concentrations, sources and processes involving volatile ... · bb event which impacted cape grim...

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Queensland University of Technology

Concentrations, sources and processes involving volatile organic compounds (VOCs) in the marine and terrestrial

background atmosphere of the Southern Hemisphere

Sarah Jane Lawson

A thesis by publication submitted in fulfilment of the requirements for

the degree of

Doctor of Philosophy (PhD)

2017

School of Chemistry, Physics and Mechanical Engineering (CPME) Science and Engineering Faculty (SEF)

Queensland University of Technology (QUT)

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Abstract Volatile Organic Compounds (VOCs) influence climate by driving secondary organic

aerosol (SOA) formation and tropospheric ozone production and by influencing the

oxidative capacity of the atmosphere. Aerosol has a major impact on the earth’s radiative

budget through direct effects (scattering and absorption of long and shortwave radiation)

and indirect effects (through changes to the microphysical and radiative properties and

lifetime of clouds) (IPCC 2007). Tropospheric ozone is the third most important

anthropogenic greenhouse gas in terms of radiative forcing (IPCC 2007) and has significant

impacts on human health and ecosystems at high concentrations. VOCs are a diverse

groups of compounds, with a range of sources (combustion, plant growth and decay,

industry) and atmospheric lifetimes varying from minutes to months. Of the perhaps 1

million VOCs theoretically in the atmosphere, only 10% of these have been measured to

date.

The aim of work this is to explore the concentrations, sources and sinks of VOCs that

participate in SOA and ozone formation processes in marine and terrestrial environments in

the Southern Hemisphere, which has been sparsely characterised to date. This work has

focused on characterising atmospheric composition over the Southern and South Pacific

Oceans, and in a biomass burning (BB) plume from a coastal heathland fire, using proton

transfer reaction mass spectrometry and a derivatization technique. This work has also

tested the ability of a chemical transport model to simulate aerosols and primary and

secondary trace gases and from a near field fire.

VOC measurements were made via PTR-MS and DNPH/HPLC during the Surface Ocean

Aerosol Production (SOAP) voyage, with a focus on short lived dicarbonyls glyoxal and

methyl glyoxal and their precursors, isoprene and monoterpenes. Glyoxal is observed over

the remote temperate oceans, even in winter in very pristine air over biologically

unproductive waters. We provide the first observations of methylglyoxal over temperate

oceans and confirm its presence alongside glyoxal. These observations support the likely

widespread contribution of these dicarbonyls to SOA formation over the ocean. At most, 1–

3 ppt of the glyoxal and methylglyoxal observed in clean marine air can be explained from

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the oxidation of their precursors, confirming a significant contribution from another source

over the ocean. GOME-2 vertical column density retrievals exceed the surface glyoxal

observations by more than 1.5 x 1014 molecules cm-2, suggesting that a distribution of

glyoxal throughout the free troposphere, as has been observed elsewhere, may be the

reason. This work highlights the degree to which atmospheric VOC sources and sinks are

still unknown, even in the relatively simple and well-mixed matrix over the remote ocean.

Data from a wide range of trace gas and aerosol measurements was collated from a

BB event which impacted Cape Grim Baseline Station in 2006. BB emission factors (EFs)

were derived using the carbon mass balance method for a range of trace gases, many never

before reported for Australian fires. Methyl halide EFs were higher than reported

elsewhere, likely due to high halogen content in vegetation in very close proximity to the

ocean. A short-lived rainfall event lead to a large increase in VOCs and CO, attributed to a

change in the combustion efficiency of the fire. This is the first study to our knowledge

which has linked rainfall with a large increase in trace gas emission ratios from BB. The

ability of biomass burning aerosol to act as cloud condensation nuclei was investigated with

56% of particles > 80 nm able to act as CCN in the fresh plume, 77% in the aged plume and

higher still (104%) in background air. Particles produced from coastal heath burned here

appear to be more hygroscopic than those from other fuel types. A particle growth event

was observed just after the direct BB plume stopped impacting the station, suggesting

particles were growing in size due to oxidation of gas phase precursors and condensation of

low-volatility products.

We tested the ability of a chemical transport model to simulate the Robbins Island BB

plume, and used the model to determine the sources responsible for the ozone

enhancement observed after the plume strikes. Key model sensitivities were explored.

Emission factors and meteorology determined whether the model predicted ozone

formation or destruction from the fire. The changing NOx EF was likely the driver of the

simulated ozone production or destruction. The model suggested the dominant source of

ozone observed was 2 day old air masses transported from Melbourne, with a contribution

of ozone formed from local BB emissions. This work shows the importance of assessing

model sensitivity to meteorology and EF, particularly for receptor sites close to the fire. We

demonstrate how a model can be used to elucidate the degree of contribution from

different sources to air quality impacts, where this is not possible using observations alone.

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Overall, this research project greatly increases the coverage of speciated VOC

measurements in poorly sampled regions in the Southern Hemisphere. This work confirms a

missing source of VOCs over the ocean, provides the first biomass burning emission factors

for many VOCs for Australian fires, and demonstrates the high sensitivity of biomass burning

models to emission inputs and meteorology.

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Keywords Volatile organic compounds (VOCs), biomass burning, marine, modelling, secondary

organic aerosol, ozone

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Statement of original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best of

my knowledge and belief, the thesis contains no material previously published or written by

another person except where due reference is made.

Signature:

QUT Verified Signature

Date: October 2017

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Table of Contents

Abstract ............................................................................................................................................ ii

Keywords .......................................................................................................................................... v

Statement of original Authorship ...................................................................................................... vi

Publications arising from this work .................................................................................................... x

Acknowledgements .......................................................................................................................... xii

Chapter 1 .......................................................................................................................................... 2

Introduction ...................................................................................................................................... 2

1.1 The scientific problem ................................................................................................................ 2

1.2 Study aims .................................................................................................................................. 2

1.3 Specific study objectives ............................................................................................................. 3

1.4 Evidence of research progress linking the research papers ....................................................... 4

2 Chapter 2 ................................................................................................................................... 8

Literature review ............................................................................................................................... 8

2.1 VOCs and SOA – background ..................................................................................................... 8

2.1.1 Properties, sources and atmospheric importance of VOCs .................................................. 8

2.2 Marine aerosol composition - evidence of SOA ........................................................................ 22

2.3 Emission of VOCs from biomass burning plumes, impact on SOA and ozone formation, and

modelling biomass burning plumes ................................................................................................................. 45

2.4 Summary of literature review .................................................................................................. 58

2.5 Literature review of experimental methods ............................................................................. 62

2.6 References ................................................................................................................................ 67

3 Chapter 3 ................................................................................................................................. 82

Seasonal in situ observations of glyoxal and methylglyoxal over the temperate oceans of the

Southern Hemisphere ................................................................................................................................ 84

3.1 Abstract .................................................................................................................................... 84

3.2 Introduction .............................................................................................................................. 85

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3.3 Methods ................................................................................................................................... 90

3.3.1 Sampling locations .............................................................................................................. 90

3.3.2 In situ measurements .......................................................................................................... 93

3.3.3 Supporting measurements for selection of clean marine periods ...................................... 96

3.3.4 Measurements for dicarbonyl yield calculations ................................................................ 98

3.4 Results and Discussion ........................................................................................................... 101

3.4.1 In situ observations in clean marine air ............................................................................ 101

3.4.2 Dicarbonyl observations in clean marine air ..................................................................... 103

3.4.3 Clean marine versus all data and comparison with other marine background observations .

........................................................................................................................................... 103

3.4.4 Calculation of expected glyoxal, methylglyoxal yields from measured VOC precursors in

clean marine air ........................................................................................................................................ 109

3.4.5 Comparison of glyoxal surface observations with satellite vertical columns .................... 112

3.5 Conclusions ............................................................................................................................. 115

3.6 Acknowledgements ................................................................................................................ 117

3.7 References .............................................................................................................................. 117

4. Chapter 4 ....................................................................................................................................124

Biomass burning emissions of trace gases and particles in marine air at Cape Grim, Tasmania. .......124

4.1 Abstract .................................................................................................................................. 126

4.2 Introduction ............................................................................................................................ 127

4.3 Methods ................................................................................................................................. 131

4.3.1 Cape Grim station location and location of fire ................................................................ 131

4.3.2 Measurements .................................................................................................................. 133

4.4 Results and discussion ............................................................................................................ 136

4.4.1 Biomass burning event 1 (BB1) February 16th 2006 .......................................................... 137

4.4.2 BB event 2 (BB2) February 23rd 2006 ................................................................................ 145

4.5 Summary and future work ..................................................................................................... 155


4.7 References .............................................................................................................................. 158

4.8 Supplementary Material ........................................................................................................ 166

5 Chapter 5 ................................................................................................................................177

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Biomass burning at Cape Grim: exploring photochemistry using multi-scale modelling ....................179

5.1 Abstract .................................................................................................................................. 179

5.2 Introduction ............................................................................................................................ 180

5.3 Methods ................................................................................................................................. 184

5.3.1 Fire and measurement details........................................................................................... 184

5.3.2 Chemical Transport Modeling ........................................................................................... 184

5.4 Results and Discussion ........................................................................................................... 191

5.4.1 Modelling Sensitivity Study ............................................................................................... 191

5.5 Exploring plume chemistry and contribution from different sources ..................................... 204

5.5.1 Drivers of ozone production .............................................................................................. 204

5.5.2 Plume age .......................................................................................................................... 207

5.6 Summary and conclusions ...................................................................................................... 209


5.8 References .............................................................................................................................. 210

6 Chapter 6 ................................................................................................................................216

Conclusions ....................................................................................................................................216

6.1 Conclusions and significances arising from experimental studies .......................................... 216

6.1.1 General comments ............................................................................................................ 216

6.1.2 Specific outcomes and the significance ............................................................................. 217

6.1.3 Recommendations for future work ................................................................................... 220

x

Publications arising from this work

Lawson, S. J., Selleck, P. W., Galbally, I. E., Keywood, M. D., Harvey, M. J., Lerot, C.,

Helmig, D., and Ristovski, Z.: Seasonal in situ observations of glyoxal and methylglyoxal over

the temperate oceans of the Southern Hemisphere, Atmos. Chem. Phys., 15, 223-240,

doi:10.5194/acp-15-223-2015, 2015. (Copernicus Feature Article, January 2015) Cited 14

times

Lawson, S. J., Keywood, M. D., Galbally, I. E., Gras, J. L., Cainey, J. M., Cope, M. E.,

Krummel, P. B., Fraser, P. J., Steele, L. P., Bentley, S. T., Meyer, C. P., Ristovski, Z., and

Goldstein, A. H.: Biomass burning emissions of trace gases and particles in marine air at

Cape Grim, Tasmania, Atmos. Chem. Phys., 15, 13393-13411, doi:10.5194/acp-15-13393-

2015, 2015. Cited 7 times.

Lawson, S. J., Cope, M., Lee, S., Galbally, I. E., Ristovski, Z., and Keywood, M. D.:

Biomass burning at Cape Grim: exploring photochemistry using multi-scale modelling,

Atmos. Chem. Phys., 17, 11707-11726, https://doi.org/10.5194/acp-17-11707-2017, 2017.

Other publications co-authored during candidature

Dunne, E, Galbally, I.E., Lawson, S.J and Patti, A. (2012). Interference in the PTR-MS

measurement of acetonitrile at m/z 42 in polluted urban air—A study using switchable

reagent ion PTR-MS. International Journal of Mass Spectrometry 319– 320 (2012) 40– 47

Emmerson, K. M., Galbally, I. E., Guenther, A. B., Paton-Walsh, C., Guerette, E.-A.,

Cope, M. E., Keywood, M. D., Lawson, S. J., Molloy, S. B., Dunne, E., Thatcher, M., Karl, T.,

and Maleknia, S. D.: Current estimates of biogenic emissions from eucalypts uncertain for

southeast Australia, Atmos. Chem. Phys., 16, 6997-7011, doi:10.5194/acp-16-6997-2016,

2016.

xi

Milic, A., Mallet, M. D., Cravigan, L. T., Alroe, J., Ristovski, Z. D., Selleck, P., Lawson, S.

J., Ward, J., Desservettaz, M. J., Paton-Walsh, C., Williams, L. R., Keywood, M. D., and

Miljevic, B.: Biomass burning and biogenic aerosols in northern Australia during the SAFIRED

campaign, Atmos. Chem. Phys., 17, 3945-3961, https://doi.org/10.5194/acp-17-3945-2017,

2017.

Mallet, M. D., Desservettaz, M. J., Miljevic, B., Milic, A., Ristovski, Z. D., Alroe, J.,

Cravigan, L. T., Jayaratne, E. R., Paton-Walsh, C., Griffith, D. W. T., Wilson, S. R., Kettlewell,

G., van der Schoot, M. V., Selleck, P., Reisen, F., Lawson, S. J., Ward, J., Harnwell, J., Cheng,

M., Gillett, R. W., Molloy, S. B., Howard, D., Nelson, P. F., Morrison, A. L., Edwards, G. C.,

Williams, A. G., Chambers, S. D., Werczynski, S., Williams, L. R., Winton, H. L., Atkinson, B.,

Wang, X., and Keywood, M. D.: Biomass burning emissions in north Australia during the

early dry season: an overview of the 2014 SAFIRED campaign, Atmos. Chem. Phys. Discuss.,

doi:10.5194/acp-2016-866, in review, 2016.

Dunne, E., Galbally, I.E., Cheng, M., Selleck, P.W., Molloy, S.B. and Lawson, S.J.:

Comparison of VOC measurements made by PTR-MS, GC-FID-MSD and DNPH derivatization-

HPLC during the Sydney Particle Study 2012: a contribution to the assessment of uncertainty

in current atmospheric VOC measurements, Atmos. Meas. Tech. Discuss., in review, 2016.

xii

Acknowledgements I would like to acknowledge the support of my supervisors, Zoran Ristovski, Melita Keywood

and Ian Galbally, and Martin Cope for helpful discussions which helped me to understand

the biomass burning event central to this work.

xiii

Dedication

This thesis is dedicated to my dear uncle, Rob Lyon (1958-2014) – a talented actor by trade,

who was also fascinated by the mysteries of the natural world.

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Chapter 1

Introduction

1.1 The scientific problem

Volatile Organic Compounds (VOCs) influence climate by driving secondary organic

aerosol (SOA) formation and tropospheric ozone production and by influencing the

oxidative capacity of the atmosphere. Aerosol has a major impact on the earth’s radiative

budget through direct effects (scattering and absorption of long and shortwave radiation)

and indirect effects (through changes to the microphysical and radiative properties and

lifetime of clouds) (IPCC 2007). Aerosols also have significant impacts on human health. Two

recent estimates indicate that SOA makes up a large proportion of the global tropospheric

aerosol budget, (Goldstein and Galbally, 2007; Hallquist et al., 2009) however the formation

mechanisms and precursors, chemical composition and degree of radiative forcing of SOA is

still poorly understood (Hallquist et al., 2009).

Tropospheric ozone is the third most important anthropogenic greenhouse gas in

terms of radiative forcing (IPCC 2007) and has significant impacts on human health and

ecosystems at high concentrations. Tropospheric ozone concentrations are increasing in the

background atmosphere, despite efforts to reduce emissions of its anthropogenic

precursors, including VOCs, and the processes driving this increase in background ozone

concentrations are not well understood (The Royal Society 2008).

1.2 Study aims

The broad aim of this research is to explore the concentrations, sources and sinks of

VOCs that participate in SOA and ozone formation processes in marine and terrestrial

environments in the Southern Hemisphere, which has been sparsely characterised to date.

The work also aims to test the ability of local and regional models to successfully simulate

atmospheric composition and processes involving VOCs. This work is focused particularly

from a) marine origin and b) biomass burning origin.

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Specific aims include:

• Measure distribution of VOCs in the remote southern hemisphere marine boundary

layer, to determine significance of ocean as a source of VOCs with high SOA potential (e.g.

isoprene, monoterpenes, glyoxal). Where possible, make VOC measurements alongside

aerosol chemical composition and distribution measurements, and ocean biogeochemical

measurements to determine the significant of ocean biological processes as a source.

• Investigate the sources and sinks of VOCs implicated in SOA and ozone formation in

biomass burning plumes. Where supporting measurements of CO and CO2 are available,

calculate biomass burning emission factors (EFs) for trace gases and aerosols

• Test the ability of chemical transport modelling to simulate composition and

transport of an Australian biomass burning plume. Integrate model and observations to

investigate processes and precursors which drive the formation of secondary species such

ozone in biomass burning plumes.

1.3 Specific study objectives

Marine VOCs

• Make atmospheric VOC measurements over the subtropical front along Chatham

Rise in the South Pacific Ocean (44º S) for 3 weeks during the Surface Ocean Aerosol

Production (SOAP) Voyage. Make VOC measurements via proton transfer reaction mass

spectrometry (PTR-MS) and also via adsorbent tubes and DNPH cartridges, analysed later by

GC-MS and HPLC.

• Integrate SOAP VOC data with other in situ aerosol and trace gas measurements

from the SOAP voyage, and with previous VOC measurements from Cape Grim Baseline

Station which samples southern ocean air. Access vertical column densities of VOCs from

satellite if possible. Use all available data to assess whether VOCs with high SOA potential

are present in significant quantities over the remote ocean and if so, what is the likely

source of these VOCs.

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These objectives are addressed in one paper in Chapter 3

Biomass burning

• Collate and interpret all available data from a fire which burned through coastal

heath near Cape Grim in 2006 (including VOC data, cloud condensation nuclei (CCN)

concentration, Condensation Nuclei (CN) concentration, black carbon, and size resolved

aerosol distributions, aerosol volatility and hygroscopic growth, ozone, CO, CO2 N2O, methyl

halides etc). Calculate Emission Factors for VOCs, other trace gases and aerosol. Investigate

changes in composition of the plume as the wind direction changes, including

enhancements in ozone and changes to the ability of particles to act as cloud condensation

nuclei. Relate changes in plume composition to those observed elsewhere in the world.

This objective is addressed in Chapter 4

Biomass burning modelling

• The observations published in Chapter 4 will be used to test a 3D Eulerian chemical

transport model’s ability to simulate the biomass burning plume strikes observed at Cape

Grim. Sensitivity studies will be undertaken (varying emissions, meteorology). The model

will be used to explore the contribution of different sources (Melbourne emissions, fire

emissions) to the enhanced ozone that was reported in Chapter 4 and to determine the age

of the air mass.

This objective is addressed in Chapter 5

1.4 Evidence of research progress linking the research papers


measurements in poorly sampled regions in the Southern Hemisphere (over the temperate

ocean and in biomass burning plumes). In this work, sources of VOCs were explored and

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quantified using surface based and remotely sensed observations, including a) oxidation of

marine-derived precursors and b) burning of vegetation. Secondary processes involving

VOCs in biomass burning plumes were explored including particle growth, changes to

particle hygroscopicity and ozone formation. Finally, chemical transport modelling was used

to identify the contribution from different sources to formation of secondary species

(ozone). A brief description of each chapter is provided below.

Marine VOCs - Chapter 3

Chapter 3 in the thesis was published as a research paper in the SOAP Special issue

of Atmospheric Chemistry and Physics in 2015 (and highlighted as a feature article by

Copernicus Editors).

This paper is based on measurements of VOCs during the Surface Ocean Aerosol

Production (SOAP) Voyage. Glyoxal and methylglyoxal are short lived VOCs which are

important SOA precursors and intermediate oxidation products of isoprene and

monoterpenes. Previous papers had reported somewhat surprisingly that glyoxal appears to

be widespread over the remote oceans while there was only one previous study of

methylglyoxal over the Northern Hemisphere tropical oceans.

In this work we made the first in situ measurements of glyoxal over the temperate

Southern Hemisphere oceans and the first in situ methyl glyoxal measurements over any

Southern Hemisphere ocean. Our results showed glyoxal and methylglyoxal were present in

significant quantities which could not be explained by their precursors which were

measured in parallel, both during the SOAP voyage, and utilising previous unpublished

measurements from Cape Grim. These observations support the likely widespread

contribution of these dicarbonyls to SOA formation over the ocean. Satellite data provided

by Belgian Institute of Aeronomy showed a large discrepancy between surface and column

measurements, suggesting glyoxal may be present in the free troposphere. This work

highlights the degree to which atmospheric VOC sources and sinks are still unknown, even in

the relatively simple and well-mixed matrix over the remote ocean.

A second paper reporting locally high concentrations of isoprene and monoterpenes

observed during SOAP is in preparation, but was not sufficiently developed to include in this

thesis.

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Biomass burning VOCs- Chapters 4, 5

Chapter 4 in the thesis was published as a research paper in Atmospheric Chemistry

and Physics in 2015.

In 2006 during the Precursors to Particles campaign at the Cape Grim Baseline Station,

smoke from a fire on nearby Robbins Island impacted the Cape Grim station, giving an

opportunity to look at the gas and aerosol emissions in very clean marine air. There are

limited measurements of biomass burning composition in temperate regions in the

Southern Hemisphere. In 2011 and 2012 I collated, analysed and interpreted a wide range

of trace gas and aerosol measurements from this event. BB emission factors (EFs) were

derived using the carbon mass balance method for a range of trace gases, many never

before reported for Australian fires. Methyl halide EFs were higher than reported

elsewhere, likely due to high halogen content in vegetation in very close proximity to the

ocean. These EF will provide important input for regional and global models. A short-lived

rainfall event lead to a large increase in VOCs and CO, attributed to a change in the

combustion efficiency of the fire. This is the first study to our knowledge which has linked

rainfall with a large increase in trace gas emission ratios from BB. The ability of BB aerosol

to act as cloud condensation nuclei was investigated; particles produced from this coastal

heath fire were more hygroscopic than those from other fuel types reported in the

literature.

Chapter 5 in the thesis is under review for Atmospheric Chemistry and Physics

Discussions.

In 2012-2013 a 3D Eulerian chemical transport model was used to simulate the

biomass burning plume observed at Cape Grim (described in Chapter 4). The aim was to use

the model to help with interpretation of observed changes in composition and influence of

different sources. The model was found to be very sensitive to several input parameters

including emissions and meteorology, and so a systematic sensitivity was undertaken using

three different sets of emission factors (corresponding to low, medium and high combustion

efficiency), two different meteorological models, and exploring spatial variability. Ozone

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production from the fire in particular had a non-linear response to changing emission

factors and meteorology with the changing NOx EF the likely driver. The model suggested

the dominant source of ozone observed (discussed in Chapter 4) was 2 day old air masses

transported from Melbourne, with a contribution of ozone formed from local BB emissions.

This work shows the importance of assessing model sensitivity to meteorology and EF,

particularly for receptor sites close to the fire. We demonstrate how a model can be used to

elucidate the degree of contribution from different sources to air quality impacts, where

this is not possible using observations alone.


measurements in poorly sampled regions in the Southern Hemisphere. This work confirms a

missing source of VOCs over the ocean, provides the first biomass burning emission factors

for many VOCs for Australian fires, and demonstrates the high sensitivity of biomass burning

models to emission inputs and meteorology.

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2 Chapter 2

Literature review

2.1 VOCs and SOA – background

2.1.1 Properties, sources and atmospheric importance of VOCs

Volatile Organic Compounds (VOCs) are compounds containing carbon and hydrogen

atoms, which may be saturated or unsaturated, aliphatic or aromatic, and may contain

substitutions of oxygen, sulphur, nitrogen, halogens and other atoms (Helmig, 2009).

Broadly speaking, VOCs have boiling points which allow them to exist in the gas phase at

ambient temperatures, however the definition of VOC is diverse. Typically VOCs are defined

by a boiling point or volatility range and ability to participate in photochemical reactions.

Methane is typically not included under the label of VOCs, as it does not contribute

significantly to ozone enhancements above background levels, due to its low reactivity and

long lifetime (Finalyson-Pitts and Pitts, 2000). Historically non-methanic organic gases in the

atmosphere were described as non-methanic hydrocarbons (NMHC). However with the

growing awareness of the importance of substituted hydrocarbons in the atmosphere, e.g.

oxygenated compounds, terms such as VOCs, Reactive Organic Gases (ROG) and Non

Methanic Organic Compounds (NMOC) have been coined to describe non methane organic

gases (Finalyson-Pitts and Pitts, 2000). In this review chapter, the term VOCs will be used. In

Chapters 4 and 5 the term NMOC is used.

The atmospheric lifetime of VOCs can vary from minutes to months, and VOCs are

removed from the atmosphere via eventual oxidation to CO2, by photolysis, and by wet and

dry deposition (Helmig, 2009). Major radicals responsible for VOC oxidation include OH and

O3, with a contribution from the nitrate radical, (NO3), at night in polluted regions. Other

radicals such as the hydroperoxyl radical (HO2), and atomic Cl and other halogens are

important in some circumstances. Photolysis is particularly important sink for oxygen-

containing VOCs (Finalyson-Pitts and Pitts, 2000).

9

VOCs are emitted from a variety of sources, both natural and anthropogenic.

Goldstein and Galbally (2007) summarise sources of VOCs as a) processes associated with

life, such as the growth, maintenance and decay of plants, animals and microbes and b)

combustion of living and dead organisms, including fossil fuel combustion and biomass

burning. Industrial sources including solvent manufacture and use, and geothermal sources

also contribute.

VOCs play an important role in tropospheric chemistry in a number of ways. Firstly

VOCs control the oxidative capacity of the atmosphere by influencing atmospheric

concentration of the OH radical. VOCs are both a sink for the OH radical (during oxidation),

and a source of OH, particularly through photolysis of oxygenated VOCs in the upper

atmosphere. Secondly, VOCs, along with NOx, fuel tropospheric ozone formation.

Tropospheric ozone is the third most important greenhouse gas in terms of anthropogenic

radiative forcing after CO2 and CH4 (IPCC, 2007), and background concentrations of ozone

are increasing worldwide (Royal Society, 2008). Finally, VOCs are responsible for the

formation of secondary organic aerosol (SOA) in the atmosphere. SOA is thought to make a

large contribution to the global aerosol budget, however the formation mechanisms,

properties and effect on radiative forcing is currently poorly understood (Hallquist et al.,

2009). In addition, VOCs may adversely impact air quality and human health, both through

direct exposure, and through their role in tropospheric ozone and SOA formation.

General description of Secondary Organic Aerosol (SOA) formation

Secondary organic aerosol is produced by gas-particle conversion processes involving

VOC precursors (Finlayson-Pitts and Pitts, 2000). As VOC precursors are oxidised, there is

addition of oxygen and more polar functional groups, which leads to products with a lower

volatility and higher water solubility. These lower volatility products can then partition into

the aerosol phase, through condensation or heterogeneous reactions. Low volatility

products can then form a particle nucleus through gas to particle transformation

(homogeneous nucleation), or can condense on to or react with an existing particle

(heterogeneous nucleation). Whether the low volatility product undergoes homogeneous or

10

heterogeneous nucleation has important implications for the aerosol population and

associated effect on climate, air quality and health – homogeneous nucleation will result (at

least initially) in a contribution of SOA to particle number, whereas heterogeneous

nucleation results only in a contribution to mass (Hallquist et al., 2009).

Each VOC can undergo multiple degradation processes and pathways, which produce

a variety of oxidation products, which may or may not contribute to SOA. It may take only

one, or several generations of oxidation of a parent VOC for products to have a sufficiently

low vapour pressure to contribute to SOA, depending on the parent VOC and atmospheric

conditions, such as temperature and relative humidity (Hallquist et al., 2009).

Whilst VOC oxidation can lead to less volatile, more polar compounds which may

participate in SOA formation, oxidation may also lead to fragmentation of the VOC, leading

to smaller, more volatile products which are eventually converted to CO2. Hence the

formation of SOA through gas to particle transfer of partially oxidised material occurs in

competition with further oxidation in the gas phase (Kroll et al, 2011). While the initial

research efforts on SOA focused on VOC oxidation in the gas phase (typical for non-water

soluble and volatile compounds), there is now growing evidence that oxidation also occurs

in the aqueous phase (important for less volatile, more water soluble compounds).

Oxidation of organic compounds in the aqueous phase (e.g. cloud droplets or aerosol water)

often leads to addition of oxygen without fragmentation due to breaking of carbon bonds.

For this reason, aqueous-phase oxidation is more likely to lead to lower volatility products

and higher SOA yield (Ervens et al., 2011). The aqueous environment also allows small water

soluble molecules like glyoxal to contribute to SOA through oligomer formation, which

increases the molecular weight and decreases volatility (Carlton et al., 2007).

Contribution of SOA to total aerosol load and global budgets

Atmospheric aerosols have a diverse range of sources which may be natural or

anthropogenic, and may be classed as primary or secondary. Primary aerosols are emitted

directly from processes such as fossil fuel combustion, biomass burning, and the suspension

of dust, sea salt and biological material through wind blowing. Secondary aerosols are

formed from gas to particle conversion in the atmosphere, and may be comprised of

inorganic material, organic material, or a mixture of both. Common inorganic components

11

of secondary aerosol include sulphate, nitrate and ammonium, and the processes involving

the gas to particle conversion of these species is relatively well understood (Hallquist et al.,

2009). However there is great deal of uncertainty surrounding the processes involving

secondary organic formation (SOA), which is thought to be a significant contributor to global

aerosol (Goldstein and Galbally, 2007; Hallquist et al., 2009).

Understanding the chemical composition and properties of atmospheric aerosol is

critical because aerosols have a strong influence on the earth’s radiative balance and

climate. Aerosols affect the radiative budget in two ways: firstly through the scattering and

absorbing both solar and terrestrial radiation (direct effect) and secondly by influencing

cloud formation and properties in their role as cloud condensation nuclei (CCN) (indirect

effect). Aerosols also play an important role in atmospheric chemistry through participation

in heterogeneous chemical reactions which influences the distribution and fate of trace gas

species (Andreae and Crutzen, 1997; Haywood and Boucher, 2000). Globally, aerosols are

understood to have a cooling affect, however the uncertainty associated with aerosol

radiative forcing is very high (IPCC, 2007) which limits the accuracy of climate models. The

uncertainty involved with the aerosol radiative effect can be reduced by understanding the

composition of atmospheric aerosol and processes which control their emission,

transformation and removal from the atmosphere. It is therefore necessary to quantify the

contribution of SOA to total aerosol load, and understand the precursors and processes

responsible for SOA formation.

However, understanding SOA formation and composition is challenging. Firstly, there

are likely to be vast numbers of VOCs in the atmosphere which may contribute to SOA

production. Goldstein and Galbally (2007) estimate up to 100,000 VOCs have been

measured in the atmosphere to date – however the authors estimate that well over 1

million different VOCs compounds with 10 carbon atoms or less are likely to exist in the

atmosphere, if all possible atom or group substitutions are considered. The identification

and quantification of this many VOCs, many of which are likely to be present at parts per

trillion (ppt) or parts per billion (ppb) concentrations, is a very large analytical challenge. In

addition to the initial identification and quantitation of the VOCs is the challenge of

understanding the interactions of such a vast range of VOCs, considering that each

individual VOC may follow a range variety of degradation pathways, depending on

atmospheric conditions (e.g. temperature, RH) and concentrations of other species e.g. OH,

12

NOx. To date, only a few VOC degradation mechanisms have been studied, and for more

complex (but ubiquitous) VOCs like aromatics and terpenes, only sparsely characterised at

molecular level (Hallquist et al., 2009).

What proportion of the > 1 million different VOCs present in the atmosphere are

likely to contribute to SOA is unclear. Kroll et al (2011) stated that oxidation of most

atmospheric organic species is likely to contribute to SOA, based on work which used the

carbon oxidation state of organic aerosol to understand composition. The carbon oxidation

state was found to increase as the OA became more highly oxidised, e.g. as the O/C ratio

increased, with an ultimate oxidation state of +4 upon complete oxidation to CO2. The

authors found that organic aerosol samples taken from a wide range of ambient conditions

(remote biogenic fresh and aged, urban, cloudwater) and sources (car exhaust, biomass

burning, chamber) all had carbon oxidation states in a relatively narrow range of -2 to +1.

The authors conclude “thus SOA is not the product of only a few select hydrocarbons but is

rather formed in the oxidation of most organic species. The only reduced species not likely

to contribute to SOA are C4 and less though these might form SOA through oligomers. The

potential formation of aerosol from such a wide variety of organic species is a likely

contributor to underestimates of SOA concentrations because models only include a select

few precursors”.

There have been several global estimates of SOA published in recent years, using

both bottom up and top down approaches. Generally speaking the bottom up approach

uses VOC fluxes and laboratory SOA yields in global models to estimate global SOA

production, while the top down approach uses a top down inverse estimate to constrain

precursor emissions to infer SOA production.

Hallquist et al (2009) provides a summary of findings from the bottom up

approaches, which estimate global biogenic SOA in the range of 9-50 TgC y-1, with smaller

contributions from anthropogenic SOA of 2-9 TgC y-1.

Goldstein and Galbally (2007) used four top down approaches to infer global SOA

production, including a) VOC mass balance estimate, b) estimate using the SOA sinks of

deposition plus oxidation, c) estimate based on the global sulphate aerosol estimates and

observed ambient ratios of organics to sulphate, and d) estimate based on SOA required to

maintain the observed vertical SOA distribution. The resulting SOA global production ranges

13

from 140-910 Tg y-1. This estimate is an order of magnitude higher than previous bottom up

estimates.

The Hallquist et al., SOA review (2009) assesses these estimates and approaches, and

states that while the top end of the Goldstein and Galbally (2007) range (910Tg C yr-1) would

require a perhaps unrealistically high global VOC to SOA conversion yield of 70%, chamber

study results used in the bottom up estimates are likely to underestimate SOA yield due to

termination of experiments before oxidation pathways are complete. Hallquist et al (2009)

go on to revise Goldstein and Galbally’s top down global estimation based on the sulphur

budget to 150 TgC y-1 total net organic carbon flux, with a range of 60-240 Tg C y-1 . The

authors then estimate the contribution of organic carbon flux from primary and secondary

sources, including primary emissions, anthropogenic SOA, and SOA from low volatility

organics co-emitted with POA during biomass burning and combustion. They estimate 10%

of the global OC is attributed to primary anthropogenic and BB emissions, 30% to secondary

anthropogenic and BB production (including oxidation of both high and low volatility gases)

with the remaining 60% attributed (by difference) to biogenic SOA. The authors state that

the values are highly uncertain, and highlight that within the VOC/SOA budget the vapour

deposition term and SOA formation term are both highly uncertain, leading to errors and

uncertainties in global models that attempt to represent SOA formation.

Important formation mechanisms of SOA

The role of organics in homogeneous nucleation (new particle formation) and heterogeneous

nucleation (particle growth)

New particle formation (NPF), or homogeneous nucleation, involves formation of

aerosols in the atmosphere from gas phase species. The first step in the formation process is

the formation of a cluster of gas phase molecules as a result of random collisions. If the

nucleus reaches a critical size, growth of the particle occurs spontaneously through

condensation of vapours onto the particle, or heterogeneous reactions. There are significant

barriers to NPF – the first is a free energy barrier needs to be overcome for the nucleus to

grow to the critical size, and the second is the Kelvin effect which leads to elevated vapour

14

pressures above small clusters and nanoparticles, which reduces condensation of vapours

and growth. However, because the formation and dissociation of clusters is occurring

continuously in the atmosphere, a few will form a critical nucleus and NPF will result (Zhang

et al., 2012). Once a new particle grows to 2-3 nm, any future growth occurs in competition

with scavenging/coagulation of the nanoparticle by existing particles, which is why NPF is

more common in regions with low background particle concentrations.

The role of VOCs in NPF

Nucleation occurs at a rate which is dependent on the chemical makeup of the

critical nucleus, and gaseous concentrations of the nucleating species (Zhang et al., 2012).

Formation of a critical nucleus may be homomolecular (involving just one type of gas

molecule) or heteromolecular (involving several types different types of gas molecules). In

reality due to the very high supersaturation required for homomolecular nucleation,

nucleation occurring in the atmosphere is nearly always heteromolecular. The most

common gas involved in formation of the critical nucleus is thought to be sulphuric acid,

owing to its low vapour pressure at typical atmospheric temperatures. When mixed with

water, the vapour pressure becomes even lower. Nucleation in the earth's atmosphere is

thought to almost always involve sulphuric acid and water molecules. In the free

troposphere, and in very cold boundary layer environments, sulphuric acid and water are

responsible for most of the nucleation observed (Kirkby et al., 2011). However, in the rest

of the boundary layer, sulphuric acid and water clusters are unstable at the higher

temperatures, resulting in rapid dissociation. It is therefore thought that other gases play a

critical role in the boundary layer, by stabilising the clusters and allow them to grow to the

critical size for further spontaneous growth. Gases commonly implicated in stabilising the

H2SO4/H2O clusters via strong hydrogen bonds are ammonia, amines, organic acids, iodine

oxides and atmospheric ions. Hence in the boundary layer binary nucleation is thought to be

rare, and is more likely to involve three (ternary) or more (multicomponent) interacting gas

phase vapours.(Zhang et al., 2012)

VOCs have an important role in atmospheric NPF/homogeneous nucleation, and

heterogeneous nucleation, as the low volatility vapours essential for particle formation and

growth are produced through photo-oxidation of many different saturated and unsaturated

15

and aromatic hydrocarbons (Zhang et al., 2012). Evidence of the contribution of organic

vapours to NPF and particle growth from laboratory and field studies is discussed below.

Challenges measuring composition of NPF

The critical nucleus is thought to be around 1 nm in diameter, making measurement

of the composition of the molecular cluster extremely challenging (Zhang et al., 2012).

Instruments such as the TD-CIMS (thermal desorption chemical ionisation mass

spectrometer) can measure aerosol composition down to sizes of around 10 nm, however

by the time the particle has grown to this size it contains several hundred to several

thousand molecules, so the gases contributing to the initial development and growth of the

critical nucleus cannot be directly measured (Zhang et al., 2012). However a combination of

measurements such as particle and ion concentration and size distribution, nucleation

rates, solubility, hygroscopicity and volatility alongside concurrent gas phase (VOC)

measurements, can be used to infer the chemical composition of particles <10 nm.

The importance of organics in NPF - Laboratory Studies

A study by Kirkby et al (2011) at the CERN Proton Synchrotron examined the gases

responsible for formation of very small clusters consisting of only a few molecules. They

found that nitrogen-containing compounds such as ammonia, amines and urea were always

present in sulphuric acid clusters containing more than four molecules, confirming the

important role of N-containing compounds in stabilising the cluster. Ammonia was also

found to have a profound effect on nucleation rates, with increases of 10-1000 fold when

ammonia was present > 100 ppt. However interestingly, the authors concluded that

atmospheric ammonia and sulphuric acid cannot explain the observed nucleation in the

boundary layer, and that other gases, most likely organics, are necessary. The study also

found that ions, formed from galactic cosmic rays, enhance nucleation rates. A further CERN

study by Kirkby et al (2016) found that ozonolysis of alpha pinene at atmospheric conditions

and in the absence of sulphuric acid led to formation of highly oxidized molecules in

minutes. These molecules then went on to form new particles, with higher nucleation rates

observed in the presence of Galactic cosmic rays, attributed to the stabilizing effect of

16

resulting ions. This study raises the possibility that new particles could be formed either in

the marine boundary layer or more likely in the free troposphere from marine-derived

terpenes. This could be a significant marine-derived source of new particles particularly in

regions with low DMS and sulfuric acid.

The importance of organics in the initial formation of the critical nucleus was also

highlighted by Metzer et al (2010). By conducting a smog chamber experiment involving

irradiation of oxides of nitrogen (NOx), sulphur dioxide (SO2) and tri methyl benzene, the

authors found that particle formation rates were proportional to the product of the

concentration of sulphuric acid and an organic molecule (which could not be identified). The

authors concluded that sulphuric acid and organic compounds are responsible both for the

formation of the critical nucleus, and also the subsequent growth of the particle.

A study in a Finland forest found that organic vapours, including amines have a

stabilising role in growing 1.5-2 nm clusters over the critical size, and then dominate the

further growth of the particles (Kulmala et al., 2013).

The role of condensation of low volatile organics, such as organic acids, is also

examined in the recent review nucleation and particle growth by Zhang et al (2012). The

authors claim that in addition contributing to particle growth by condensation, organic acids

(such as pinonic, formic, acetic, benzoic) are likely to form hydrogen bonds with sulphuric

acid and/or water molecules. According to the review, laboratory experiments show an

inconsistent contribution of organic acids to nano particles, with little organic acid content

found in laboratory- derived particles, but substantial organic acids in ambient particles. The

authors hypothesise that this may be due to the more complex matrix in ambient air, and

possible synergistic interactions between organic acids and alkyl amines, which forms stable

salts and reduces the volatility of both components (Zhang et al., 2012).

Findings from studies investigating heterogeneous reactions of VOCs on particles are

also reviewed by Zhang et al (2012). A brief summary of the discussion in this review is given

below.

Heterogeneous reactions of aldehydes

17

Studies of heterogeneous reactions of gas phase aldehydes and particles have shown

somewhat inconsistent results. Larger aldehydes such as octanal and 2,4 hexadienal have

been found to contribute to SOA growth via aldol condensation and polymer formation in a

variety of smog chamber and flow tube experiments, (Zhang et al (2012) and references

therein). However when more atmospherically relevant concentrations of aldehydes

(including formaldehyde) were combined with sulphuric acid seed particles in a chamber by

Kroll et al (2005), no uptake was observed. However, more recently heterogeneous uptake

of 2,4 hexadienal into sulphuric acidic nano particles has been observed using nano TDMA,

and the resulting aldol reaction products have been observed by TD-IC-CIMS. The authors

conclude that higher aldehydes may contribute to growth of freshly nucleated acidic

particles through aldol condensation (Zhang et al., 2012).

Heterogeneous reactions of dicarbonyls

Zhang et al (2012) reports that while glyoxal and methyl glyoxal both contribute to

SOA, studies generally show glyoxal is more implicated in growth of sulphuric acid

nanoparticles. This is thought to be related to the much higher Henry's law constant of

glyoxal compared to methyl glyoxal, resulting in higher aqueous concentrations of glyoxal in

the nanoparticles when particles are exposed to the same gaseous concentration of both

dicarbonyls. Some studies suggest the contribution of glyoxal to particle growth can be

affected by humidity, particle size (with glyoxal uptake occurring more readily in > 6 nm

particles) and particle acidity. Glyoxal typically forms oligomers in SOA, and hydrated glyoxal

may form organosulphates on reaction with sulphuric acid.

Heterogeneous reactions of alcohols

Zhang et al (2012) reports that several studies have investigated uptake of alcohols

into sulphuric acid, with findings differing between dilute sulphuric acid (resulting in

reversible physical adsorption) and concentrated sulphuric acid (resulting in reversible alkyl

hydrate formation). At lower temperatures, the reaction with concentrated sulphuric acid

eventually leads to sulphate ester formation, however with the warmer temperatures in the

18

boundary layer, the reaction between alcohols and sulphuric acid is only likely to be

important with very acidic newly nucleated particles.

Heterogeneous reactions of isoprene oxidation products

Dihydroxyepoxides (i.e epoxydiols), which are a recently discovered product of

isoprene oxidation in low NOx conditions, have been implicated in the formation of SOA, by

uptake on to existing aerosols in a variety of chamber experiments (Zhang et al., 2012). The

epoxides undergo hydrolysis in particles with a variety of pH’s, which suggests they may be a

precursor to the sulphate esters (organosulphates) that have been observed in ambient SOA

(Zhang et al., 2012). Lin et al, (2012) recently confirmed the link between the isoprene-

derived epoxides and SOA. The authors observed uptake of isoprene derived epoxydiol on

sulphate seed aerosols in chamber experiments, and found that the SOA tracers chemically

characterised from the chamber experiment (including 2-methyltetrols, C5-alkene triols,

dimers and organosulphates), were in good agreement with SOA tracers observed in

ambient aerosol. This provides convincing evidence of the contribution of isoprene to SOA

and particle growth.

The importance of organics in NPF - Field studies

There have been a number of recent studies that have observed NPF in marine and

terrestrial environments, and have attributed a likely role for organics (i.e. VOCs). While the

focus of this review is VOCs and SOA in background marine and biomass burning

environments, the VOCs implicated in terrestrial biogenic NPF, isoprene and monoterpenes,

are also emitted by the ocean. Hence findings from terrestrial NPF studies which implicate

isoprene and monoterpenes may be relevant for marine NPF. Hence studies of NPF in both

marine and terrestrial environments which suggest a role for organics are summarised

below.

Modini et al (2009) observed frequent NPF in clean marine air on the coast just

south of the Great Barrier Reef. The particles grew to 20-50 nm over 1-4 hours. The

volatility and hygroscopic properties of particles >10 nm were measured during these

formation events with a VH-TDMA (Volatility and Hygroscopicity Tandem Differential

19

Mobility Analyser). The new particles were found to be internally mixed and had a

significant organic component (20-40%) with the remaining 60-80% attributed to sulphates.

The strongest NPF event had an air back trajectory in the direction of the highly biologically

productive Great Barrier Reef, suggesting biologically produced precursor VOC may have

been responsible. However in the absence of gas phase measurements the precursor/s

could not be identified.

Vaatovaara et al (2006) investigated the composition of newly formed nano particles

(<20 nm) at Mace Head in spring and autumn. NPF is common at Mace Head, and is strongly

influenced by iodine compounds emitted from local macroalgae (Saiz-Lopez et al., 2012).

The particle formation events observed by Vattavaara et al (2006) occurred during low tide

and high solar radiation, which is typical for Mace Head. The authors used an Ultra Fine

Organic (UFO) TDMA in June, and PHA-UCPC (pulse height analyser-ultrafine condensation

particle counter) in October to investigate the chemical composition of newly formed

particles. Both instruments were 'calibrated' before ambient measurements by exposure to

typical and/or likely components of marine aerosol such as NaCl , ammonium sulphate and

bisulphate, I2O4 and I2O5 (known to be important at Mace Head), and various organic acids

to determine the instrument response (via particle growth rates at different conditions).

The authors concluded that iodine oxides were responsible for nucleation of particles, but

further growth of 5-6 nm particles was due to organic compounds. Whilst the compounds

could not be identified from the measurements, the authors speculated that isoprene or

monoterpene-dervied SOA was likely responsible.

Grose et al (2007) investigated a possible link between NPF and methyl iodide

emitted from phytoplankton and macroalgae in the waters surrounding Cape Grim Baseline

Station. An ocean-air methyl iodine flux was observed in nearby waters, with the bull kelp

near the shore identified as the strongest source. However in contrast to Mace Head, at

Cape Grim there were no regular NPF events at low tide, and the only NPF event at the

station (identified by an increase in 3-10 nm particles) was not accompanied by elevated

methyl iodide at the station. Concurrent PTR-MS measurements at the station did not

identify any organic precursors. The study concluded that iodine emissions are not an

important source of new particles at Cape Grim perhaps due to the location of the station

on the cliff top, or low amounts of iodine present in the local seaweed compared to Mace

Head.

20

A study in subtropical Brisbane, Australia found regular occurrence of NPF at night

throughout the year, corresponding mainly with air masses coming off the ocean to the east

(Salimi et al., 2016). The authors speculate that marine biogenic precursors such as dimethyl

sulfide may be responsible, however further characterization of particle precursors in the air

masses was required.

Two publications describe terrestrial NPF in a eucalypt forest at Tumbarumba,

Australia, (Suni et al., 2008; Ristovski et al., 2010) . An Air Ion Spectrometer (AIS) was used

at the site, which can detect charged particles down to 0.3 nm. Suni et al (2008) reported

NPF events occurred on 42% of days, both during the daytime and night-time. The high

growth rates observed, which exceeded particle growth rates observed in boreal European

forests, were attributed to high concentrations of condensable vapours. The highest growth

rates were in summer, and occurred when air masses came from forests rather than

agricultural land, suggesting the precursor may be biogenic emissions of terpenes. Ristovski

et al (2010) investigated the chemical composition of nanoparticles using VH-TDMA. The

authors found that one component of the particles had higher lower volatility and lower

hydroscopic growth, and the other component had lower volatility with higher hygroscopic

growth. The first component was attributed to oxidation products of monoterpenes, with

the volatility curve from particles at Tumbarumba showing excellent agreement with the

volatility curve obtained during chamber measurements of monoterpene oxidation. The

broadness of the curve suggested several different organic species were likely present. The

sulphate component was identified as ammonium bisulphate or ammonium sulphate.

Calculations suggested that the sulphate component was responsible for < 6% of the particle

growth, highlighting the importance of organics at this site.

Finally, at a study at a forested site in Finland, Laaksonen et al (2008) also found that

monoterpene and sesquiterpene oxidation products play an important role in growth of

new particles. AMS measurements showed that particles > 50 nm, measured during and

after NPF events, contained similar organic substances, thought to be mono and

sesquiterpene oxidation products. Filter analysis showed the organic component was mostly

aldehydes. The composition of 10-50 nm particles was investigated with TDMA, which

showed that growth rates of particles via ethanol uptake was strongly correlated with gas

phase monoterpene oxidation products, measured by CIMS ( chemical ionisation mass

21

spectrometer). Particle growth rates during nucleation also correlated with these biogenic

oxidation products.

Future work required to improve understanding of the role of organics in NPF

Zhang et al (2012) suggests further field and laboratory measurements are needed to

improve understanding of NPF in the atmosphere. In particular, the authors highlight a

requirement for simultaneous identification and quantification of the gas-phase nucleating

vapours (including organic vapours, i.e. VOCs) and the chemical composition of the natural

and ionic clusters and nanoparticles.

Aqueous phase SOA and organics

Formation of SOA in aqueous solution can occur in cloud droplets, or in aerosol

water. Gas phase VOC (usually highly water soluble) diffuse into the solution, and are

oxidised in the aqueous phase by OH or another radical. The major source of OH in the

aqueous phase is uptake from the gas phase (Ervens et al., 2011).

SOA formation is more favourable in the aqueous phase compared to the gas phase,

because when oxidation occurs in the aqueous phase, C-C bonds are usually left intact,

which leads to lower volatility products. In the gas phase oxidation usually leads to the

breaking of C-C bonds, which leads to smaller, more volatile products (Carlton et al., 2007).

Hence aqueous phase SOA typically has a higher O/C ratio than gas phase SOA, and is more

hydrophyllic (polar), so is more likely to act as CCN. Aqueous phase SOA is often composed

of oligomers and other high molecular weight molecules. Aqueous phase SOA has a shorter

atmospheric lifetime than gas phase SOA, through removal by wet deposition

(Myriokefalitakis et al., 2011).

A recent review of aqueous phase SOA formation by Ervens et al (2011) suggests

that SOA formed in aqueous phase is likely to be as important globally as SOA formed in the

gas phase, and a recent global modelling study of oxalate also concludes that aqueous SOA

contributes significantly to the global SOA burden (Myriokefalitakis et al., 2011). Despite

this, SOA formation in clouds is not yet routinely parametised in models. Ervens et al (2011)

22

hypothesise that the reason models typically under predict the O/C ratio in SOA may be due

to omission of aqueous phase SOA, especially as under prediction of O/C is usually

correlated with RH and sulphate.

An in-depth discussion of typical components of aqueous SOA can be found in the

Dicarbonyls, carboxylic acids and dicarboxylic acids Section.

The next section will give some general information about the composition of marine

aerosol, with a focus on SOA, then Section 2.2 will discuss the SOA formation potential of

different groups of VOCs in the marine boundary layer (MBL). However, much of the general

information on VOCs in Section 2.2 is also relevant for SOA in BB plumes.

2.2 Marine aerosol composition - evidence of SOA

Natural aerosols, including sea spray and secondary aerosols originating from marine

dimethyl sulphide (DMS), have been shown to strongly affect the uncertainty of cloud

radiative forcing in global climate models, highlighting a need to understand the

composition and microphysical properties of marine aerosol in very pristine marine

environments (Carslaw et al., 2013).

Marine aerosols are comprised of a mixture of inorganic and organic components. Of

the inorganic components, sea salt typically dominates the total mass, with contributions

from non sea salt (nss) sulphate (DMS oxidation product). The organic fraction of marine

aerosol can be divided into water insoluble organic matter (WIOM) and water soluble

organic matter (WSOM). WIOM is typically comprised of primary organic matter which is

transferred from the surface micro layer of the ocean to the atmosphere during bubble

burst, which may comprise polysaccharides, proteins, nucleic acids, lipids and exopolymer

substances (Ovadnevaite et al., 2011a). WSOM is sometimes used as a proxy for SOA, and

may comprise components such as MSA (DMS oxidation product) and dicarboxylic acids,

such as oxalate, and amines. However recent studies have suggested that as WIOM ages

and becomes more oxidised, it may then become water soluble – hence WSOM may refer to

SOA, but also to aged POA (Decesari et al., 2011; Ovadnevaite et al., 2011b; Rinaldi et al.,

2010). The overlapping properties of primary and secondary organic aerosol were recently

summarised by Rinaldi et al (2010):

23

“Primary and secondary organic components must be considered as closely correlated

in marine aerosol as the oxidation products of biogenic primary organics can lead to the

production of both oxidised aerosol components (belonging to broad category of SOA) and

of volatile low molecular weight products, which can partition into the gas phase and

influence the multiphase photochemical evolution of the marine troposphere (including

SOA formation)“.

Hence, when attempting to distinguish between WSOM which has formed from gas to

particle conversion, and WSOM which consists of highly oxidised POA, the use of chemical

tracers is valuable and will be discussed further below. Studies also typically look at

correlations between WSOM and MSA and WIOM to determine whether the WSOM is likely

to be of primary or secondary origin.

Importance of POA versus SOA

Studies have observed a higher proportion of OC (both primary and secondary) in

marine aerosol in the more biologically active summer months (Sciare et al., 2009; Facchini

et al., 2008) due to accumulation of biological material in the ocean surface layer, and

increased biological processes, such as photosynthesis. Some studies have found a higher

proportion of WSOM (i.e SOA or aged POA) in marine aerosol in the summer months (e.g.

Rinaldi et al, 2011 during the Marine Aerosol Production study at Mace Head), others a

higher proportion of WIOM (fresh POA), (e.g. Claeys et al 2010, Sciare et al 2009 at

Amsterdam Island).

There has been an increased awareness of the contribution of POA to marine OA in

recent years, and the molecular composition of POA is the subject of intense study. A recent

global modelling study suggests that the POA contribution to total OA (7.9-9.4 Tg y-1) is

comparable to that from MSA (5 Tg y-1) but dominates the contribution from monoterpene

and isoprene derived SOA (0.2 Tg y-1) (Meskhidze et al., 2011). The major contribution of

POA is significant because of the likely contribution it makes to CCN number in the MBL, and

resulting effects on cloud properties. Whilst studies investigating the effect of POA on CCN

have shown differing results (Ovadnevaite et al., 2011a; Meskhidze et al., 2011; Orellana et

al., 2011; Westervelt et al., 2012; Topping et al., 2013), there is recent suggestion that sea

salt particles enriched with POA can be more effective CCN than particles dominated by

24

sulphate over the same supersaturation range, (Ovadnevaite et al., 2011a), and that even

POA which are emitted below the critical diameter for droplet activation may still serve as a

nucleus for sulphate condensation (Meskhidze et al., 2011), and thereby influence CCN

number.

Meskhidze et al (2011) showed the while isoprene and monoterpene derived SOA

can contribute up to 20% of submicron OM mass in some regions, (highlighting the

importance of regional sources), the modelling results suggested isoprene and

monoterpene-derived SOA and MSA have little effect on the size distribution or chemical

composition of climatically relevant aerosols. In any case, many studies show a significant

proportion of WSOC in marine aerosol, and as the identity of most of this WSOC is currently

unknown, a major contribution by SOA cannot be ruled out. Studies that suggest a

contribution of SOA to marine aerosol are explored below.

Evidence of SOA contribution to marine aerosol- field studies

In 2006, the Marine Aerosol Project was carried out which combined field

measurements at the Mace Head coastal site, simultaneous shipboard measurements off

the West Ireland coast, and shipboard bubble bursting experiments. During the high

biological activity periods in MAP, WSOC contributed an average of 23% to the total aerosol

mass (Rinaldi et al., 2010). Decesari et al (2011) used NMR and AMS to chemically

characterise submicron organic carbon from aerosol sampled at the coastal site. The

authors used factor analysis to identify a contribution to total WSOC from three different

groups: a) MSA and LMW acids, b) aliphatics containing amines (NMR)/NaCl with internally

mixed organics (AMS), and c) aliphatics containing n-alkanoic acids/oxygenated organic

aerosol (AMS). These findings suggest the WSOC fraction is comprised of material both from

gas to particle conversion, and aged POA. Facchini et al (2008) identified SOA from biogenic

amines as an important contributor to WSOC during the MAP. Diethyl and dimethyl

ammonium salts (DMA+ and DEA+) were found in submicron aerosol in significant amounts

in samples taken at both Mace Head and on the ship, and had higher concentrations in clean

marine air in periods of high biological production, pointing to a biogenic source. The

authors speculated that the salts are most likely formed from reaction of gaseous amines

(dimethyl and diethyl amines) with sulphuric acid or sulphates, and that the gaseous amines

25

may be an end product of marine OM degradation. Importantly, an absence of DMA+ and

DEA+ in bubble bursting experiments confirmed a gas to particle (SOA) source. Finally

Rinaldi et al (2010) reports that using a combination of HPLC-TOC and IC it was possible to

chemically characterise ~80% of the WSOC from MAP Mace Head and ship aerosol samples

into 3 classes according to their acid base properties: Neutral-basic compounds (32%),

including DEA+ and DMA+, mono-diacids (42%), including MSA and oxalic acid, and

polyacids (4%). However, the molecular composition of 28% of the mono-di acids and 16%

of the neutral-basic component could not be identified.

Muller et al (2009) also found that biogenic amines contribute to SOA at the Cape

Verde Islands, with DEA+, DMA+ and mono methyl amine (MA+) detected in submicron

particles during a winter algal bloom. This finding, which followed the initial report of amine

salts in Mace Head aerosol (Facchini et al., 2008) confirmed the likely importance of amines

to SOA formation in the MBL, though contributions to total OC at Cape Verde were quite

low at 0.2-2% (water solubility of OC was not measured). In a separate publication, Muller

et al (2010) reports that oxalate and MSA are the major organic compounds identified in

aerosol filter samples from Cape Verde, with malonate and succinate also detected in most

samples. The overall mass of PM10 is dominated by sea salt and mineral dust, but during

clean marine periods, organic compounds made up ~60% of the mass of the smallest size

fraction (50-140 nm), however the composition or water solubility of this OC is not

specified.

In summer at Mace Head, Ovadnevaite et al (2011b) observed very high

concentrations of submicron OA with an AMS over a 30 hour period, with concentrations

peaking at 3.8 mg m-1, the highest ever reported in a clean marine environment.

Measurements indicated that 37% of the mass was hydrocarbon-like compound (attributed

to primary sea spray emissions), and 63% was oxygenated hydrocarbon compound,

indicating chemical aging of POA, or SOA. However, the authors concluded that the

oxygenated hydrocarbon mass was likely dominated by aged POA rather than SOA due to a)

the hydrocarbon-like and oxygenated hydrocarbon mass being highly correlated, and the

total OA and nss-sulphate concentrations being anti correlated, indicating different

processes were responsible. There was no evidence of amine-derived SOA during this study,

in contrast to observations at Mace Head by Facchini et al (2008).

26

Studies of aerosol chemical composition at Amsterdam Island (Claeys et al., 2010)

show that sea salt dominates on a mass basis, making up 83% of the fine fraction (<PM2.5)

and 91% of the course fraction (>PM2.5). WSOC made up only 2.8% of the mass of the

PM2.5, and contained MSA, dicarboxylic acids, and organosulphates. However, a large

proportion of the WSOC (75% of the fine fraction and 95% of the coarse) could not be

identified, and the authors attribute the unidentified mass as oxidised POA due to the

presence of POA tracers. Tracers of isoprene –derived SOA, 2-methyltetrol sulphates were

targeted but not found, giving no evidence of isoprene –derived SOA in these samples.

Sciare et al (2009) examined the long term record of OA <10 µm at Amsterdam Island and

found that during most of the year non-MSA WSOC (i.e total WSOC excluding the

contribution from MSA) and WIOC make an equal contribution to the OA component.

However during the summer months the WIOC component increases significantly,

coinciding with the peak in marine productivity, while the non-MSA WSOC shows only a

small increase. MSA increases substantially in the summer months and makes a contribution

to WSOM ranging from 4 to 70%. The summer peaks in non-MSA WSOC correspond with

the MSA peaks and do not correspond with the WIOC peaks, suggesting an SOA source for

the WSOC, however the authors state that oxidised POA cannot be ruled out. Ion chemistry

analysis was performed but the authors do not discuss the contribution of oxalate, formate,

acetate or other species analysed to the WSOC.

At Cape Grim during the biologically active summer period, Fletcher et al (2007)

measured particle hydroscopic properties and volatilities, and attributed the presence of a

non-hygroscopic material with a similar volatility to ammonium sulphate/bisulphate to

organic matter – however the source of the OM (i.e primary or secondary) could not be

determined from these measurements.

Finally, Fu et al (2011) characterised the molecular composition of organic aerosol

from samples taken from a round the world cruise in the low to mid latitudes of the

Northern Hemisphere. GC-MS was used to characterise the samples, which allowed the

classification of the following groups: n alkanes, fatty acids, alcohols, sugars, lignin/resin

acids, sterols, aromatic acids, hopanes, PAH and biogenic SOA tracers. Total OC was also

measured. However, due to the analysis technique, there were several important groups

which could not be detected, including HULIS (Humic-like substances), dicarboxylic acids,

amines, proteins and organosulfates, and only ~5% of the total mass could characterised on

27

a molecular level. Biogenic SOA tracers from oxidation of isoprene and monoterpenes were

detected in all samples, and specific tracers detected included 2-methylglyceric acid, three

C5-alkene triols, and two diasteroerisometric 2-methyltetrols (isoprene oxidation), pinoic

and pinic acids, 3-hydroxyglutaric acid and 3-methyl-1,2,3-butane tricarboxylic acid (pinene

oxidation) and β-caryophyllinic acid, (β-caryophyllene oxidation). Biogenic SOA accounted

for 0.5-20% of all organic material, and 0.05-1.5% of total organic carbon in marine aerosols.

Six hydroxyl acids were detected, including malic and glycolic acid and were correlated with

biogenic SOA tracers suggesting a common source. The high biogenic SOA component was

attributed in part to transport of SOA from continental sources, due to higher SOA observed

over coastal areas, and correlation of biogenic SOA with PAHs. Source apportionment

indicated that atmospheric oxidation was the most important contributor to marine

organics over the tropical oceans, including the region off the Californian coast, South China

Sea and North Indian Ocean and that primary marine emissions and biomass burning

influences were minor. However in other regions, such as the Western North Pacific in

winter and spring, biomass burning plumes were found to significantly influence the

chemical composition of marine aerosol.

The study by Fu et al (2011) is interesting because it highlights the possible

interactions between continental and marine aerosols and reactive gases, through long

range transport, as well as photochemical aging and sea-air flux. The significant continental

influence seen in the composition of marine aerosols in this study also suggests that findings

from studies which focus on aerosol composition and processes in very clean marine air may

be only representative of limited parts of the ocean.

Potential VOC contributors to marine aerosol SOA

Isoprene and monoterpenes

The ocean was first reported as a biological source of isoprene in 1992 based on field

measurements (Bonsang et al., 1992) and a source of monoterpenes in 2008, based on a

combination of laboratory and field measurements (Yassaa et al.). There have been several

studies since the first report by Bonsang et al (1992) which have investigated biological

processes and emission rates of isoprene in the laboratory (see review by Shaw et al (2010)

for a comprehensive summary). Phytoplankton are generally reported to be the dominant

28

source of isoprene and monoterpenes, but studies have also confirmed emission of isoprene

from seaweed in rockpools (Broadgate et al., 2004) and microphyobenthic communities in

an estuary (Exton et al., 2012). Isoprene emissions are highly dependent on phytoplankton

species and amount of cell chlorophyll, and have been found to generally increase with

available light and seawater temperature (Shaw et al., 2010). Isoprene in seawater has a

lifetime of 19 days, due to its slow reaction with OH, and so is removed from seawater

predominantly by sea-air exchange (Shaw et al., 2010).

Recently, laboratory studies identified the sea surface microlayer (SML) as a

potentially important source of isoprene in the MBL, from photochemical reactions

involving fatty acids as surfactants and dissolved organic matter as photosensitizers (Ciuraru

et al., 2015a).

Several field studies have confirmed non negligible ambient concentrations of

isoprene in the remote MBL; Williams et al (2010) observed isoprene of up to 200 ppt

during bloom conditions over the South Atlantic, Colomb et al (2009) observed

concentrations of between 30-70 ppt over ocean fronts in the Southern Indian Ocean,

Galbally et al (2007) and Lawson et al (2011a) observed isoprene concentrations of 14-21

ppt in clean marine air at Cape Grim during summer, and ~20 ppt was observed over the

Chatham Rise in the Southern Pacific during the SOAP cruise in 2012 (unpublished data).

Shaw et al (2010) provides a full summary of concentrations obtained during field studies.

There are relatively fewer measurements of monoterpenes in the remote MBL, but Yassa et

al (2008) observed levels of up to 100-200 ppt over an active bloom, Colomb et al (2009)

observed between 20-40 ppt over ocean fronts and Lawson et al (2011a) observed 25 ppt at

Cape Grim.

The discovery that the ocean is a source of isoprene and monoterpenes is significant

due to the high SOA formation potential of these species when oxidised (Hallquist et al.,

2009). This in turn may lead to modification of aerosol properties in the MBL, such as CCN

concentration, which can have indirect climate effects. Meskhidze and Nenes et al (2006)

speculated that isoprene-derived SOA over a bloom site in the Southern Ocean may

responsible for the observed change in cloud microphysical and radiative properties at the

same site. The authors made a severe overestimation of atmospheric isoprene

concentrations at the site, which led to the conclusion that isoprene –derived SOA could

29

explain 100% of the change in cloud properties. However, even when the much lower

atmospheric isoprene concentrations were confirmed, the authors maintained there was a

likely relationship between biological emissions and cloud properties at that site.

Several recent global modelling studies have examined the contribution of isoprene-

derived SOA to marine aerosol. These studies on the whole indicate the isoprene is unlikely

to be a significant contributor globally, but may make a significant contribution on a local

scale. For example, Arnold et al (2009) used a top down approach to estimate global

isoprene emissions, by scaling model predictions to observed laboratory fluxes. They

concluded that isoprene derived-SOA makes a <1% contribution to the OC observed at

marine sites at Mace Head, the Azores and Amsterdam Island. The author cites limited

atmospheric isoprene data as a factor limiting accurate prediction of isoprene SOA.

Gantt et al (2009) used laboratory fluxes of isoprene and satellite imagery of

phytoplankton distribution to determine hourly global isoprene emissions and isoprene

derived-SOA, as well as simulating primary organic carbon, with a focus on < 1µm particles.

The authors found a similar global isoprene source to Arnold et al (2009), and conclude that

on a global scale isoprene-derived SOA contributes <0.2% to total marine OC. However, in

contrast to Arnold et al (2009), Gantt et al (2009) investigate the spatial and temporal effect

of isoprene on isoprene-derived SOA, and find that over the tropics, marine isoprene may

contribute 30% to marine OC, and during midday hours, when isoprene emission and

oxidation is at its greatest, may contribute up to 50% of the submicron OC over vast areas of

the ocean.

The minor contribution of marine isoprene and monoterpenes to marine aerosol on

a global scale was reported by Gantt et al (2010) in a further modelling study. The authors

found that marine isoprene and monoterpenes contribute < 5% to total marine organic

PM2.5, with POA dominating the total organic mass.

The global modelling study of isoprene and monoterpene emissions by Luo and Yu et

al (2010) uses bottom up and top down methods for determining global fluxes, and finds a

very large discrepancy between the two methods. The bottom up approach uses laboratory

flux data, and finds a global flux of isoprene (0.32 Tg y-1) which agrees well with estimates by

Arnold (2009)and Gantt et al (2009), and a smaller monoterpene global flux (0.013 Tg y-1)

reflecting the small flux observed in the laboratory. However, in the top down method, the

30

model was fitted to observed atmospheric concentrations from the OOMPH cruise over the

South Atlantic (Yassaa et al., 2008), and the resulting global fluxes required to explain the

observations were extremely large: 11.6 Tg y-1 isoprene and 29.5 Tg y-1 monoteprenes,

which are by far the largest ever reported, and if correct would indicate a very significant

marine source of SOA from monoterpenes. However a weakness of the top down approach

was that the model was fit to data from only one experiment (OOMPH), which reported

concentrations of isoprene and monoterpenes which were at the upper end of the range

reported elsewhere for isoprene (there are insufficient monoterpene measurements for

comparison). The scarcity of measurements in particular of monoterpenes of the ocean,

means that the very large discrepancy between top down and bottom up approaches

cannot currently be resolved.

Finally, there has not been any evidence to date of ocean derived-isoprene and

monoterpene SOA in marine aerosol. Fu et al (2011) detected SOA tracer molecules from

isoprene and mono and sesquiterpene oxidation in all samples from a round-the-world

cruise – however terrestrial influence was likely. Claeys et al (2010) found no evidence of

isoprene SOA tracers (such as 2-methyl tetrol sulphates) in aerosol samples from

Amsterdam Island.

While several of the modelling studies discussed above (Arnold et al., 2009; Gantt et

al., 2009; Gantt et al., 2010) find the overall contribution of isoprene and monoterpene-

derived SOA is small in terms of total mass, it is possible that the isoprene and monoterpene

oxidation products may make a more significant contribution to particle number in the MBL,

through NPF and contribution to formation of the critical nucleus. The difficulty in

identifying gas species contributing to molecular clusters (highlighted previously in the NPF

section) currently prevents an understanding of the role of isoprene and monoterpenes in

these processes. However the importance of organic molecules in formation of the critical

nucleus (Kirkby et al., 2011; Metzger et al., 2010, Kirkby et al., 2016) suggests a likely role for

these ocean-derived VOCs.

An understanding of the contribution of other secondary aerosol species such as

isoprene and monoterpene derived-SOA to the CCN activity of marine aerosol is still

emerging. Meskhidze and Nenes et al. (2006) suggested a link between isoprene-derived

SOA over a phytoplankton bloom site and cloud microphysical and radiative properties in

31

the Southern Ocean, while Lana et al. (2012) found a correlation between modelled

secondary sulphur and organic aerosols and variability of cloud microphysics derived from

satellite observations over the remote mid and high latitude ocean.

In the summary of future research directions, Shaw et al (2010) highlight a need for

additional field and laboratory measurements of monoterpenes, due to sparse datasets and

high SOA formation potential. A multi-faceted approach to further work is recommended,

by combining laboratory, field and modelling studies in order to reconcile uncertainties in

marine fluxes of isoprene and monoterpenes highlighted above and their contribution to

SOA and local photochemistry. Finally, the authors highlight the importance of making

concurrent chemical and biological measurements: 'it is important to make trace gas

measurements in air and water phases in concert with a suite of detailed biological and

environmental measurements such as aerosol chemical composition, and size distribution,

meteorological parameters, physical water parameters and cell counts of dominant

phytoplankton species.”

DMS

Dimethyl sulfide (DMS) and its oxidation products have been the focus of a great

deal of research in the MBL over the past 25 years, prompted by publication of the CLAW

hypothesis in 1987 (Charlson et al., (1987)). CLAW proposed a biological regulation of

climate via a feedback loop involving phytoplankton emissions. Specifically CLAW proposed

that DMS emissions from phytoplankton would lead to an increase in CCN, which would in

turn increase cloud albedo, with the resulting change in temperature and radiation resulting

in altered DMS emissions from phytoplankton.

DMS contributes to aerosol in the MBL in the following way. DMSP

(dimethylsulphoniumpropionate) is released by marine phytoplankton, which is

enzymatically cleaved to DMS in surface waters. An alternate competing degradation

pathway of DMSP involves demethylated to methane thiol (methyl mercaptan). When

released to the atmosphere, DMS is oxidised predominantly by OH to form sulphur dioxide

and methane sulphonic acid (MSA). Sulphur dioxide can be taken up into particles, or can be

further oxidised to sulphuric acid. MSA and sulphuric acid can condense on to existing

particles (heterogeneous nucleation), and sulphuric acid can combine with other gas phase

molecules, such as ammonia, to form new particles (homogeneous nucleation).

32

The final products of DMS oxidation in particles are therefore nss sulphate and MSA. Both

are water soluble.

Atmospheric DMS has a lifetime of about 1.5 days in temperate regions, and

average concentrations in the MBL have found to vary from 1 ppt at the South Pole

(Beyersdorf et al., 2010), to 100 ppt at Cape Grim (Lawson et al., 2011a; Galbally et al.,

2007), to 250 ppt over highly biologically active ocean fronts (Colomb et al., 2009).

Concentrations of almost 1000 ppt (1 ppb) were observed over the Chatham Rise in the

South Pacific during the SOAP campaign as part of this work (unpublished data).

Concentrations of atmospheric DMS are generally higher in summertime due to increased

biological activity in surface waters.

While MSA and sulphuric acid can condense on to existing particles in the MBL, there

is very limited evidence for NPF from sulphuric acid in the MBL – due to the high load of sea

salt in the MBL, sulphur dioxide or sulphuric acid molecules are scavenged by existing sea

salt particles so that there is insufficient sulphuric acid for NPF to occur. However there is

experimental evidence that DMS-derived sulphuric acid nucleation does occur frequently in

the free troposphere (FT), due to the low background aerosol concentration, and low

temperatures. These sulphuric acid particles that form in the FT may then undergo

condensation and coagulation before being entrained to the MBL. Interestingly, a modelling

study has shown that that DMS and its oxidation products advected to the FT can travel

hundreds or even thousands of km over several days while nucleation and growth is

occurring, before particles are entrained to the boundary layer (BL). Therefore there may

be a disconnect between regions of high DMS flux, and regions with DMS-derived CCN.

(Korhonen et al., 2008).

DMS does have an important role in modifying chemical properties of marine

aerosol as indicated by the ubiquitous presence of MSA and nss sulphate in marine aerosol

around the world (see previous discussion of marine aerosol). But the link between DMS

and climate as proposed in CLAW has never been proven, and the perceived importance of

DMS as CCN precursor has lessened with increased knowledge about alternate source of

CCN such as sea salt and primary organics. Recently a review by Quinn and Bates (2011)

critically assessed the current knowledge of the CLAW hypothesis and found no compelling

evidence for climate regulation by sulphur emissions from phytoplankton. Firstly, the review

examined whether DMS is a significant global source of CCN in the MBL, and found that

33

while there is evidence for a strong seasonal relationship between CCN, DMS, nss sulphate

and MSA at several remote oceanic sites (including Cape Grim), this does not necessarily

indicate than CCN number is sensitive to an increase in DMS emissions. The authors also

point out the significant contribution of other sources to CCN in the MBL such as wind-

blown salt and primary organics, which were not thought to contribute to CCN when the

CLAW hypothesis was established. The review relies on several global modelling studies to

assess the relationship between DMS and climate on a global or hemispheric scale. One

such modelling study of the Southern Hemisphere (Korhonen et al., 2008) show that at

some sites such as Cape Grim, DMS emissions do significantly increase CCN in summer

(mostly due to advection of DMS-derived sulphuric acid particles from the FT), but further

south, sea salt dominates CCN all year round. Korhonen et al (2008) also shows that in this

the Southern Ocean CCN has a low sensitivity to a decrease in DMS, due to the abundance

of other CCN sources, including entrainment of sulphur-derived CCN from continental

sources. The review goes on to examine whether there is evidence for DMS-derived CCN

leading to increased cloud albedo (as proposed by CLAW), and conclude that on the

contrary, some studies show increased CCN can in fact lead to increased evaporation and

decreased cloud cover. Finally, the authors cite further modelling studies that find that the

sensitivity of DMS emissions to changes in radiation, cloud cover and surface temperature is

weak.

Despite the lack of clear evidence supporting the CLAW hypothesis, research on DMS

still continues, because of the important role it has in modifying chemical properties of

marine aerosol, particularly in the summer months at remote locations like Cape Grim.

Oxygenated VOCs

Formaldehyde, acetaldehyde, acetone and methanol are abundant and ubiquitous in

the atmosphere. These OVOCs have a wide range of sources, including photochemical

production, biomass burning, and direct biogenic and oceanic emissions. The high water

solubility of formaldehyde, acetaldehyde, acetone and methanol leads to strong likelihood

of uptake into cloud droplets and aerosol water, where they may act as precursors for

dicarbonyls and carboxylic acids which then contribute to SOA through chemical processing

in the aqueous phase. In addition short-lived OVOCs such as acetaldehyde (average lifetime

34

in of 1 day) and HCHO (average lifetime of a few hours) play in important role in the

formation of ozone.

Despite the importance of these compounds, their global budgets are still uncertain.

Recent studies have confirmed emission of small oxygenated molecules such as

formaldehyde, acetaldehyde and acetone from the degradation of chromomorphic

dissolved organic matter (CDOM) in ocean waters, (de Bruyn et al., 2012). Recent irradiation

laboratory studies involving surfactants (fatty acids) and photosensitisers (humic acid) show

production of a wide variety of saturated and unsaturated aldehydes, and even alkanes,

acids and dienes, which suggests that the photochemical reactions in the ocean SML may be

an important source of these compounds (Ciuraru et al., 2015b). However investigation of

the chemical and biological processes that occur in the SML are still in the early stages, and

whether the ocean is an overall source or sink of these compounds of is not well known, and

adds uncertainty to global models.

A brief discussion of sources, sinks and typical concentrations in the background

marine atmosphere is given below.

Formaldehyde

Formaldehyde is the most abundant carbonyl in the troposphere. It is an

intermediate species formed from the oxidation of precursor hydrocarbons, primarily

methane, but also oxidation of non methane hydrocarbons (NMHC) of biogenic and

anthropogenic origin. Formaldehyde is also directly emitted from BB, biological processes

and anthropogenic sources, however on a global scale, the photochemical source of

formaldehyde is thought to dominate the primary sources (Vrekoussis et al., 2010). Because

methane (which is the main photochemical source of formaldehyde) has a lifetime of

several years, changes in concentrations of formaldehyde can be attributed to local short

lived NMHC precursors, such as isoprene, and so enhanced formaldehyde concentrations

can be used to identify photochemical hotspots. Sinks of formaldehyde are photolysis, OH

oxidation, wet and dry deposition and multiphase chemistry, (Vrekoussis et al., 2010) where

it may contribute to reactions in solution resulting in SOA formation. Formaldehyde

concentrations are strongly influenced by variations in concentrations OH and NOx.

35

Vrekkousis et al (2010) investigated the global sources of formaldehyde and glyoxal

using data retrieved from the GOME (Global Ozone Monitoring Experiment) Satellite

instrument. They found hotspots of HCHO and glyoxal were highly correlated indicating

common sources, and found highest concentrations over regions with strong biogenic

emissions, and over regions heavily impacted by BB and anthropogenic emissions. The

authors use a ratio of glyoxal to formaldehyde to classify sources of the precursors: they

conclude that higher ratios are indicative of biogenic emissions, whereas lower ratios

indicate anthropogenic emissions and nitrogen dioxide (areas influenced by BB were

excluded from the ratio analysis).

Concentrations of HCHO over the ocean are typically 0.3 – 1 ppb (Zhou and Mopper,

1993; Ayers et al., 1997) but may be higher over marine photochemical hotspots (Sabolis et

al., 2011).

Acetone

Acetone is the second most abundant carbonyl in the atmosphere. Its main source in

the atmosphere is thought to be oxidation of iso alkanes (Fischer et al., 2012) but is also is

produced from oxidation of biogenic VOCs, and direct emission from BB, terrestrial and

ocean biogenic sources, including metabolism and decay. It has a global average lifetime of

~15 days and is lost through photolysis, OH oxidation and uptake into terrestrial and

biogenic processes. In the upper troposphere it can be a source of OH and is a precursor of

PAN, with implications for tropospheric ozone formation (Fischer et al., 2012).

There is uncertainty in the literature as to whether the ocean is a source or a sink of

acetone. There are studies which have observed fluxes of acetone from the ocean,

particularly during periods of high biological productivity and strong sunlight e.g. (Sinha et

al., 2007). Sjostedt et al (2012) observed negative correlations of acetone and methanol

with DMS over biologically active Arctic waters, and speculated Arctic waters may act as a

sink for these gases.

Fischer et al (2012) recently used a global model to investigate the sea air exchange

of acetone and provided a global budget. The study found that the ocean plays a major role

globally in controlling atmospheric concentrations of acetone, due to the biological

processes in the ocean which both consume and produce acetone. The study concluded that

36

the ocean can be a source of sink of acetone, depending on the atmospheric concentrations

and ocean surface temperature. Overall the tropical oceans were found to be a net source

of acetone, and the Southern Ocean a weak sink, but on a global scale the ocean and

atmosphere are in near equilibrium. Typical concentrations of acetone over the ocean range

from 50- 500 ppt (Colomb et al., 2009; Lawson et al., 2011a).

Acetaldehyde

Acetadehyde contributes to formation of ozone, PAN and HOx radicals in the

atmosphere.

Millet et al (2010) recently constructed a global budget of acetaldehyde which

included a satellite derived estimate of acetaldehyde flux from the ocean based on coloured

dissolved organic matter (CDOM). The authors concluded that photochemical production

from the ocean is the second largest source of acetaldehyde, with the largest source being

from oxidation of hydrocarbons, including alkanes, alkenes and ethanol the main

precursors. Decaying terrestrial plant matter is the third largest source, and BB is a small

global source. Destruction of acetaldehyde is dominated by OH oxidation. The authors find

good agreement between model prediction and observed levels of acetaldehyde except in

polluted urban areas and in the free troposphere, indicating an incomplete understanding of

acetaldehyde precursors, and a possible missing source. This is discussed further in

'Evidence for missing sources“ below.

Typical concentrations of acetaldehyde in the MBL range from 50-500 ppt (Lawson

et al., 2011a; Zhou and Mopper, 1993; Colomb et al., 2009).

Methanol

Methanol is the most abundant non-methane organic gas in the atmosphere, with

predominantly biogenic sources. It has a lifetime of several days, and is a significant source

of formaldehyde and CO (Millet et al., 2008).

Galbally and Kirstine (2002) constructed the first global budget of methanol which

estimated plant growth emissions dominated methanol emissions globally, with small

37

contributions from decay of plant matter, biomass burning, atmospheric production and

anthropogenic sources. The ocean was thought to be a large methanol sink. More recently

Millet et al (2008) provided a revised budget which proposed a reduced plant emission

source based on aircraft and surface measurements of methanol in Northern America, and a

significant ocean source, of the same magnitude as the plant emission source. Uptake by

the ocean and oxidation by OH are thought to be dominant sinks.

Both Galbally and Kirstine (2002) and Millet et al (2008) list biomass burning as a

small global source. Typical concentrations of methanol over the ocean range from 0.5 – 1

ppb (Galbally et al., 2007; Colomb et al., 2009)

Dicarbonyls, carboxylic and dicarboxylic acids

Dicarbonyls, carboxylic acids and dicarboxylic acids contribute to SOA through

oxidation in aqueous solution. Due to their higher O/C ratios, these compounds are highly

water soluble. Glyoxal and methyl glyoxal are the two simplest dicarbonyls, formic and

acetic acid are the simplest carboxylic acids, and oxalate, malonate and succinate are the

three simplest dicarboxylic acids. Due to the low volatilities of these compounds, they can

be found in either gas or aerosol phase in the atmosphere, but in typical atmospheric

conditions glyoxal and methyl glyoxal are predominantly found in the gas phase, formic and

acetic acid may be in the gas or aerosol phase, and oxalate, malonate and succinate are

typically found in the aerosol phase.

There has been a great deal of focus recently on the global sources of glyoxal and

methyl glyoxal and their role in formation of SOA. A global modelling study by Fu et al

(2008) found that oxidation of biogenic isoprene is the precursor for 47% of glyoxal and 79%

of methyl glyoxal with acetylene and the aromatics, and acetone also important

photochemical precursors for glyoxal and methyl glyoxal respectively. In addition to the

dominant photochemical source, biomass burning is also an important direct source of

these compounds. Lifetimes of glyoxal and methyl glyoxal are short at 2.9 and 1.6 hours

respectively, with destruction primarily by photolysis (Fu et al., 2008). Some recent

observations of glyoxal over the remote ocean in the absence of sufficient precursor VOCs

including isoprene has lead to speculation about alternative sources of these compounds,

38

including volatilisation of POA (Ervens et al., 2011; Fu et al., 2008). This is discussed further

in Section 2.2.3.3 ’Evidence for Unknown Sources“ below.

It is thought that the vast majority of SOA formation from glyoxal and methyl glyoxal

takes place in clouds (Carlton et al., 2007; Fu et al., 2008). The three mechanisms thought to

lead to SOA production from glyoxal and methyl glyoxal in solution are summarised by Fu et

al (2008) as 1) oxidation to non volatile organic acids, eg glyoxylic (C2), pyruvic, oxalic, 2)

oligomerisation of glyoxal and methyl glyoxal and 3) oxidation to organic acids which then

oligermerize.

Oxalate appears to be a particularly important end product of VOC oxidation, as it is

ubiquitous in cloud water, and exists in the aerosol phase once the cloud droplet

evaporates. Myriokefalitakis et al.,(2011) recently published a global modelling study of

oxalate which detailed spatial and temporal distribution for oxalate, its sources, sinks and

atmospheric lifetime. The authors report that all oxalate is formed from reaction in solution,

with 79% of the precursor gases originating from biogenic sources, mostly isoprene.

Finally Paulot et al (2009) published a global modelling study of formic and acetic

acids which showed that the source of these carboxylic acids is dominated by

photochemical oxidation of BVOCs, particularly isoprene. However they are also directly

emitted from a wide range of sources including BB and anthropogenic sources. Due to the

slow reaction rate of formic and acetic acids with OH, wet and dry deposition are the

dominant sinks.

Laboratory studies of aqueous phase SOA formation

Carlton et al (2007) investigated the oxidation mechanism of glyoxal in aqueous

solution, and found glyoxal was oxidised to formic acid and large multifunctional groups,

and degradation of the large multifunctional groups then created oxalic acid. The authors

speculated that the multifunctional groups were likely to be oligomers with carboxylic acid

or alcohol functional groups. This new mechanism was at odds to the accepted mechanism

for oxalic acid, which involved oxidation of glyoxal to glyoxlyic acid, to oxalic acid. The

authors said that their findings were able to explain the presence of oxalic acid and

multifunctional groups observed when cloud droplets evaporate, and provided confirmation

39

that glyoxal does contribute to SOA formation in clouds. When their new mechanism was

used in a model, the agreement between model and observations was much improved.

Tan et al (2012) investigated OH oxidation of acetic acid in solution to determine its

potential as an SOA precursor. Acetic acid was oxidised to glyoxylic, glycolic and oxalic acids,

but no oligomers formed, in contrast to methyl glyoxal which promoted oligomer formation.

Radical mechanisms for oligomer formation from methyl glyoxal were proposed. Lee et al

(2011) used AMS to investigate aging of ambient biogenic SOA in cloud water. The authors

found that the aqueous oxidation of SOA was more representative of ambient

measurements, and was more highly oxidised when glyoxal was present in the cloudwater.

Some recent work has focussed on aqueous oxidation of isoprene and its first stage

oxidation products methacrolein and methyl vinyl ketone (MVK) which contribute to gas

phase SOA. Methacrolein and MVK are water soluble, whereas isoprene has low water

solubility. Liu et al (2012b) found that aqueous phase oxidation of methacrolein and methyl

vinyl ketone by OH lead to the formation of oligomers, with masses up to 1400 amu. When

the solution was nebulised these oligomers formed SOA, with yields of SOA of 4-10% for

MVK. Huang et al (2011) investigated oxidation of isoprene itself in solution. While isoprene

has low water solubility, the authors reason due to its abundance isoprene may react on

water drop surfaces with aqueous phase OH, and oxidation product yields may be higher

than during gas phase oxidation. Isoprene was oxidised in bulk water by OH, and the rate

constant of OH and isoprene reaction was determined. There was a particularly high yield of

MVK (24%), compared to 11% yield of MACR, which was double the typical ratio during gas

phase oxidation. Yields of methyl glyoxal and glyoxal were 11 and 4% respectively, with

significant oxalic acid production. The observed oxidation products accounted for 50% of

the isoprene oxidised, with the remainder likely to be due to high MW compounds not

measured. This study explored the possibility that the range of VOC thought to be important

in aqueous phase SOA may be wider than currently accepted.

Evidence for aqueous phase SOA in MBL

There is considerable evidence that the dicarbonyls, and particularly glyoxal, makes

an important contribution to the organic component of marine aerosol over the remote

oceans. Both dicarbonyls have been found in marine aerosols over the Atlantic Ocean (van

40

Pinxteren and Herrmann, 2013) and Pacific Oceans (Bikkina et al., 2014), with dicarbonyl

mass positively correlated to organic acids (including oxalic acid) and ocean biological

activity. Oxalic acid has been consistently found in pristine marine aerosol from remote sites

including Amsterdam Island (Claeys et al., 2010), Mace Head (Rinaldi et al., 2010), Cape

Verde (Muller et al., 2010) and Cape Grim (unpublished data), with highest concentrations

during the biologically active summer months, coinciding with maximum concentrations of

DMS oxidation products methanesulfonic acid (MSA) and non-sea-salt sulphate. Rinaldi et

al. (2011) reported that oxalic acid in submicron marine aerosol from Mace Head and

Amsterdam Island showed a similar seasonal cycle to SCIAMACHY glyoxal columns, and a

chemical box model was able to explain the observed oxalate using the glyoxal columns.

Evidence for unidentified OVOCs over the ocean

There is increasing observation of small reactive OVOCs over the ocean, such as

glyoxal and HCHO, which cannot be explained by current knowledge of their precursors. This

indicates a local missing source of these carbonyls which is not currently understood.

Wittrock et al. (2006) reported enhanced concentrations of formaldehyde and

glyoxal from SCIAMACHY satellite retrievals over tropical oceans, but were unable to

reproduce observations using a global model. More detailed global modelling studies by Fu

et al. (2008) and Myriokefalitakis et al. (2008) were also unable to reproduce SCIAMACHY

glyoxal column retrievals over the tropical oceans, highlighting the possibility of unknown

biogenic marine sources. Later satellite retrievals of glyoxal from SCIAMACHY (Vrekoussis et

al., 2009), GOME-2 (Lerot et al., 2010) and recently from OMI (Miller et al., 2014) have

provided further evidence of the widespread presence and seasonal modulation of glyoxal

over oceans.

Glyoxal and methylglyoxal were first observed in the atmosphere and seawater in

the Caribbean and Sargosso Seas as early as 1989 (Zhou and Mopper, 1990) where

concentrations of glyoxal and methylglyoxal in seawater were 4 and 2 orders of magnitude

too low to explain the atmospheric concentrations. MAX-DOAS retrievals observed

hundreds of ppt glyoxal in the Gulf of Maine (Sinreich et al., 2007) and an average of 63 ppt

glyoxal over the remote Tropical Pacific (Sinreich et al., 2010). The Sinreich et al. (2010)

measurements were sufficiently far from land that the glyoxal observed was either from

41

unrealistically high mixing ratios of long lived terrestrial precursors, or more likely a

substantial unknown source possibly of marine origin, in support of earlier modelling and

satellite studies. The widespread presence of glyoxal over the remote oceans was recently

confirmed by Mahajan et al. (2014), who reported MAX-DOAS and long-path DOAS

differential slant column densities from 10 field campaigns in both hemispheres in tropical

and temperate regions. A global average value of about 25 ppt was reported with an upper

limit of 40 ppt, however over the Southern Hemisphere oceans glyoxal concentrations were

mostly below instrument detection limits.

In 2014 an additional source of glyoxal in the MBL was identified in laboratory

studies (Zhou et al., 2014), when oxidation of the sea surface microlayer (SML) led to

emission of low molecular weight oxygenated compounds including glyoxal. However, the

atmospheric yields of glyoxal were low, attributed to the fast irreversible hydrolysis of

glyoxal which prevents transfer of glyoxal to the atmosphere. Van Pinxteren and Herrmann

(2013) observed a glyoxal enrichment factor of 4 in SML compared to the bulk ocean, but

the concentration observed was several orders of magnitude too low to explain

concentrations of 10s of ppt typically seen in the MBL (Sinreich et al., 2010). The first eddy-

covariance flux measurements of glyoxal were recently made over the oceans, using an in

situ Fast Light Emitting Diode Cavity Enhanced Differential Optical Absorption Spectroscopy

(LED-CE-DOAS instrument) (Coburn et al., 2014). Negative flux (glyoxal transfer into the

ocean) was observed in both hemispheres during the day, and a positive flux from the ocean

in the Southern Hemisphere at night. However, despite this first evidence of a direct oceanic

source of glyoxal to the atmosphere, the positive flux at night could explain only 4 ppt of the

glyoxal observed in the overlying atmosphere (some 30 % of the overnight increase),

implying the contribution of another night-time production mechanism.

The production of glyoxal from photochemical processing of organic aerosol is a

possible contributor (Vrekoussis et al., 2009; Stavrakou et al., 2009; Bates et al., 2012)

though this remains unconfirmed. Volkamer (2012) recently report unexpectedly high

glyoxal concentrations in the free troposphere, which suggests a local source in the free

troposphere, and also adds uncertainty interpreting column measurements of glyoxal from

satellite, which assume 100% of glyoxal is in the boundary layer.

A more in depth understanding is currently hindered by a lack of dicarbonyl

observations in the MBL. While recent studies have contributed substantial additional

42

observations of glyoxal over the remote oceans (Coburn et al., 2014; Mahajan et al., 2014),

there have been no studies which have made parallel measurements of gas phase

precursors, and so expected yields of glyoxal are only estimates. No in situ observations of

glyoxal have been reported over temperate oceans of either hemisphere, and prior to this

work there was only one previous study reporting methylglyoxal observations over the

world’s oceans (in the tropical northern hemisphere, NH)(Zhou and Mopper, 1990). With

the exception of the Caribbean and Sargasso Sea measurements (Zhou and Mopper, 1990),

all column and in situ observations of glyoxal over the remote oceans had used optical

measurement techniques (Mahajan et al., 2014; Sinreich et al., 2010; Sinreich et al., 2007;

Coburn et al., 2014). Finally, given the challenges in retrieving low vertical column densities

(VCDs) of glyoxal over the ocean from satellite observations (Lerot et al., 2010; Vrekoussis et

al., 2009; Miller et al., 2014), more ground based and in situ measurements are required.

Sabolis et al., (2011) used satellite observations to identify a hotspot of

formaldehyde over the Mediterranean Sea, which had concentrations 4-5 times higher than

the remote Pacific Ocean. There was no correlation between areas of high net primary

productivity and formaldehyde, and models were not able to reproduce the observed

formaldehyde hotspot. The authors state that likely concentrations of isoprene and

monoterpenes would not explain the high formaldehyde, however acknowledged the

current high discrepancy in isoprene and monoterpene oceanic emissions as reported by

Luo and Yu (2010). Due to the high water solubility of formaldehyde, emission from the

ocean microlayer due to photochemical degradation of DOM seems unlikely. The authors

conclude that photochemical degradations of DOM in submicron aerosol is a suggested

source of the elevated formaldehyde.

In addition, Millet et al (2010) find that their model under predicts acetaldehyde in

areas with high urban pollution, which they attribute to an underestimate of hydrocarbon

precursors, but perhaps more interestingly severely under predicts acetaldehyde in the free

troposphere compared to aircraft observations. This is puzzling because the observed

PAN/NOx ratios are well simulated by the model indicating that the model is capturing the

gas phase chemistry.

Photo-degradation of atmospheric primary biogenic organic matter has been

suggested in the literature as a source of small reactive oxygenated compounds (Rinaldi et

al., 2011; Sabolis et al., 2011) and there is a general growing acceptance that photo-

43

degradation of semi volatile POA may be a source of small reactive oxygenated gas phase

species which can then go on to contribute to SOA after further oxidation, or dissolve into

the aqueous phase where they may also form SOA (Paulot et al., 2011;Ervens et al., 2011).

This is in contrast to previous held views that small oxygenated gas phase molecules were

produced solely from oxidation of gas phase parent compounds.

It is likely that some or all of the missing sources of small oxygenated compounds as

detailed above may be related to photo-degradation of POA. Transportation of POA to the

FT, with photolysis/photo-degradation resulting in local release of gaseous species could

explain the higher than expected concentrations of acetaldehyde and glyoxal recently

observed in the free troposphere observed by Millet et al (2010) and Volkamer et al (2012).

Amines

As discussed previously, Facchini et al (2008) detected diethyl and dimethyl

ammonium salts (DMA+ and DEA+) in aerosol at Mace Head, while Muller et al (2009)

detected DMA+, DEA+ and monomethyl amine (MA+) in aerosol at Cape Verde. These salts

are likely formed from the reaction of gaseous biogenic amines with sulphuric acid or

sulphates. In a recent review of atmospheric amines, Ge et al (2011a) reports that little is

currently known about the thermodynamic or kinetic properties of amines, despite their

ubiquitous presence in the atmosphere and unique ability (particularly the aliphatic amines)

to undergo fast acid-base reactions to form salts. The most common and abundant amines

in the atmosphere are the low-molecular weight aliphatic amines with carbon numbers of 1-

6, such as methylamine (MA), dimethylamine (DMA), trimethylamine(TMA), ethylamine

(EA), diethylamine (DEA), triethylamine (TEA), 1-propanamine and 1-butanamine(Ge et al.,

2011a). While the low molecular weight amines (eg dimethyl and trimethyl amine)

implicated in formation of ammonium salts reported by Facchini et al (2008) and Muller et

al (2009), have relatively high vapour pressures, their high water solubility means they can

contribute to SOA formation in aqueous solution (Ge et al., 2011a). Amines may also react

with organic acids to yield amides, and react with NOx and O3 to form products which

contribute to SOA.

Amines are emitted to the atmosphere from a wide range of anthropogenic sources

such as animal husbandry, industry, and natural sources including geologic (soil, volcanoes),

44

vegetation, ocean (biodegradation of marine organic matter and during excretion and

metabolism of marine organisms) and BB emissions (C1-C5 amines) (Ge et al., 2011a). They

may then participate in a wide variety of atmospheric processes (physical, chemical and

photochemical), including reaction with gas phase oxidants (OH, O3, NOx), uptake and

processing in cloud water, surface deposition (both wet and dry), gas-particle conversion,

acid-base chemistry, and may participate in homogeneous nucleation by stabilising acid

clusters in the critical nucleus. The lifetime of amines with respect to OH oxidation is short

(a few hours) in contrast with 72 days for ammonia. Ge et al (2011b) reviews global budget

estimates for methyl amines, with trimethyl amine the most abundant amine in the

atmosphere. Dominant global sources of methyl amines are animal husbandry, marine and

BB. The global emissions of methyl amines are thought to be much smaller (285 ± 78 Gg N a-

1) than ammonia. (~30,000 ± 50,000 Gg N a-1)

In part two of the atmospheric amine review, Ge et al., (2011b) compile data for

thermodynamic properties of amines which control gas/particle partitioning, and find that

several common amines have a similar or greater tendency to partition into the particle

phase than ammonia, with partitioning into aqueous aerosol favoured for acidic aerosols.

Several recent laboratory studies have further explored the processes by which

amines may contribute to SOA. Yin et al (2011) found when EA, DEA and TEA were exposed

to liquid sulphuric acid, irreversible uptake occurred, with uptake coefficients increasing

with acidity, and fastest uptake observed for DEA. Chan and Chan (2012) observed the RH of

50-75%, heterogenous uptake of about 40-ppm TEA by aqueous ammonium salts of sulfate,

bisulphate, nitrate, chloride and oxalate led to increases in particle mass of over 90% with

complete displacement of ammonium by tri ethyl ammonium. Liu et al (2012a)investigated

the reactivity of ammonium salts with MA under dry conditions, and observed exchange

reactions between MA and ammonium nitrate, bisulphate and chloride and a simple acid-

base reaction with ammonium sulphate. These initial studies suggest an important role for

gaseous amines in atmospheric aerosol formation.

Organic Iodine compounds

Organic iodine compounds emitted from the ocean have been found to play an

important role in initiating homogeneous nucleation in the MBL, particularly at Mace Head,

45

Ireland. Organic iodine compounds also influence to oxidising capacity of the atmosphere

through their role in ozone destruction and changes to concentration of OH (Saiz-Lopez et

al., 2012).

The thesis did not focus on organic iodine or other halogenated compounds –

however a brief summary of their role in aerosol formation in the MBL is given below.

Micro and macro algae in the ocean are thought to be the dominant source of

organic iodine compounds such as methyl iodide (CH3I), ethyl iodide (C2H5I), and propyl

iodide (1- and 2-C3H7I), more reactive polyhalogenated compounds such as

chloroiodomethane

(CH2ICl), bromoiodomethane (CH2IBr), and diiodomethane

(CH2I2). Another biological (but inorganic) source of atmospheric iodine has also been

identified as I2 emitted directly from kelp (Saiz-Lopez et al., 2012). Concentrations of

atmospheric organic iodines generally decrease in the order CH3I > C2H5I ≈ C3H7I > CH2ICl

>CH2I2> CH2IBr, with concentrations near the coast higher than the open ocean, due to the

local coastal emission from macroalgae. Organic iodines have been observed in coastal

regions in many parts of the world (Saiz-Lopez et al., 2012).

After release from the ocean, organic iodines photolyse in the atmosphere,

generating iodine atoms which then go on to react with a variety of other radicals including

NO2, ozone, OH and HO2. Homogeneous nucleation from iodine compounds is thought to

result from recombination reactions of IO and OIO radicals to form higher oxides which

condense to form particles. The composition of aerosol particles formed from these

recombination reactions are I2O5 and I2O4. However there is still a great deal of uncertainty

regarding gas phase reactions of IO and OIO and higher oxides, interactions with NOx and

aqueous phase iodine chemistry (Saiz-Lopez et al., 2012).

Methyl iodine, as well as a range of other organic halogenated compounds (such as

methyl bromide, di bromomethane, chloro methane, dichloro ethane) are also emitted from

BB.

2.3 Emission of VOCs from biomass burning plumes, impact on SOA

and ozone formation, and modelling biomass burning plumes

46

Biomass burning (BB) is the largest global source of primary carbonaceous fine aerosols

and the second largest source of trace gases (Akagi et al., 2011). Species directly emitted

from fires include carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), nitrogen

oxides (NOx), ammonia (NH3),VOCs, carbonyl sulfide (COS), sulfur dioxide (SO2) and

elemental and organic carbonaceous and sulphate-containing particles (Keywood et al.,

2011). Secondary species that are formed from BB precursors include ozone (O3),

oxygenated NMOCs, inorganic and organic aerosol (OA). The complex mixture of reactive

gases and aerosol in BB plumes can act as short lived climate forcers. Direct climate forcing

may occur from black carbon and tropospheric ozone formation and organic aerosol with

indirect effects due to aerosol transformations and changes to the CCN population. While

BB plumes often have the greatest impact on the atmosphere close to the source of the fire,

once injected into the free troposphere plumes may travel long distances, so that climate

and air quality affects may be regional or even global. For example, a recent modelling study

highlighted the large contribution that BB makes to the burden of certain NMOC in the

background atmosphere, particularly in the Southern Hemisphere (Lewis et al., 2013).

With some studies predicting that future changes to the climate will result in increasing

fire frequency (Keywood et al., 2011), it is essential to understand the composition of fresh

plumes, how they vary temporally and spatially, and the way in which the chemical

composition is transformed with aging. This will provide the process understanding to allow

models to more accurately predict regional air quality impacts and long term climate effects

of BB.

This review considers the BB emissions only from burning of natural terrestrial

vegetation e.g. forest and savanna fires (including wildfires and prescribed fires) and

excludes BB associated with disposal of crop residues and rubbish, and domestic heating

and cooking etc.

Estimation of SOA in aged biomass burning plumes

Once emitted, the composition of BB plumes can change extremely rapidly, with

destruction of highly reactive species, coagulation of particles, and formation of secondary

species such as ozone, oxygenated VOCs and secondary organic and inorganic aerosol

47

occurring on a timescale of minutes to hours (Akagi et al., 2012; Vakkari et al., 2014).

Particles typically become more oxygenated, and particle size often increases as primary

particles are coated either with low-volatility oxidation products of co-emitted organic and

inorganic gases, or with co-emitted semi volatile primary organics (Sahu et al., 2012; Vakkari

et al., 2014; Akagi et al., 2012).

There have been a few studies in very recent years which have investigated

evolution of OA in BB plumes. The evolution of BB plumes has been investigated either

through smog chamber studies, or field measurements, typically from aircraft platforms. A

common method of estimating SOA contribution to OA in BB plumes involves use of a ratio

which gives the enhancement of OA to a non-reactive tracer to normalise for mixing, which

is typically CO, but in some cases may be CO2 or POA.

To date, the findings have been highly variable, but generally show that SOA

production in BB plumes is smaller than production in urban plumes (Akagi et al., 2012). In

some cases the enhancement ratio of OA increases with plume aging, (Akagi et al., 2012;

Yokelson et al., 2009; Hennigan et al., 2011), in some cases there is a decrease in the OA

ratio (Hecobian et al., 2012; Akagi et al., 2011). However several studies (even those that

showed a decrease in the OA ratio) have found that aging of aerosol is always accompanied

by increasing oxidation state of the OA, and lowering of volatility. This is a strong indicator

that the photo-oxidation continues to transform the POA as the plume ages. The

investigators hypothesise that the reason for the decreasing OA ratio may be due to

evaporation of semi volatile POA during plume dilution (Cubison et al., 2011) or photo-

oxidation of POA (Hennigan et al., 2011). Hence VOCs in the BB plume may be produced

through aging of POA, as well as emitted directly from fuel combustion.

Cubison et al (2011) took all OA enhancement estimates from different studies and

calculated a global net OA source due to aging of BB plumes to be 8 ± 7 Tg OA yr−1, which is

5% of recent total OA source estimates. Hallquist et al (2009) also estimated the

contribution of both fresh and aged BB emissions on the global OC budget, and estimated

BB contributes 28% to the global OC budget of 150 Tg C y-1, with 7% due to primary BB

emissions, and 21% due to secondary processes (9% due to semi volatile vapours emitted

with POA and oxidised in the gas phase, and 11% due to gas to particle conversion). These

estimates suggest that BB is an important contributor to global OA, through production of

SOA and chemical processing of POA.

48

Keywood et al (2011a) collected aerosol samples of fresh BB smoke near a major

bushfire in Victoria, Australia, and also collected samples of aged BB smoke which had

travelled from the same fire over a distance of approximately 300 km. Fresh smoke

contained more high volatility OC while the aged smoke contained more low volatility OC.

The authors attribute this difference to the condensation of the emitted VOCs with the

aerosols as the plume aged, and also to gas to particle transformation in the aging plume. A

slightly higher OC/EC ratio in the fresh smoke compared to the aged smoke also supported

possible SOA formation.

Akagi et al (2012) attributed a change in the chemical composition of black carbon

particles to SOA. The investigators found up to 85% of the BC particles may have been

thickly coated over a 4 hour aging period following emission and that a mix of organic and

inorganic species were likely responsible. The authors concluded that the coating on the BC

particles would change the chemical properties of the particles, including direct radiative

forcing, and indirect forcing, through increased hygroscopicity and ability to form CCN.

A further pathway for SOA formation in BB plumes is in aqueous solution during

processing by clouds, however this potentially important pathway has been little studied.

Yokelson et al (2003) found that cloud processing of a BB plume from an African Savannah

fire lead to rapid changes in gas phase chemistry. After the plume travelled through a large

cumulus cloud, the plume had reduced ratios to CO for methanol, NO2, SO2 and acetic acid,

which suggests some uptake into the cloud droplets. The ratio of formaldehyde to CO

increased which could be due to conversion of methanol to formaldehyde. The authors

pointed out that ‘dirty clouds’ which have had contact with BB plumes would have high

potential for multiphase chemistry.

There has been some recent evidence of dramatic new particle formation (NPF) in

BB plumes via a smog chamber study by Hennigan et al., (2012). The authors burned fuels

typically found in the Northern United States, and found that approximately 30-60 minutes

after the chamber was irradiated with UV light there was a NPF event in each burn, with

rapidly growing particles which they attributed to SOA. These particles grew to CCN size and

increased CCN concentrations significantly. The authors found that when these new

particles associated with BB were included in global models as CCN they exerted a

significant cooling affect (through increased cloud albedo). However, to my knowledge such

consistent and dramatic NPF and growth to CCN size has not been demonstrated in ambient

49

data. Other studies give evidence of the potential for BB plumes to enhance concentrations

of CCN. Pratt et al (2011) found that fresh BB particles from prescribed burns in rural US

were highly active as CCN compared to background aerosol and Andreae et al (2002) found

that over the Amazon in dry season with extensive BB, the CCN number increased by 10 to

20 times compared to the wet season, and that >50% of the organic fraction of aerosol was

water soluble. The hygroscopicity of the particles which acted as CCN in these studies

indicates the particles were likely composed of oxidised POA, and/or SOA.

While BB is recognised as a major source of CCN (Andreae et al., 2002), the

hygroscopicity of fresh BB particles varies enormously from weakly to highly hygroscopic

and fuel type appears to be a major driver of the variability (Pratt et al., 2011; Engelhart et

al., 2012; Petters et al., 2009) along with particle morphology (Martin et al., 2013). As

particles ages, in addition to becoming larger, they also generally become more hygroscopic

and more easily activated to CCN, however, this is dependent on the initial composition and

hygroscopicity of the particle, as well as the hygroscopicity of the coating material (Martin

et al., 2013; Engelhart et al., 2012). Most studies of CCN in BB plumes to date have been

chamber studies, and there are few ambient studies which have examined the ability of BB

particles to act as CCN in fresh and aged plumes. The enhanced number of CCN as a result of

BB may lead to reduced cloud droplet size, and initiation of precipitation at higher cloud

base heights than would occur in clean conditions. BB aerosols may also provide thermal

stability in the lower troposphere and suppress convective cloud formation (Keywood et al.,

2011b).

Chemical tracers of SOA in biomass burning plumes

Aerosol Mass Spectrometry (AMS) has been used in recent years to monitor masses

which are tracers for SOA and POA, to look for changes in aerosol composition in aged BB

plumes. Mass 44 is typically used as a tracer for oxygenated OA (SOA), while mass 60 and

mass 70 are used as a tracer for POA (specifically levoglucosan-like compounds) (Cubison et

al., 2011; Hennigan et al., 2011). These studies have found consistently in laboratory and

field experiments that as the plume ages, mass 44 increases (due to condensation of SOA or

50

oxidation of POA) and mass 60 and mass 70 decreases, due to oxidation of levoglucosan-

type compounds. Mass 44 was found to be correlated with increased hygroscopicity and

ability of the aerosol to form CCN (Cubison et al., 2011). In a chamber study, Hennigan et al

(2011) found that a very rapid decrease in mass 70 that could not be explained by levels of

OH present (OH was estimated by decay rate of aromatics). The authors suggest that

evaporation and reaction of semi-volatile vapours is an important mechanism for POA

processing, as well as oxidation by OH. In a field study, Pratt et al (2011) also found that the

OH concentration in the plume could not explain the enhancement in the O/C ratio in OA as

the plume aged. The enhancement in the O/C ratio was attributed it to volatilisation of less

oxygenated organics after dilution or heterogeneous reaction and condensation of highly

oxygenated species like dicarboxylic acids. Indeed dicarboxylic acids were identified in BB

aerosol by Pratt et al., (2011), who found that particles contained internally mixed oxalate,

malonate and succinate which increased in abundance with age.

These findings highlight the important role that gaseous organics play in the photo

oxidation of BB POA, and the resulting change in aerosol properties.

Evidence of tropospheric ozone production in biomass burning plumes

Ozone is typically destroyed by reaction with NO in close proxmity to the fire,

however once the plume is diluted, ozone enhancement is often observed (typically

normalised to CO). In a recent summary of a number of studies, the enhancement of ozone

to CO typically increases with the age of the plume (Jaffe and Wigder, 2012). However there

is significant variation in ozone enhancements observed between studies which is thought

to be dependent on several factors such as precursor emissions (resulting from fuel and

combustion efficiency), meteorology, the aerosol effect on plume chemistry and radiation,

and photochemical reactions. Hobbs et al (2003), Yokelson et al (2009) and Akagi et al

(2012), report an increase in O3/CO ratio with plume aging while Yokelson et al (2003)

reported an initial destruction of O3 by NO, followed by rapid production. Observations of

O3/CO range from 0.1 to 0.9, and the significant variation in O3 production rates is thought

to be dependent on several factors such as precursor emissions (resulting from fuel and


and photochemical reactions.

51

The degree of ozone formation in a BB plumes depends on transformation and

dilution of the plume during transport and interaction of the BB plume with other emission

sources (Keywood et al., 2011b). Several of the studies mentioned previously which

examined the affect of the aging BB plume on OA, also examined formation of tropospheric

ozone.

Estimates of global ozone production from BB emissions are currently uncertain –

however a recent estimate by Jaffe et al (2012) using observed O3/CO ratios, estimated 170

Tg of O3 per year is a result of BB emissions, which is 3.5% of all global tropospheric O3

production.

Some studies have suggested that O3 production is higher when BB and urban

plumes are mixed, compared to O3 production from BB emissions alone, but it is not known

whether this is due to a feature of the mixed emission sources, or because O3 formation is

suppressed due to high NO levels near the fire (Hecobian et al., 2012;Jaffe and Wigder,

2012). Jaffe and Widger et al (2012) also highlighted the importance of cloud processing and

aerosol heterogeneous reactions on O3 precursors – however a lack of observations

preclude an in-depth understanding of these factors.

A more thorough characterisation of formation processes of O3 and its precursors in

BB plumes, particularly of the oxygenated organics and their photochemical reactions as

highlighted above, will be necessary to improve model predictions of O3 formation. An

understanding of the O3 formation due to BB is necessary to understand the contribution to

radiative forcing, and as ozone air quality standards become more stringent worldwide.

Role of specific VOCs in SOA and ozone production in biomass burning plumes

A number of studies have measured speciated VOCs in BB plumes, and papers such

as Andreae and Merlet (2001) and more recently Akagi et al (2011) have compiled emission

factors of trace gas species from BB studies from around the world. With development of

new technology, including the now widespread use of PTR-MS, there has been in increase in

the number of speciated VOCs which can be quantified in BB plumes in real time. As

52

demonstrated by the recent compilation in Agaki et al (2011), there are a wide variety of

speciated VOC which have been measured in BB plumes, and typically small OVOCs such as

methanol and formaldehyde and small alkanes have the highest emission factors.

However despite the expansion of the range of VOCs which can be quantified, there

are several regions of the globe which have been sparsely studied, or not studied at all. For

example Agaki et al (2011) reports VOC emission factors for temperate forests which are

based only on measurements from the Northern Hemisphere (North America).

As highlighted in the discussion on BB and SOA above, VOCs in BB plumes may be

directly emitted from fire, may be oxidation products of parent VOC, or may be formed

through volatilisation of semi-volatile POA or heterogeneous reactions involving POA. In

several recent studies there has been an attempt to identify VOC that may be contributing

to SOA formation, directly through condensation or heterogeneous reaction, or indirectly by

being a precursor to a VOC that has a sufficiently low vapour pressure. This has been

typically investigated by calculating Normalised Excess Mixing Ratios, which is a ratio of

excess VOC to CO, to normalise for mixing (similar to the ratios used to OA and O3 discussed

above). When the NEMR increases over time, this indicates that the VOC is being produced

during photochemical processing, when it decreases, this indicates the VOC is being

consumed/oxidised.

All studies that investigate changes in NEMRs report reactive, primary emitted VOC,

typically alkenes and terpenes, decrease during plume aging, while small oxygenated VOCs

(oxidation products) increase with time. During the BB chamber study in which rapid NPF

occurred, Hennigan et al (2012) reported a rapid decrease in isoprene and monoterpenes

NEMR when the UV light was turned on (indicating photo-oxidation) and a gradual increase

in acetone and acetic acid NEMR. Hobbs et al (2003) reported a decrease in the NEMR of

small alkenes and an increase in the acetic acid NEMR during aging of a savanna fire plume.

Akagi et al (2012) found rapid a decrease in ethene and propene NEMR and an increase in

formic and acetic acid NEMR, with a factor of 7 increase for formic acid, indicating

significant production. Formaldehyde also increased initially then levelled off, suggesting a

balance between formaldehyde production and destruction in the plume.

Recently, remote sensing from satellite instruments have provided a global view of

VOCs associated with biomass burning. Vrekoussis et al (2010) reported global SCIAMACHY

satellite data for glyoxal and formaldehyde, and found elevated glyoxal and formaldehyde

53

columns were co-located, indicating a common source, and found elevated concentrations

of both gases over areas impacted by BB. In a separate paper by Vrekoussis et al (2009),

glyoxal columns from SCIAMACHY showed good correlation with fire count data, and

showed a BB burning and photochemical hotspot over northern Australia during the dry

season, and almost no glyoxal in the wet season, presumably due to less production and

increased removal by wet deposition. The authors conclude that a high glyoxal to

formaldehyde ratio indicated a strong BB source. There is currently no published data for

glyoxal in Australia to compare with these satellite observations.

The BB plume is a highly reactive environment as demonstrated by estimated levels

of OH, (Hobbs et al., 2003; Yokelson et al., 2009; Akagi et al., 2012), which significantly

reduce lifetimes of VOCs in the BB plume compared to non BB conditions. Emission of

HONO and HCHO can also provide a significant HOx source (Jaffe and Wigder, 2012). For a

NEMR to increase during plume aging, the production of the VOC must be greater than the

destruction. Hornbrook et al., (2012) compared observed VOC chemical evolution in two

fresh plumes (Canadian and Californian) with VOC chemical evolution predicted by a box

model which incorporated the Master Chemical Mechanism. The model predicted that most

low molecular weight oxygenated VOC would decrease over 3 days as the plume aged (i.e

destruction would dominate production), however overall observations were scattered and

did not show a clear trend with time for many species.

Evidence for unidentified VOCs in biomass burning plumes

An increasingly wide range of sophisticated instruments are being used to measure

the trace gas and aerosol composition and microphysical properties in BB plumes. This has

led to a higher proportion of VOC being quantified than ever. However there is significant

evidence that a large proportion of VOCs in BB plumes are still not being identified. A

compilation of NMOC measurements from 71 laboratory fires using southwest US fuels

found that even using a range of techniques, the mass of unidentified VOCs was significant

(up to 50%) (Yokelson et al., 2013), though recent work using high-resolution PTR-TOF-MS

has allowed an at least tentative identification of up to 93% of VOCs (Stockwell et al., 2015).

Flow reactor experiments have suggested the mass of OA formed in aged BB plumes

exceeds the mass of known VOCs precursors, suggesting either unknown VOC precursors,

54

and/or highlighting the important contribution of semi and intermediate volatile species to

the increase in OA observed (Ortega et al., 2013). Yokelson et al (2009) concluded that

unidentified high MW VOC may have contributed to their observed rapid secondary

formation of organic and inorganic aerosol in Yucatan, which saw the PM2.5/CO ratio more

than double in less than two hours. When Trentham et al (2005) designed a photochemical

model to reproduce the chemical evolution measurements of Hobbs et al (2003), they found

that the modelled O3/CO ratio was too low, but that the ratio was in good agreement with

the observations when initial emissions of VOC were increased in the model by 30% as a

surrogate for unmeasured VOC. These studies highlight the need to include these

unidentified gas phase organics in models to capture the additional reactivity they provide,

and include their contribution to OA as the plume ages.

Akagi et al (2011) summarises the implications for this unidentified and often

unmeasured VOCs: firstly, these VOCs provide additional reactivity in BB plumes which are

not captured in models, and secondly because of their high mass, are likely contributors to

SOA when cooled or oxidised. Perhaps related to the missing VOC reported by previously

mentioned studies is an unknown source of formic and acetic acids in BB plumes reported

by Paulot et al., (2011). The authors report their global model underestimates

concentrations of formic and acetic acids in areas impacted by BB, and speculate that in BB

plumes there may be an unknown long lived secondary source of dicarboxylic acids, possibly

associated with the aging of organic aerosols.

Lack of BB field observations in the Southern Hemisphere

In recent years there have been a number of intensive field and laboratory studies

which have characterised both fresh emissions and aged BB emissions. However there are

several regions of the globe where BB emissions, including emission factors (EF), have been

sparsely characterised. For example, EF data has been published for only a few trace gases

in the temperate forests of Southern Australian (Paton-Walsh et al., 2012; Paton-Walsh et

al., 2005; Paton-Walsh et al., 2008; Paton-Walsh et al., 2014). The lack of Australian

temperate EF was evident in a recent compilation of EF by Akagi et al (2011), in which all

temperate VOC EF reported were from the northern hemisphere from mostly coniferous

forests. Species emitted during combustion can be strongly dependent on vegetation type

55

(e.g. Simpson et al., (2011)), and emission factors from northern hemisphere coniferous

forests are not likely to be representative of Australia’s temperate dry sclerophyll forests.

Hence using EF from boreal and tropical forest fires to model BB plumes in temperate

regions adds uncertainty to the model outcomes (Akagi et al., 2011). Therefore more

detailed chemical measurements in the field of BB plumes in the Southern Hemisphere

temperate regions are necessary.

Whilst laboratory studies are of high value, they are not able to reproduce the

conditions of BB plume aging, which are dependent on long range transport, cloud

processing, varying meteorological conditions and interactions between trace gases and

aerosols in the plume and ambient air.

Modelling biomass burning plumes

To be able to accurately predict and assess the impact of BB on human health, air

quality and climate, models must be able to realistically simulate the chemical and physical

processes that occur in a plume as well as plume transport and dispersion. In the case of BB

plumes close to an urban centre/sensitive receptor, models can be used to reduce risks on

community by predicting where and when a BB plume will impact, the concentrations of

toxic trace gases and particles in the plume, and potential impact of BB mixing with other

sources. Models also allow investigations of contributions from BB and other sources on

observed air quality when multiple sources are contributing. Understanding the relative

importance of different sources is important when formulating policy decisions to improve

air quality.

Lagrangian parcel models are typically used to investigate photochemical

transformations in BB plumes as they are transported and diluted downwind (Jost et al.,

2003; Trentmann et al., 2005; Mason et al., 2006; Alvarado and Prinn, 2009; Alvarado et al.,

2015) while three dimensional Eulerian grid models have been used to investigate transport

and dispersion of plumes, plume age, as well as contributions from different sources. 3D

Eulerian grid models vary from fine spatial resolution on order of kms (Luhar et al., 2008;

Keywood et al., 2015; Alvarado et al., 2009; Lei et al., 2013) to coarse resolution of up to

400km in global models (Arnold et al., 2015; Parrington et al., 2012).

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Broadly speaking, models used for simulating BB plumes can consist of a) description

of emissions source b) determination of plume rise c) vertical transport and dispersion and

d) chemical transformations in the plume (Goodrick et al., 2013). There are challenges

associated with accurately representing each of the four components of BB models. The

description of emissions source includes a spatial and temporal description of the area

burnt, fuel load, combustion completeness, and trace gas and aerosol emission factors per

kg of fuel burned. The area burned is often determined by a combination of hotspot and fire

scar data, measured by satellite. Cloud cover may lead to difficulties in obtaining area burnt

data, while scars from small fires may be difficult to discern against complex terrain, and low

intensity fires may not correspond with a detectable hotspot (Meyer et al., 2008). Emission

factors are determined experimentally either by field or laboratory measurements, and are

typically grouped by biome type. In some regions, such as SE Australia, biomes have been

sparsely characterised (Lawson et al., 2015). Furthermore, biomed –averaged EF are

incorporated into models, which do not account for complex intra-biome variation in EF as a

result of impact of temporal and spatial differences in environmental variables, including

impact of vegetation structure, monthly rainfall (van Leeuwen and van der Werf, 2011) and

even short term rainfall meteorological events (Lawson et al., 2015). Finally, the very

complex mixture of trace gases and aerosols in BB plumes creates analytical challenges in

quantifying EF, especially for semi and low volatility organics which are challenging to

measure and identify but contribute significantly to secondary aerosol formation and

photochemistry within the plume (Alvarado and Prinn, 2009; Alvarado et al., 2015; Ortega et

al., 2013).

Meteorology has a large impact on the ability of models to simulate the timing and

magnitude and even composition of BB plume impacts in both local and regional scale

models (Lei et al., 2013; Luhar et al., 2008; Arnold et al., 2015). Plume rise is a description of

how high the buoyant smoke plume rises and the vertical distribution of trace gases and

aerosols in the plume. This is still a large area of uncertainty in BB models, with a

generalised plume rise approach typically used which may include either homogenous

mixing, prescribed fractions of emissions distributed according to mixing height, use of

parametisations, and finally plume rise calculated according to atmospheric dynamics. A key

driver of this uncertainty is the complexity of fire behaviour resulting in high spatial and

57

temporal variability of pollutant and heat release, which drives variability in plume rise

behaviour, such as multiple updraft cores (Goodrick et al., 2013).

Vertical transport and dilution in models is driven by meteorology, particularly wind

speed and direction. For example, too-high wind speeds can lead to modelled pollutant

levels which are lower than observed (eg (Lei et al., 2013)) while small deviations in wind

direction lead to large concentration differences between modelled and observed,

particularly when modelling emissions of multiple spatially diverse fires (Luhar et al., 2008).

Dilution of BB emissions in large grid boxes in global models may also lead to discrepancies

between modelled and observed NOx, O3 and aerosols (Alvarado et al., 2009).

Finally, models use a variety of different gas-phase and aerosol-phase physical and

chemical schemes, which vary in their ability to accurately represent chemical

transformations, including formation of ozone and organic aerosol (Alvarado and Prinn,

2009; Alvarado et al., 2015). Validating and constraining chemical transformations in models

requires high quality, high time resolution BB observations of a wide range of trace gas and

aerosol species, including important but infrequently measured species such as OH and semi

volatile and low volatility VOCs. Field observations whilst scarce are particularly valuable

because the processes and products of BB plume processing are dependent on long range

transport, cloud processing, varying meteorological conditions and heterogeneous

reactions.

Sensitivity studies have allowed the influence of different model components (emissions,

plume rise, transport, chemistry) on model output to be investigated. Investigating the

impact of different model components is particularly important in formation of secondary

species such as ozone, which have a non-linear relationship with emissions. Studies have

found that modelled ozone concentration from BB emissions is highly dependent on a range

of factors including a) meteorology (plume transport and dispersion) in global (Arnold et al.,

2015) and high resolution (Lei et al., 2013)Eulerian grid models, b) absolute

emissions/biomass burned (Pacifico et al., 2015; Parrington et al., 2012), c) model grid size

resulting in different degrees of plume dilution (Alvarado et al., 2009), and oxidative

photochemical reaction mechanisms in Lagrangian parcel models (Mason et al., 2006).

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2.4 Summary of literature review

A summary of the main knowledge gaps as identified by the literature review are given

below.

Uncertainties in the global budget of SOA.

The global budget of SOA is highly uncertain due to vast number of atmospheric VOCs

present in the atmosphere, and the likelihood that only a small proportion of these have

been identified to date (Goldstein and Galbally 2007). Of those identified, the degradation

pathways which lead to SOA formation have been determined for only a small number of

VOCs. According to a recent study (Kroll et al., 2011) it is likely that almost all VOCs >C4

contribute to SOA through their degradation in the gas phase, and VOCs <C4 are likely to

contribute through SOA formation in the aqueous phase. This is contrary to mechanisms

describing SOA formation in models, in which only a few select VOCs undergo oxidation

leading to SOA, and aqueous SOA formation (in cloud droplets and aerosol water) are

typically not parametrised.

What is the role of organic molecules in homogeneous nucleation (NPF)?

A number of studies have suggested that there is an important role for organic

molecules in stabilising the critical nucleus (size of 1 nm) via formation of hydrogen bonds.

The role of organics is likely to be particularly important in the boundary layer, where, in the

absence of stabilising molecule/s higher temperatures cause dissociation of H2SO4 and H2O

molecules. In addition, once the critical nucleus is formed, condensation of organics are

thought to be important in growing particles to multi nanometer size. The identities of these

organic molecules, and their sources/precursors are currently unknown. There is therefore

a need to…” simultaneously identify and quantify the gas-phase nucleating vapours

(including organic vapours, i.e. VOCs) and the chemical composition of the natural and ionic

clusters and nanoparticles”(Zhang et al. 2012).

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The interrelatedness of primary and secondary aerosol

In recent years there has been a blurring of distinction and increasing acceptance of

the interrelatedness between primary aerosol and secondary aerosol. The close relationship

between primary and secondary aerosol has been recognised in the literature in

publications on both the clean marine boundary layer, and the terrestrial biosphere

impacted by BB. For example in the MBL, water soluble organic carbon (WSOC) was

previously used as a proxy for SOA, however it is now accepted that WSOC may also be

comprised of photochemically aged/oxidised POA (eg (Sciare et al. 2009, Claeys et al. 2010).

Similarly in air impacted by BB, POA emitted from biomass burning plumes is no longer

viewed as non-volatile and non-reactive. Instead POA is now thought to be semi volatile

and may partly evaporate during plume dilution and undergo photo-oxidation in the gas

phase e.g. (Cubison et al. 2011, Hennigan et al. 2011).

A major implication of the newly recognised semi volatility of POA in both marine and

BB impacted air is that secondary aerosol is not necessarily defined as being formed just

from gas to aerosol phase transfer–secondary aerosol may also be defined as

photochemically processed POA, including oxidised semi volatile vapours which evaporated

from the POA, were oxidised in the gas phase then re-condensed. A second implication is

that the chemical processing of POA may be a source of small reactive oxygenated

compounds –VOCs such as glyoxal, formaldehyde– which can then go on to contribute to

photochemical reactions and also contribute to SOA after further oxidation, or dissolve into

the aqueous phase where they may also form SOA. This is in contrast to previous held views

that small oxygenated gas phase molecules were produced solely from oxidation of gas-

phase parent compounds. Hence a more recent view of SOA formation is that it can be

formed from traditional gas to particle transfer (from oxidation of VOCs emitted directly

from combustion or the ocean), or may be formed from VOCs produced when POA

evaporates or photo-degrades.

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Knowledge gaps specific to the marine boundary layer:

Evidence for an unidentified source of OVOCs over the ocean

There are increasing reports of observations (both in situ and remotely sensed) of

small reactive OVOCs over the remote ocean, such as glyoxal and HCHO, which are present

in concentrations that cannot be explained by concentrations of their gaseous precursors

(Sinreich et al 2010, Coburn et al. 2014, Sabolis et al 2012, Meskhidze et al. 2011). This

suggests an incomplete understanding about the chemical processes driving formation of

these species, which is further hampered by a severe lack of observations over the remote

oceans. For example there are currently no published data for glyoxal and methyl glyoxal

over the ocean in the Southern Hemisphere.

It is likely that some or all of the missing sources of small oxygenated compounds may

be related to photo-degradation of primary organic aerosol (POA) (see section on

interrelatedness of POA/SOA above). More observations of these small OVOCs and their

gaseous precursors in the remote MBL are needed to confirm or negate this.

Scarcity of monoterpene and isoprene measurements

There is a scarcity of measurements in particular of monoterpenes (but also isoprene)

over the remote oceans, particularly in the southern hemisphere (Shaw et al. 2010).

Modelling studies which have assessed the contribution of monoterpenes and isoprene to

organic aerosol over the oceans have relied on the observations from only a handful of

studies, which adds a large amount of uncertainty to global findings. Modelling studies to

date generally suggest that globally isoprene and monoterpenes contribute almost

insignificantly to marine aerosol in terms of mass, however modelling studies have yet to

determine whether oxidation products of these species may play an important role in

stabilising the critical nucleus in homogeneous nucleation, and hence have a major impact

on particle number. Some modelling studies have reported that the contribution of isoprene

and monotepenes to SOA is highly spatially variable and may be locally important (Gantt et

al. 2009).

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Knowledge gaps specific to biomass burning emissions

Lack of BB field observations and emission factors (EF) in temperate Southern Hemisphere

The composition of fresh smoke is dependent on many factors including combustion

efficiency, local meteorology, terrain, seasonality and fuel (vegetation) (Akagi et al. 2011).

However in the temperate regions of Southern Australia, emission factors have only been

reported for a few traces gases to date (Paton-Walsh et al 2008, Paton-Walsh, Deutscher et

al. 2010). For this reason models which assess the effect of BB on air quality and climate in

Australia typically use emission factors from the temperate northern hemisphere which are

unlikely to be representative of Australian fuel and conditions, adding major uncertainty to

model results.

Challenges in measuring and modelling changing BB plume composition

The changes that occur in the composition of BB plumes as plumes are diluted and

chemically processed are complex and challenging to measure and to model. For example,

the production and transformation of aerosols in aging BB plumes and effect of these

transformations on CCN and cloud properties is poorly understood, and not currently

included in climate models. BB is a major source of tropospheric ozone precursors, and

ozone is typically formed in dilute and photochemically aged BB plumes. However the

drivers of ozone formation are not well understood, and is challenging to model due to an

incomplete understanding of precursor emissions (including high molecular weight VOCs)

and photochemical reactions (Jaffe and Wigder 2012). The cloud processing of trace gases

within BB plumes, for example aqueous oxidation of water soluble VOCs, is likely to have an

important influence on the composition of the plumes but has been little studied. There is

also an incomplete understanding about the interactions between BB plumes and urban

plumes, and how this may influence photochemistry and formation of secondary pollutants

such as ozone. The mixing of BB and urban emissions is pertinent as BB often occurs on the

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fringes of populated regions, and even when fires occur in remote regions, plumes can be

transported long distances and are likely to interact with other sources.

2.5 Literature review of experimental methods

The following techniques have been used to measure VOCs and to undertake the modelling.

PTR-MS

Proton transfer reaction mass spectrometry, PTR-MS, was developed in the late

1990s by Professor Werner Lindinger and co-workers at the University of Innsbruck, Austria

(Lindinger et al., 1998). The PTR-MS uses chemical ionisation to measures the presence and

concentration of chemical species in the air, allowing real time monitoring of a wide range

of organic compounds in the gas phase. PTR-MS is also employed to measure gas phase

VOCs in medical, food science and industrial applications.

The PTR-MS used in this work was manufactured by Ionicon Analytik, Innsbruck,

Austria. It contains an ion source, which is a hollow cathode where the chemical ionising

agent is produced, a drift tube, where the chemical ionisation reaction occurs, a quadrupole

mass spectrometer which separates ions according to their mass to charge (m/z) ratio and a

secondary electron multiplier (SEM) which detects and counts incoming ions from each m/z

channel at a resolution of 1 amu.

The traditional and most common way of operating the PTR-MS is to use H3O+ ions

as the chemical ionising agent. H3O+ are produced in the ion source via electron impact

ionisation of water vapour. The ion source is optimised to produce reagent H3O+ ions with

a purity of >95% but there are always some O2+, NO+, H3O+·(H2O)n and other ions present in

the reagent ion matrix as a result of air back-streaming into the ion source (de Gouw and

Warneke, 2007). When the H3O+ and chemical species collide in the drift tube, proton

transfer to the chemical species will occur if the chemical species has a proton affinity

greater than water. Using H3O+ as the chemical ionising reagent ion allows a wide range of

organic compounds to be detected, including those containing oxygen, nitrogen and sulfur,

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aromatic hydrocarbons and some alkenes and alkynes. Alkanes are not detected.

Formaldehyde has a proton affinity which is very similar to water, so continuous exchange

of the proton occurs, leading to a very poor response for formaldehyde which is highly

dependent on humidity in the drift tube. The permanent constituents of air, oxygen,

nitrogen, etc. have a proton affinity less than water and so are not detected by the PTR-MS.

In most cases the transfer of the proton leads to no fragmentation of the chemical

species and the mass recorded is that of the chemical species plus 1 amu, due to the

attached proton. Where fragmentation does occur the mass recorded is that of the charged

fragment plus 1 amu. Tables of the fragmentation patterns (where fragmentation occurs) of

many chemical species are available (Ionicon, 2007 unpublished; Warneke et al., 2003) but

do not contain fragmentation patterns for all species at all PTR-MS operating conditions.

The minimum detectable limits of the PTR-MS for many compounds are around 100

ppt (0.1 ppb, or 1 in 1010) for a dwell time of 1 second. The detection limit decreases

(sensitivity increases) for longer dwell times (measurement periods).

It is possible to quantify a chemical species measured with the PTR-MS using a

theoretical mathematical formula based on kinetic theory of the interaction between the

chemical species and reagent ion in the drift tube. However precise knowledge of

parameters is required, including the reaction rate constant, k, which is determined either

experimentally or theoretically, and typically makes the largest contribution to the error in

the calculation. It is therefore desirable to calibrate the PTR-MS with VOCs of interest for

more accurate quantitation (de Gouw and Warneke, 2007). The PTR-MS also produces a

signal at most masses when VOC free air is introduced (eg such as interference ions created

in the ion source and outgassing of materials in the ion source and drift tube) and it is

necessary to make regular corrections of this ‘background’ signal in ambient data.

A significant challenge associated with the PTR-MS quadrupole system which uses

only H3O+ as the reagent ion is that it cannot distinguish between two or more chemical

species that have the same molecular mass, which is particularly a problem in complex air

mixtures such as urban areas and BB plumes. Additional issues with identification may result

from fragmentation or chemical species or cluster ion formation (de Gouw and Warneke,

2007). These issues can be addressed in a number of ways, which often involve making

concurrent measurements with another technique. A number of studies have made

measurements with PTR-MS alongside GCMS, FTIR etc, both in clean (marine) and polluted

64

environments, to determine which VOCs contribute to each mass (Karl et al., 2007a; de

Gouw and Warneke, 2007; Christian et al., 2004; de Gouw et al., 2003a; de Gouw et al.,

2003b; Warneke et al., 2011; Yokelson et al., 2007). Pre-separation of VOCs in the sample air

with GC has also been employed (Warneke et al., 2003).

Recently, a modified PTR-MS system has become available with a switchable reagent

ion capability (PTR-SRI-MS) which allows switching between 3 reagent ions: O2+, NO+ and

H3O+ (Jordan et al., 2009). The addition of two further reagent ions O2+ and NO+ was

designed to allow detection of chemical species with a proton affinity less than water, and

also identify isomeric species. Where use of H3O+ leads mostly to protonation of the parent

ion with some fragmentation, NO+ mainly leads to the parent ion after charge transfer, and

O2+ leads to a high degree of fragmentation of the parent ion due to charge transfer (Jordan

et al., 2009). Jorden et al (2009) report some initial applications for the SRI including use of

O2+ to distinguish between ethylbenzene and xylene using differences in fragmentation

ratios, which are indistinguishable at M107 using H3O+, and using differences in reaction

probabilities between aldehydes and ketones and NO+ to distinguish between aldehydes

and ketones at the same molecular mass (e.g. acetone/propanal).

Dunne et al., (2012) used the PTR-SRI-MS during a study in an urban location to

investigate the contribution of interfering species to m/z 42, which is typically considered a

unique mass for the biomass burning tracer acetonitrile. The authors used the O2+ reagent

ion to quantify the contribution from C3H6+ to m/z 42 in H3O+ mode, which is produced by

reactions of O2+ with alkanes and alkenes. The authors recommend that the interference of

C3H6+ to m/z 42 can be quantified and corrected by using O2+ mode or by reducing the

amount of O2+ in the H3O+ primary reagent ion mode. Work on the SRI capability is still

under development worldwide, with very limited publications to date on the use of PTR-SRI-

MS in atmospheric measurements.

There are a great number of studies which have successfully used PTRMS with just

the H3O+ reagent ion to quantify a large range of VOCs in a variety of environments, both

clean and polluted. PTR-MS has been used to measure VOCs in marine air at the Cape Grim

Baseline station Tasmania (Galbally et al., 2007; Lawson et al., 2011a), and used on ships

during cruises in the Southern Indian Ocean (Colomb et al., 2009), South Atlantic Ocean

(Williams et al., 2010) , North West Indian ocean (Warneke and de Gouw, 2001) and

Canadian Archipelago (Sjostedt et al., 2012).It has been used alongside other instruments to

65

quantify VOCs from BB emissions in chamber studies (Christian et al., 2004; Karl et al.,

2007b; Warneke et al., 2011) and in the field from aircraft and ground stations (Hornbrook

et al., 2012; Murphy et al., 2010; Yokelson et al., 2007).

Supporting measurements to aid identification of PTR-MS masses

2,4-DNPH cartridges with HPLC analysis

Aldehyde, ketones and dicarbonyls can be measured by sampling air through

Supelco LpDNPH air monitoring cartridges. Aldehyde, ketones and dicarbonyls are trapped

on high purity silica adsorbent coated with 2,4-dinitrophenylhydrazine(2,4-DNPH), where

they are converted to the hydrazone derivatives. Ozone scrubbers are placed in front of the

DNPH cartridge in environments with elevated ozone, to remove the possibility of ozone

reacting with derivatised carbonyls. The derivatives are eluted from the cartridge in

acetonitrile and analysed by HPLC. The analysis of carbonyls is based on EPA Method

TO11A. A range of aldehydes, ketones and dicarbonyls are quantified up to C8 with this

technique.

DNPH samples can be taken alongside PTR-MS measurements, to assist with identification

for masses which may have more than one contributing compound, e.g. m/z 59 (acetone,

propanal), or to measure VOCs not easily detectable with PTR-MS (e.g. formaldehyde,

glyoxal and methyl glyoxal). A disadvantage of this off line technique is that high time

resolution data is not achieved. Also, the derivatisation of the carbonyls and manual sample

handling in the field may lead to sample losses and other errors (Hopkins et al., 2003). The

DNPH derivative method may be unsuitable for some carbonyls – for example the acrolein-

DNPH derivative has recently been reported as unstable on the cartridge. This technique has

been used recently to measure aldehydes, ketones and dicarbonyls in urban air (Lawson et

al., 2011b; Molloy et al., 2012, Cheng et al., 2016), and has been used to quantify

dicarbonyls in a variety of continental and marine sites as summarised by Fu et al (2008).

Adsorption tubes with TD-GC-FID/MS analysis

66

A wide range of alkanes, alkenes and aromatics with S, N, O and other substitutions

can be measured by drawing air through adsorption tubes, which may be packed with a

variety of sorbents, depending on the target VOCs. The tubes can then be thermally

desorbed to drive the VOCs into a GC system, which has both FID and MS detectors, used

for quantification and identification respectively. Two adsorbent tubes are typically joined in

sequence to assess breakthrough of VOCs from the front to the back tubes, and the total air

sampled is guided by safe sampling volume data to minimise breakthrough of key VOCs.

Sampling and the thermal desorption method are conducted according to USEPA

Compendium method TO-17 (USEPA TO-17). A number of gas standards containing a range

of aromatics, alkanes, sulphur and oxygen containing VOCs are used for calibration of the

system, and field blanks are employed. Generally VOCs in the range of C5 to C15 can be

quantified with the current system configuration at CSIRO Marine and Atmospheric

Research.

Adsorbent tube samples can be taken alongside PTR-MS measurements, to assist

with identification for masses which may have more than one contributing compound, e.g.

GC/MS is able to identify the individual monoterpenes which on the PTR-MS are all

measured at m/z 81 or m/z 137. As for the 2,4-DNPH technique, a disadvantage of this

technique is that high time resolution data is not achieved, and manual handling of samples

in the field may lead to sample losses. This technique has been used recently to measure a

range of ambient VOCs in urban air (Lawson et al., 2011b; Molloy et al., 2012, Cheng et al.,

2016), and TD-GCMS was used to identify the first monoteprene emissions from the ocean

(Yassaa et al., 2008).

Chemical transport modelling

The modelling that will be employed in this work involves coupling of prognostic

meteorological models, an air emissions inventory, chemical transport and particle

dynamics modelling capability into a chemical transport model (CTM). The CTM is a three-

dimensional Eulerian chemical transport model which models the emission, transport,

chemical transformation, wet and dry deposition of a coupled gas and aerosol phase

species. Meteorological models are being used to predict meteorological fields including

wind velocity, temperature, and water vapour mixing ratio (including clouds),

67

radiation and turbulence. Two of CSIRO’s meteorological models will be used in this work:

TAPM (The Air Pollution Model), and CCAM (Cubic Conformal Atmospheric Model).

Photochemical reactions of gas phase species use the Carbon Bond 5 mechanism

including updated chemistry for toluene (Sarwar et al., 2011) which include the gas phase

precursors for secondary (gas and aqueous phase) inorganic and organic aerosol mass.

Secondary organic aerosol (SOA) mass is being modelled using the Volatility Basis Set

approach (Donahue et al., 2006)The modelling used in this work will incorporate large scale

processes (by incorporating the Australia domain), as well as fine scale processes, down to

grid size of 400m.

The CTM is in the process of being configured to run in a two-moment, multi-

component, multi-model configuration, which will allow incorporation of the Global Model

of Aerosol Processes model; (Mann et al., 2010). GLOMAP will simulate particle number,

including processes such as particle nucleation, coagulation and condensational growth

within the CTM. Model development, setup and model runs will be carried out by members

of the Weather and Environmental Prediction program at CSIRO Aspendale.

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3 Chapter 3

Seasonal in situ observations of glyoxal and methylglyoxal

over the temperate oceans of the Southern Hemisphere

S.J. Lawson1, P.W. Selleck1, I.E. Galbally1, M.D. Keywood1, M.J. Harvey2, C. Lerot3, D.

Helmig4, and Z. Ristovski 5

[1] {Commonwealth Scientific and Industrial Research Organisation, Oceans and

Atmosphere Flagship, Aspendale, Australia}

[2] {National Institute of Water and Atmospheric Research, Wellington, New Zealand}

[3] {Belgian Institute for Space Aeronomy, Brussels, Belgium}

[4] {Institute of Arctic and Alpine Research, University of Colorado, Boulder, USA}

[5]{International Laboratory for Air Quality & Health, Queensland University of

Technology, Brisbane, Australia.}

Published in Atmospheric Chemistry and Physics, 15 (2015),Pages 223-240. STATEMENT OF JOINT AUTHORSHIP

The authors listed below have certified* that: 1. they meet the criteria for authorship in that they have participated in the conception,

execution, or interpretation, of at least that part of the publication in their field of expertise;

2. they take public responsibility for their part of the publication, except for the responsible author who accepts overall responsibility for the publication;

3. there are no other authors of the publication according to these criteria; 4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or

publisher of journals or other publications, and (c) the head of the responsible academic unit, and

5. they agree to the use of the publication in the student’s thesis and its publication on the QUT ePrints database consistent with any limitations set by publisher requirements.

In the case of this chapter: Chapter 3

83

Title: Seasonal in situ observations of glyoxal and methylglyoxal over the temperate oceans

of the Southern Hemisphere (2015, published)

Contributor Statement of contribution Sarah Lawson (candidate)

Identified scientific problem, developed scientific design, contributed to scientific method, conducted field work, analysed and interpreted data, wrote manuscript

Paul Selleck Developed analytical method, conducted laboratory work, contributed to methods section of manuscript

Ian Galbally Contributed to scientific design, conducted field work, reviewed the manuscript

Melita Keywood Reviewed the manuscript Mike Harvey Produced figures, contributed to scientific design, reviewed the

manuscript Christophe Lerot Contributed data, data interpretation and writing of the discussion

section Detlev Helmig Contributed data and reviewed manuscript Zoran Ristovski Reviewed the manuscript

Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their certifying

authorship.

_______________________ ____________________ ______________________

Name Signature Date

84

Seasonal in situ observations of glyoxal and methylglyoxal

over the temperate oceans of the Southern Hemisphere

S.J. Lawson1, P.W. Selleck1, I.E. Galbally1, M.D. Keywood1, M.J. Harvey2, C. Lerot3, D.

Helmig4, and Z. Ristovski 5

[1] {Commonwealth Scientific and Industrial Research Organisation, Oceans and

Atmosphere Flagship, Aspendale, Australia}

[2] {National Institute of Water and Atmospheric Research, Wellington, New Zealand}

[3] {Belgian Institute for Space Aeronomy, Brussels, Belgium}

[4] {Institute of Arctic and Alpine Research, University of Colorado, Boulder, USA}


Technology, Brisbane, Australia.}

Correspondence to: S.J Lawson ([email protected])

3.1 Abstract Dicarbonyls glyoxal and methylglyoxal have been measured with 2,4-

dinitrophenylhydrazine (2,4-DNPH) cartridges and high performance liquid chromatography

(HPLC), optimised for dicarbonyl detection, in clean marine air over the temperate Southern

Hemisphere (SH) oceans. Measurements of a range of dicarbonyl precursors (volatile

organic compounds, VOCs) were made in parallel. These are the first in situ measurements

of glyoxal and methylglyoxal over the remote temperate oceans. Six 24 hour samples were

collected in summer (Feb-Mar) over the Chatham Rise in the South West Pacific Ocean

during the Surface Ocean Aerosol Production (SOAP) voyage in 2012, while 34 24 hour

samples were collected at Cape Grim Baseline Air Pollution Station in late winter (Aug-Sep)

2011. Average glyoxal mixing ratios in clean marine air were 7 ppt at Cape Grim, and 23 ppt

over Chatham Rise. Average methylglyoxal mixing ratios in clean marine air were 28 ppt at

Cape Grim and 10 ppt over Chatham Rise. The mixing ratios of glyoxal at Cape Grim are the

lowest observed over the remote oceans, while mixing ratios over Chatham Rise are in good

agreement with other temperate and tropical observations, including concurrent MAX-

DOAS observations. Methylglyoxal mixing ratios at both sites are comparable to the only

other marine methylglyoxal observations available over the tropical Northern Hemisphere

85

(NH) ocean. Ratios of glyoxal : methylglyoxal > 1 over Chatham Rise but <1 at Cape Grim,

suggest different formation and/or loss processes or rates dominate at each site. Dicarbonyl

precursor VOCs, including isoprene and monoterpenes, are used to calculate an upper

estimate yield of glyoxal and methylglyoxal in the remote marine boundary layer and

explain at most 1-3 ppt of dicarbonyls observed, corresponding to 10% and 17% of the

observed glyoxal and 29% and 10% of the methylglyoxal at Chatham Rise and Cape Grim,

respectively, highlighting a significant but as yet unknown production mechanism. Surface –

level glyoxal observations from both sites were converted to vertical columns and compared

to average vertical column densities (VCDs) from GOME-2 satellite retrievals. Both satellite

columns and in situ observations are higher in summer than winter, however satellite

vertical column densities exceeded the surface observations by more than 1.5x1014

molecules cm-2 at both sites. This discrepancy may be due to the incorrect assumption that

all glyoxal observed by satellite is within the boundary layer, or may be due to challenges

retrieving low VCDs of glyoxal over the oceans due to interferences by liquid water

absorption, or use of an inappropriate normalisation reference value in the retrieval

algorithm. This study provides much needed data to verify the presence of these short lived

gases over the remote ocean and provide further evidence of an as yet unidentified source

of both glyoxal and also methylglyoxal over the remote oceans.

3.2 Introduction Natural aerosols, including sea spray and secondary aerosols originating from marine

dimethyl sulphide (DMS), have been shown to strongly affect the uncertainty of cloud

radiative forcing in global climate models, highlighting a need to understand the

composition and microphysical properties of marine aerosol in very pristine marine

environments (Carslaw et al., 2013). While primary emissions, including wind-blown sea salt,

make a large contribution to aerosol mass in the remote marine boundary layer (MBL),

organic carbon can make a significant contribution to the mass of submicron marine aerosol

in the more biologically active summer months (O'Dowd et al., 2004; Facchini et al., 2008a;

Sciare et al., 2009; Ovadnevaite et al., 2011b). This organic carbon may be primary organic

matter, including polymer microgels, viruses, bacteria, colloids and organic detritus, directly

transferred from bulk water and the sea surface microlayer (SML) of the ocean to the

86

atmosphere during bubble burst (Orellana et al., 2011; Facchini et al., 2008b). The organic

carbon may also comprise secondary aerosol, formed from oxidation of gas phase ocean-

derived volatile organic compounds (VOCs) such as DMS, isoprene and monoterpenes (Shaw

et al., 2010).

The organic component of marine aerosol is chemically complex and requires multiple

state of the art techniques to elucidate (Fu et al., 2013; Fu et al., 2011; Decesari et al., 2011;

Rinaldi et al., 2010; Claeys et al., 2010). A further challenge is the more recent blurring of

distinction between primary and secondary organics, in which oxidative ageing and

evaporation of semi volatile primary organic aerosol (POA) leads to production of gas phase,

volatile, low molecular weight products, which may then go to form secondary organic

aerosol (SOA) (Donahue et al., 2014). The resulting photochemically processed POA may

have similar chemical properties to, and is sometimes loosely classified as SOA. (Rinaldi et

al., 2010; Decesari et al., 2011; Ovadnevaite et al., 2011b). This interrelatedness of primary

and secondary organics adds considerable complexity to understanding the formation and

chemical processing of organic aerosols in the MBL.

The influence of organics on cloud condensation nuclei (CCN) activity of marine

aerosol in general appears to be highly variable and investigations have mostly focused on

primary organic aerosol (Ovadnevaite et al., 2011a; Meskhidze et al., 2011; Orellana et al.,

2011; Westervelt et al., 2012; Topping et al., 2013). The contribution of DMS oxidation

products to the CCN population over the remote Southern Ocean has been well established

(Korhonen et al., 2008; Ayers and Gras, 1991), however an understanding of the

contribution of other secondary aerosol species such as isoprene and monoterpene derived-

SOA to the CCN activity of marine aerosol is still emerging. Meskhidze and Nenes et al.

(2006) suggested a link between isoprene-derived SOA over a phytoplankton bloom site and

cloud microphysical and radiative properties in the Southern Ocean, while Lana et al. (2012)

found a correlation between modelled secondary sulphur and organic aerosols and

variability of cloud microphysics derived from satellite observations over the remote mid

and high latitude ocean.

The alpha dicarbonyl glyoxal (CHOCHO) is an important SOA aerosol precursor which

in recent years has found to be widespread in the marine boundary layer (MBL), both via

column measurements (Lerot et al., 2010; Vrekoussis et al., 2009; Mahajan et al., 2014;

Wittrock et al., 2006) and in situ measurements (Coburn et al., 2014). The dominant source

87

of glyoxal is oxidation of parent VOCs, with isoprene globally the most important precursor

(explaining 47% of glyoxal formation) (Fu et al., 2008). Glyoxal has a global average lifetime

of about 3 hours (Fu et al., 2008; Myriokefalitakis et al., 2008; Stavrakou et al., 2009), and is

highly water soluble and so can diffuse into aerosol or cloud water where it is converted to

SOA through formation of low volatility products such as organic acids and oligimers (Ervens

et al., 2011; Kampf et al., 2013; Sedehi et al., 2013; Lee et al., 2011; Lim et al., 2013). Alpha

dicarbonyl methylglyoxal (CHOCCH3O), a close relative of glyoxal, also forms low volatility

products in the aqueous phase (Tan et al., 2012; Sedehi et al., 2013; Lim et al., 2013), has a

short global lifetime of 1.6 hours and is produced by oxidation of gas phase parent

compounds, predominantly isoprene (Fu et al., 2008). Destruction of both dicarbonyls is

mainly via photolysis, followed by reaction with OH (Myriokefalitakis et al., 2008, Fu et al.,

2008). The global sources of glyoxal and methylglyoxal are significant (45 Tg C a-1 and 140 Tg

a-1 globally), and their SOA yield, which occurs mainly in clouds, is comparable in magnitude

to SOA formation from other oxidation products of biogenic VOCs and aromatics (Fu et al.,

2008). Major oxidation products of glyoxal and methylglyoxal at in-cloud relevant

concentrations are oxalic and pyruvic acids (Lim et al., 2013).

There is considerable evidence that the dicarbonyls, and particularly glyoxal, makes an

important contribution to the organic component of marine aerosol over the remote

oceans. Both dicarbonyls have been found in marine aerosols over the Atlantic Ocean (van

Pinxteren and Herrmann, 2013) and Pacific Oceans (Bikkina et al., 2014), with dicarbonyl

mass positively correlated to organic acids (including oxalic acid) and ocean biological

activity. Oxalic acid has been consistently found in pristine marine aerosol from remote sites

including Amsterdam Island (Claeys et al., 2010), Mace Head (Rinaldi et al., 2010), Cape

Verde (Muller et al., 2010) and Cape Grim (unpublished data), with highest concentrations

during the biologically active summer months, coinciding with maximum concentrations of

DMS oxidation products methanesulfonic acid (MSA) and non-sea-salt sulphate. Rinaldi et

al. (2011) reported that oxalic acid in submicron marine aerosol from Mace Head and

Amsterdam Island showed a similar seasonal cycle to SCIAMACHY glyoxal columns, and a

chemical box model was able to explain the observed oxalate using the glyoxal columns.

However, significant unknowns remain. There are currently insufficient methylglyoxal

observations to confirm its presence and importance to SOA formation over the remote

oceans, and understanding the source of the observed glyoxal in the MBL has proven

88

challenging. If the production of glyoxal is indeed due only to oxidation of precursor VOCs,

calculating the expected yield of glyoxal should be straightforward in this relatively simple

and well mixed chemical matrix over the remote ocean. However, there has been

consistent suggestion that glyoxal concentrations in the MBL are in excess of the yields

expected from its precursors. Wittrock et al. (2006) reported enhanced concentrations of

formaldehyde and glyoxal from SCIAMACHY satellite retrievals over tropical oceans, but

were unable to reproduce observations using a global model. More detailed global

modelling studies by Fu et al. (2008) and Myriokefalitakis et al. (2008) were also unable to

reproduce SCIAMACHY glyoxal column retrievals over the tropical oceans, highlighting the

possibility of unknown biogenic marine sources. Later satellite retrievals of glyoxal from

SCIAMACHY (Vrekoussis et al., 2009), GOME-2 (Lerot et al., 2010) and recently from OMI

(Miller et al., 2014) have provided further evidence of the widespread presence and

seasonal modulation of glyoxal over biologically active oceans, although in some regions,

such as the temperate SH oceans, the columns are close to satellite detection limits.

Glyoxal and methylglyoxal were first observed in the atmosphere and seawater in the

Caribbean and Sargosso Seas as early as 1989 (Zhou and Mopper, 1990) where

concentrations of glyoxal and methylglyoxal in seawater were 4 and 2 orders of magnitude

too low to explain the atmospheric concentrations. MAX-DOAS retrievals observed

hundreds of ppt glyoxal in the Gulf of Maine (Sinreich et al., 2007) and an average of 63 ppt

glyoxal over the remote Tropical Pacific (Sinreich et al., 2010). The Sinreich et al. (2010)

measurements were sufficiently far from land that the glyoxal observed was either from

unrealistically high mixing ratios of long lived terrestrial precursors, or more likely a

substantial unknown source possibly of marine origin, in support of earlier modelling and

satellite studies. The widespread presence of glyoxal over the remote oceans was recently

confirmed by Mahajan et al. (2014), who reported MAX-DOAS and long-path DOAS

differential slant column densities from 10 field campaigns in both hemispheres in tropical

and temperate regions. A global average value of about 25 ppt was reported with an upper

limit of 40 ppt, however over the Southern Hemisphere oceans, particularly in sub tropical

and temperate regions, glyoxal mixing ratios were mostly below instrument detection limits.

In 2014 an additional source of glyoxal in the MBL was identified in laboratory studies

(Zhou et al., 2014), when oxidation of the sea surface microlayer (SML) led to emission of

low molecular weight oxygenated compounds including glyoxal. However, the atmospheric

89

yields of glyoxal were low, attributed to the fast irreversible hydrolysis of glyoxal which

prevents transfer of glyoxal to the atmosphere. Van Pinxteren and Herrmann (2013)

observed a glyoxal enrichment factor of 4 in SML compared to the bulk ocean, but the

concentration observed was several orders of magnitude too low to explain mixing ratios of

10s of ppt typically seen in the MBL (Sinreich et al., 2010). The first eddy-covariance flux

measurements of glyoxal were recently made over the oceans, using an in situ Fast Light

Emitting Diode Cavity Enhanced Differential Optical Absorption Spectroscopy (LED-CE-DOAS

instrument) (Coburn et al., 2014). Negative flux (glyoxal transfer into the ocean) was

observed in both hemispheres during the day, and a positive flux from the ocean in the SH

at night. However, despite this first evidence of a direct oceanic source of glyoxal to the

atmosphere, the positive flux at night could explain only 4 ppt of the glyoxal observed in the

overlying atmosphere (some 30 % of the overnight increase), implying the contribution of

another night-time production mechanism.

Despite these recent advances in our understanding of glyoxal production processes,

our current inability to reconcile the presence of these short lived gases over the remote

ocean suggests we have not identified a significant source of glyoxal. It is likely that this

unidentified source also contributes to the glyoxal production in polluted terrestrial

environments, but is masked by a large contribution from anthropogenic precursors such as

acetylene. The production of glyoxal from photochemical processing of organic aerosol is a

possible contributor (Vrekoussis et al., 2009; Stavrakou et al., 2009; Bates et al., 2012)

though this remains unconfirmed. An additional source may be entrainment of glyoxal and

its precursors from the free troposphere into the MBL, particularly in light of recent

observations of non-negligible mixing ratios of glyoxal in the free troposphere (Volkamer,

2014).

A more in depth understanding is currently hindered by a lack of dicarbonyl

observations in the MBL. While recent studies have contributed substantial additional

observations of glyoxal over the remote oceans (Coburn et al., 2014; Mahajan et al., 2014),

there have been no studies which have made parallel measurements of gas phase

precursors, and so expected yields of glyoxal are only estimates. No in situ observations of

glyoxal have been reported over temperate oceans of either hemisphere, and there is only

one previous study reporting methylglyoxal observations over the world’s oceans (in the

tropical northern hemisphere, NH) (Zhou and Mopper, 1990). With the exception of the

90

Caribbean and Sargasso Sea measurements (Zhou and Mopper, 1990), all column and in situ

observations of glyoxal over the remote oceans have used optical measurement techniques

(Mahajan et al., 2014; Sinreich et al., 2010; Sinreich et al., 2007; Coburn et al., 2014). Finally,

given the challenges in retrieving low VCDs of glyoxal over the ocean from satellite

observations (Lerot et al., 2010; Vrekoussis et al., 2009; Miller et al., 2014), more ground

based measurements are required.

We provide much needed in situ glyoxal and methylglyoxal data from the very sparsely

measured temperate oceans of the Southern Hemisphere. Observations have been made

using derivatisation of dicarbonyls on 2-4 DNPH cartridges and analysis with HPLC, which is

an alternative measurement technique to the optical techniques used widely for oceanic

glyoxal observations to date. Measurements have been made in two seasons, summer and

winter, and auxiliary measurements, including carbon dioxide, radon and particles have

been used to conclusively remove the possibility of any terrestrial influence on the

dicarbonyl observations. This is the first study to concurrently measure a range of

dicarbonyl precursors (VOCs), so that the yield of dicarbonyls from its gas phase precursors

can be conclusively determined. Finally we provide the first methylglyoxal observations over

the temperate remote ocean.

3.3 Methods

3.3.1 Sampling locations

Dicarbonyl in situ observations were made at the Cape Grim Baseline Air Pollution

Station (CGBAPS) and during a voyage over the Chatham Rise in the South West Pacific

Ocean during the Surface Ocean Aerosol Production (SOAP) study (see Fig. 1).

91

Figure 1. Cape Grim and Chatham Rise sampling locations

Cape Grim Baseline Air Pollution Station The Cape Grim Baseline Air Pollution Station (BAPS) is located on the north-west tip of

the island state of Tasmania, Australia, (40.683◦S 144.689◦E). CGBAPS is a World

Meteorological Organisation (WMO) Global Atmosphere Watch (GAW) Global Station and

hosts a wide variety of long term measurements including greenhouse gases, ozone

depleting substances, aerosols, radon and reactive gases. The station is situated on a cliff

94m above sea level and when the wind blows from the south westerly ‘Baseline’ sector the

air that arrives at the station has travelled over the Southern Ocean several days prior with

no terrestrial influence.

A total of 33 samples were collected from the 26 August – 29 September in 2011 (late

winter-early spring). Each 24 hour sample consisted of approximately 2000L of air drawn

through a 2,4-DNPH S10 Cartridge (Supelco) at a flow rate of 1.8L min-1.

Ambient air was sampled down a 150mm diameter stainless steel inlet stack which

extends 10m above the roof deck and is 104m above sea level. The flow rate of the intake

was 235 L min-1 to ensure laminar flow was maintained. The DNPH cartridges were loaded in

92

a custom designed ‘Sequencer’ which allows up to 16 cartridges to be automatically

sampled for a predefined time and sequence. The Sequencer drew air via a 3m length of ¼

inch PFA tubing which was extended into the centre of the stainless steel intake stack.

Samples were collected in all wind directions and an ozone scrubber (KI impregnated filter)

was placed in front of the cartridges. Chlorophyll-a, a measure of ocean biological activity, is

low in the Southern Ocean in winter, with typical values of 0.1-0.2 mg m-3 (Bowie et al.,

2011). Air temperatures throughout the sampling period ranged from 7ºC - 13ºC with an

average of 10ºC and total rainfall during the sample period at the station was 90 mm. The

average relative humidity was 78%.

Data collected from concurrent and continuous carbon dioxide, particle count and

radon measurements at Cape Grim have been included in this work as indicators for clean

marine air. VOC data from canister samples collected at Cape Grim for the NOAA

Halocarbon (HATS) group and the Carbon Cycle Network have been used to calculate

dicarbonyl yields.

Surface Ocean Aerosol Production (SOAP) voyage The SOLAS-endorsed Surface Ocean Aerosol Production Study in 2012 investigated

links between ocean biogeochemistry, air-sea exchange of trace gases and particles, and the

composition of the overlying atmosphere (Landwehr et al., 2014). Measurements were

made on board the RV Tangaroa over Chatham Rise, located over the biologically productive

subtropical oceanic front. Six dicarbonyl samples were collected from the 29th Feb- 6th

March 2012 (late summer). Each 24 hour sample consisted of approximately 1400L of air

drawn through a 2,4 DNPH S10 Cartridge (Supelco) at a flow rate of 1.3 L min-1. Cartridges

were loaded in the “Sequencer’ which drew air off a 25m 3/8 inch PFA inlet line with a flow

rate of 10 L min-1. Inlet losses were determined to be <2% for isoprene, monoterpenes,

methanol and dimethyl sulphide however losses were not specifically tested for dicarbonyls

due to the absence of a gaseous calibration standard. The sample inlet line pulled air from

the crow’s nest of the vessel above the bridge, some 28 m above sea level. To avoid ship

exhaust from aft of the inlet being drawn into the PFA inlet line and sampled on to the

cartridges, a baseline switch was developed and deployed using a CR3000 micrologger

control system (Campbell Scientific, Logan UH). The switch used 1 Hz wind data from the

vessel port/starboard pair of Wind Observer anemometers (Gill Instruments, Lymington,

93

U.K.) and was configured to switch pumps off within 1 second of detecting non-baseline

conditions. The “baseline” was defined as: a five second running average relative

windspeed > 3 ms-1 and 5 second vector averaged relative wind direction outside of the aft

(135° to 225° relative degrees) wind-direction with meteorological convention of 0° at the

bow. Five minute duration under accepted windspeed and direction was required before

turning on. During experimentation and for much of the “steaming” transit legs, the vessel

was oriented into the wind for as much of the time as possible in addition to dedicated

periods of steaming into the wind. This resulted in a high frequency (~75%) of baseline

conditions throughout the voyage.

During the voyage three distinct phytoplankton blooms were sampled, and dicarbonyl

samples reported in this work were taken over the third bloom during the last 6 days of the

voyage. Underway chlorophyll-a during this period ranged between 0.3-0.9 mg m-3 (10th-90th

percentile) with median of 0.5 mg m-3 (calibrated against discrete data with data

corresponding to sky irradiance >50W/m-2 removed, i.e. to exclude daytime data affected by

photo-quenching). The bloom consisted of a mixed phytoplankton population of

coccolithophores, small flagellates and dinoflagellates, and had a deep cold mixed layer

characteristic of Sub-Antarctic waters. During the 6 days of sampling the vessel moved

between 44.928ºS and 41.261ºS and 172.768ºE to 175.168ºE, including transiting to

Lyttelton Port on the East Coast of the New Zealand South Island for several hours on the 1st

March to exchange staff. However due to south westerly winds during this period and high

wind speeds (average of 13 ms-1 and max of 29 ms-1), air was predominantly of marine

origin. Air temperatures during the sampling period ranged from 10ºC -18 ºC with an

average of 13ºC and total rainfall during the 6 day sample period was 3.0 mm. The average

relative humidity was 80% during the voyage.

Parallel measurements also made during the SOAP voyage which have been utilised in

this work, include online VOCs via Proton Transfer Reaction Mass Spectrometry (PTR-MS)

and carbon dioxide and particle concentrations.

3.3.2 In situ measurements

DNPH cartridges and HPLC analysis During sampling, carbonyls and dicarbonyls were trapped on S10 Supelco cartridges,

containing high purity silica adsorbent coated with 2,4-dinitrophenylhydrazine (2,4-DNPH),

94

where they are converted to the hydrazone derivatives. Samples were refrigerated

immediately after sampling until analysis. The derivatives were extracted from the cartridge

in 2.5 ml of acetonitrile and analysed by a High Performance Liquid Chromatography (HPLC)

system consisting of a Dionex GP40 gradient pump, a Waters 717 autosampler, a Shimadzu

System controller SCL-10A VP, a Shimadzu diode array detector (DAD) SPD-M10A VP, a

Shimadzu Column Oven CTO-10AS VP and Shimadzu CLASS-VP chromatography software.

The compound separation was performed with two Supelco Supelcosil LC-18 columns in

series, 5 µm, 4.6 mm ID x 250 mm in length, Part No 58298. The chromatographic

conditions include a flow rate of 1.6 ml min-1 and an injection volume of 25 µl, and the DAD

was operated in the 220nm to 520nm wavelength range. The peaks were separated by

gradient elution with an initial mobile phase of 64% acetonitrile and 36% deionised water

for 10 minutes, then a linear gradient to 100% acetonitrile at 20 min, and column

temperature of 30°C. The deionised water used for analysis was 18.2 MΩ.cm grade

produced from a Millipore Milli-Q Advantage 10 system and HPLC grade acetonitrile was

purchased from Merck.

Standards for glyoxal and methylglyoxal were prepared by making hydrazone crystals

from glyoxal (40% wt in H2O), methylglyoxal (40% wt in H2O) and derivatisation reagent

2,4,DNPH (all from Sigma-Aldrich). The crystals were weighed and dissolved in acetonitrile

to produce a stock standard for the glyoxal and methyglyoxal derivatives, which was used to

make up a range of standards from 0.125 to 1.000 µg ml-1 which gave a linear response with

a correlation coefficient of 0.999 for both derivatives.

The DAD enables the absorption spectra of each peak to be determined. The mono

carbonyl DNPH derivatives all have a similar shaped absorption spectrum with a maximum

absorption near 360nm. In contrast, the dicarbonyls glyoxal and methylglyoxal have

absorption spectra which differ in shape to the monocarbonyls, and have a maximum

absorption near 435nm (Fig. 2). The difference in the spectra highlights which peaks in the

chromatograms are mono- or dicarbonyl DNPH derivatives and along with retention times

allows identification of the glyoxal and methylglyoxal peaks. Quantifying the dicarbonyl

DNPH derivatives at 435nm results in increased peak height and also has the added benefit

of reducing the peak area of any co-eluting mono-carbonyl DNPH derivatives (Fig. 3). All

samples, blanks and standards for glyoxal and methylglyoxal in this work were quantified

using absorption at 435 nm which as discussed above is optimised for dicarbonyl detection.

95

Figure 2. Absorption spectra for monocarbonyl formaldehyde (green), dicarbonyls glyoxal (blue line) and methylglyoxal (red line).

96

Figure 3. Example of sample chromatogram from Chatham Rise, using absorption at 360 nm (pink line) and 435 nm (black line).

Sample recovery was determined by spiking 1µg of glyoxal and methylglyoxal on to

DNPH cartridges – recoveries were 96 ±0.3 % for glyoxal and 111 ±8 % for methylglyoxal.

The degree of derivatisation was examined in these spiked cartridges to ensure both

carbonyl groups in the glyoxal and methylglyoxal molecules had reacted with the 2-4, DNPH

(Wang et al., 2009;Olsen et al., 2007). Analysis of samples that had been extracted within

the last 24 hours showed a second smaller peak indicating that ~ 5% of the glyoxal had

reacted to form mono-derivatives rather than bis-derivatives (e.g. only one carbonyl group

had reacted). However analysis of samples that were held for > 24 hours after extraction

showed all of the mono derivative had been converted into the bis-derivative. As all samples

were extracted then held for at least 24 hours before analysis, complete derivatisation to

the bis-derivative is expected. For methylglyoxal there was no evidence of any mono-

derivatives. The total mass of carbonyls and dicarbonyls sampled on the DNPH cartridges

was at most 7% of the cartridge capacity, and collection efficiencies of >93% have been

determined for carbonyls on DNPH cartridges at similar flow rates to those used here (Zhang

et al., 1994; Slemr, 1991; Grutter et al., 2005). Hence no significant losses of dicarbonyls

during sampling are expected.

The minimum detectable limit (MDL) for glyoxal and methylglyoxal were calculated

from the standard deviation of field blanks collected during the study period based on the

principles of ISO 6879 (ISO, 1995). Field blanks were opened and installed in the Sequencer

sampling train for the same time period as samples. MDLs for a 24 hour sample were 1 ppt

(glyoxal) and 1.7 ppt (methylglyoxal) during SOAP, and 0.6 ppt (glyoxal) and 0.9 ppt

(methylglyoxal) at Cape Grim. Glyoxal and methylglyoxal mixing ratios were above MDLs in

all 24 hour samples.

3.3.3 Supporting measurements for selection of clean marine periods

HYSPLIT (https://ready.arl.noaa.gov/HYSPLIT.php) 96 hour air-mass back trajectories

(300m above sea level) were used as an additional means of identifying clean marine

samples from Cape Grim and the Chatham Rise (see Figs. 4a and 4b). Specific surface

measurements used to indicate clean marine air for each site are discussed below.

97

Cape Grim Atmospheric radon-222, carbon dioxide and particle concentration data were used to

select dicarbonyl samples with clean marine origin and no terrestrial influence.

Atmospheric radon-222 is a useful atmospheric tracer to determine the degree of

contact between an air parcel and a terrestrial surface, due to the much larger flux of radon

from terrestrial surfaces compared with the ocean. Hourly atmospheric radon-222

measurements at Cape Grim are made on air taken from a 70 inlet (height above sea level

164 m) and using the dual-flow loop two filter method. See Zahorowski et al. (2013) for

details of the measurement technique and application of radon data to identify clean

marine air.

Carbon dioxide (CO2) concentrations may be used to indicate whether an air mass is

primarily of marine origin or has had recent contact with land. Terrestrial contact results in

enhancement or draw down of CO2 depending on the land use and anthropogenic sources.

Continuous CO2 measurements at Cape Grim are sampled via a 70m inlet and measured via

a continuous, ultra precise CSIRO LOFLO NDIR system, described elsewhere (Steele et al.,

2014). Hourly averaged CO2 concentrations were used in this work.

Particle concentration may be used as an indicator of an air mass history, as recent

contact with a terrestrial surface leads to particle concentrations enhanced above low

concentrations typically found in marine air. Measurements of condensation (CN) nuclei

greater than 10 nm in diameter (CN>10nm) are made at Cape Grim using a 3010 CPC TSI

particle counter, sampling from the 10 m sample inlet described in previous section (Gras,

2009). Hourly averaged particle concentration data was used in this work.

Baseline status at Cape Grim Air is automatically classified as Baseline at Cape Grim (e.g. clean marine air) using a

combination of wind direction (190º and 280º) and a seasonally adjusted particle

concentration (CN>10nm) threshold based upon the previous five year’s particle

concentration data (Keywood, 2007). This Baseline status was used to identify clean marine

samples.

SOAP Voyage Carbon dioxide and particle concentrations (CN>10 nm) were used to identify

dicarbonyl samples with a clean marine origin and no terrestrial influence during the

voyage.

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Carbon dioxide (CO2) measurements were made continuously using a Picarro Cavity

Ringdown laser (CRDS). The instrument was calibrated before and during the voyage using

three reference calibration tanks. The CO2 intake through 6 mm Decabon tubing, from

alongside the crow’s nest had a flow rate of 300 mL min-1.

CN>10nm concentrations were measured with a 3010 CPC TSI particle counter.

Antistatic (copper coil) polyurethane ducting was used as a common aerosol inlet, and

sampled air from the main radar tower at 21 m in height above sea level. The inlet was

10 cm in diameter, 30 m in length, with a flow rate of 800 L m-1. The CPC intake was

connected to the common aerosol inlet via ¼ inch stainless steel tubing. Inlet loss tests

indicated particle loss rates were ~15% for total particle counts.

3.3.4 Measurements for dicarbonyl yield calculations

A High Sensitivity Proton Transfer Reaction-Mass Spectrometer (PTR-MS) (Ionicon

Analytik) was used to measure VOCs in real time during the SOAP voyage. Details on PTR-MS

measurements are given in Galbally et al. (2007) and some additional information is

provided here.

The PTR-MS ran with inlet and drift tube temperature of 60ºC, 600V drift tube, ~2.2

mbar drift tube pressure, which equates to an energy field of 133 Td. The O2+ signal was

<1% of the primary ion H3O+ signal. The PTR-MS sampled from a 25 m PFA 3/8 inch inlet line,

which had a continuous flow of 10 L min-1, except during ‘non baseline’ periods when the

inlet pump switched off and the PTR-MS sampled room air through a VOC scrubber. The

Baseline status was logged on two separate programs and PCs and so room air

measurements were removed from the data. The PTR-MS measured in scan mode in the

range of m/z 21 – m/z 155 with a dwell time of 10 seconds per mass, allowing 3 full scans of

the mass range per hour. Measurement of background signal resulting from interference

ions and outgassing of materials was achieved by passing ambient air through Platinum

coated glass wool catalyst at 350ºC for 30 minutes 4 times per day. An interpolated

background signal was used for background correction. All species used in this work were

calibrated daily by introducing a known flow of calibration gas to VOC-free ambient air

which had previously passed through the catalyst. Calibrations and background

measurements were carried out using an automated calibration system, see Galbally et al.

99

(2007). Calibration gases used were ~ 1 ppm custom VOC mixture in nitrogen Apel Riemer

(~1ppm acetone, benzene, toluene, m-xylene, a-pinene) and a custom gas mixture from

Scott Specialty Gases (~ 1ppm isoprene and 1,8- cineole).

The MDL for a single 10 s measurement of a selected mass was determined using the

principles of ISO6879 (ISO, 1995) i.e. 5% of the 10 s background measurements give a false

positive reading. MDLs were as follows: m/z 59 (acetone) 17 ppt, m/z 69 (isoprene) 28 ppt,

m/z 79 (benzene) 16 ppt, m/z 93 (toluene) 16 ppt, m/z 107 (sum xylenes) 19 ppt, m/z 137

(sum monoterpenes) 66 ppt. In contrast to the first week of the voyage, the mixing ratios of

VOCs during the last 6 days of the voyage were low and subsequently many of the VOCs

were below detection limit. The percentage of observations above MDL during this period

are as follows m/z 59 – (95%), m/z 69- (14%), m/z 79- (15%), m/z 93- (14%), m/z 107 – (7%),

m/z 137-(4%). Where the observation was lower than MDL, the half MDL value was

substituted. Hence due to the high periods of time that VOCs were below MDLs, the

reported concentrations used for yield calculation are strongly influenced by the MDL.

Reported mixing ratios of dicarbonyl precursors and dicarbonyl yields calculated with PTR-

MS data are therefore likely to be an upper limit.

VOC Flask data VOCs measurements from flasks collected at Cape Grim in Baseline conditions were

used to provide supplementary mixing ratios for species which were not targeted, or could

not be measured with sufficient senstivity by PTR-MS at Cape Grim and during the SOAP

voyage.

Stainless steel and glass flasks have been collected for and analysed by the National

Oceanic Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL)

Global Monitoring Division (GMD) Halocarbons (HATS) group with Gas Chromatography (GC)

techniques since the early 1990s (Montzka et al., 2014). In this work, benzene and acetylene

mixing ratios (analysed with GC-mass spectrometry detection (Rhoderick et al., 2014;Pétron

et al., 2012)) from August- September 2011 were utilised, as well as acetylene values in

March 2011, as a proxy for mixing ratios during SOAP. Benzene values are calculated from

an average of 6 pairs of flasks (2 glass and 4 stainless steel pairs) while acetylene values are

from 3 pairs of stainless steel flasks in Aug-Sep 2011 and a single pair of stainless steel flasks

100

in March 2011. There is low interannual variability in benzene and acetylene at Cape Grim,

so the values used, which correspond to the same sample periods as dicarbonyls, were

representative of typical values for these months.

Glass flasks collected in Baseline Air at Cape Grim for the NOAA Carbon Cycle Group

are analyzed for VOCs by an automated gas chromatography system at the University of

Colorado’s Institute of Arctic and Alpine Research ( INSTAAR) (Helmig et al., 2014; Helmig et

al., 2009). Average propane, iso-butane, n-butane, iso-pentane, and n-pentane mixing raios

were utilised from 5 pairs of glass flasks collected in Aug-Sep 2011 (filtered data). Flask data

were also used to estimate alkane mixing ratios during the SOAP voyage (average mixing

ratios from flasks sampled in March between 2005-2014). The following number of flasks

were used in calculating average values for March: propane (4 pairs and 2 single flasks), n-

butane (9 pairs and 4 single flasks), iso-butane (10 pairs and 2 single flasks), n-pentane (7

pairs and 2 single flasks) and iso-pentane (10 pairs and 2 single flasks).

Additional VOC measurements from Cape Grim were utilised for the dicarbonyl yield

calculations, including online PTR-MS measurements in clean air at Cape Grim in February

(summer) 2006 (Galbally et al., 2007), online PTR-MS measurements in clean air in spring

(November) 2007 (Lawson et al., 2011), in which data has been further filtered to include

only Baseline hours and stainless steel canisters which were collected at Cape Grim

between 1998 – 2000 and analysed at Aspendale with GC with flame ionisation detection

(FID) (Kivlighon, 2001). Further details of how these data were utilised is provided in Table 3.

OH and ozone concentrations Precursor (VOC) lifetimes at Chatham Rise and Cape Grim were calculated using

estimated OH concentrations, and measured ozone mixing ratios from Cape Grim in March

and August –September respectively.

[OH] was estimated from a simple steady state chemical model where: ( ) + ( ) (1) ( ) + → 2 (2)

OH is presumed to be removed overwhelmingly by reaction with carbon monoxide

and methane (Sommariva et al., 2004). J(O1D) is estimated from UV-B measurements for

2000 – 2005 inclusive (Wilson, 2014). All other chemical parameters are measured at Cape

101

Grim (hourly averages) except for ozone where climatological values were used. The full

temperature dependence of reaction rates was used.

Average measured ozone mixing ratios in Baseline air at Cape Grim were taken from

Molloy et al. (2014).

3.4 Results and Discussion

3.4.1 In situ observations in clean marine air

Selection of clean marine samples Five of the 33 samples from Cape Grim, and 2 of the 6 samples from Chatham Rise

were identified as coming from a clean marine back trajectory air over the 24 hour sampling

period. Mixing ratios of glyoxal and methylglyoxal at Cape Grim and Chatham Rise in clean

marine air alongside supporting measurements are shown in Table 1. Air mass back

trajectories (96 hour) for these clean marine samples are shown in Figs. 4a and 4b.

Table 1. Mixing ratios of glyoxal and methylglyoxal in clean marine air at Cape Grim and Chatham Rise, with supporting measurements of carbon dioxide, condensation nuclei (CN) > 10 nm and atmospheric radon-222. Values are averageSD. n stands for the number of 24 h samples.

Samples were identified as being of clean marine origin in the following way. Samples

from Cape Grim were initially identified as those for which > 90% of the sample hours were

Site Season Glyoxal (ppt)

Methylglyoxal (ppt)

CO2 (ppm)

CN>10nm (particles

cm-3)

Radon (mBq m-3)

% Baseline

hours

Cape Grim n=5

Winter/Spring (Aug-Sep)

7 ± 2 28 ± 11 388.84 ± 0.12

194 ± 110 43± 14 95

SOAP voyage

n=2

Summer (Feb-Mar)

23 ± 8 10 ± 10 388.54 ± 0.82

328 ± 1591 n/a n/a

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classified as Baseline according to the criteria described previously. Between 92-97% of the

sampling time was Baseline for the clean marine samples. Chatham Rise clean marine

samples were initially identified using the HYSPLIT air mass back trajectories, and in situ

measured wind direction. As an additional indicator of clean marine baseline air, concurrent

measurements of in situ continuous CO2, and CN > 10nm were calculated for Cape Grim and

SOAP samples (see Table 1). Concurrent atmospheric radon-222 concentrations were also

calculated for Cape Grim samples.

The pristine marine nature of these samples is clearly demonstrated by these

supporting measurements. The particle concentration (CN>10nm) at Cape Grim during

sampling of clean marine samples was 194 particles cm-3, lower than the typical

concentration of ~400 particles cm-3 in Baseline air in August/September (Gras, 2014).

Particle concentrations corresponding to the Chatham Rise clean marine samples are also

low (328 particles cm-3) but with a large standard deviation of 1591 particles cm-3. This is

due to short-lived, major enhancements (up to 30,000 particles cm-3) of CN, which

correspond to measured enhancements in black carbon, identifying ship exhaust. This raises

the possibility that there may have been a minor influence of ship exhaust on the VOC

measurements, even though the VOC and aerosol inlets were not co-located. While glyoxal

and methylglyoxal have been identified in medium duty diesel exhaust (Schauer et al.,

1999), and so could be emitted by the ship’s diesel engine, Schauer et al. (1999) showed

that oxygenated VOCs such as acetaldehyde and acetone were present in mixing ratios 10-

20 times higher than glyoxal. No coincident spike in acetaldehyde or other VOCs were seen

with the particle peaks – therefore it is unlikely that ship exhaust had any influence on the

glyoxal or methylglyoxal measured.

Average CO2 concentrations during clean marine samples were 388.84 (std dev 0.12)

and 388.54 ppm (std dev 0.8) at Cape Grim and SOAP respectively, very close to Southern

Ocean Baseline concentrations in August 2011 (388.51 ppm) and March 2012 (388.69 ppm)

(http://www.csiro.au/greenhouse-gases/). The higher standard deviation from Chatham

Rise was due to positive CO2 excursions above background and is therefore likely also a

minor impact of ship exhaust.

Finally the atmospheric radon-222 concentration of 43 mBq m-3 at Cape Grim is

indicative of clean marine air. This value compares well to a median baseline sector value of

103

42 mBq m-3 and is much lower than the median non-baseline value of 378 mBq m-3 reported

by Zahorowski et al (2013).

3.4.2 Dicarbonyl observations in clean marine air

The glyoxal mixing ratio at Cape Grim in winter is low (7 ± 2 ppt), and in contrast is

higher over Chatham Rise in summer (23 ±8 ppt). The low standard deviations indicate

consistency in glyoxal mixing ratios in clean marine air at both sites. The higher mixing ratios

in summer compared to winter are in agreement with higher VCDs of glyoxal in summer

compared to winter over the temperate SH oceans as observed by SCIAMACHY and GOME-2

(Vrekoussis et al., 2009; Lerot et al., 2010) (see Sec. 3.4.5 for further discussion of satellite

comparison).

In contrast to glyoxal, the methylglyoxal mixing ratios in pristine marine air are higher

at Cape Grim (28 ± 11ppt) compared to Chatham Rise (10 ± 10 ppt). The average ratio of

glyoxal:methylglyoxal in clean marine air is ~4 over Chatham rise (range 1.7-5.9) while at

Cape Grim the average ratio is 0.3 (range 0.2-0.4) Given that many of the gas phase

precursors of methylglyoxal are also precursors of glyoxal this major difference in ratios at

the two sites is striking, and is also seen when taking into account non-pristine marine

samples. Possible reasons for this difference are discussed below.

3.4.3 Clean marine versus all data and comparison with other marine

background observations

Cape Grim and Chatham Rise dicarbonyl observations from clean marine samples and

for all samples are presented in Table 2. For comparison, other studies reporting mixing

ratios of glyoxal and methylglyoxal from remote temperate and tropical oceans are also

presented. Where other studies have explicitly excluded possibility of terrestrial influence

via back trajectories or other means, these values are listed as ‘clean marine.’ Where

possibility of terrestrial influence has not been investigated, values are listed as ‘all data’,

however values listed under ‘all data’ are not necessarily affected by air of terrestrial origin.

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Table 2. Glyoxal and methylglyoxal compared to dicarbonyl measurements from other remote oceanic sites. Data is listed as being of clean marine origin where the study explicitly excludes terrestrial influence. All concentrations are in ppt. Values are mean_SD; SH stands for Southern Hemisphere; NH stands for Northern Hemisphere. aMahajan et al. (2014) (only data above MDL has been included in average). bCoburn et al. (2014). cSinreich et al. (2010). dZhou and Mopper (1990).

Glyoxal Average mixing ratios of glyoxal at both Cape Grim and Chatham Rise are higher when

averaging all samples (which include air from all wind directions and hence terrestrial

sources), compared to clean marine samples. This is expected as the terrestrial environment

is a major source of important biogenic dicarbonyl precursor gases isoprene and alpha-

pinene, and is also a source of precursors from anthropogenic and biomass burning sources,

including longer lived gases such as acetylene, benzene, acetone, alkanes and >C2 alkenes

which can travel long distances before being oxidised. Higher standard deviations in all

Temperate ocean

Tropical ocean

Southern Ocean (Cape Grim)

This work

South West

Pacific (Chatham

Rise) This work

South West

Pacific (Chatham

Rise)a

North Pacific

& Atlantica

Tropical Pacific

& Atlantica

Eastern Tropical Pacificb

Tropical Pacificc

Caribbean and

Sargasso Sead

Clean marine origin

All data

7 ± 2

10 ± 6

23 ± 8

30 ± 12

-

23 ± 10

-

25 ± 13

-

24 ± 12 (SH)

26 ± 15 (NH)

43 ± 9 (SH)

32 ± 6 (NH)

-

63 ± 21

-

-

80

Clean marine origin

All data

28 ± 11

57 ± 32

10 ± 10

19 ± 14

- -

- -

- -

- -

- -

-

~10

105

samples compared to clean marine samples likely reflects a greater variation in

concentrations of precursor gases resulting from differing wind directions. Interestingly, at

Cape Grim, the average enhancement in glyoxal when including data from all wind direction

is only 3 ppt, even though a further 28 samples have been included. This suggests terrestrial

sources have a minimal contribution to glyoxal mixing ratios at Cape Grim in winter.

The glyoxal mixing ratio from Cape Grim of 7 ppt in clean marine conditions and 10

ppt in all conditions, is the lowest mixing ratio that has been reported over the world’s

oceans to date. This low mixing ratio is supported in part by the study by Mahajan et al.

(2014), in which many of the observations over the temperate SH oceans were below

detection limits. The glyoxal mixing ratio from Chatham Rise all data (30 ±12 ppt) compares

well to the mixing ratio derived from MAX- DOAS measurements during the same voyage

(23 ± 10 ppt) (Mahajan et al., 2014). Despite the techniques employing different approaches

(in situ derivatised samples versus column measurement) this suggests good agreement

between these techniques at these low mixing ratios. Recent inter-comparisons of

dicarbonyl measurement techniques have examined the relationship between optical and

derivatisation techniques, but with a focus on a wider range of mixing ratios than observed

over the remote ocean (Thalman et al., 2014; Pang et al., 2014).

The glyoxal mixing ratios from Chatham Rise also compare well to those observed by

Mahajan et al. (2014) over the North Pacific and Atlantic (25 ± 13 ppt) and tropical Pacific

and Atlantic (24 ± 12 ppt SH, 26 ± 15 ppt NH). It should be noted that these Mahajan et al.

(2014) values were calculated only from data above the instrument detection limit and so

contain a positive bias and are upper estimates. Chatham Rise mixing ratios are also similar

to the Eastern Tropical Pacific NH average (32 ± 6 ppt) (Coburn et al., 2014) but somewhat

lower than those observed in the SH Eastern Tropical Pacific (43 ± 9 ppt) (Coburn et al.,

2014), and over the Tropical Pacific (63 ± 21 ppt) (Sinreich et al., 2010). The Caribbean Sea

value of 80 ppt is the highest average mixing ratio reported over the oceans and

substantially higher than mixing ratios observed in this study, although the variation of this

value is not given (Zhou and Mopper, 1990). Overall, the synthesis of glyoxal observations

from this and other studies provides compelling evidence for the widespread presence of

glyoxal, in non-negligible mixing ratios, in the atmosphere over the remote oceans.

106

Methylglyoxal Mixing ratios of methylglyoxal at Cape Grim and Chatham Rise are higher when

considering all data, and have greater variation, reflecting substantial influence of terrestrial

precursors. In particular, mixing ratios of methylglyoxal at Cape Grim in all samples are

approximately twice the mixing ratios of clean marine samples. This significant

enhancement at Cape Grim in all data is likely due to substantial terrestrial influence at Cape

Grim when considering all wind directions. The station is bounded by farmland to the east

and south-east, and mainland Australia, and the city of Melbourne is ~300km north across

Bass Straight. The greater enhancement of methylglyoxal compared to glyoxal in all data

from Cape Grim may be due to the much higher yield of methylglyoxal from isoprene and

monoterpenes compared to glyoxal, and the rich source of methylglyoxal precursors from

urban regions including alkenes and alkanes > C2 (Fu et al., 2008).

The only other observations of methylglyoxal over the world’s oceans come from the

Caribbean Sea (Zhou and Mopper, 1990), with an approximate value of ~ 10 ppt which is

somewhat lower than that observed at Cape Grim, but in agreement with Chatham Rise

mixing ratios in this study.

Differences between dicarbonyl ratios at Cape Grim and Chatham Rise The average ratio of glyoxal: methylglyoxal is 3.8 over Chatham Rise (range 1.7-5.9) in

clean marine air and 2.3 in all samples (range 1.2-5.9). At Cape Grim the average ratio is 0.3

(range 0.2-0.4) in clean marine air and 0.2 in all samples (range 0.1-0.4). The dominance of

methylglyoxal at Cape Grim and glyoxal at Chatham Rise points to a major difference

between sites and warrants further investigation.

The back trajectories of air in clean marine samples at both Cape Grim and Chatham

Rise indicate that the air sampled at both sites originated from the Southern Ocean, from a

latitude of 55 - 65ºS 96 hours prior (Fig. 4a and b). A major difference between the back

trajectories of the two sites is the longitude, with Cape Grim back trajectories covering 50 ºE

- 140ºE and the trajectories from the more easterly located Chatham Rise covering 90º E -

175ºE. The 3-D trajectory altitude (not shown), suggests that air from all clean oceanic

samples at both sites travelled in the lower 750m of troposphere 24 hours prior, and which

up to 48 hours prior had originated at a height of between 500-1500m (Chatham Rise) and

300-1200m (Cape Grim). No clear differences in vertical back trajectories between sites, or

relationship between height and mixing ratios were evident.

107

Figure 4 (a) HYSPLIT 96-hour back trajectory for the five clean marine samples from Cape Grim. (b) HYSPLIT 96-hour back trajectory for the two clean marine samples from the SOAP Voyage

Because glyoxal and methylglyoxal are so short lived, their observed mixing ratios are

due to equilibrium between local production and loss. Therefore the difference in ratios

between sites indicates a major difference or differences in production or loss rates.

If differences in ratios are due to differing rates of production of dicarbonyls, this

could be due to a) varying concentrations of precursor gases, b) different emission rates of

dicarbonyls from the SML, or c) other unconfirmed production mechanisms. Methylglyoxal

and glyoxal have a number of overlapping gas phase precursors, and while there are

precursors specific to each (e.g. acetylene, acetone and benzene for glyoxal and higher

alkanes and alkenes for methylglyoxal) (Fu et al., 2008), in clean marine conditions

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precursor mixing ratios are unlikely to differ significantly between sites. The calculated yield

of dicarbonyls from parallel or best estimate precursor mixing ratios at both sites is low and

so other production mechanisms must be dominating at these sites.

Emission of glyoxal from the SML has only very recently been reported for the first

time (Zhou et al., 2014), and there is no evidence as yet of direct emission of methylglyoxal

from the oceans. It is likely that methylglyoxal is emitted from the oceans: it has been

measured alongside glyoxal in the SML in concentrations which are enhanced above the

bulk water, indicating its production in the SML, (van Pinxteren and Herrmann, 2013; Zhou

and Mopper, 1990). However, the relative abundance of methylglyoxal in the SML

compared to glyoxal is highly uncertain, but the studies that have investigated this have

found higher concentrations of glyoxal compared to methylglyoxal by a factor of 3 (van

Pinxteren and Herrmann, 2013) and 5 (Zhou and Mopper, 1990). Meanwhile, a laboratory

study which detected glyoxal from oxidation of the SML did not find evidence for

methylglyoxal production (Zhou et al., 2014). It is therefore possible that the ratio of

glyoxal:methylglyoxal is higher at Chatham Rise compared to Cape Grim due to enhanced

direct emission of glyoxal from biologically productive waters which were targeted over

Chatham Rise, in contrast to Cape Grim which in winter samples air which has passed over

waters of low biological productivity. The likelihood of SML as a major source of glyoxal is

uncertain given the modest atmospheric yields of glyoxal in laboratory studies (Zhou et al.,

2014) and modest positive fluxes of glyoxal from the tropical ocean (Coburn et al., 2014).

However emission of dicarbonyls from the temperate oceans has not been studied and so

the temperate SML, as a source of dicarbonyls particularly in biologically active regions such

as Chatham Rise, cannot be discounted.

It is also possible that the difference in dicarbonyl ratios between the two sites is due

in part to differences in loss rates between glyoxal and methylglyoxal. The major sink for

both dicarbonyls is photolysis which is unlikely to explain the difference in observed ratios.

Other sinks include oxidation by OH, irreversible uptake into cloud droplets and particles

followed by conversion to SOA, or wet or dry deposition (Fu et al., 2008). Satellite imagery

shows both sites had partial cloud cover during the sampling periods, however a major

difference was the amount of rainfall that occurred at Cape Grim (90 mm over 33 days)

compared to during dicarbonyl sampling on over Chatham Rise (3 mm over 6 days).

Specifically during sampling of the Cape Grim clean marine samples, 1-7 mm of rain fell each

109

day, (sum 14.0 mm for 5 samples), while during clean marine Chatham Rise samples 0-

0.4mm fell each day (sum 0.57mm for 2 samples). It is possible that due to the higher

Henry’s law constant of glyoxal compared to methylglyoxal (Kroll et al., 2005; Zhou and

Mopper, 1990; Betterton and Hoffmann, 1988), glyoxal was more efficiently removed from

the atmosphere via wet deposition at Cape Grim due to its more rapid uptake into aqueous

particles. While wet deposition is a globally minor sink, it is likely to be important at night in

the absence of other major sinks (Fu et al., 2008). However, the reason for this difference

cannot conclusively be determined. The only other observations of glyoxal and methyglyoxal

over the open ocean for comparison are in the tropics (Zhou and Mopper, 1990) and show

an average glyoxal mixing ratio far in excess of methylglyoxal mixing ratio. This is in direct

contrast to Cape Grim, and in partial agreement with the Chatham Rise results. Production

and loss processes at each site could be explored with chemical modelling.

3.4.4 Calculation of expected glyoxal, methylglyoxal yields from measured VOC

precursors in clean marine air

Expected yields of glyoxal and methylglyoxal were calculated, based, where possible,

on parallel precursor VOC measurements over Chatham Rise and Cape Grim (Table 3).

Where a concurrent measurement of a precursor was not available, an estimate was made.

All estimated precursor mixing ratios are identified, and the source of the estimate is given

in Table 3. Where no observations of the precursor at the site were available (e.g. toluene

and xylene in winter at Cape Grim), but observations of a similar compound class were

available (e.g. benzene) the mixing ratio of benzene was used as a reliable upper estimate

for shorter-lived toluene and xylenes. Where no observations of the precursor were

available, and no measurements of compounds from a similar class were available, mixing

ratios were based on the same precursor species at a different site (e.g. summer Cape Grim

acetylene, alkene and alkane observations were used for Chatham Rise). In other cases, in-

situ observations from the site in the same season were used, but based on measurements

several years prior (e.g. Cape Grim ethene and propene). Where observations from the

specific season were not available, e.g. winter isoprene, acetone and monoterpenes at Cape

Grim, a spring or summer value was used, which for isoprene and monoterpenes are likely

to be an upper estimate. Three dicarbonyl precursors, glycoaldehyde, methyl butenol and

hydroxyacetone, were excluded from the calculation as all are emitted from terrestrial

110

processes (biomass burning and biogenic emission) and are short-lived, so are unlikely to

contribute to dicarbonyl production over the remote ocean.

Table 3. Calculated dicarbonyl yields based on precursor data from Cape Grim and Chatham Rise. Yields and dicarbonyl lifetimes based on Fu et al. (2008). Where supplementary (e.g. non-parallel) measurements were used, these are denoted as follows: a Cape Grim flasks (NOAA HATS analysis). b Kivlighon (2001). c Cape Grim flasks (INSTAAR analysis). d Galbally et al. (2007) (upper estimate Cape Grim summer). e Lawson et al. (2011) (Cape Grim baseline spring)

The expected mixing ratios of dicarbonyls that could be explained by oxidation of each

precursor were calculated according to the following equation: = × × (3)

Where MRdicarbonyl is mixing ratio of dicarbonyl, MRprecursor is mixing ratio of precursor,

Ydicarbonyl = yield of dicarbonyl, prec = lifetime of precursor and dicarbonyl = lifetime of

dicarbonyl.

Global annual mean molar yields of glyoxal and methylglyoxal from precursor gases

were taken from Fu et al. (2008). Lifetimes of all precursors were calculated based on

average daytime concentrations of [OH] of 8.7×105 molecules cm-3 at Chatham Rise in

Precursor precursor mixing ratios

(ppt) glyoxal yield (ppt)

methylglyoxal yield

(ppt)

Chatham Rise

Cape Grim

Chatham Rise

Cape Grim

Chatham Rise

Cape Grim

acetylene 3a 39a 0.02 0.08 n/a n/a ethene 51b 31b 0.22 0.06 n/a n/a

propene 17b 8b n/a n/a 0.04 0.03 propane 33c 35c n/a n/a 0.02 0.02

alkanes >C3^ 54 52c n/a n/a 0.02 0.02

isoprene 14 14d 0.85 0.43 1.89 1.97 benzene 8 9a 0.02 0.01 n/a n/a toluene 9 9* 0.08 0.03 0.03 0.03

xylenes sum 10 9* 0.27 0.10 0.22 0.19 monoterpenes 32 17e 0.83 0.44 0.69 0.48

acetone 89 118d n/a n/a 0.01 0.02 sum yield (ppt) 2.3 1.2 2.9 2.8

% explained 10 17 29 10

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March and 3.7×105 molecules cm-3 at Cape Grim in August-September, except for

monoterpenes (proxy for alpha-pinene), isoprene and propene lifetimes which were based

on the [OH] stated above and [ozone] of 4.9×1011 molecules cm-3 at Chatham Rise and

8.0×1011 molecules cm-3 at Cape Grim. Global average lifetimes of glyoxal (2.9 hours) and

methylglyoxal (1.6 hours) were used (Fu et al., 2008).

Table 3. shows that the small proportion of glyoxal and methylglyoxal production

accounted for is largely driven by isoprene and monoterpenes. The precursors can explain at

most 1-3 ppt of glyoxal and methylglyoxal at these two sites, which equates to only 17% and

10% of glyoxal and 10% and 29% of methylglyoxal over Cape Grim and Chatham Rise,

respectively. By dividing the difference between the measured and calculated mixing ratios

by the average global lifetime of glyoxal and methylglyoxal, the production rate in the

boundary layer required to reconcile the measured and calculated dicarbonyl mixing ratios

can be determined. For glyoxal, the additional production rate required is 48 ppt/day (97

ppt C/day) and 172 ppt/day (343 ppt C/day) while for methylglyoxal the additional

production rate required is 378 ppt/day (1135 ppt C/day) and 106 ppt/day (318 ppt C/day)

at Cape Grim and Chatham Rise.

As mentioned previously, the isoprene and monoterpene mixing ratios over Chatham

Rise were below the instrument detection much of the time, and substitution of half MDLs

may result in an upper estimate of mixing ratios for these species. Regardless, this is the first

study which has used concurrent measurements of these important precursors to constrain

the yields of glyoxal and methylglyoxal. As parallel isoprene and monoterpene mixing ratios

were not available at Cape Grim the yield calculation used summer and spring isoprene and

monoterpene mixing ratios which are likely to result in an upper estimate of dicarbonyl

mixing ratios resulting from precursor oxidation. Conversely using the global average

lifetimes of glyoxal and methylglyoxal is likely to lead to an underestimate of the mixing

ratio of dicarbonyls at Cape Grim, as actual dicarbonyl lifetimes in winter at Cape Grim are

likely to be longer than the global average. The calculation also does not take into account

diurnal variation in production and loss rates. However, Coburn et al. (2014) showed that

while glyoxal over the Eastern Tropical Pacific varied by approximately 15 ppt (~30%)

between night and day, it did not decrease below 30 ppt at night (average both

hemispheres). The approach used here should therefore give a good approximation of the

24 hour dicarbonyl mixing ratio expected from oxidation of precursors. The absence of

112

photolytic destruction and OH oxidation of dicarbonyls at night (the dominant known sinks),

coupled with an absence of dicarbonyl production through OH oxidation of precursors at

night (the dominant known source) likely contributes to the relatively constant mixing ratios

between day and night.

The low proportion of dicarbonyl mixing ratios that can be explained by oxidation of

precursors calculated here supports previous claims that there is unlikely to be sufficient

levels of VOC precursors over the remote oceans to explain the non-negligible levels of

glyoxal observed (Coburn et al., 2014; Sinreich et al., 2010). For the first time, we show that

the same applies to methylglyoxal over the ocean.

The large proportion of glyoxal and methylglyoxal which cannot be explained by the

precursor mixing ratios confirms the importance of other production mechanisms. As

discussed previously, it is unclear whether positive SML fluxes are sufficiently large to

explain the unaccounted for portion of these gases at Cape Grim and Chatham Rise. Kwan

et al (2006) estimated that OH oxidation of organic aerosol (OA) may result in a production

rate of up to 70 ppt C/day of OVOCs in the FT and a production rate of up to ~500 ppt C/day

OVOCs in the lower continental troposphere in the summertime. The combined boundary

layer production rate of glyoxal and methylglyoxal at Chatham Rise needed to reconcile the

measured and calculated mixing ratios is 661 ppt C/day, in reasonable agreement with the

Kwan et al (2006) estimate, while the Cape Grim glyoxal and methylglyoxal flux (1232 ppt

C/day) is a factor of 2-3 times higher. Whilst the Kwan et al (2006) estimates contained

significant uncertainties, and used continental measurements, they do suggest that

oxidation of OA may make a non-negligible contribution to the dicarbonyl mixing ratios.

Another possible production mechanism is oxidation of as yet unidentified gas phase

precursors.

3.4.5 Comparison of glyoxal surface observations with satellite vertical columns

In situ glyoxal mixing ratios from Cape Grim and Chatham Rise were converted into

vertical column densities (VCDs) and compared with glyoxal VCDs from GOME-2 on Metop–

A.

Mixing ratios were converted to VCDs assuming that all glyoxal observed was well

mixed within the boundary layer, and assuming standard conditions throughout the

boundary layer of temperature (25ºC) and pressure (1 atm). Boundary layer heights of

113

850m were used for both Chatham Rise (average of daytime and nocturnal radiosonde

flights) and an average modelled value for Cape Grim in all wind directions (unpublished

data, see Zahorowski et al. 2013 for model details). Chatham Rise surface observations were

compared to an average GOME-2 column for March 2012 in the region 40-º50 S and 170-

180ºE, while Cape Grim surface observations were compared to GOME-2 columns taken

from August-September 2011 in the region 39-42ºS and 143-147ºE.

In the Austral summer, GOME-2 glyoxal columns over the temperate oceans in the SH

are low, but somewhat higher than other remote regions, while in winter the columns are

among the lowest observed globally (Lerot et al., 2010). There is low inter-annual variability

in the GOME-2 glyoxal VCDs at both the regions encompassing Cape Grim and Chatham

Rise, but a clear seasonal cycle, with a maximum VCD in the summer months (Dec- Feb) and

a minimum in the autumn-winter months (May-August). The 2007-2012 average seasonal

variability of GOME-2 glyoxal VCDs from the sites above is shown in Figure 5. In June, VCDs

cannot be calculated due to insufficient satellite sensitivity resulting from observation

geometry (low angle of the sun).

114

Figure 5. Seasonal glyoxal VCDs retrieved from GOME-2 and calculated from surface-based observations at Cape Grim and Chatham Rise. GOME-2 values are for all data averaged between 2007 and 2012, for regions encompassing Cape Grim and Chatham Rise. Error bars for surface-based VCDs are insignificant Cape Grim VCDs calculated from in situ observations are 2.1 × 1013 molecules cm-2

(standard error of mean 2.1 × 1012), while Chatham Rise calculated VCDs are 6.3 × 1013

molecules cm-2 (standard error of mean 1.0 × 1013). In comparison, satellite retrieved VCDs

are 1.8 × 1014 molecules cm-2 for the Cape Grim region and 2.4 ×1014 molecules cm-2 for the

Chatham Rise region (both with an uncertainty of ± 1.3× 1014 molecules cm-2) (Figure 5).

While both satellite columns and in situ columns observe higher VCD over Chatham Rise in

summer than Cape Grim in winter, the satellite VCDs exceed in situ VCDs at both sites by

>1.5 x 1014 molecules cm-2. There are several possible factors that may be contributing to

the higher satellite VCD. The first reason may be due to the assumption that all of the

glyoxal observed by the satellites is in the boundary layer. Aircraft measurements made as

part of the TORERO campaign (Tropical Ocean Troposphere Exchange of Reactive Halogens

and OVOCs), have confirmed the widespread presence of glyoxal in the free troposphere

(Volkamer, 2014). If glyoxal is also present in the free troposphere over the temperate SH

115

oceans, the satellite, which is sensitive to the entire troposphere, would indeed observe

glyoxal VCD larger than VCD calculated from in situ measurements, which represent only the

boundary layer. If glyoxal is widespread in the free troposphere this clearly has important

implications for the interpretation of satellite column data. Another reason may be due to

the significant challenges in retrieving low VCDs of glyoxal over the oceans, due to

interferences by liquid water absorption, discussed elsewhere (Vrekoussis et al., 2009; Lerot

et al., 2010). Also, the satellite glyoxal retrieval algorithm includes a normalization

procedure comparing actual retrieved columns to a reference value taken in a reference

sector. As discussed in Miller et al. (2014), this reference value might be too large. Another

possible reason is that the satellite VCDs are measured only once per day during the satellite

overpass at 9:45am, whereas the in situ observations reported here are 24 hour averages. If

there is a diurnal variation in glyoxal mixing ratios at these sites, as was shown over the

Tropical Pacific Ocean (Coburn et al., 2014), the satellite VCD may not be representative of

the 24 hour average.

Difficulty reconciling satellite VCDs and in situ observations over the ocean has

recently been noted elsewhere, including the tendency of satellite VCDs to exceed in situ

measurements, particularly in regions with low VCDs which are at the limits of satellite

sensitivity (Coburn et al., 2014; Mahajan et al., 2014). Further comparisons between in situ

and satellite observations over the oceans are needed, including characterisation of the

vertical distribution of glyoxal.

3.5 Conclusions This work confirms the presence of short lived dicarbonyl glyoxal over the remote

temperate oceans, even in winter in very pristine air over biologically unproductive waters.

We provide the first observations of methylglyoxal over temperate oceans and confirm its

presence alongside glyoxal. These observations support the likely widespread contribution

from these dicarbonyls to SOA formation over the ocean.

Glyoxal mixing ratios at Cape Grim in winter are the lowest measured over the ocean,

while glyoxal at Chatham Rise is similar to other temperate mixing ratios, and similar to or at

the lower end of tropical observations. Methylglyoxal observations at Cape Grim and

116

Chatham Rise are comparable to the only other observations available (tropical Northern

Hemisphere ocean).

Chatham Rise glyoxal observations from this study agree well with observations made

via MAX-DOAS on the same voyage, suggesting a good agreement between the technique

used in this work (DNPH derivatisation with HPLC analysis optimised for dicarbonyl

detection) and the optical technique.

Different ratios of glyoxal: methylglyoxal were observed at the two locations, with an

average ratio in clean marine air of ~4 over Chatham Rise (range 1.7-5.9) and 0.3 at Cape

Grim (range 0.2-0.4). The reasons for this are unexplained but may be due to a larger

positive flux of glyoxal from biologically active waters over Chatham Rise, and/or to

preferential loss of glyoxal over methylglyoxal by wet deposition at Cape Grim. Chemical

modelling is suggested to better constrain the productions and loss mechanisms.

Expected yields of glyoxal and methylglyoxal were calculated based on parallel

measurements of precursor VOCs, including isoprene and monoterpenes. At most, 1-3 ppt

of the glyoxal and methylglyoxal observed in clean marine air can be explained from

oxidation of these precursors, confirming a significant contribution from another source

over the ocean. While the SML has recently been confirmed as a direct source of glyoxal

both in the field and in laboratory studies, it seems unlikely this positive flux is a sufficiently

large to explain the atmospheric concentrations observed. Other possible, but unconfirmed

sources may include oxidation of as-yet unidentified gas precursors, or atmospheric

oxidation of organic aerosol.

Glyoxal observations were converted to VCDs and compared with GOME-2 satellite

VCDs. While in situ and satellite observations both observe a higher glyoxal VCD in summer,

the satellite VCD exceeds the surface observations by more than 1.5x1014 molecules cm-2.

Recent observations of glyoxal in the free troposphere suggest that this discrepancy is least

in part due to the incorrect assumption that all glyoxal observed over the ocean by satellites

is in the MBL. Other reasons for the discrepancy may be due to challenges in retrieving low

VCDs of glyoxal over the oceans, including accounting for interference by liquid water

absorption and selection of an appropriate normalisation reference value in the retrieval

algorithm. Further comparisons are needed, including characterisation of the vertical

distribution of glyoxal.

117

3.6 Acknowledgements We thank Rob Gillett and Min Cheng (CSIRO), Nigel Somerville, Jeremy Ward and Sam

Cleland (Cape Grim BAPS) and Nick Talbot (NIWA) for assistance with sampling and analysis

and James Harnwell (CSIRO) for design and construction of Sequencer sampling unit. We

thank Cliff Law for excellent leadership during SOAP voyage and officers and crew of R.V.

Tangaroa and NIWA Vessels for logistics support.

Radon data and boundary layer heights courtesy of Alastair Williams, Scott Chambers

and Alan Griffiths (ANSTO), carbon dioxide Cape Grim data courtesy of Paul Krummel and

Paul Steele, CSIRO, and CGBAPS. Benzene and acetylene data from GCMS analysis of NOAA

HATS flasks collected at Cape Grim courtesy of Steve Montzka, (NOAA). OH concentration

data from Cape Grim provided by Stephen Wilson (University of Wollongong). Chatham Rise

carbon dioxide data provided by John McGregor (NIWA) and chlorophyll-a data provided by

Cliff Law (NIWA).

Sarah Lawson would like to acknowledge the NIWA Visiting Scientist Scheme and

CSIRO’s Capability Development Fund for providing financial support for her participation in

the SOAP voyage.

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4. Chapter 4

Biomass burning emissions of trace gases and particles in

marine air at Cape Grim, Tasmania. S.J. Lawson1, M.D. Keywood1, I.E. Galbally1, J.L. Gras1, J.M. Cainey1,2, M.E. Cope1, P.B.

Krummel1, P.J. Fraser1, L.P. Steele1, S.T. Bentley†, C.P Meyer1, Z. Ristovski3 and A.H.

Goldstein4

[1]{Commonwealth Scientific and Industrial Research Organisation, Oceans and

Atmosphere, Aspendale, Australia}

[2]{formerly from the Bureau of Meteorology, Smithton, Tasmania, Australia}


Technology, Brisbane, Australia}

[4]{Department of Civil and Environmental Engineering, University of California,

Berkeley}

Correspondence to: S. J. Lawson ([email protected])

Published in Atmospheric Chemistry and Physics, 15 (2015),Pages 13393–13411

STATEMENT OF JOINT AUTHORSHIP







125


Title: Biomass burning emissions of trace gases and particles in marine air at Cape Grim,

Tasmania. (2015, published)


Identified scientific problem, analysed and interpreted data, wrote manuscript

Melita Keywood Contributed data, contributed to analysis and interpretation, produced figures, reviewed manuscript

Ian Galbally Contributed data, reviewed manuscript John Gras Contributed data, reviewed manuscript Jill Cainey Contributed to scientific design, reviewed the manuscript Martin Cope Contributed to interpretation Paul Krummel Contributed data and reviewed manuscript Zoran Ristovski Reviewed the manuscript Paul Fraser Contributed data and reviewed manuscript Paul Steele Contributed data and reviewed manuscript Simon Bentley Contributed to data analysis Mick Meyer Contributed to data analysis Zoran Ristovski Reviewed the manuscript Allen Goldstein Reviewed the manuscript



authorship.

_______________________ ____________________ ______________________

Name Signature Date

126

Biomass burning emissions of trace gases and particles in marine air at Cape Grim, Tasmania.

S.J. Lawson1, M.D. Keywood1, I.E. Galbally1, J.L. Gras1, J.M. Cainey1,2, M.E. Cope1, P.B.

Krummel1, P.J. Fraser1, L.P. Steele1, S.T. Bentley†, C.P Meyer1, Z. Ristovski3 and A.H.

Goldstein4

[1]{Commonwealth Scientific and Industrial Research Organisation, Oceans and

Atmosphere, Aspendale, Australia}

[2]{formerly from the Bureau of Meteorology, Smithton, Tasmania, Australia}


Technology, Brisbane, Australia}

[4]{Department of Civil and Environmental Engineering, University of California,

Berkeley}


4.1 Abstract Biomass burning (BB) plumes were measured at the Cape Grim Baseline Air Pollution

Station during the 2006 Precursors to Particles campaign, when emissions from a fire on

nearby Robbins Island impacted the station. Measurements made included non methane

organic compounds (NMOCs) (PTR-MS), particle number size distribution, condensation

nuclei (CN) > 3 nm, black carbon (BC) concentration, cloud condensation nuclei (CCN)

number, ozone (O3), methane (CH4), carbon monoxide (CO), hydrogen (H2), carbon dioxide

(CO2), nitrous oxide (N2O), halocarbons and meteorology.

During the first plume strike event (BB1), a four hour enhancement of CO (max ~2100

ppb), BC (~1400 ng m-3) and particles > 3 nm (~13,000 cm-3) with dominant particle mode of

120 nm were observed overnight. A wind direction change lead to a dramatic reduction in

BB tracers and a drop in the dominant particle mode to 50 nm. The dominant mode

increased in size to 80 nm over 5 hours in calm sunny conditions, accompanied by an

increase in ozone. Due to an enhancement in BC but not CO during particle growth, the

presence of BB emissions during this period could not be confirmed.

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The ability of particles > 80 nm (CN80) to act as CCN at 0.5% supersaturation was

investigated. The ∆CCN/∆CN80 ratio was lowest during the fresh BB plume (56±8%), higher

during the particle growth period (77±4%) and higher still (104±3%) in background marine

air. Particle size distributions indicate that changes to particle chemical composition, rather

than particle size, are driving these changes. Hourly average CCN during both BB events

were between 2000-5000 CCN cm-3, which were enhanced above typical background levels

by a factor of 6-34, highlighting the dramatic impact BB plumes can have on CCN number in

clean marine regions.

During the 29 hours of the second plume strike event (BB2) CO, BC and a range of

NMOCs including acetonitrile and hydrogen cyanide (HCN) were clearly enhanced and some

enhancements in O3 were observed (∆O3/∆CO 0.001-0.074). A shortlived increase in NMOCs

by a factor of 10 corresponded with a large CO enhancement, an increase of the NMOC/CO

emission ratio (ER) by a factor of 2 – 4 and a halving of the BC/CO ratio. Rainfall on Robbins

Island was observed by radar during this period which likely resulted in a lower fire

combustion efficiency, and higher emission of compounds associated with smouldering. This

highlights the importance of relatively minor meteorological events on BB emission ratios.

Emission factors (EF) were derived for a range of trace gases, some never before

reported for Australian fires, (including hydrogen, phenol and toluene) using the carbon

mass balance method. This provides a unique set of EF for Australian coastal heathland fires.

Methyl halide EFs were higher than EF reported from other studies in Australia and the

Northern Hemisphere which is likely due to high halogen content in vegetation on Robbins

Island.

This work demonstrates the substantial impact that BB plumes can have on the

composition of marine air, and the significant changes that can occur as the plume interacts

with terrestrial, aged urban and marine emission sources.

4.2 Introduction Biomass burning (BB) is the largest global source of primary carbonaceous fine

aerosols and the second largest source of trace gases (Akagi et al., 2011). Species directly

emitted from fires include carbon dioxide (CO2), methane (CH4), carbon monoxide (CO),

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nitrogen oxides (NOx), ammonia (NH3), non methane organic compounds (NMOCs), carbonyl

sulfide (COS), sulfur dioxide (SO2) and elemental and organic carbonaceous and sulphate-

containing particles (Keywood et al., 2011). Secondary species that are formed from BB

precursors include ozone (O3), oxygenated NMOCs and inorganic and organic aerosol (OA).

The complex mixture of reactive gases and aerosol that make up BB plumes can act as short

lived climate forcers (Keywood et al., 2011). While BB plumes often have the greatest

impact on the atmosphere close to the source of the fire, once injected into the free

troposphere plumes may travel long distances, so that climate and air quality affects may be

regional or even global. A recent modelling study by Lewis et al., (2013) for example

highlighted the large contribution that BB emissions make to the burden of several NMOC in

the background atmosphere, particularly in the Southern Hemisphere.

With some studies predicting that future changes to the climate will result in

increasing fire frequency (Keywood et al., 2011), it is essential to understand the

composition of fresh plumes, how they vary temporally and spatially, and the way in which

the chemical composition is transformed with aging. This will provide the process

understanding to allow models to more accurately predict regional air quality impacts and

long term climate affects of BB.

Characterising BB plumes is challenging for several reasons, and significant knowledge

gaps still exist. BB plumes contain extremely complex mixtures of trace gases and aerosols,

which vary substantially both spatially and temporally. The initial composition of BB plumes

is dependent on the combustion process and efficiency of combustion, which has a complex

relationship with environmental variables. Combustion efficiency (CE) is a measure of the

fraction of fuel carbon completely oxidised to CO2. However it is difficult to measure all the

carbon species required to calculate CE, and so modified combustion efficiency (MCE),

which closely approximates the CE, is often used instead, where MCE = ∆CO2/ (∆CO+∆CO2)

(Ferek et al., 1998) where ∆ refers to excess or above-background quantities. The efficiency

of fire combustion depends on fuel size, density and spacing, fuel moisture content, local

meteorology (including temperature, windspeed and precipitation), and terrain (van

Leeuwen and van der Werf, 2011), and MCE can vary substantially spatially and temporally

within one fire. The EF of trace gas and aerosol species are in many cases strongly tied to

the efficiency of combustion. Species such as CO, organic carbon, and NMOCs tend to be

emitted at higher rates in smouldering fires which burn with low MCE (i.e. have a negative

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relationship with MCE), while other species such as CO2 and black carbon (BC) are emitted

at higher rates in flaming fires with higher MCE (e.g. have a positive relationship with MCE)

(Andreae and Merlet, 2001).

Once emitted, the composition of BB plumes can change very rapidly, with destruction

of highly reactive species, coagulation of particles, and formation of secondary species such

as O3, oxygenated NMOCs and secondary organic and inorganic aerosol occuring on a

timescale of minutes to hours (Akagi et al., 2012; Vakkari et al., 2014). Particles typically

become more oxygenated, and particle size often increases as primary particles are coated

either with low-volatility oxidation products of co-emited organic and inorganic gases, or

with co-emited semi volatile primary organics (Sahu et al., 2012; Akagi et al., 2012; Vakkari

et al., 2014). Changes that occur in the composition of the plume can be highly variable and

drivers of variability are difficult to quantify. One example is the large variability in the net

OA enhancement in aged BB plumes, with studies reporting both enhancements and

decreases in the OA/CO ratio with plume aging (Yokelson et al., 2009; Hennigan et al., 2011;

Cubison et al., 2011; Akagi et al., 2012; Hecobian et al., 2012).

While BB is recognised as a major source of CCN (Andreae et al., 2002), the

hygroscopicity of fresh BB particles varies enormously from weakly to highly hygroscopic

and fuel type appears to be a major driver of the variability (Pratt et al., 2011; Engelhart et

al., 2012; Petters et al., 2009) along with particle morphology (Martin et al., 2013). As

particles age, in addition to becoming larger, they also generally become more hygroscopic

and more easily activated to CCN. However, this is dependent on the initial composition and

hygroscopicity of the particle, as well as the hygroscopicity of the coating material (Martin

et al., 2013; Engelhart et al., 2012). Most studies of CCN in BB plumes to date have been

chamber studies, and there are few ambient studies which have examined the ability of BB

particles to act as CCN in fresh and aged plumes.

Ozone is typically destroyed by reaction with nitric oxide (NO) in close proximity to the

fire, however once the plume is diluted, O3 enhancement is often observed (typically

normalised to CO). In a recent summary of a number of studies, the enhancement of O3 to

CO typically increases with the age of the plume (Jaffe and Wigder, 2012). However there is

significant variation in O3 enhancements observed between studies which is thought to be

dependent on several factors such as precursor emissions (resulting from fuel and


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and photochemical reactions. Many challenges remain in modelling the transformation

processes that occur in BB plumes, such as O3 formation and changes to particle properties,

in part due to a lack of high-quality real-time observations (Jaffe and Wigder, 2012; Akagi et

al., 2012).

In recent years there have been a number of intensive field and laboratory studies

which have characterised both fresh emissions and aged BB emissions. However there are

several regions of the globe where BB emissions, including emission factors (EF), have been

sparsely characterised. For example, EF data has been published for only a few trace gases

in the temperate forests of Southern Australian (Volkova et al., 2014; Paton-Walsh et al.,

2012; Paton-Walsh et al., 2005; Paton-Walsh et al., 2014; Paton-Walsh et al., 2008). The lack

of Australian temperate EF was evident in a recent compilation of EF by Akagi et al., (2011),

in which all temperate EF reported were from the Northern Hemisphere (NH) from mostly

coniferous forests. Species emitted during combustion can be strongly dependent on

vegetation type (e.g. Simpson et al 2011), and EFs from NH coniferous forests are unlikely to

be representative of for example Australia’s temperate dry sclerophyll forests. Using EF

from boreal and tropical forest fires to model BB plumes in temperate regions adds

uncertainty to the model outcomes (Akagi et al., 2011), and more detailed chemical

measurements of BB plumes in the Southern Hemisphere temperate regions are needed.

An increasingly wide range of sophisticated instruments are being used to measure

the trace gas and aerosol composition and microphysical properties in BB plumes. This has

led to a higher proportion of NMOC being quantified than ever. Despite this, there is

significant evidence that a large proportion of NMOCs in BB plumes are still not being

identified. A compilation of NMOC measurements from 71 laboratory fires using a range of

techniques, found that the mass of unidentified NMOC was significant (up to 50%) (Yokelson

et al., 2013), though recent work using high-resolution proton transfer reaction – time of

flight – mass spectrometry (PTR-TOF-MS) has allowed at least tentative identification of up

to 93% of NMOC (Stockwell et al., 2015). Flow reactor experiments have indicated the mass

of OA formed in aged BB plumes exceeds the mass of known NMOC precursors, suggesting

unknown NMOC precursors, and/or highlighting the important contribution of semi and

intermediate volatile species to the increase in OA observed (Ortega et al., 2013). Inclusion

of unidentified semi volatile organics in a recent photochemical modelling study of young BB

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plumes allowed successful simulation of O3 and OA, if reasonable assumptions were made

about the chemistry of the unidentified organics (Alvarado et al., 2015).

Finally, with increasing global population and urbanisation, it is likely that BB events

will increasingly impact human settlements, either through close proximity of fires or

transport of plumes to urban areas. Consequently a greater understanding is needed of the

interactions between BB and urban emissions. These interactions are complex and have not

been significantly studied to date, although there is evidence that interactions between

these two sources may significantly change the resulting processes and products in plume

aging. For example Jaffe and Wigder (2012), Wigder et al., (2013) and Akagi et al., (2013)

show that O3 formation is enhanced when NOx-limited BB plumes mix with NOx- rich urban

emissions. Deposition of nitrogen-containing pollutants from major urban areas may also

enhance emission of NOx and other nitrogen containing trace gases in BB plumes (Yokelson

et al., 2007). Hecobian et al., (2012) found higher concentrations of inorganic aerosol

components in aged BB plumes that had mixed with urban emissions compared to BB

plumes, which were attributed to higher degree of oxidative processing in the mixed

plumes.

In this study we have investigated the chemical composition of fresh BB plumes in

marine air at the Cape Grim Baseline Air Pollution Station. The BB event occurred

unexpectedly during the Precursors to Particles campaign (Cainey et al., 2007), which aimed

to investigate new particle formation in clean marine air. Despite the opportunistic nature

of this work and lack of targeted BB measurements, a wide variety of trace gas and aerosol

species were quantified which provide valuable information on the composition of BB

plumes in this sparsely studied region of the world.

4.3 Methods

4.3.1 Cape Grim station location and location of fire

The Cape Grim Baseline Air Pollution Station is located near the north-west tip of the

island state of Tasmania, Australia, 40.7◦ S latitude and 144.7◦ E longitude (see Fig 1). The

station is situated on top of a cliff 94 m above mean sea level. When the wind blows from

the south west sector (the Roaring Forties) the air that impacts the station is defined as

Baseline and typically has back trajectories over the Southern Ocean of several days. In

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northerly wind directions, urban air from the city of Melbourne some 300 km away is

transported across the ocean (Bass Strait) to the station. North west Tasmania has a mild

temperate climate, with average February temperatures of 15 ± 2º C , RH 75 ±1 2%,

windspeed of 9 ± 4 m s-1 (where ±is 1 std dev) and 25 mm precipitation.

Figure 1. Location of Cape Grim and Robbins Island in NorthWest Tasmania, Australia. Area burned is shown. From 30 January to 24 February 2006 (the Austral late summer), the Precursors to

Particles (P2P) campaign was undertaken (Cainey et al., 2007). On the 15th of February 2006,

in the middle of P2P, a fire was ignited on nearby Robbins Island, which lies across farmland

20km east of Cape Grim. Robbins Island (9748 ha) is separated from the Tasmanian

mainland by a tidal passage 2km across, and has been a freehold property used for the

grazing of sheep and cattle since the 1830s (Buckby, 1988). The vegetation consists of

grazed pastures and native vegetation, mostly disturbed coastal heathland (largely endemic

Epacridaceae, Leptospermum) and woodland (Leptospermum, Melaleuca and Eucalyptus

nitida) with shrubs interspersed by tussock grasses (Poa spp) and sedges (Kitchener and

Harris, 2013). The fire burned 2000 ha, mostly coastal heath, over a period of 2 weeks. The

vegetation burned is comparable in structure to the mid and lower story vegetation in

Australian temperate forest and savannah woodland, though lacks the coarse woody debris

and the dominant upper story of trees found particularly in temperate Australian forests.

On two occasions an easterly wind advected the BB plume directly to the Cape Grim Station.

The first plume strike (BB1) occurred from 02:00 – 06:00 (Australian Eastern Standard Time -

133

AEST) on the 16th February, with light easterly winds of 3 m s-1 and temperature of 13 ºC

and RH of 96 %. The second, more prolonged plume strike (BB2) occurred from 23:00 on

23rd February to 05:00 on the 25th February, with strong easterly winds ranging from 10-16

m s-1, temperatures of 16-22 ºC and RH from 75-95 %.

4.3.2 Measurements

During P2P, a number of additional instruments were deployed to run alongside the

routine measurements. All the measurements made during BB1 and BB2 (routine and P2P

measurements) are listed in Table 1, with references supplied for further information. Some

additional information is provided here. All levels of trace gases are expressed as volume

mixing ratios. As the focus of P2P was clean marine air, PM2.5 and PM10 filter samples were

not collected during the BB events.

NMOCs (PTR-MS) Details on PTR-MS measurements are given in Galbally et al (2007) and some

additional information is provided here.

The PTR-MS ran with inlet and drift tube temperature of 75ºC, 600V drift tube, 2.2

mbar drift tube pressure, which equates to an energy field of 140 Td. The O2+ signal was ~2%

of the primary ion H3O+ signal. The PTR-MS ran in multiple ion detection (MID) mode in

which 26 masses were selected. Masses included in this work were identified by reviewing

instrument intercomparison studies of BB plumes (Christian et al., 2004; Karl et al., 2007b;

de Gouw and Warneke, 2007; Stockwell et al., 2015). Protonated masses were identified as

m/z 28 hydrogen cyanide (HCN), m/z 31 formaldehyde (HCHO), m/z 33 methanol (CH3OH),

m/z 42 acetonitrile (C2H3CN), m/z 45 acetadehyde (C2H4O), m/z 47 formic acid (HCOOH),

m/z 59 acetone and propanal (C3H6O), m/z 61 acetic acid (CH3COOH), m/z 63 dimethyl

sulphide - DMS (C2H6S), m/z 69 furan/isoprene (C4H4O/C5H8), m/z 71 methacrolein/methyl

vinyl ketone - MVK (C4H6O), m/z 73 methylglyoxal (C3H4O2)/methyl ethyl ketone - MEK

(C4H8O), m/z 79 benzene (C6H6), m/z 85 2-furanone (C4H4O2), m/z 87 2,3-butanedione

(C4H6O2) m/z 93 toluene (C7H8), m/z 95 phenol (C6H6O), m/z 107 ethylbenzene + xylenes

(C8H10), m/z 121 C3 benzenes (C9H12), m/z 137 monoterpenes (C10H16) + unknowns (C8H8O2).

These are expected to be the dominant compounds contributing to these masses. However,

due to the inability of the PTR-MS to differentiate between species with the same molecular

134

mass, a contribution from other compounds not listed here cannot be ruled out.

Protonated masses m/z 46, m/z 101, m/z 113 and m/z 153 were measured but not

identified, but their concentrations have been reported in this work with the aim of

quantifying as much emitted volatile carbon as possible.

During the campaign the PTR-MS was calibrated for the following compounds using

certified gas standards from Scott Specialty Gases, USA and National Physical Laboratory,

UK: methanol, acetaldehyde, acetone, isoprene, MVK and methacrolein, MEK, benzene,

toluene, ethylbenzene, 1,2,4 trimethylbenzene and formaldehyde. Calibration data were

used to construct sensitivity plots which were used to calculate approximate response

factors for other masses not specifically calibrated. Due to having proton affinities similar to

water, formaldehyde and HCN responses are highly dependent on humidity of the sample

air. The changing response of the PTR-MS for these compounds was calculated every 10

minutes by taking the response of the dry formaldehyde calibration gas, then adjusting this

based on the measured water content of the sample air and relationship between response

and humidity as reported in Inomata et al (2008). Corrections were made to the response of

m/z 61 and m/z 137 for known losses due to fragmentation of acetic acid and

monoterpenes at those masses. Dunne et al (2012) reported a significant interference to the

acetonitrile signal at m/z 42 from the 13C isotopologues of C3H5+ and the product ion C3H6+

from reactions involving O2+ and alkanes/alkenes. A detailed correction for this interference

was not possible here, due to an absence of m/z 41, and alkane and alkene measurements.

However, during a BB event, Dunne et al., (2012) calculated a 20% contribution to m/z 42

from non-acetonitrile ions: to reflect this interference the m/z 42 signal during the BB

events has been reduced by 20%. Minimum detectable limits (MDLs) were calculated

according to the principles of ISO 6869 (ISO, 1995) and ranged from 2 – 563 ppt for a one

hour measurement. Where measured levels were below the MDL, a half MDL value was

substituted.

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Table 1 Measurement Summary, table taken from Lawson et al., (2015)

136

4.4 Results and discussion A time series of CO, BC and particle number > 3nm clearly shows the two events (BB1

and BB2) where plumes from the Robbins Island fire impacted the Cape Grim Station (Fig.

2). A detailed times series of these two events are presented here, with discussion of the

influence of photochemistry, meteorology and air mass back trajectory on changing

composition of trace gases and aerosol.

Figure 2. Time series of carbon monoxide (CO), black carbon (BC) and particles >3 nm (CN3) for the study period (BB1 and BB2 shown).

137

Figure 3 Time series from BB1 including a particle size and number contour plot, wind direction (degrees), ozone (O3), carbon monoxide (CO), black carbon (BC) and HFC-134a. Periods A–F are discussed in the text. 4.4.1 Biomass burning event 1 (BB1) February 16th 2006

Brief plume strike, particle growth and ozone enhancement Fig. 3. shows a time series plot from BB1, including both the fresh plume and the

changing composition with changing wind direction. A particle size and number contour

plot, wind direction, O3, CO, BC and urban tracer HFC-134a are shown. Periods of interest

are labelled as Periods A-F (Fig 3.) which are discussed below and summarised in Table 2.

Average particle size distributions for periods corresponding to Periods A-F are presented in

Fig. 4. NMOC data is not available from BB1. The matching air mass back trajectories for

periods corresponding to Periods A-F are shown in Supp Fig. 1a-f.

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Table 2 Summary of periods described in the text for BB1 and BB2 (as shown in Figs. 3 and 6).

EventDate and Time Period Air Mass Origin Marker Species

Comments

BB 116/02/2006 2:00 A Ocean & NW Tasmania CO, BC, low O3, particles (uni modal)

Fresh Plume

16/02/2006 6:00 B Ocean & NW Tasmania BC, O3, particle growth

Particle growth

16/02/2006 12:00 C mainland Australia O3 (overnight enhancement)

Background terrestrial

17/02/2006 6:00 D Melbourne O3, particles, HFC-134a

Urban

17/02/2006 15:00 E Ocean Particles (bi-modal)

Clean Marine

18/02/2006 0:00 F Ocean & mainland Australia O3, HFC-134a

Marine with minor terrestrial

BB223/02/2006 23:00 A Ocean & NW Tasmania CO, BC, Acetonitrile, particles

Fresh Plume

24/02/2006 23:00 B mainland Australia CO, NMOC (Acetonitrile)

Fresh plume + precipitation

25/02/2006 5:00 C Melbourne HFC-134a, O3

Urban

25/02/2006 23:00 D Ocean Low particles, HFC-134a

Clean Marine

139 | P a g e

Figure 4. Average particle size distributions (with log scale on both axes) from BB1 corresponding to periods shown in Fig. 3 including (a) the fresh plume (Period A) and particle growth (Period B) (b) background terrestrial (Period C) and urban terrestrial (Period D) and (c) clean marine (Period E) and marine and minor terrestrial (Period F).

140 | P a g e

Period A. The fresh BB plume is visible from ~02:00-06:00 (Fig. 3.) through high

particle number concentrations corresponding with elevated CO and BC. The BB particles

have a single, broad size distribution with a dominant mode of 120 nm (Fig. 4a), indicating

fresh BB aerosol (Janhäll et al., 2010). The O3 mixing ratio during this period is 10 ppb which

is lower than background concentration of about ~15 ppb, likely due to titration by NO

emitted from the fire. The HYSPLIT back trajectory (Supp Fig. 1a) indicates that air which

brought the plume to Cape Grim had previously passed over the north west corner of

Tasmania and the Southern Ocean.

Period B. Just after 06:00 (Fig. 3.), a slight wind direction change results in dramatically

reduced particle concentration, CO and BC. The dominant mode of the particles drops from

about 120 nm to 50 nm, but the distribution remains broad and uni-modal (Fig. 4a). From

7:00 – 12:00 there is a gradual increase in the dominant mode of particles from 50 nm to 80

nm, which is accompanied by an increase in ozone from 12 to 20 ppb. The winds were light

(1 m s-1) and variable, the temperature mild (19ºC) and skies clear during this period. An

increase in particle size and ozone in calm and sunny conditions suggests that particles were

growing in size due to oxidation of gas phase precursors and condensation of low volatility

products. An alternative but less likely explanation is that the light and variable winds were

bringing increasingly larger particles and ozone to the station over several hours. There is a

small enhancement of BC above background concentrations (12 - 194 ng m-3,) while the

particle size is increasing, suggesting that the station may be on the edge of the plume

during this period, however CO is not enhanced alongside BC and so influence of BB

emissions cannot be confirmed. The HYSPLIT trajectory (Supp Fig. 1b) shows that air

arriving at the station is almost entirely of marine origin but had some contact with the

vegetated and sparsely populated North West coast of Tasmania and appears to pass over

Robbins Island before arriving at Cape Grim.

Period C. At midday, the dominant particle mode stops increasing and is stable, and

BC drops to background levels. An easterly wind overnight brings air from the sparsely

populated and forrested coast of south eastern Australia (Supp Fig. 1 c) which leads to a

further decrease in particle number, but a continued increase in O3. The meteorology and

night-time increase in O3 is suggestive of a transported continental aged air mass arriving at

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Cape Grim, rather than local production. The average particle size distribution over this

period (Fig. 4b) is a single broad distribution with a dominant mode of around 60nm, and is

similar in shape to the distribution during the particle growth event.

Period D. A strong urban influence is visible in the early morning on the 17th February

(Fig. 3.), when air is transported directly from metropolitan region of Melbourne ~300 km

directly to the north (Supp Fig. 1d). O3 peaks at ~30 ppb, accompanied by particle number

concentrations of similar magnitude to the direct BB plume the previous day, but without

the elevated CO or BC. The significant urban influence is confirmed by a peak in HFC-134a,

an urban tracer which is widely used in motor vehicle air conditioning and domestic

refrigeration (McCulloch et al., 2003). The average particle size distribution (Fig. 4b) shows a

single broad distribution with a dominant mode of 90 nm.

Period E. In mid afternoon on the 17th February a westerly wind from the ocean

sector leads to a sudden drop in HFC-134a, O3 and particle number. HYSPLIT trajectories

suggest the air mass passed over the ocean for at least 60 hours prior to arriving at Cape

Grim (Supp Fig. 1 E). The particle size distribution changes from uni-modal to bi-modal,

with dominant modes at around 50nm and 160 nm (Fig. 4c). This bi-modal distribution is

typical of clean marine air and aerosols are likely dominated by non sea salt sulphate and

sea salt particles, which in the larger mode have been cloud processed (Lawler et al., 2014;

Cravigan et al., 2015).

Period F. At midnight on the 18th February, (Fig. 3.) terrestrial influence from

mainland Australia is visible (Supp Fig. 1f), with an increase in O3, HFC-134a and an increase

in particle number in the 60 – 200nm size range. Over the next 24 hours, decreasing O3 and

particle number suggests the air is becoming increasingly free of terrestrial influence.

However the HYSPLIT trajectory (Supp 1F) shows that some terrestrial influence from

mainland Australia remains for the next 24 hours. This is also shown by HFC-134a values

which are slightly higher than during clean marine period (Event E), and a uni-model average

particle size distribution (Fig. 4c), which resembles the terrestrially-influenced distributions

corresponding to Periods B, C, D and F.

It is interesting to note that while size distributions have been described as uni-modal

for Periods B, C, D and F, Fig. 4 a-c shows evidence of a second minor mode at around 160-

170 nm in each of these terrestrially-influenced periods. Due to the strong marine influence

142 | P a g e

of the air arriving at Cape Grim, the 160-170 nm mode in these periods can likely be

attributed to cloud processed non sea salt sulphate and sea salt aerosol, and corresponds to

the second larger mode (160 nm) in the clean marine period of Fig. 3 Period E.

Of interest is the contribution that the BB emissions from the Robbins Island fire had

on the O3 enhancement (Fig. 3). Determining the contribution is challenging given the

variety of emission sources impacting Cape Grim (BB, terrestrial, marine, urban), and

understanding the transport and mixing of these emissions. During BB1 the HFC-134a

indicates an increasing influence from urban air from mainland Australia (indicating a likely

source of O3 or O3 precursors), and indeed the O3 and HFC-134a concentrations do increase

in parallel (Fig. 3). However, some of the increases in O3 occured when there was minimal

urban influence, for example during the particle growth event (Fig. 3 Period B), and may

have been driven by emissions from the local fire. Use of a chemical transport model to

determine influence of fire emissions on O3 formation will be reported in a follow up paper

by Lawson et al (2016).

Ability of particles in BB event 1 (BB1) to act as CCN The ability of particles to act as CCN at 0.5% supersaturation was investigated during

the fresh BB plume (Fig. 3 Period A) and the particle growth period (Period B). The CCN

activity of particles was also calculated during the 24 hours of Period F, chosen due to the

absence of BB tracers during this period, and predominance of marine air with some minor

terrestrial influence. The average hourly ratio of CCN number to condensation nuclei (CN)

number > 80 nm (CN80, measured using the SMPS) was calculated. CN80 was chosen based

on a study by Petters et al., (2009) which suggested even weakly hygroscopic BB aerosols

began to activate to CCN at a diameter of approximately 80 nm and larger. Given this, any

observed difference in the CCN/CN80 ratio may then be due to either different chemical

composition between the particles, and/or differences in particles size distributions, as

larger particles are more easily activated to CCN. The CCN/CN80 ratio has only been

calculated for BB1 because there are no aerosol size distribution measurements (and hence

no CN80 measurements) for BB2.

Fig. 5a shows the CCN/CN80 expressed as a percentage for the fresh plume (Period A),

particle growth event (Period B), and background marine/terrestrial (Period F). Error bars

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are ±1 standard error of the mean. Fig. 5b shows the absolute number concentration of CCN

during these periods.

Figure 5. (a) Average ratios of CCN=CN>80 (hourly) in BB1 during fresh plume (Fig. 3, Period A), particle growth event (Fig. 3, Period B), and in marine air with minor terrestrial influence (Fig. 3, Period F). (b) Average absolute number concentrations of CCN (hourly) during the same periods. Error bars are one standard error of the mean.

The CCN/CN80 ratio during the fresh BB plume strike (Period A) is 56±8%. Petters et

al., (2009) show that in laboratory BB measurements the CCN activation of 80 nm particles

ranges from a few % for low or weakly hygroscopic fuels to up to 60% for more hygroscopic

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fuels such as chamise, suggesting that the particles produced from coastal heath burned

here may be more hygroscopic than those from other fuel types.

The CCN/CN80 ratio is substantially higher during the particle growth event (Period B)

(77±4%). Fig. 4a shows that the average dominant diameter of particles shifts from around

120 nm during Period A to around 60nm during Period B. The smaller diameter during

Period B suggests that particle size is not the reason for the increased CCN/CN80 ratio

during the particle growth period, but is likely due to a changing chemical composition of

particles between the two periods, with more hygroscopic particles measured during the

particle growth period compared to the fresh BB particles. Volatility and hygroscopicity

measurements of particles are available from Period A using a volatility and hygroscopic

tandem differential mobility analysis (VH-TDMA) system (Fletcher et al., 2007). These

measurements focused on the composition of 60 nm particles, and suggested they

consisted of a non-hygroscopic 23-nm core, a hygroscopic layer to 50 nm and a hydrophobic

outer layer to 60 nm (possible homogeneously mixed). There was some evidence that the

particle core contained two different types of particle, possibly due to merging of marine

and BB particles. The suggested mix of hygroscopic and non-hygroscopic materials in fresh

BB particles is in agreement with the fact that only 56% of these particles were hygroscopic

enough to act as CCN. While the composition of the fresh BB particles may only be inferred

from these measurements, the non-hygroscopic core may be black carbon or primary

organic aerosol, the hygroscopic component an inorganic material such as sea salt or

ammonium nitrate or sulphate or a hydroscopic organic such as MSA which is abundant in

the marine boundary layer at Cape Grim in summer. The hydrophobic outer layer may be a

hydrocarbon-type organic, with a low O:C ratio, which was co-emitted in the fire and

condensed on to the particle as the plume cooled and was transported to Cape Grim

(Fletcher et al., 2007). Unfortunately no hygroscopicity or volatility measurements are

available from Period B (particle growth).

During background marine Period F, all (104 ± 3%) of the particles >80 nm could act as

CCN (Fig. 5a). A value of more than 100% is not physically possible but is due to uncertainty

associated with the different techniques (SMPS and CCN) and measurement

synchronisation. This result is in agreement with the work of Fletcher et al (2007) who

reported that the fresh BB particles from BB1-A had a lower hygroscopic growth factor than

145 | P a g e

marine particles. Fig. 4 a and 4 c shows the average size distribution of particles during

Period F (background marine/terrestrial) is very similar to period B (particle growth), despite

the air masses coming from different directions (westerly and easterly respectively). As

discussed previously, both these periods have a predominant marine back trajectory and

some terrestrial influence. It is therefore likely that the main difference between the

particle composition between these two periods, and the reason for the lower CCN/CN ratio

during Period B is the presence of non or weakly-hygroscopic >80 nm particles, such as the

BC which elevated above background levels during Period B.

Sea salt and non sea salt sulphate aerosol are important sources of CCN in the marine

boundary layer (Korhonen et al., 2008; Quinn and Bates, 2011) and are likely the main

source of CCN in Period F (and possibly Period B). The fact that all particles >80nm could act

as CCN in Period F suggests that any non-hygroscopic terrestrial particles which reached

Cape Grim during this time were likely to have been aged and oxidised during the several

hundred kms during transport from the mainland.

Finally, the absolute number concentration of CCN in the fresh plume (A) (hourly

average) was ~2000 cm-3 (Fig. 5b), with minute average concentrations up to ~5500 CCN cm-

3. In contrast, the average number of CCN during particle growth (Period B) was a factor of 3

lower at ~700 CCN cm-3. During the background marine/terrestrial Period (F) in BB1, the CCN

is 320 CCN cm-3, with low variability, a value which is within the range of typical pristine

marine values (Gras, 2007). Overall, CCN were enhanced by a factor ~6 and a factor of ~30

above background levels in BB1 and BB2 respectively (see Table 3). Despite the modest

ability of fresh BB particles to form CCN (CCN/CN80 ratio of 56%), the very high numbers of

particles ejected into the marine boundary layer during the fire highlights the dramatic

impact BB plumes can have on the CCN population, particularly in clean marine regions.

4.4.2 BB event 2 (BB2) February 23rd 2006

Interplay between emissions, meteorology and sources BB2 was of much longer duration than BB1, and lasted about 29 hours. Fig. 6 shows a

time series including wind direction and rainfall, O3, CO, BC, BB tracer acetonitrile,

acetonitrile/CO ratio (where CO > 400 ppb) and urban tracer HFC-134a. Periods of interest

are highlighted as A-D (Fig 6.), summarised in Table 2 and discussed below. Particle size

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distribution data is not available for BB2. The matching air mass back trajectories for the

events highlighted in Fig. 6. are shown in Supplementary Fig. 2. a-d.

Figure 6. Time series from BB2 including wind direction and rainfall, CN3 (particle number >3 nm), CO (carbon monoxide), BC (black carbon), acetonitrile and ratio of acetonitrile to CO, O3 and HFC-134a. Events corresponding to Periods A–D are discussed in the text. Period A. For the first 24 hours of BB2, there is clear elevation in CO, BC and

acetonitrile, due to the easterly wind advecting the plume directly to Cape Grim. The

acetonitrile mixing ratio is ~ 1 ppb and is enhanced by a factor of 30 above typical

background levels at Cape Grim of ~35 ppt (Table 3). The acetonitrile ratio to CO is also

relatively constant during this time (1-2 ppt/ppb). An ozone peak of 27 ppb (minute data)

occurs in mid afternoon on 24 February, corresponding to an hourly Normalised Excess

Mixing Ratio (NEMR) ∆O3/∆CO of 0.05 (where NEMR is an excess mixing ratio normalised to

a non-reactive co-emitted tracer, in this case CO, see Akagi et al., 2011).

Period B. At 04:00 on 25th February acetonitrile peaks by a factor of 17 over 2 hours

(Fig. 6 Period B) with a smaller peak at 23:00 on the 24th February. Almost all masses on

the PTR-MS increased at the same time as acetonitrile, including masses corresponding to

HCN, methanol, acetaldehyde, acetone, furan/isoprene and benzene). The corresponding

CO peak at 04:00 (~1500 ppb) increased by a factor of 21 and is the largest peak from BB2.

The corresponding BC peak at 04:00 is much smaller than peaks which occurred earlier in

BB2. This large enhancement in CO and NMOCs but modest enhancement in BC suggests a

147 | P a g e

decrease in the combustion efficiency during this time. This is further supported by

increases in the ratio of acetonitrile to CO (where CO > 400 ppb) by a factor of ~3 during the

peak periods (Fig. 6), and a decrease in the ratios of BC to CO during peak periods (average

0.9± 0.3 ng m-3ppb-1) compared to non-peak periods (2.2± 0.1 ng m-3ppb-1).

A small amount of rainfall recorded at Cape Grim (1.4 mm) corresponds with the

second peak (Fig. 6). Archived radar images from the Bureau of Meteorology (West Takone

128 km, 10 min resolution) confirm that very light patchy rain showers occurred on Robbins

Island at 23:10 followed by intermittent rain showers of light to moderate intensity from

12:40 until 05:40, on the 25th February (S. Baly, pers com). The total rainfall amount that

fell on Robbins Island was between 1-5 mm (www.bom.gov.au). Evidence of rainfall

coinciding with an enhancement in NMOC ER to CO, and a decrease in BC ER to CO suggests

that the rainfall changed the combustion processes of the fire. The enhanced ERs of NMOCs

to CO which are associated with low-efficiency, smouldering combustion, can therefore

attributed to a short term decrease in combustion efficiency, driven by rainfall. Due to the

small number of data points (2) it is not possible to calculate reliable ER to CO during this

shortlived event. But this time series highlights the importance of relatively minor

meteorological events on BB emission ratios.

While the elevated concentrations of BB tracers CO, BC and acetonitrile during this

period are attributed to emissions from the local fire, back trajectories (Supp Fig. 3B) show

that air arriving at Cape Grim had previously passed over the Australian mainland. The

increasing anthropogenic influence is also supported by increasing levels of HFC-134a and a

corresponding increase in O3 which peaks at 34 ppb (minute data) at 1:00-2:00, with an

hourly NEMR for ∆O3/∆CO of 0.07 (the highest observed).

Period C. With a change in wind direction further to the north from 5:00 onwards (Fig.

6), BB tracers BC, CO and acetonitrile all decrease to background levels, indicating fire

emissions are no longer impacting the station. Ozone begins to increase at 8:00 and reaches

~40 ppb 3 hours later, corresponding with a maximum HFC-134a mixing ratio of ~ 35 ppt.

The air mass back trajectory (Supp Fig. 2 C) confirms that air from Melbourne is impacting

the station during this period

Period D. As wind moves further into the west in to the clean marine sector (Supp Fig.

2D), O3 and HFC-134a decrease to background levels.

148 | P a g e

This time series highlights possible interplay of sources and meteorology on the

observed trace gases and particles. The very large increase of NMOCs and CO observed

during the rainfall period shows the potentially large effect of quite minor meteorological

events on BB emission ratios. While other studies have found a link between fuel moisture,

MCE and emissions of PM2.5, (e.g. Watson et al., 2011 ; Hosseini et al., 2013) this is the first

study to our knowledge which has linked rainfall with a large increase in trace gas emission

ratios from BB.

This work also highlights the large influence that BB plumes can have on the

composition of air in the marine boundary layer. During the direct plume strikes, absolute

numbers of particles > 3nm increased from 600 to 25,000 particles cm-3 (hourly average). In

BB2, as was the case in BB1, the O3 concentrations closely correspond with the HFC134a

concentrations. This suggests that transport of photochemically processed air from urban

areas to Cape Grim is the main driver of the O3 observed but does not rule out possible local

O3 formation from BB emissions. NEMRs of ∆O3/∆CO ranged from 0.001-0.074 during BB2

which are comparable to NEMRs observed elsewhere in BB plumes <1 hr old (Yokelson et

al., 2003; Yokelson et al., 2009).

Chemical composition of BB2 and selection of in-plume and background periods The composition of the fresh plume during BB2 was explored by determining for

which trace gas and aerosol species the enhancement above background concentrations

was statistically significant. Emission ratios (ER) and particle number to CO were then

calculated for these selected species and converted to emission factors (EF).

The first 10 hours of Period A from BB2 (from 23:00 on the 23rd Feb to 09:00 on the

24th Feb) was selected to characterise the fresh plume composition. During this time, the air

which brought the Robbins Island BB emissions to Cape Grim had previously passed over the

ocean and so was free of terrestrial or urban influence (NOAA HYSPLIT Supp Fig. 2A). While

fresh BB emissions were measured at Cape Grim beyond 10:00 on the 24th Feb, the air at

this time had prior contact with the Australian mainland, including the Melbourne region

and so was considered unsuitable for characterising the BB plume. During the selected time

period, wind speeds of 16 m s-1 meant that the plume travelled the 20 km to Cape Grim over

a period of about 20 minutes, which allows the plume to cool to ambient temperatures but

ensured minimum photochemical processing of the plume (Akagi et al., 2011). Advection of

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the plume to the site occurred primarily at night so minimal impact of photochemical

reactions on the plume composition is expected (Vakkari et al., 2014). It is therefore

assumed that the enhancement ratios measured at Cape Grim during this time are

unaltered from the original emission ratios. Finally, photos indicate the Robbins Island fire

plume was well mixed within the boundary layer and was not lofted into the FT, allowing

representative ‘fire-averaged’ measurements to be collected (Akagi et al., 2014).

Background concentrations of gas and particle species were determined from fire-free

periods in early March 2006 which had a very similar air back trajectory to trajectories

during the fire (not shown). Concentrations of long lived urban tracers (not emitted from

fires) including HFC-32, HFC-125a and HFC-134a were also used to match suitable

background time periods with the fresh plume period.

Table 3 lists the gas and aerosol species measured, whether concentrations were

statistically higher in the plume compared to background air, average background

concentrations, average in-plume concentrations, emission ratios (ER) to CO and EF (g kg-1).

Details of ER and EF calculations are given below. Hourly average data were used for these

calculations.

Species emitted in BB event 2 (BB2) –t tests Hypothesis testing using the student t tests (one sided) were carried out to determine

whether concentrations in the BB plume (x1) were significantly higher than concentrations

observed in the background periods (x2), with a 95% level of significance. Table 3 shows

which species were statistically enhanced in the BB plume, and hence assumed to be

emitted from the fire (x1-x2>0) and those which were not statistically enhanced in the BB

plume (x1-x2=0). While the vast majority of species measured were found to be significantly

enhanced in the BB plume, there were a number of species including DMS, chloroform,

methyl chloroform, dichloromethane, carbon tetrachloride, bromoform and the urban

tracers HFC-032, HFC-125 and HFC-134a which were not significantly enhanced. DMS has

consistently been found to be emitted from BB in many studies (as summarised by Akagi et

al 2011). However, in this study due to close proximity to the ocean, the likely emission of

DMS from the BB was likely obscured by the high variability in the background

concentration. The absence of emission of chloroform, methyl chloroform,

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Table 3. Summary of species measured in BB2 coastal heathland fire including background concentration, plume concentration, ER to CO and EFs. Table taken from the published paper, Lawson et al., (2015)

151 | P a g e

dichloromethane, carbon tetrachloride, tribromomethane and the HFCs are in agreement

with a recent study of boreal forest emissions by Simpson et al (2011).

Calculation of Emission Ratios to CO Excess mixing ratios (Δx) were calculated for species that were statistically higher in

the plume compared to background air by subtracting background mixing ratios from the

hourly in-plume mixing ratios. Emission ratios to CO were then calculated by plotting Δx

versus ΔCO, fitting a least squares line to the slope and forcing the intercept to zero

(Yokelson et al., 1999). Emission ratios (ER) to CO and the R2 of the fit are reported in Table

3.

The excess mixing ratios of all species significantly enhanced in the plume correlated

with the excess mixing ratios of CO with an R2 value of ≥0.4, with the exception of CO2,

HCHO, HCOOH, m/z 101, N2O, and CCN number concentration (see discussion below and

Fig. 3b). ER plots for BC, CN>3nm, H2, CH4, C2H6, C6H6, CH3COOH, C6H6O and C2H3N are

shown in Fig. 7. The ER of CO to particle number (38 cm-3 ppb-1) agrees well with a literature

averaged value of 34 ± 16 cm-3 ppb-1 (Janhall., et al 2010). The H2 ER to CO (0.10) is lower

than the range reported from BB emissions (0.15-0.45), as summarised by Vollmer et al.,

(2012).

There is a low correlation between mixing ratios of CO and CO2 (ER to CO R2 = 0.15,

see Table 3). This is in part because CO and CO2 are emitted in different ratios from different

combustion processes (smouldering and flaming respectively) and may also be influence by

variability in background levels of CO2 (Andreae et al., 2012). Of the other species with R2

values of <0.4, HCHO and HCOOH are both emitted directly from BB, but are also oxidation

products of other species co-emitted in BB. It is therefore possible that in the 20 minute

period between plume generation and sampling, chemical processing has led to generation

of these compounds in the plume, which has changed the ER to CO. In addition, sampling

losses of HCOOH down the inlet line are possible (Stockwell et al., 2014). The lack of

relationship between ∆CO and ∆N2O is likely because N2O is an intermediate oxidation

product which is both formed and destroyed during combustion. Studies of emissions from

Savanna burning in Northern Australia have found N2O to be insensitive to changes in MCE

(Meyer and Cook, 2015; Meyer et al., 2012; Volkova et al., 2014). A further reason for a lack

of correlation with ∆CO for ∆N2O is that as for CO2, the plume enhancement of ∆N2O is

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relatively small compared to the observed variability in background concentrations. Finally

the lack of correlation between ∆CCN and ∆CO may be due interaction of plume aerosol

with background sources of CCN, such as sea salt, and the change in particle properties and

composition in the 20 minutes after emission.

Figure 7 Emission ratios (ER) of several trace gas and aerosol species to CO during Period A in BB2.

Calculation of MCE and EF and comparison with other studies Combustion efficiency (CE) is a commonly used measure of the degree of oxidation of

fuel carbon to CO2. Combustion efficiency is commonly approximated as the modified

combustion efficiency (MCE) (Yokelson et al., 1999), which is calculated using the following

equation

COCOCOMCE

Δ+ΔΔ

=2

2 (1)

In this study the 10 hour integrated MCE was 0.88, which indicates predominantly

smouldering combustion. The ER of BC to CO reported here is in good agreement with BC to

153 | P a g e

CO ERs in smouldering fires (MCE <0.9) reported by Kondo et al., (2011) and May et al.,

(2014) which suggests that the excess CO2, and MCE has been determined reliably.

Whole of fire Emission factors were calculated according to the Carbon Mass Balance

method (Ward and Radke, 1993). Emission factors were calculated relative to combusted

fuel mass (Andreae and Merlet, 2001), assuming 50% fuel carbon content by dry weight

according to the following equation.

12

)()/(10005.0

])[][]([

][)/(

42

xMWkggCHCOCO

xkggEFx ×××Δ+Δ+Δ

Δ=

(2)

The Carbon Mass Balance method assumes all volatilised carbon is detected, including

CO2, CO, hydrocarbons and particulate carbon. Here the major volatile carbon components

of CO2, CO and CH4 were used in the EF calculation, so the resulting EF may be

overestimated by 1-2 % (Andreae and Merlet, 2001).

For comparison with the carbon mass balance method, EF were also calculated using

an average CO EF for temperate forests from Akagi et al., (2011), which corresponds to an

MCE of 0.92 (see Supplementary material for EF and method details). Trace gas EF

calculated using an assumed CO EF were generally 50% lower than EF calculated using the

carbon mass balance method.

Table 4 shows EFs calculated from this study (Carbon Mass Balance Method)

compared with other Australian BB studies both of eucalypt and schlerophyll forest fires in

temperate south eastern Australia (Paton-Walsh et al., 2005; Paton-Walsh et al., 2008;

Paton-Walsh et al., 2014), and tropical savanna fires in northern Australia (Paton-Walsh et

al., 2010; Meyer et al., 2012; Hurst et al., 1994a; Hurst et al., 1994b; Shirai et al., 2003;

Smith et al., 2014). The fire in this study (41ºS) is >1000 km south of the temperate forest

fires used for comparison (33-35ºS), and some 2500 km South East of the tropical savannah

fires in the comparison (12-14ºS).

EF from this study reported in Table 4 are within 50% of the EFs from the other South

Eastern Australian studies except for acetic acid, which is 5 times lower than the EF reported

by Paton-Walsh et al., (2014). EF from this study are also within 50% of temperate NH EF

(temperate forests and chaparral) except for hydrogen, acetic acid and the methyl halides

and within 80% of the average tropical savannah EF, with the exception of acetic acid and

the methyl halides.

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The acetic acid EF from this study is significantly lower than reported from Australian

and NH temperate studies, though the variability reported elsewhere is large. Acetic acid

may form rapidly in BB plumes (Akagi et al., 2012), which adds uncertainty to the EF in

plumes which are sampled some distance downwind of emission. The lower EF reported in

this work may be due to inlet losses, or another loss process such as nocturnal uptake of

acetic acid on to wet aerosols (Stockwell et al., 2014).

The methyl halides EF from this study are in the same proportion as seen elsewhere

(i.e. EF (CH3Cl) > EF(CH3Br) > EF(CH3I) but the EF magnitudes are substantially higher. The

CH3Cl EF from this study is more than a factor of 4 higher than elsewhere in Australia and

the NH, the CH3Br EF between 5 and 11 times higher and CH3I EF a factor of about 3 times

higher than elsewhere. EF calculated by the alternative ER to CO method (Supplementary

material) gives methyl halide EFs which are 30% lower but still much larger than those

observed elsewhere. It is likely that the high methyl halide EFs reported here are due to

high halogen content of soil and vegetation on the island, due to very close proximity to the

ocean, and transfer of halogens to the soil via sea spray (McKenzie et al., 1996). Chlorine

and bromine content in vegetation has been shown to increase with proximity to the coast

(McKenzie et al., 1996; Stockwell et al., 2014) and methyl chloride and hydrochloric acid EF

are impacted by the chlorine content of vegetation (Reinhardt and Ward, 1995; Stockwell et

al., 2014).

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Table 4. Comparison of emission factors with other studies. Table taken from the published paper, Lawson et al., (2015) 4.5 Summary and future work

The opportunistic measurement of BB plumes at Cape Grim Baseline Air Pollution

Station in February 2006 has allowed characterisation of BB plumes in a region with few BB

measurements. Plumes were measured on two occasions (events BB1 and BB2) when the

plume was advected to Cape Grim from a fire on Robbins Island some 20 km to the east.

156 | P a g e

The fresh plume had a large impact on the number of particles at Cape Grim, with

absolute numbers of particles > 3 nm increasing from 600 cm-3 in background air up to

25,000 cm-3 during the fresh plume in BB2 (hourly average) and CCN increasing from 160

cm-3 in background air up to 5500 cm-3 (hourly average). The dominant particle diameter

mode measured in BB1 was 120 nm.

After a slight wind direction change, BB tracers BC and CO decreased dramatically and

the dominant particle mode decreased to 50nm. A gradual increase in particle size to 80nm

was observed over 5 hours, in calm sunny conditions, alongside a modest increase in ozone.

BC was present above background levels during particle growth but CO was not significantly

elevated, so the presence of the fire emissions during particle growth cannot be

determined. During BB1, the ability of particles > 80nm to act as CCN at 0.5%

supersaturation was investigated, including during the fresh BB, particle growth and

background terrestrial/marine periods. The ∆CCN/∆CN80 ratio was lowest during the fresh

BB plume strike (56±8%), higher during the particle growth event (77±4%) and is higher still

(104±3%) in background marine air.

Enhancements in O3 concentration above background were observed following the

direct plume strikes in BB1 and during the direct plume strike in BB2, with NEMRs

(∆O3/∆CO) of 0.001-0.074. It is likely that some of the O3 enhancement that occurred during

the particle growth event in BB1 was driven by fire emissions. However on other occasions

enhancement of O3 which occurred at night, corresponding with enhancements of urban

tracer HFC-134a was most likely due to air being transported from mainland Australia.

Chemical transport modelling will be used in a follow up paper to elucidate the sources, and

where possible the species responsible for the O3 enhancement and change in particle

hygroscopicity observed, as well as the age of the urban emissions transported from

Melbourne.

The more prolonged BB2 allowed determination of emission ratios (ER) to CO and

Emission Factors (EF) for a range of trace gas species, CN and BC using the carbon mass

balance method. These EF, which were calculated from nocturnal measurements of the BB

plume, provide a unique set of emission estimates for a wide range of trace gases from

burning of coastal heathland in temperate Australia.

157 | P a g e

A very large increase in emissions of NMOCs (factor of 16) and CO (factor of 21), and a

more modest increase in BC (factor of 5) occurred during BB2. The ratio of acetonitrile to CO

increased by a factor of 2-3 and the ratio of BC to CO halved during this period. This change

in emission ratios is attributed to decreased combustion efficiency during this time, due to

rainfall over Robbins Island. Given that air quality and climate models typically use a fixed EF

for trace gas and aerosol species, the impact of varying emissions due to meteorology may

not be captured by models.

More broadly, given the high variability in reported EF for trace gas and aerosol

species in the literature, the impact of EF variability on modelled outputs of both primary BB

species (i.e. CO, BC, NMOCs) and secondary BB species (i.e. O3, oxygenated NMOCs,

secondary aerosol) is likely to be significant. In the next phase of this work, in addition to

exploring the chemistry described above with chemical transport modelling, we will also

systematically explore the sensitivity of these models to EF variability, as well as spatial and

meteorological variability.

4.6 Acknowledgements The Cape Grim program, established by the Australian Government to monitor and

study global atmospheric composition, is a joint responsibility of the Bureau of Meteorology

(BOM) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). We

thank the staff at Cape Grim and staff at CSIRO Oceans and Atmosphere (Rob Gillett, Suzie

Molloy) for their assistance and input. We acknowledge the NOAA Air Resources Laboratory

(ARL) for the provision of the HYSPLIT transport and dispersion model used in this

publication. West Takone radar images courtesy of Stuart Baly (BOM). We thank both

reviewers for insightful comments and suggestions which have been incorporated into the

manuscript.

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4.8 Supplementary Material

Fig 1a. Air mass back trajectory corresponding to BB1 Period A, fresh BB plume. Three back trajectories have been run and finish at 3:00, 4:00 and 5:00 on 16th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 1b. Air mass back trajectory corresponding to BB1 Period B, particle growth event. Three back trajectories have been run and finish at 8:00, 10:00 and 12:00 on 16th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 1c. Air mass back trajectory corresponding to BB1 Period C, mainland influence (background). Four back trajectories have been run and finish at 21:00 on the 16 February, 0:00, 3:00 and 6:00 on 17th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 1d. Air mass back trajectory corresponding to BB1 Period D, mainland influence (urban). Five back trajectories have been run and finish at 8:00, 9:00, 10:00, 11:00 and 12:00 on 17th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 1e. Air mass back trajectory corresponding to BB1 Period E, clean marine air. Four back trajectories have been run and finish at 15:00, 16:00, 17:00 and 18:00 on 17th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 1 f. Air mass back trajectory corresponding to BB1 Period F, marine air with minor terrestrial influence. Four back trajectories have been run and finish at 6:00, 11:00, 16:00 and 21:00 on 18th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 2a. Air mass back trajectory corresponding to BB2 Period A, fresh BB plume. Ten back trajectories have been run and finish at 1:00, 2:00. 3:00, 4:00, 5:00, 6:00, 7:00, 8:00, 9:00 and 10:00 on 24th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 2b. Air mass back trajectory corresponding to BB2 Period B, mainland influence (background). . Four back trajectories have been run and finish at 1:00, 2:00. 3:00, 4:00 on 25th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 2c. Air mass back trajectory corresponding to BB2 Period C, mainland influence (urban). Four back trajectories have been run and finish at 9:00, 11:00, 13:00 and 15:00 on 25th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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Fig 2d. Air mass back trajectory corresponding to BB2 Period D, clean marine air. Back trajectory ends at 23:00 on the 25th February 2006 Australian Eastern Standard Time (AEST). Yellow circle indicates approximate fire location.

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EFs (g/kg fuel) were calculated using the equation detailed in Andreae et al., (2001),

using CO as the reference gas:

)()(

)()/()( COEF

COMWXMWCOXERXEF ××= (1)

Where EF (X) is the calculated emission factor in g/kg fuel, ER (X/CO) is the molar

emission ratio with respect to CO, MW(X) is the molecular weight of the trace species, MW

(CO) is the molecular weight of CO, and EF(CO) is the emission factor of CO. The EF (CO)

used was the temperate average EF from Akagi et al., (2011) (original publication) of 89 ±32

g CO kg -1 fuel, which corresponds to MCE of 0.92.

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5 Chapter 5

Biomass burning at Cape Grim: exploring photochemistry

using multi-scale modelling S. J. Lawson1, M. Cope1,, S. Lee1, I.E Galbally1 , Z. Ristovski2and M.D. Keywood1

[1] CSIRO Oceans and Atmosphere, Aspendale

[2] Queensland University of Technology


This paper was reviewed in Atmospheric Chemistry and Physics Discussions (5 Dec 2016). The final version of the paper was published in Atmospheric Chemistry and Physics on 5 October 2017: Lawson, S. J., Cope, M., Lee, S., Galbally, I. E., Ristovski, Z., and Keywood, M. D.: Biomass burning at Cape Grim: exploring photochemistry using multi-scale modelling, Atmos. Chem. Phys., 17, 11707-11726, https://doi.org/10.5194/acp-17-11707-2017, 2017. STATEMENT OF JOINT AUTHORSHIP








Title: Biomass burning at Cape Grim: using high resolution modelling to explore model

sensitivity and photochemistry (in review at ACPD)

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Identified scientific problem, developed scientific design, analysed data, interpreted data, wrote manuscript

Martin Cope Developed scientific method (model), contributed to scientific design, contributed to interpretation, reviewed manuscript

Sunhee Lee Contributed to scientific method (model) Melita Keywood Reviewed manuscript Ian Galbally Reviewed manuscript Zoran Ristovski Reviewed manuscript



authorship.

_______________________ ____________________ ______________________

Name Signature Date

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Biomass burning at Cape Grim: exploring photochemistry

using multi-scale modelling

S. J. Lawson1, M. Cope1,, S. Lee1, I.E Galbally1, Z. Ristovski2 and M.D. Keywood1

[1] CSIRO Climate Science Centre Aspendale

[2] Queensland University of Technology


5.1 Abstract We have tested the ability of high resolution chemical transport modelling (CTM) to

reproduce biomass burning (BB) plume strikes and ozone (O3) enhancements observed at

Cape Grim in Tasmania Australia from the Robbins Island fire. The model has also been used

to explore the contribution of near-field BB emissions and background sources to O3

observations under conditions of complex meteorology. Using atmospheric observations,

we have tested model sensitivity to meteorology, BB emission factors (EF) corresponding to

low, medium and high modified combustion efficiency (MCE) and spatial variability. The use

of two different meteorological models varied the first (BB1) plume strike time by up to 15

hours, and duration of impact between 12 and 36 hours, while the second plume strike

(BB2) was simulated well using both meteorological models. Meteorology also had a large

impact on simulated O3, with one model (TAPM-CTM) simulating 4 periods of O3

enhancement, while the other model (CCAM) simulating only one period. Varying the BB

EFs which in turn varied the non methanic-organic compound (NMOC) / oxides of nitrogen

(NOx) ratio had a strongly non-linear impact on O3 concentration, with either destruction or

production of O3 predicted in different simulations. As shown in the previous work (Lawson

et al., 2015), minor rainfall events have the potential to significantly alter EF due to changes

in combustion processes. Models which assume fixed EF for O3 precursor species in an

environment with temporally or spatially variable EF may be unable to simulate the

behaviour of important species such as O3.

TAPM-CTM is used to explore the contribution of the Robbins Island fire to the

observed O3 enhancements during BB1 and BB2. Overall, the model suggests the dominant

source of O3 observed at Cape Grim was aged urban air (age = 2 days), with a contribution of

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O3 formed from local BB emissions. The model indicates that in an area surrounding Cape

Grim, between 25 - 43% of O3 enhancement during BB1 was formed from BB emissions

while the fire led to a net depletion in O3 during BB2.

This work shows the importance of assessing model sensitivity to meteorology and EF,

and the large impact these variables can have in particular on simulated destruction or

production of O3. This work also demonstrates how a model can be used to elucidate the

degree of contribution from different sources to atmospheric composition, where this is

difficult using observations alone.

5.2 Introduction Biomass burning (BB) makes a major global contribution to atmospheric trace gases

and particles with ramifications for human health, air quality and climate. Directly emitted

species include carbon monoxide (CO), carbon dioxide (CO2), oxides of nitrogen (NOx),

primary organic aerosol (POA), non-methanic organic compounds (NMOC) and black carbon

(BC), while chemical transformations occurring in the plume over time lead to formation of

secondary species such as O3, oxygenated NMOC and secondary aerosol. Depending on a

number of factors, including magnitude and duration of fire, plume rise and meteorology,

the impact of BB plumes from a fire may be local, regional or global.

BB plumes from wildfires, prescribed burning, agricultural and trash burning can have

a major impact on air quality in both urban and rural centres (Keywood et al., 2015; Luhar et

al., 2008; Reisen et al., 2011; Emmons et al., 2010; Yokelson et al., 2011) and regional scale

climate impacts (Andreae et al., 2002; Keywood et al., 2011b; Artaxo et al., 2013; Anderson

et al., 2016). In Australia, BB from wild and prescribed fires impacts air quality in both rural

and urban areas (Keywood et al., 2015; Reisen et al., 2011; Luhar et al., 2008; Keywood et

al., 2011a) as well as indoor air quality (Reisen et al., 2011). More generally, as human

population density increases, and as wildfires become more frequent (Flannigan et al., 2009;

Keywood et al., 2011b), assessing the impact of BB on air quality and human health

becomes more urgent (Keywood et al., 2011b; Reisen et al., 2015). In particular, particles

emitted from BB frequently lead to exceedances of air quality standards, and exposure to

BB particles has been linked to poor health outcomes including respiratory effects,

cardiovascular disease and mortality (Reisen et al., 2015; Reid et al., 2016; Dennekamp et

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al., 2015). There is also increasing evidence that mixing of BB emissions with urban

emissions results in enhanced photochemistry and production of secondary pollutants such

as secondary aerosol and O3 (Jaffe and Wigder, 2012; Akagi et al., 2013; Hecobian et al.,

2012), which may result in more significant health impacts than exposure to unmixed BB or

urban emissions.

To be able to accurately predict and assess the impact of BB on human health, air

quality and climate, models must be able to realistically simulate the chemical and

microphysical processes that occur in a plume as well as plume transport and dispersion. In

the case of BB plumes close to an urban centre or other sensitive receptor, models can be

used to mitigate risks on community by forecasting where and when a BB plume will impact,

the concentrations of toxic trace gases and particles in the plume, and potential impact of

the BB plume mixing with other sources. Models also allow investigation of the

contributions from BB and other sources on observed air quality when multiple sources are

contributing. Understanding the relative importance of different sources is required when

formulating policy decisions to improve air quality.

Lagrangian parcel models are often used to investigate photochemical

transformations in BB plumes as they are transported and diluted downwind (Jost et al.,

2003; Trentmann et al., 2005; Mason et al., 2006; Alvarado and Prinn, 2009; Alvarado et al.,

2015) while three-dimensional (3D) Eulerian grid models have been used to investigate

transport and dispersion of plumes, plume age, as well as contributions from different

sources. 3D Eulerian grid models vary from fine spatial resolution on order of kms (Luhar et

al., 2008; Keywood et al., 2015; Alvarado et al., 2009; Lei et al., 2013) to a resolution of up to

hundreds of km in global models (Arnold et al., 2015; Parrington et al., 2012).

Broadly speaking, models used for simulating BB plumes comprise a) description of

the emissions source b) a determination of plume rise c) treatment of the vertical transport

and dispersion and d) a mechanism for simulating chemical transformations in the plume

(Goodrick et al., 2013). There are challenges associated with accurately representing each of

these components in BB modelling. The description of emissions source includes a spatial

and temporal description of the area burnt, the fuel load, combustion completeness, and

trace gas and aerosol emission factors per kg of fuel burned. The area burned is often

determined by a combination of hotspot and fire scar data, determined from retrievals from

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satellite (Kaiser et al., 2012; Reid et al., 2009). Cloud cover may lead to difficulties in

obtaining area burnt data, while scars from small fires may be difficult to discern against

complex terrain, and low intensity fires may not correspond with a detectable hotspot

(Meyer et al., 2008). Emission factors are determined experimentally either by field or

laboratory measurements, and are typically grouped by biome type. In some regions, such

as SE Australia, biomes have been sparsely characterised (Lawson et al., 2015).

Furthermore, models use biome–averaged EF which do not account for complex intra-

biome variation in EF as a result of temporal and spatial differences in environmental

variables. This includes factors such as impact of vegetation structure, monthly average

monthly rainfall (van Leeuwen and van der Werf, 2011) and the influence of short term

rainfall events (Lawson et al., 2015). Finally, the very complex mixture of trace gases and

aerosols in BB plumes creates analytical challenges in quantifying EF, especially for semi and

low volatility organics which are challenging to measure and identify but contribute

significantly to secondary aerosol formation and photochemistry within the plume (Alvarado

and Prinn, 2009; Alvarado et al., 2015; Ortega et al., 2013).

Plume rise is a description of how high the buoyant smoke plume rises above the fire,

and consequently the initial vertical distribution of trace gases and aerosols in the plume

(Freitas et al., 2007). This is still a large area of uncertainty in BB models, with a generalised

plume rise approach typically used which may include either homogenous mixing,

prescribed fractions of emissions distributed according to mixing height, use of

parametisations, and finally plume rise calculated according to atmospheric dynamics. A key

driver of this uncertainty is the complexity of fire behaviour resulting in high spatial and

temporal variability of pollutant and heat release, which drives variability in plume rise

behaviour, such as multiple updraft cores (Goodrick et al., 2013).

Transport and dilution in models is driven by meteorology, particularly wind speed

and direction, wind shear and atmospheric stability. Meteorology has a large impact on the

ability of models to simulate the timing and magnitude and even composition of BB plume

impacts in both local and regional scale models (Lei et al., 2013; Luhar et al., 2008; Arnold et

al., 2015). For example, too-high wind speeds can lead to modelled pollutant levels which

are lower than observed (e.g. Lei et al., (2013)) while small deviations in wind direction lead

to large concentration differences between modelled and observed, particularly when

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modelling emissions of multiple spatially diverse fires (Luhar et al., 2008). Dilution of BB

emissions in large grid boxes in global models may also lead to discrepancies between

modelled and observed NOx, O3 and aerosols (Alvarado et al., 2009).

Finally, models use a variety of gas-phase and aerosol-phase physical and chemical

schemes, which vary in their ability to accurately represent chemical transformations,

including formation of O3 and organic aerosol (Alvarado and Prinn, 2009; Alvarado et al.,

2015). Validating and constraining chemical transformations in models requires high quality,

high time resolution BB observations of a wide range of trace gas and aerosol species,

including important but infrequently measured species such as OH and semi volatile and low

volatility NMOC. Field observations, whilst often temporally and spatially scarce, are

particularly valuable because the processes and products of BB plume processing are

dependent on long range transport, cloud processing, varying meteorological conditions and

heterogeneous reactions.

Sensitivity studies have allowed the influence of different model components

(emissions, plume rise, transport, chemistry) on model output to be investigated. Such

studies are particularly important in formation of secondary species such as O3 which have a

non-linear relationship with emissions. Studies have found that modelled O3 concentration

from BB emissions is highly dependent on a range of factors including a) meteorology

(plume transport and dispersion) in global (Arnold et al., 2015) and high resolution (Lei et

al., 2013) Eulerian grid models, b) absolute emissions/biomass burned (Pacifico et al., 2015;

Parrington et al., 2012), c) model grid size resulting in different degrees of plume dilution

(Alvarado et al., 2009), and oxidative photochemical reaction mechanisms in Lagrangian

parcel models (Mason et al., 2006).

In this work we test the ability of a high resolution 3D Eulerian grid chemical transport

model to reproduce BB plume observations of the Robbins Island fire reported in Lawson et

al., (2015) with a focus on CO, BC and O3. The fire and fixed observation site (Cape Grim)

were only 20km apart, and so simulation of the plume strikes is a stringent test of the

model’s ability to reproduce windspeed and direction. We undertake sensitivity studies

using varying emission factors associated with a low, medium and high Modified

Combustion Efficiency (MCE), which in turn changes the NMOC / NOx ratio, in contrast to

other sensitivity studies which typically vary emissions linearly. We also test the model

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sensitivity to meteorology by utilising two different meteorological models. Plume rise and

chemical mechanism are held constant. Finally, we use the model to separate the

contribution of the Robbins Island fire emissions and urban emissions to the observed O3

enhancements at Cape Grim reported in Lawson et al., (2015), and use the model to

determine the age of the O3-enhanced air parcels..

5.3 Methods

5.3.1 Fire and measurement details

Details of the fire and measurements are given in Lawson et al (2015). Briefly, biomass

burning (BB) plumes were measured at the Cape Grim Baseline Air Pollution Station during

the 2006 Precursors to Particles campaign, when emissions from a fire on nearby Robbins

Island impacted the station. Fire burned through native heathland and pasture grass on

Robbins Island some 20 km to the east of Cape Grim for two weeks in February 2006. Plume

strikes occurred on two occasions when an easterly wind advected the BB plume directly to

Cape Grim. The first plume strike (BB1) occured from 02:00 – 06:00 (Australian Eastern

Standard Time - AEST) on the 16th February while the second, more prolonged plume strike

(BB2) occurred from 23:00 on 23rd February to 05:00 on the 25th February. In northerly

winds, urban air from Melbourne city (population 4.2 million) 300 km away is transported to

Cape Grim. Further details can be found in Lawson et al., (2015).

A wide variety of trace gas and aerosol measurements were made during the plume

strikes (Lawson et al., 2015). In this work, measurements of black carbon (BC), carbon

monoxide (CO) and ozone (O3) concentrations are compared with model output.

5.3.2 Chemical Transport Modeling

Simulations were undertaken with a chemical transport model (CTM), coupled offline

with two meteorological models (see below). The CTM is a three-dimensional Eulerian

chemical transport model with the capability of modelling the emission, transport, chemical

transformation, wet and dry deposition of a coupled gas and aerosol phase atmospheric

system. The CTM was initially developed for air quality forecasting (Cope et al., 2004) and

has had extensive use with shipping emission simulations (Broome et al., 2016), urban air

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quality (Cope et al., 2014; Galbally et al., 2008), biogenic (Emmerson et al., 2016) and

biomass burning studies (Keywood et al., 2015; Meyer et al., 2008; Luhar et al., 2008).

The chemical transformation of gas-phase species was modelled using an extended

version of the Carbon Bond 5 mechanism (Sarwar et al., 2008) with updated toluene

chemistry (Sarwar et al., 2011). The mechanism also includes the gas phase precursors for

secondary (gas and aqueous phase) inorganic and organic aerosols. Secondary inorganic

aerosols are assumed to exist in thermodynamic equilibrium with gas phase precursors and

were modelled using the ISORROPIA-II model (Fountoukis and Nenes, 2007). Secondary

organic aerosol (SOA) was modelled using the Volatility Basis Set (VBS) approach (Donahue

et al., 2006). The VBS configuration is similar to that described in Tsimpidi et al., (2010). The

production of S-VI in cloud water was modelled using the approach described in Seinfeld

and Pandis (1998). The boundary concentrations in the model for different wind directions

were informed by Cape Grim observations of atmospheric constituents during non BB

periods (Lawson et al., 2015). In this work the modelled elemental carbon (EC) output was

considered equivalent to the BC measured with aethalometer at Cape Grim.

Horizontal diffusion is simulated according to equations detailed in Cope et al (2009)

according to principles of Smagorinsky et al., (1963) and Hess (1989). Vertical diffusion is

simulated according to equations detailed in Cope et al., (2009) according to principles of

Draxler and Hess (1997). Horizontal and vertical advection uses the approach of Walcek et

al., (2000).

Meteorological models

Prognostic meteorological modelling was used for the prediction of meteorological

fields including wind velocity, temperature, and water vapour mixing ratio (including

clouds), radiation and turbulence. The meteorological fields force key components of the

emissions and the chemical transport model. Two meteorological models were used in this

work. CSIRO’s TAPM (Hurley, 2008b), a limited area, nest-able, three-dimensional Eulerian

numerical weather and air quality prediction system, and CSIRO’s Conformal Cubic

Atmospheric Model (CCAM) a global stretched grid atmospheric simulation model

(McGregor, (2015) and references therein). The model was run using five nested

computational domains with cell spacings of 20 km, 12 km, 3 km, 1 km and 400 m (Figure 2).

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This multi-scale configuration was required in order to capture a) large scale processes such

as windblown dust, sea salt aerosol and ambient fires; b) transport of the Melbourne urban

plume to Cape Grim; c) transport of the Robbin’s Island smoke plume between the point of

emission and Cape Grim.

In this work the CTM coupled with CCAM meteorological model is referred to as CTM-

CCAM, while the CTM coupled with the TAPM meteorological model is referred to as TAPM-

CTM.

Figure 1. The five nested computational domains used in this work, showing cell spacing of 20 km, 12 km, 3 km, 1 km and 400 m.

Emission inventories

Anthropogenic emissions

Anthropogenic emissions for Victoria were based on the work of Delaney et al (2011).

No anthropogenic emissions were included for Tasmania. The north west section of

Tasmania has limited habitation and is mainly farmland, and so the influence of Tasmanian

anthropogenic emissions on Cape Grim are expected to be negligible.

Natural and Biogenic emissions

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The modelling framework includes methodologies for estimating emissions of sea salt

aerosol (Gong, 2003) emissions of windblown dust (Lu and Shao, 1999); gaseous and aerosol

emissions from managed and unmanaged wild fires (Meyer et al., 2008); emissions of

NMOC from vegetation (Azzi et al., 2012) and emissions of nitric oxide and ammonia from

vegetation and soils. Emissions from all but the wildfires are calculated inline in the CTM at

each time step using the current meteorological fields. There were no other major fires

burning in Victoria and Tasmania during the study period.

Biomass Burning emissions – Robbins Island fire

The base case fire emission factors used in the model were Savanna category emission

factors from Andreae and Merlet (2001). Three different sets of fire emission factors were

used to test the sensitivity of the model. We used reported EF of CO and CO2 from

temperate forests (Akagi et al., 2011), to calculate a typical range of MCEs for temperate

fires, including an average (best estimate) of 0.92, a lower (0.89) and upper estimate (0.95).

Fires with MCEs of approximately 0.90 consume biomass with approximately equal amounts

of smouldering and flaming, while MCEs of 0.99 indicate complete flaming combustion

(Akagi et al., 2011). Therefore the calculate range of MCEs (0.89 - 0.95) correspond to fires

in which both smouldering and flaming is occurring, with a tendency for more flaming

combustion in the upper estimate (0.95) compared to a tendency of more smouldering in

the lower estimate (0.89). The CO EF for lower, best estimate and upper MCE were taken as

minimum, mean and maximum EF for temperate forests summarised by Agaki et al 2011.

For all other species, the savannah fuel EF (Andreae and Merlet, 2001) were adjusted

according to published relationships between MCE and EF (Meyer et al., 2012; Yokelson et

al., 2007; Yokelson et al., 2003;Yokelson et al., 2011) . For example to adjust from the

savannah EF (corresponding to an MCE of 0.94) to our temperate ‘best estimate’ EF

(corresponding to MCE of 0.92), all VOC EF’s were increased by a factor of 1.3, as an

approximate response based on relationships between MCE and EF for CH4 (Meyer et al.,

2012), methanol (Yokelson et al., 2007), HCN and formaldehyde (Yokelson et al., 2003). The

savannah BC EF (Andreae and Merlet, 2001) was reduced by 30%, and the OC EF was

increased by 20%, based on the relationship reported in Yokelson et al., 2011, in which

smouldering results in lower EC and higher OC emission. The Andreae and Merlet (2001)

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savannah NO EF from was reduced by 30% according to the relationship in (Yokelson et al.,

2007). Table 1 shows emission factors used which correspond to the three MCEs.

We recognise calculating EF in this way is highly approximate and likely to differ

significantly from actual emission factors of the Robbins Island fire. However the purpose of

including a range of EF was to explore the model sensitivity to EF. The inherent error in

these emission factors needs to be taken into account when interpreting model outputs

specific to the Robbins Island fire. While EFs for some species emitted from the Robbins

Island fire have been calculated (Lawson et al., 2015), these EF are only available for a

subset of species required by the Carbon Bond 5 model chemical mechanism. For this

reason, existing EF currently used in the model for savannah modelling were adjusted as

described above to better reflect the likely range of EF expected in temperate fires.

MCE EF g/kg -1 0.89 0.92 0.95

NO 0.8 2.7 4.7CO 121 89 57PAR 2.33 2.02 1.4OLE 0.81 0.7 0.49TOL 0.3 0.26 0.18XYL 0.07 0.06 0.04BNZ 0.35 0.3 0.21FORM 0.63 0.55 0.38ALD2 0.75 0.65 0.45EC25 0.16 0.29 0.45EC10 0.03 0.05 0.08

Table 1. EF used in sensitivity studies, corresponding to low, medium and high MCEs. A subset of the total species included in the CB05 lumped chemical mechanism are shown. NO = nitric oxide, CO =carbon monoxide, PAR=paraffin carbon bond, OLE= terminal olefin carbon bond, TOL=toluene and other monoalkyl aromatics, XYL=xylene and other polyalkyl aromatics, BNZ =benzene, FORM=formaldehyde, ALD2=acetaldehyde, EC25=elemental carbon <2.5 µm, OC=primary organic carbon < 2.5 µm

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The hourly diurnal emissions from the fire were calculated using the Macarthur Fire

Danger Index (FDI) (Meyer et al., 2008) in which the presence of strong winds will result in

faster fire spread and enhanced emissions, compared to periods of lower wind speeds

(Figure 2). The effect of windspeed on the fire behaviour and emissions in particularly

important during the second BB event in which the winds ranged from 10 to 15 m s-1.

An image of the fire scar on Robbins Island at the end of February was the only

information available about the area burned and there was no detailed information

available about the direction of fire spread. The fire burnt over the two week period, and

the area burnt was subdivided into hourly amounts burnt using a normalised version of the

Macarthur Fire Danger Index. Therefore area burnt was divided up into 250 m grids, and the

model assumed that an equal proportion of each grid burned simultaneously over the two

week period. The fuel density used was 18.7 t-C/ha, based on CSIRO’s BIOS-2 model.

http://carbonwaterobservatory.csiro.au/bios2.html.

Finally, based on photographs of the plume, the plume rise parametrisation was

modified in the model so that the plume was well mixed between the ground and the

minimum of the boundary layer height and 200 m with the latter included to account for

some vertical mixing of the buoyant smoke plume even under conditions of very low

planetary boundary layer height.

The high wind speeds particularly during the second BB event, also suggest that the

plume was not likely to be sufficiently buoyant to penetrate the planetary boundary layer.

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Figure 2 Base hourly diurnal emissions and revised emissions calculated using the Macarthur Fire Danger Index (FDI), in which the presence of strong winds results in faster fire spread and enhanced emissions. Revised emissions were used in all simulations.

Plume Rise

The chemical transport model calculates plume rise from buoyant sources and/or

sources with appreciable vertical momentum within the computational time step loop. In

the case of industrial sources (such as power stations) plume rise is calculated by

numerically integrating state equations for the fluxes of moment and buoyancy according to

the approach used in TAPM (Hurley, 2008a). In the case of landscape fires, there are a

hierarchy of approaches which can be used (Paugam et al., 2016), including rule-of-thumb,

simple empirical approaches, and deterministic models varying in complexity from analytic

solutions to cloud resolving numerical models. The Robbin’s Island fire was a relatively low

energy burn (Lawson et al., 2015), and as noted by Paugam et al., (2016) the smoke from

such fires is largely contained within the planetary boundary layer (PBL). Given that ground-

based images of the Robbin’s Island smoke plume support this hypothesis, in this work we

adopted a simple approach of mixing the emitted smoke uniformly into the model layers

contained within the PBL.The plume was well mixed between the minimum of the PBL

height and 200m above the ground, with the latter included to account for some vertical

mixing of the buoyant smoke plume even under conditions of very low PBL height. The high

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wind speeds particularly during the second BB event, also suggest that the plume was not

likely to be sufficiently buoyant to penetrate the PBL.

5.4 Results and Discussion

5.4.1 Modelling Sensitivity Study

The ability of the model to reproduce the two plume strikes (BB1 and BB2, described

in Lawson et al (2015)) was tested. The sensitivity of the model to meteorology, emission

factors and spatial variability was also investigated and is discussed below. Observation and

model data shown are hourly averages. Error! Reference source not found. summarises

main findings of the model sensitivity study. A MODIS Truecolour Aqua image of the

Robbins Island fire plume is shown in Error! Reference source not found. from the 23

February 2006, with the modelled plume during the same period.

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Sensitivity

study Species TAPM-CTM

simulation CCAM-CTMsimulation

Comments/drivers of model outputs

Meteorology

BC and CO BB1 plume strike +3 hrDuration 12 hr (actual 5 hr)

BB2 plume strike 0 hr Duration 50 hr (actual 57 hr)

BB1 plume strike -12 hrDuration 36 hr intermittent

(actual 5 hr)

BB2 plume strike 0 hr Duration 57 hr (actual 57 hr)

Narrow BB plume. Differences in plume strike due to timing and duration driven by timing of wind

direction change, windspeeds

Concentrations driven by directness of plume hit and PBL height

O3 4 O3 peaks simulated (2 observed, 2 not)

1 O3 peak simulated (observed)

Dilution of precursors due to dispersion and PBL height (and EF – see below)

Emission Factors

BC and CO BC peak magnitude varies by factor 3, CO factor 2 with different EF runs

As for TAPM -CTM Concentrations vary according to EF input ratios.

O3 2 peaks with high EF sensitivity, 2 peaks with no EF sensitivity

1 peak with no EF sensitivity NO EF (varies with MCE) drives destruction or production of O3 in fire related peaks.

MCE 0.89 TAPM-CTM simulation gives best agreement with observations

Spatial Variability

CO Differences of up to > 500 ppb in grid points 1 km apart (BB2)

n/a Narrow BB plume

O3 Differences of up to 15 ppb in grid points 1 km apart (BB1)

n/a Narrow ozone plume generated downwind of fire

Table 2. Summary of sensitivity study results, including Meteorology, Emission Factors and Spatial Variability.

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Figure 3. MODIS Truecolour Aqua image of the Robbins Island fire plume (left) with plume circled and model output (right) for the same period

Sensitivity of model to meteorology Primary species BC and CO Figure 3 shows a typical output of spatial plots from CCAM-CTM for BB1 with the

model output every 12 hours shown. The narrow BB plume is simulated intermittently

striking Cape Grim (until 17 Feb 4:00), and then the plume is swept away from Cape Grim

after a wind direction change.

The simulated and observed time series concentrations of CO and BC for the two

different models (TAPM-CTM and CCAM-CTM) and for 3 different sets of EF are shown in

Error! Reference source not found.. TAPM-CTN and CCAM-CTM both reproduce the

observed plume strikes (BB1 and BB2). The impact of meteorology on the plume strike

timing and duration is discussed below. Both models overestimate the duration of BB1 and are a few hours out in the timing of the plume strike. TAPM-CTM predicts the timing of BB1 to be about 3 hours later than occurred (BC data) and predicts that BB1 persists for 12 hours (actual duration 5 hours). CCAM-CTM predicts that BB1 occurs 12 hours prior to the observed plume strike and predicts that the plume intermittently sweeps across Cape Grim for up to 36 hours ( Figure 3) (5 hours actual). Both models indicate that the plume is narrow and

meandering.

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Figure 3. Model output of BC for CCAM-CTM at 12 hour time intervals during BB1, showing the Robbins Island BB plume strike intermittently striking Cape Grim (until 17 Feb 4:00), and then the change in plume direction with wind direction change.

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In contrast, both models successfully predict the timing and duration of BB2. TAPM-

CTM correctly predicts the timing of the first enhancement of BC prior to BB2 (if the first BC

enhancement on the 22 Feb at 20:00 is included) and predicts that BB2 persists for 50 hours

(actual duration 57 hours). CCAM-CTM correctly predicts the timing and duration of BB2 (57

hours modelled and observed).

The difference between the TAPM and CCAM simulated wind direction is driving these

differences. In both BB1 and BB2, the plume strike at Cape Grim occurred just prior to a

wind direction change from easterly (fire direction), to north-north westerly. The timing of

the wind direction change in the models is therefore crucial to correctly predicting plume

strike time and duration. In BB1 CCAM predicts an earlier wind direction change with higher

windspeeds which advects the plume directly over Cape Grim while TAPM predicts a later

wind change, lower windspeeds and advection of only the edge of the plume over Cape

Grim. In BB2, both models predict similar wind speeds and directions, and a direct ‘hit’ of

the plume over the station.

The magnitudes of the BC and CO peaks shown are also influenced by meteorology.

Overall, CCAM-CTM predicts higher concentrations of CO and BC in BB1, and TAPM predicts

higher concentrations in BB2. Assuming a constant EF, peak magnitudes are influenced by

several factors including wind direction (directness of plume hit), wind speed (degree of

dispersion and rate of fuel combustion) and PBL height (degree of dilution). In BB1, the

larger BC and CO concentrations in CCAM are likely due to the direct advection of the plume

over the site compared to only the plume edge in TAPM. In BB2, both CCAM and TAPM

predict direct plume strikes, and the higher CO and BC peaks in TAPM are likely due to a

lower PBL in TAPM which leads to lower levels of dilution and more concentrated plume.

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Figure 5. Simulated CO with a) TAPM and b) CCAM, BC with c) TAPM and d) CCAM and ozone with e) TAPM and f) CCAM. Coloured lines represent different MCE EF simulations, black symbols are observations

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Ozone Error! Reference source not found. e-f shows the simulated and actual O3

concentration time series for TAPM-CTM and CCAM-CTM for 3 different sets of EF. The two

observed O3 peaks which followed BB1 and BB2 can clearly be seen in the time series.

Again the simulated meteorology has a major impact on the ability of the model to

reproduce the magnitude and timing of the observed O3 peaks. TAPM reproduces both of

the major O3 peaks observed following BB1 and BB2, with the timing of the first peak within

5 hours of the observed peak and the second within 8 hours of the observed peak. The

model also shows 2 additional O3 peaks about 24 hours prior to the BB1 and BB2 peaks

respectively which were not observed at the Cape Grim. The magnitude of these additional

peaks shows a strong dependency on the EF suggesting an influence of fire emissions. This is

discussed further below.

Compared to TAPM, CCAM generally shows only minor enhancements of O3 above

background. Both TAPM and CCAM show depletion of O3 below background levels which

was not observed, and this is discussed further in the following section.

To summarise, the impact of using two different meteorological models for a primary

species such as BC was to vary the modelled time of impact of the BB1 plume strike by up to

15 hours (CCAM -12 and TAPM +3 hours, where actual plume strike time = 0 hours) and to

vary the plume duration between 12 and 36 hours (actual duration 5 hours).

For O3, the use of different meteorological models lead to one model (TAPM)

reproducing both observed peaks plus two additional peaks, while the other model (CCAM)

captured only one defined O3 peak over the time series of 2 weeks.

Sensitivity of model to Emission Factors Primary species – CO and BC

Error! Reference source not found. a-d shows the simulated and observed

concentrations of BC and CO for combustion MCEs of 0.89, 0.92 and 0.95 (see Method

Section). Because CO has a negative relationship with MCE, and BC has a positive

relationship with MCE, the modelled BC concentrations are highest for model runs using the

highest MCE, while the modelled CO concentrations are highest for model runs using the

lowest MCE (Error! Reference source not found.).

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Changing the EF from low to high MCE varies the modelled BC concentrations during

BB1 and BB2 by a factor of ~3 for BC and a factor of ~2 for CO, and for these primary

pollutants this is in proportion to the difference in EF input to the model.

Observed CO and BC peaks were compared in magnitude to peaks simulated using

different EF in CCAM-CTM and TAPM-CTM. In TAPM, the simulation with the lowest

combustion efficiency EFs (MCE 0.89) gives closest agreement to the CO observations, while

the run with the medium combustion efficiency EFs (MCE 0.92) gives best agreement with

BC observations. For CCAM, the lowest MCE model run (0.89) provides the best agreement

with observations for CO for BB and BB2, while for BC, model runs corresponding to the low

MCE 0.89 (BB1) and high MCE 0.95 (BB2) provide the best agreement with observations.

As discussed, the magnitude of the modelled concentration is a function of both the

input EF, the wind speed (rate of fuel burning, dispersion) and the mixing height which

controls the degree of dilution after plume injection. Hence a good agreement between the

magnitude of the model and observed peaks is not necessarily indicative that a suitable set

of EF has been used. As discussed previously there is also uncertainty in the derivation of EF

as a function of MCE, as these were based on relationships from a small number of studies.

However interestingly, in most cases, model simulations with EF corresponding to the low

MCE 0.89 appear to best represent the observations, which is in agreement with the

calculated MCE of 0.88 for this fire (Lawson et al., 2015).

Secondary species – ozone

For secondary species such as O3 (Error! Reference source not found.e-f), the

relationship between EF precursor gases and model output is more complex than for

primary species such as CO and BC, because the balance between O3 formation and

destruction is dependent on the degree of dilution of the BB emissions and also factors such

as the NMOC composition and the NMOC/NOx ratio.

TAPM-CTM (Error! Reference source not found.e) reproduces the magnitude of both

observed peaks following BB1 and BB2 (BB1 max observed = 33 ppb, modelled = 31 ppb,

BB2 max observed = 34 ppb, modelled = 30ppb). Interestingly the magnitude of O3 for these

two peaks is the same for different EF inputs of O3 precursors from the Robbins Island fire,

suggesting that the BB emissions are not responsible for these enhancements. In contrast,

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the two additional peaks modelled but not seen in the observations are heavily dependent

on the input EF. For the first additional peak modelled prior to BB1, all EF runs result in an

O3 peak, with the medium MCE model scenario resulting in highest predicted O3. For the

second additional modelled peak prior to BB2, only the lowest MCE model run results in a

net O3 production, while medium and high MCE runs lead to net O3 destruction.

This differing response to EF for the TAPM runs suggests the importance of the NO EF

on O3 production in BB plumes. Unfortunately there were no oxides of nitrogen

measurements made during the fire to test the model. For the first simulated additional

peak prior to BB1, while the medium NO EF (MCE 0.92) resulted in the highest O3 peak (with

corresponding NO of 3.7 ppb, NO2 4.5 ppb) the lower NO EF in the 0.89 MCE run perhaps

indicates insufficient NO was present to drive O3 production (corresponding NO 0.5 ppb,

NO2 1.5 ppb), which is in line with studies which have shown that BB plumes are generally

NOx limited (Akagi et al., 2013; Jaffe and Wigder, 2012; Wigder et al., 2013). Conversely the

highest input NO EF (MCE 0.95) lead to net destruction of O3 (NO 9 ppb, NO2 7 ppb), which

is due to titration of O3 with the larger amounts of NO emitted from the fire in these runs as

indicated by excess NO (NO/NO2 ratio > 1) at Cape Grim (where NO has a positive

relationship with MCE). For the second additional peak prior to BB2, only the lowest NO EF

run (MCE 0.89) resulted in net production of O3 (NO 1.5 NO2 2.6)– in the medium and high

MCE runs the background O3 concentration is completely titrated (0 ppb) with NO

concentrations of 10 and 20 ppb and NO/NO2 ratios of 1.3 and 2.6 respectively.

Unlike the simulation, the observations do not show significant reduction of O3 below

background levels. The lower MCE (0.89) TAPM-CTM model simulation predicts no O3

titration and is in best agreement with the observations. This suggests that EF

corresponding to lower MCE (0.89) are most representative of the combustion conditions

during the Robbins Island fire, and as stated previously is in agreement with the calculated

MCE of 0.88 for BB2 (Lawson et al., 2015). Again however it should be recognised that the

absolute concentrations of NO in the plume, which determines O3 production or

destruction, are not only driven by EF but also dependent on the degree of dilution, which is

driven by meteorology and mixing height.

In contrast, the CCAM-CTM model (Error! Reference source not found.f) simulations

reproduce only the first observed O3 peak associated with BB1 (modelled = 27 ppb,

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measured = 34 ppb). This modelled O3 peak does not show an influence of MCE on O3

concentration, in agreement with TAPM, again suggesting no influence from fire emissions.

The CCAM model runs also show significant titration of O3 during BB1 and BB2 for the

medium and high MCE model runs, with ~24 and ~48 hours of significant O3 depletion below

background concentrations being modelled for each event, which was not observed.

To summarise, the impact of EF on primary species such as BC and CO was that the

modelled peak concentrations varied in proportion with the variation in the input EFs,

(factor of ~3 BC and ~2 CO). For the secondary species O3, the EF of precursor gases,

particularly NOx, had a major influence (along with meteorology) on whether the model

predicted net production of O3, or destruction of background O3, as was particularly evident

in TAPM.

As shown in the previous work (Lawson et al., 2015), minor rainfall events have the

potential to significantly alter EF due to changes in combustion processes. This work

suggests that varying model EF has a major impact on whether the model predicts

production or destruction of O3, particularly important at a receptor site in close proximity

to the BB emissions. Models which assume a fixed EF for O3 precursor species in an

environment with temporally variable EF may therefore be challenged to correctly predict

the behaviour of an important species such as O3.

Given that TAPM-CTM meteorological model with EF corresponding to the low

combustion efficiency (MCE 0.89) provides an overall better representation of the timing

and magnitude of both primary and secondary species during the fire, this configuration has

been used to further explore the spatial variability in the next section, as well as drivers of

O3 production and plume age described in the following sections.

Sensitivity of modelled concentrations to spatial variability

The near-field proximity of the Robbins Island fire (20 km) to Cape Grim, the

narrowness of the BB plume and the spatial complexity of the modelled wind fields around

north Tasmania are likely to result in strong heterogeneity in the modelled concentrations

surrounding Cape Grim. We investigated how much model spatial gradients vary by

sampling the model output at 4 grid points sited 1 km to the north, east, south and west of

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Cape Grim. The TAPM-CTM model runs with EF corresponding to the MCE of 0.89 were

used for the spatial analysis.

Figure 6a shows a time series of the modelled CO output of the difference between

Cape Grim and each grid point 1km either side, where plotted CO concentration is other

grid point [CO] (N,S,E,W) –Cape Grim [CO].

.

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Figure 6 a Spatial variability of a) CO b) cumulative CO and c) spatial variability of O3. All plots show 4 grid points surrounding Cape Grim over two weeks of fire (BB1 and BB2 shown)

The figure clearly shows that there are some large differences in the modelled

concentrations of CO between grid points for both BB1 and BB2. Particularly large

differences were seen for BB2 with the north gridpoint modelled concentrations in BB2 over

500 ppb lower than at Cape Grim grid point, while at the Southerly grid point the modelled

CO was up to 350 ppb higher. Smaller differences of up to 250 ppb between the east and

Cape Grim grid points were observed for BB1. This indicates the plume from the fire was

narrow and had a highly variably impact on the area immediately surrounding Cape Grim.

Error! Reference source not found.b shows the observed cumulative concentration of

CO over the 56 hour duration of BB2 at Cape Grim, as well as the modelled cumulative

concentration at Cape Grim and at the four gridpoints either side. This figure shows both

the variability in concentration with location, but also with time. Beyond the 10 hour mark,

the model shows major differences in cumulative CO concentrations between the 5

gridpoints (including Cape Grim), highlighting significant spatial variability. For example at

the end of BB2 (hour 56), the model predicts that the cumulative modelled CO

concentration at Cape Grim is 24% lower than the cumulative concentration 1 km south and

47% higher than the cumulative concentration 1 km north. The modelled cumulative CO

concentrations at the South gridpoint at hour 56 is almost twice as high as the north

modelled concentration 2 km away (82% difference). This high variability modelled

between sites which are closely located highlights the challenges with modelling the impact

of a near field fire at a fixed single point location. This also highlights the high spatial

variability which may be missed in similar situations by using a coarser resolution model

which would dilute emissions in a larger gridbox.

Error! Reference source not found.c shows a time series of the modelled O3 output of

the difference between Cape Grim and each gridpoint 1km either side, where plotted O3

concentration is other location [O3] (N,S,E,W) – Cape Grim [O3].

The modelled concentrations very similar at all grid points when BB emissions are not

impacting. The variability increases at the time of BB1 and BB2, with differences mostly

within 2-3 ppb, but up to 15 and 10 ppb at east and west sites for BB1. This largest

difference corresponds to the additional modelled O3 peak which showed strong

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dependency on EF, and provides further evidence that local BB emissions are driving this

enhancement.

The model output for O3 for BB1 (Figure 4) shows O3 enhancement downwind of the

fire at 11:00 and 13:00 on the 16 February. The very localised and narrow O3 plume is

dispersed by the light (2 m s-1) and variable winds, and Cape Grim is on the edge of the O3

plume for much of this period, explaining the high variability seen in Error! Reference

source not found.c.

Figure 4. O3 enhancement downwind of the fire during BB1 at 11:00 and 13:00 on the 16 February, for TAPM-CTM including fire and Melbourne emissions. The spatially variable plume and complex wind fields are shown. Scale is ppb.

In summary there is a large amount of spatial variability is the model for primary

species such as CO during the BB events, with differences of > 500 ppb in grid points 1 km

apart. This is due to the close proximity of the fire to the observation site and narrow plume

non-stationary meteorology. For O3, there is up to 15 ppb difference between grid points

for a narrow O3 plume which is formed downwind of the fire.

The highly localised nature of the primary and in some cases secondary species seen

here highlights the benefits of assessing spatial variability in situations with a close

proximity point source and a fixed receptor (measurement) site. Due to the spatial

variability shown for O3 in BB1, model data from all 5 grid points are reported in Section 5.5.

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5.5 Exploring plume chemistry and contribution from different

sources

5.5.1 Drivers of ozone production

In previous work on the Robbins Island fire, it was noted that the increases in O3

observed after both BB1 and BB2 were correlated with increased concentration of HFC134a.

This indicated that transport of photochemically processed air from urban areas to Cape

Grim was likely the main driver of the O3 observed, rather than BB emissions (Lawson et al.,

2015). However, an O3 increase was observed during particle growth (BB1) when urban

influence was minimal which suggested O3 growth may also have been driven by emissions

from local fire. Normalised Excess Mixing Ratios (NEMR) observed during BB2 were also in

the range of those observed elsewhere in young BB plumes (Lawson et al., 2015).

In this section, we report on how TAPM-CTM was used to determine the degree to

which the local fire emissions, and urban emissions, were driving the O3 enhancements

observed.

The model was run using TAPM-CTM with EF corresponding to the lowest MCE of

0.89, as discussed previously. Three different emission configurations were run to allow

identification of BB-driven O3 formation; a) with all emission sources (Eall); b) all emission

sources excluding the Robbins Island fire (EexRIfire); and c) all emission sources excluding

anthropogenic emissions from Melbourne (EexMelb).

The enhancement of O3 due to emissions from the Robbins Island fire was calculated

by

ERIfire = Eall – EexRIfire (1)

The enhancement of O3 due to emissions from anthropogenic emissions in Melbourne

was calculated by

EMelb = Eall – EexMelb (2)

In this way the contribution was estimated from the two most likely sources

(emissions from the Robbins Island fire and transported emissions from Melbourne on the

Australian mainland).

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Figure 5 a) Simulated contribution to O3 formation at Cape Grim from Robbins Island fire emissions (red line) and Melbourne emissions (green line). Observations are black symbols. The periods corresponding to BB1 and BB2 are shaded; b) simulated contribution of the fire to excess O3 for BB1 and BB2 at all 5 grid points surrounding Cape Grim, where upper and lower diamonds are minimum and maximum contribution.

206 | P a g e

Due to the high spatial variability of O3 for BB1 discussed in the previous section, ERIfire

and EMelb was calculated for all 5 locations (Cape Grim and 1 km north, south, east and

west).

The O3 modelled times series for the EexRIfire and the EexMelb runs shows distinct O3

peaks driven by the Robbins Island fire emissions and distinct peaks from the Melbourne

anthropogenic emissions (Figure 5). The 2 peaks attributed to the fire occur during, or close

to the plume strikes, and are short lived (3 and 5 hour) events. These same two peaks

showed a strong dependence on model EF. In contrast, the two peaks attributed to

transport of air from mainland Australia are of longer duration, and occur after the plume

strikes.

The O3 peaks which were observed following BB1 and BB2 correspond with the

modelled O3 peak in which the Robbins Island fire emissions were switched off, confirming

that the origin of the two observed O3 peaks is transport from mainland Australia, as

suggested by the observed HFC-134a. Of the 2 modelled Robbins Island fire-derived O3

peaks, the first modelled peak (33 ppb) corresponds with a small (21 ppb) observed peak

during BB1 (Period B in Lawson et al., 2015), but the second modelled fire-derived O3 peak is

not observed. As shown in Figure 4, according to the model the O3 plumes generated from

fire emissions were narrow and showed a strong spatial variability. Given this, it is

challenging for the the model to predict the exact timing and magnitude of these highly

variable BB generated O3 peaks impacting Cape Grim. This is likely why there is good

agreement in timing and magnitude between model and observations for the large scale,

spatially homogeneous O3 plumes transported from mainland Australia, but a lesser

agreement for the locally formed, spatially variable O3 formed from local fire emissions.

Given the challenges in modelling narrow locally formed O3 plumes and the

dependence on meteorology in particular, we analysed a longer period surrounding BB1 and

BB2 (32 and 71 hours) to remove this temporal variability. We calculated the overall

contribution of the Robbins Island fire to total excess (excess to background) O3 (including

anthropogenic O3) for these periods. To capture some of the spatial variability, model

output at the 4 locations around Cape Grim was included in the calculation.

The contribution of the Robbins Island fire emissions to the excess O3 was calculated

by:

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ERifire/ (ERifire + EMelb) x 100 (3)

Where the contribution can be positive (O3 enhanced above background levels) or

negative (O3 depleted below background levels).

Figure 5 shows the modelled contribution of the Robbins Island fire emissions to

excess O3 for the period surrounding BB1 and BB2, where the box and whisker values are

the % contributions at each of the 5 sites (Cape Grim and 1 km either side). The model

indicates that for an area 4 km2 surrounding Cape Grim, the Robbins Island fire emissions

contributed between 25 to 43% of the total excess O3 during BB1 and contributed -4 to -6 %

to the excess O3 during BB2. In other words, during BB1, the fire emissions had a net

positive contribution to the O3 in excess of background, while during BB2 the fire emissions

had a net destructive effect on the excess O3. The higher variability in the contribution for

BB1 reflects the high spatial variability discussed previously.

In summary, running the model with and without the Robbins Island fire emissions

allowed clear separation of the fire-derived O3 peaks from the anthropogenic derived O3

peaks, and allowed estimation of the fire contribution to total excess O3 during BB1 and

BB2. While the contributions of BB emissions to O3 are only estimates due to the issues

discussed previously, this work demonstrates how a model can be used to elucidate the

degree of contribution from different sources, where this is not possible using observations

alone.

5.5.2 Plume age

The model was used to estimate the age of air parcels reaching Cape Grim over the

two week period of the Robbins Island fire. The method has been described previously in

Keywood et al., (2015). Briefly, two model simulations were run for scenarios which

included all sources of nitric oxide (NO) in Australia ; the first treated NO as an unreactive

tracer, the second with NO decaying at a constant first order rate. The relative fraction of

the emitted NO molecules remaining after 96 hours was then inverted to give a molar-

weighted plume age.

Figure 6 shows a time series of the modelled NO tracer (decayed version), modelled

plume age (hours) and the observed O3. Direct BB1 and BB2 plume strikes can be clearly

seen with increases in NO corresponding with a plume age of 0-2 hours. The plume age

then gradually increases over 24 hours in both cases, peaking at 15:00 on the 17th February

208 | P a g e

during BB1 (aged of plume 40 hours) and peaking at 17:00 on the 25th February during BB2

(age of plume 49 hours). The peak observed O3 enhancements correspond with the

simulated plume age in both BB1 and BB2 (with an offset of 2 hours for BB1), and the

observed HFC-134a, suggesting that the plume which transported O3 from Mebourne to

Cape Grim was approximately 2 days old. The model also simulates a smaller NO peak

alongside the maximum plume age, indicating transport of decayed NO from the mainland

to Cape Grim.

Figure 6 Simulated plume age (green line), simulated combustion tracer (NO) (red line), observed O3 (black symbols) and HFC-134a (orange symbols) over 2 week duration of the fire.

As reported in Lawson et al., (2015), during BB2 NEMRs of ∆O3/∆CO ranged from

0.001-0.074, in agreement with O3 enhancements observed in young BB plumes elsewhere

(Yokelson et al., 2003; Yokelson et al., 2009). However, the modelling reported here

suggests that almost all of the O3 observed during BB2 was of urban, not BB origin. This

suggests NEMRs should not be used in isolation to identify the source of observed O3

enhancements, and highlights the value of utilising air mass back trajectories and modelling

to interpret the source of O3 enhancements where there are multiple emission sources.

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5.6 Summary and conclusions In this work we have used a unique set of opportunistic BB observations at Cape Grim

Baseline Air Pollution Station to test the ability of a high resolution (400m grid cell) chemical

transport model to reproduce primary (CO, BC) and secondary (O3) BB species in challenging

non-stationary, inhomogeneous, and near field conditions. We tested the sensitivity of the

model to three different parameters (meteorology, MCE and spatial variability) while

holding the plume rise and the chemical mechanisms constant. We found meteorology, EF

and spatial variability have a large influence on the model output mainly due to the close

proximity of the fire to the receptor site (Cape Grim). The lower MCE (0.89) TAPM-CTM

model simulation provided best agreement with observed concentrations, in agreement

with the MCE calculated from observations of 0.88 (Lawson et al., 2015). The changing EFs,

in particular NO dependency on MCE, had a major influence on the ability of the model to

predict O3 concentrations, with a tendency of the model in some configurations to both fail

to simulate observed O3 peaks, and to simulate complete titration of O3 which was not

observed. As shown in the previous work (Lawson et al., 2015), minor rainfall events have

the potential to significantly alter EF due to changes in combustion processes. This work

suggests that varying model EF has a major impact on whether the model predicts

production or destruction of O3, particularly important at a receptor site in close proximity

to the BB emissions. Models which assume a fixed EF for O3 precursor species in an

environment with temporally and spatially variable EF may therefore be challenged to

correctly predict the behaviour of important species such as O3.

There were significant differences in model output between Cape Grim and grid points

1 km away highlighting the narrowness of the plume and the challenge of predicting when

the plume would impact the station. This also highlights the high spatial variability which

may be missed in similar situations by using a coarser resolution model which would dilute

emissions in a larger gridbox.

The model was used to distinguish the influence of the two sources on the observed

O3 enhancements which followed BB1 and BB2. Transport of a 2 day old urban plume some

300km away from Melbourne was the main source of the O3 enhancement observed at

Cape Grim over the two week period of the fire. The model suggests the Robbins Island fire

contributed approximately 25-43% of observed O3 to the BB1 O3 enhancement, but for BB2

210 | P a g e

the fire caused a net O3 depletion below background levels. Despite NEMRs of ∆O3/∆CO

during BB2 being similar to that observed in young BB plumes elsewhere, this work suggests

NEMRs should not be used in isolation to identify the source of observed O3 enhancements,

and highlights the value of utilising air mass back trajectories and modelling to interpret the

source of O3 enhancements where there are multiple emission sources.

5.7 Acknowledgements The Cape Grim program, established by the Australian Government to monitor and

study global atmospheric composition, is a joint responsibility of the Bureau of Meteorology

(BOM) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). We

thank the staff at Cape Grim and staff at CSIRO Oceans and Atmosphere for providing data

for this paper. Thanks to Nada Derek (CSIRO) for formatting figures.

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6 Chapter 6

Conclusions

6.1 Conclusions and significances arising from experimental studies

6.1.1 General comments

The aim of this research project was to explore the concentrations, sources and sinks of

VOCs that participate in SOA and ozone formation processes in marine and terrestrial

environments in the Southern Hemisphere, which has been sparsely characterised to date.

In this work, sources of VOCs were explored and quantified using surface based and

remotely sensed observations, including a) oxidation of marine-derived precursors and b)

burning of vegetation. This work greatly increases the coverage of speciated VOC

measurements in poorly sampled regions in the Southern Hemisphere, including over the

remote ocean and in biomass burning plumes.

VOC measurements were made during the Surface Ocean Aerosol Production (SOAP)

campaign and were integrated with satellite data. These measurements confirmed the

presence of important SOA precursors over Southern Hemisphere oceans, and highlight a

major unknown source. VOC measurements from the Robbins Island biomass burning

plume at Cape Grim were integrated with a wide variety of other trace gas and aerosol

measurements, and were used to characterise the changing composition of a BB plume and

to calculate the first ever emission factors for this type of fire in Australia. These unique set

of emission factors will be crucial for models to be able to accurately predict spatial and

temporal impacts of primary and secondary biomass burning species. Model sensitivity

studies have been undertaken and have determined the impact of varying biogenic and

biomass burning emission rates, and varying meteorology on the ability of the models to

simulate atmospheric concentrations. This highlights the importance of assessing model

sensitivities particularly for secondary species. Chemical transport modelling has been used

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to interpret a complex photochemistry event involving multiple sources, and to calculate

plume age, demonstrating the usefulness of integrating models and observations.

6.1.2 Specific outcomes and the significance

Specific outcomes and significance is provided below for each chapter:

Chapter 3 - Marine VOCs

• This work confirms the presence of short-lived dicarbonyl glyoxal over the remote

temperate oceans, even in winter in very pristine air over biologically unproductive waters.

We provide the first observations of methylglyoxal over temperate oceans and confirm its

presence alongside glyoxal. These observations support the likely widespread contribution

of these dicarbonyls to SOA formation over the ocean

• Chatham Rise glyoxal observations from this study agree well with observations

made via MAX-DOAS on the same voyage, suggesting a good agreement between the

technique used in this work (DNPH derivatisation with HPLC analysis optimised for

dicarbonyl detection) and the optical technique. Such a comparison between methods has

not been undertaken previously at concentrations typical of the marine boundary layer.

• Expected yields of glyoxal and methylglyoxal were calculated based on parallel

measurements of precursor VOCs, including isoprene and monoterpenes. At most, 1–3 ppt

of dicarbonyls observed, corresponding to 10% and 17% of the observed glyoxal and 29 and

10% of the methylglyoxal at Chatham Rise and Cape Grim, respectively can be explained

from the oxidation of these precursors, confirming a significant contribution from another

source over the ocean. This is the first study to concurrently to use concurrent

measurements of dicarbonyl precursors to constrain the expected yield of dicarbonyls.

• Glyoxal observations were converted to vertical column densities (VCDs) and

compared with GOME-2 satellite VCDs. The satellite VCD exceeds the surface observations

by more than 1.5x1014 molecules cm-2. Recent observations of glyoxal in the free

troposphere suggest that this discrepancy may at least in part be due to the incorrect

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assumption that all glyoxal observed over the ocean by satellites is in the marine boundary

layer.

• This work highlights the degree to which atmospheric VOC sources and sinks are still

unknown, even in the relatively simple and well-mixed matrix over the remote ocean

Chapter 4 – biomass burning data synthesis and interpretation

• Biomass Burning emission factors (EFs) were derived for a range of trace gases, some

never before reported for Australian fires, (including hydrogen, phenol and toluene) using

the carbon mass balance method. This provides a unique set of EFs for Australian coastal

heathland fires, essential information for models to predict air quality and climate impacts.

Methyl halide EFs were higher than EFs reported from other studies in Australia and the

Northern Hemisphere which is likely due to high halogen content in vegetation on Robbins

Island due to very close proximity to ocean.

• The ability of biomass burning aerosol to act as cloud condensation nuclei was

investigated. The ΔCCN/ΔCN80 ratio was lowest during the fresh BB plume (56 ± 8 %),

higher during the particle growth period (77 ± 4 %) and higher still (104 ± 3 %) in background

marine air. Particle size distributions indicate that changes to particle chemical composition,

rather than particle size, are driving the different ΔCCN/ΔCN80 ratios between the three

periods. Particles produced from coastal heath burned here appear to be more hygroscopic

than those from burning of other biomass fuel types.

• A particle growth event was observed in sunny calm conditions, alongside increasing

ozone concentrations, just after the direct BB plume stopped impacting the station. This

suggests particles were growing in size due to oxidation of gas phase precursors and

condensation of low-volatility products. The presence of BB emissions during this period

could not be confirmed.

• Enhancements in O3 concentration above background were observed following the

direct plume strikes in BB1 and during the direct plume strike in BB2, with NEMRs (ΔO3

/ΔCO) of 0.001–0.074. It is likely that both the fire emissions and urban air from Melbourne

are contributing to the observed ozone. Further work suggested use of chemical transport

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modelling to determine the sources responsible for the ozone enhancement and age of the

urban emissions transported from Melbourne.

• A shortlived rainfall event led to a large increase in NMOC (VOCs) and CO, attributed

to a change in the combustion efficiency of the fire. This is the first study to our knowledge

which has linked rainfall with a large increase in trace gas emission ratios from BB and

highlights that using biome and annually-averaged trace gas emission factors in models will

not capture significant changes in emissions due to environmental variables.

Chapter 5 – Modelling of biomass burning event

• The ability of chemical transport models to simulate the Robbins Island fire plume

strikes at Cape Grim was tested. Key model sensitivities were explored – the plume rise and

chemical schemes were held constant while the meteorology and emissions varied. The

model was used to determine the sources of ozone enhancement observed in Chapter 4.

• The use of two different meteorological models varied the timing of the plume

strikes, and determined whether or not the models simulated production or destruction of

ozone resulting from fire emissions.

• Varying EFs according to a low, medium and high MCE scenarios had a major

influence on whether models predicted production or destruction of ozone. As biomass

burning plumes are NOx limited, the changing NOx EF with MCE was likely the driver of the

simulated ozone output

• The model results suggested the dominant source of ozone observed was 2 day old

air masses transported from Melbourne, with a contribution of ozone formed from local BB

emissions. This was despite the observed excess ozone to excess CO enhancements

(NEMRs) being comparable with those reported elsewhere from young biomass burning

plumes. This suggests that using NEMRs alone may not be a reliable method to identify

whether ozone enhancements are from biomass burning emissions. The model indicates

that in an area surrounding Cape Grim, between 25 - 43% of ozone enhancement during

BB1 was from BB emissions while the fire led to a net depletion in ozone during BB2.

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• This is one of only a few studies which model the interaction between BB and urban

plumes worldwide. This work shows the importance of assessing model sensitivity to

meteorology and EF, particularly for receptor sites close to the fire. This work also

demonstrates how a model can be used to elucidate the degree of contribution from

different sources to air quality impacts, where this is not possible using observations alone.

6.1.3 Recommendations for future work

• VOCs are still very sparsely characterised in the SH, and more spatial and temporal

coverage of VOC measurements are required, especially in remote and background regions

including over the oceans, in biomass burning plumes and forested areas. This will allow

testing and development of chemical models so that VOC emissions and processes are

realistically represented. Recent deployment of new measurement instrumentation such as

high-resolution proton-transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) has

allowed identification of a large proportion of the VOC species in complex air mixtures such

as biomass burning emissions in laboratory settings. These instruments will provide

powerful information when deployed in field settings.

• A recent intercomparison study of glyoxal and methyl glyoxal measurement methods

by Thalman et al., (2015) examined whether reactions involving O3 in Teflon tubing could be

a source of glyoxal, creating a measurement artefact. While the study overall found that

there was no affect of O3 flowing in Teflon tubing on glyoxal and methyl glyoxal

concentrations, the authors noted that some groups had noticed generation of small

quantities of glyoxal in some tubing when O3 flowed through it. The authors recommended

further study on the role of O3 at very low glyoxal concentrations. In this work, the good

level of agreement in glyoxal concentrations measured using DNPH and MAX-DOAS

techniques during the SOAP voyage provides a level of confidence in the DNPH derivitisation

results reported here. However, while the concentration of O3 over the Southern Ocean

during the glyoxal and methyl glyoxal measurements were low (20 ppb and 32 ppb in

summer and winter respectively), and likely lower than the O3 levels used in the Thalman et

al., (2015) experiments (quoted O3 level 0-250 ppm), it is possible that some insitu

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production of dicarbonyls occurred in the sample lines. Further investigations should be

undertaken to investigate measurement artefacts when measuring dicarbonyls at these very

low levels.

• The comparison of surface and satellite glyoxal data (Chapter 3) highlights the

importance of vertically characterising the distribution of key VOCs in the atmosphere, and

the limitations of comparing in situ surface measurements with remotely sensed column

measurements. Discrepancies between surface and satellite based measurements are often

attributed to instrumental challenges. Vertical profile in situ measurements will allow a

more meaningful comparison with satellite measurements.

• VOC flux measurements are needed over the ocean to determine direction of ocean-

atmosphere exchange of key SOA precursors such as glyoxal, and the magnitude of the flux.

This is particularly relevant given photochemical reactions in the surface microlayer have

recently been identified as a source of climatically important VOCs including glyoxal and

isoprene.

• The SOAP voyage measurements showed locally high concentrations of isoprene and

monoterpenes during parts of the voyage (manuscript in prep). While modelling studies

have shown that isoprene and monoterpene oxidation production are unlikely to contribute

to mass of marine aerosols, whether they contribute to aerosol number (which potentially

has a more significant climate impact) remains unclear. Further modelling studies exploring

this issue are needed.

• The biomass burning observations interpretation (Chapter 4) highlighted the large

impact a minor rainfall event can have on fire combustion efficiency and hence emissions.

The biomass burning modelling (Chapter 5) showed that changing input emission factors in

models has a large impact on the model’s ability to simulate secondary species such as

ozone. A much larger database of experimental emission factors is needed for biomass

burning modelling, which represent environmental variables which change temporally and

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spatially such as vegetation structure and fuel moisture. To ensure realistic outputs of

primary and secondary BB species, EFs used by models must be able to respond dynamically

to changes in environmental variables, such as vegetation structure and fuel moisture,

rather than using a static EF value.

concentrations, sources and processes involving volatile ... · bb event which impacted cape grim...

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