african dust over greece (acept-air life+) and over airuse...

Evaluating contributions of African dust to PMx over Europe LIFE+ Networking Barcelona 27-28th April, 2015

African dust over Greece (ACEPT-AIR LIFE+) and over AIRUSE LIFE+ cities

1

NCSR “Demokritos”, Athens Greece

Konstantimos Eleftheriadis


Relevant AIMS and SCOPE of AIRUSE and ACEPT AIR LIFE+

Reduction of exposure to PM requires quantitative apportionment of source

contributions

Mitigation measures are better targeted when the natural and anthropogenic

sources are well quantified

2

Extensive and intensive measurement campaigns

focused on chemical properties at AIRUSE and

ACEPT AIR sites

Source apportionment analysis by PMF

Alternative techniques for quantification of

Saharan dust


3

Assessment of the capacity of the available methods to identify and quantify

the contribution from the examined natural sources.

Application of natural source estimation guidelines to available data in 4

Mediterranean/South European countries (Greece, Italy, Spain and Portugal)

Comparative analysis of natural contribution with respect to other

anthropogenic sources in at least one urban area (Athens)

Sensitivity results on the uncertainty of the quantification of the natural

sources contribution

Assessment of temporal resolution of the quantified natural contribution with

respect to the averaging period of the reported limit values

Assessment of the spatial uncertainty of the quantification of the natural

sources


NATURAL CONTRIBUTIONS are considered from 3

types of sources

FOCUS ON AFRICAN DUST

SEA SALT

SEA SALT

SEA SALT


Estimation of Saharan dust contribution

Saharan dust can be found as a separate source by receptor modeling. Composition very

similar to earth’s crust. Otherwise the Saharan dust is included in the Mineral dust

source identified by the model

Mineral dust can be also calculated based on average crust composition from stoichiometry as: Al2O3 + SiO2 + 1.42*Fe + 1.94*Ti + 1.2*nssK + nssMg + nssCa + CO3 (nss corresponds to non sea spray fraction).

Florence (FI-UB),


Estimation of Saharan dust contribution

In cases of non resolved separate source Saharan dust contribution was estimated from the mineral dust PMx component from SA analysis or stoichiometry . Assuming

Mineral dust = local dust contribution + Saharan dust

Saharan dust event days identified from a suite of air mass/dust transport modelling tools

methodology for net dust calculation (Escudero et al., 2007) is applied to the mineral dust PMx component

Estimation of Saharan dust contribution to PM10 at ATH-SUB


African dust (and Sea salt) mean contribution normalized over an annual

basis for the AIRUSE cities


PM2.5

Contributions to the annual means POR BCN FI MLN ATH

µg/m3

SS 1.3 0.4 0.1 0.5 0.2 AF_DUST 0.1 0.1 0.2 0.1 0.7 Tot natural 1.4 0.5 0.3 0.6 0.9 % SS 5 3 1 1 2 AF_DUST 1 1 1 1 6 Tot natural 6 4 2 2 8

Contributions to the mean from days of exceedance POR BCN FI MLN ATH

µg/m3

SS 0.1 0.3 0.4 0.7 0.3 AF_DUST 0.1 0.1 0.3 0.1 10 Tot natural 0.2 0.4 0.7 0.8 10.3 % SS 1 1 1 1 1 AF_DUST 1 1 1 1 45 Tot natural 2 2 2 2 46

Contributions to the annual means

POR BCN FI MLN ATH µg/m3

SS 4.4 1.5 0.6 N.D. 1.6

AF_DUST 0.2 0.3 0.7 N.D. 3.0 Tot natural 4.6 1.8 1.3 N.D. 4.6 %

SS 13 6 3 N.D. 7 AF_DUST 1 1 4 N.D. 14 Tot natural 14 7 7 N.D. 21

Contributions to the mean from days of exceedance

POR BCN FI MLN ATH µg/m3

SS 2.9 0.8 0.5 N.D. 4.9 AF_DUST 0.2 0.3 0.2 N.D. 38 Tot natural 3.1 1.1 0.7 N.D. 43 %

SS 3 2 1 N.D. 7 AF_DUST 1 1 1 N.D. 52 Tot natural 4 3 2 N.D. 59

PM10

Natural contributions to PMx


Seasonal variability of natural source

contribution

9


Application of natural source estimation

guidelines

10

Annual mean 90.4 perc.

Athens

greater

area

AIRUSE

sites

SUBURBAN SUBURBAN URBAN/TRAFFIC URBAN/TRAFFIC


Natural contribution with respect to other

anthropogenic sources

11

PM10

PM2.5

ATH-SUB

PM10

ATH-SUB

ATH-TR

PM10

2013 AIRUSE ATHENS


12

PM2.5

All days

PM10

Exceedance days

PM2.5

PM10

ATH-SUB



2013 AIRUSE ATHENS


Application of natural source estimation

guidelines

13

Urban-

Traffic

stations

Suburban

stations

90.4 perc. Annual

mean


14

Seasonal variability of

Saharan Net Dust in

Athens


15

All days

PM10

Exceedance

days PM10



2013 AIRUSE ATHENS

2011-2012 ACEPT-AIR ATHENS

ATH-SUB ATH-SUB

Exceedance

days

All days


Sensitivity analysis: Regional background concentrations

16 Project meeting - Florence, 23-3-2015

Quantification of Saharan dust:

Regional background: For Athens the 50th percentile was finally selected

Spatial variability in suburban background

PM10 concentrations


Sensitivity analysis: Selection of forecasts models



Sensitivity analysis: Use of alternative input data

18

PM10 concentrations (PM10)

PM10 mineral component calculated by stoichiometry (MIN-STOICH)

PM10 mineral source quantified by PMF (MIN-PMF) Florence


Sensitivity analysis: Use of alternative input data

19

Athens


Sensitivity analysis: Saharan and local dust profiles

20


Sensitivity analysis: Coarse versus PM10 mass concentration


Athens


Sensitivity analysis: Comparison with transport models

22

SKIR-0 SKIR-1 SKIR-2 SKIR-3 NMMB SKIR-0 SKIR-1 SKIR-2 SKIR-3 NMMB

Pearson 0.83 0.86 0.87 0.87 0.77 0.93 0.94 0.94 0.92 0.91

Slope 0.95 1.50 1.93 2.34 1.54 2.13 3.23 3.99 4.56 3.30

Intercept -0.17 -0.26 -0.38 -0.01 2.19 -0.26 -0.38 -0.35 0.46 1.57

Athens


Potential Source Contribution Function analysis previous example

5 day back trajectory analyses by HYSPLIT model at 12h intervals (Draxler and Rolph,2003)

Northern hemisphere divided from 42-90 degrees latitude to 3oX3o grid cells

80th percentile of 12 h mean

Black Carbon concentration peak values

PSCF probability map of BC aerosol for the summer half-year (May–October) at Zeppelin station (black circle) for the period1998–2007.

Eleftheriadis, K.; Vratolis, S.; Nyeki, S. (2009) Aerosol black carbon in the European Arctic: Measurements at Zeppelin

station, Ny-Alesund, Svalbard from 1998-2007 GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L02809,

doi:10.1029/2008GL035741, 2009


AIRUSE PSCF DESCRIPTION

•5 day back trajectory analyses by HYSPLIT

model at 3h intervals at 3 heights (Draxler and

Rolph,2003)

•Northern hemisphere divided from -40 to 80

degrees latitude to 1oX1o grid cells

•Trajectory end points above 3000 m excluded

•PSCFij=mij/nij

•mij=number of end points in a cell that have

equivalent concentration >90th percentile of

the concentration observed

•nij=number of trajectory end points in a cell

•A Weighting function is also apllied:

Fig. 1 Athens 7-day, 5-day and 3-day Trajectory endpoints

Net Dust derived from PM10 datasetSQrt


Input concentration data for analysis

Saharan Net dust PM10 calculated from PM10 concentrations (Escudero & Querol, 30 days moving 40th/50th percentile of PM10)

Saharan Net dust calculated from PM10 mineral dust component calculated by stoichiometry (MINeral PM10-STOICH)

Saharan Net dust calculated from PM10 mineral dust source quantified by PMF (MINeral PM10-PMF)


Sahara PSCF sensitivity analysis

Fig. 2 Athens 5 -day

Trajectory endpoints

for Net Dust PM10

dataset

Saharan Net dust PM10 for different trajectory time intervals

Fig. 3 Athens 3 -day

Trajectory endpoints for Net

Dust PM10 dataset.

Fig. 2 Athens 7-day

Trajectory endpoints

for Net Dust PM10

dataset


Source regions for SAHARA/DESERT DUST

Fig. 2 Sahara PSCF Analysis,

77 event days /382.

Sahara dust origin derived from NET DUST contributions from PM10 mass concentration

Fig. 3 Sahara PSCF Analysis,

18 event days /365.

Porto

WESTERN

SAHARA,

ALGERIA,

MAURITANIA

Athens


Source regions for SAHARA/DESERT DUST

Sahara PSCF Analysis, 41

event days /365.

Sahara dust origin derived from NET DUST contributions from PM10 mass concentration

Barcelona

Sahara PSCF Analysis, 28

event days/414.

Florence Porto

Sahara PSCF Analysis,

18 event days /365.



Fig. 10 MINeral PM10-

PMF (identified factor),

51 event days /218.

Saharan Dust for different input concentration calculations

Fig. 11 MINeral PM10-STOICH

, 41 event days/365.

Fig. 9 Net dust PM10

dataset, 41 event

days /365.

Florence


Fig. 12:MINeral PM10-PMF (40th

percentile on mineral dust factor),

44 event days /128.

Fig. 13: Net dust PM10 dataset,

77 event days /382.


Athens Saharan Dust for different input concentration calculations


Sensitivity analysis on identification of potential single component tracers

Fig. 14: PM10 Carbonate

(CO3) concentration dataset,

208 days.

PSCF analysis on single component ( CO3 , Al & Si) concentration data

Fig. 16: PM10 Si

concentration dataset,

196 days.

Fig. 15: PM10 Al

concentration dataset ,

196 days.

Development of A Cost Effective Policy Tool for PM reduction in Urban

AIR. Challenges and achievements

K. Eleftheriadis1, E. Diapouli1, C. Samara2, A. Kungolos3, A. Proyou4, M. Lazaridis5

1Institute of Nuclear and Radiological Science & Technology, Energy & Safety,

N.C.S.R. Demokritos, Athens, Greece 2Department of Chemistry, Aristotle University of Thessaloniki

3Department of Planning & Regional Development, University of Thessaly 4AXON Enviro Group Ltd

5Department of Environmental Engineering, Technical University of Crete


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THE AIRUSE CONSORTIUM

X. Querol1, A. Karanasiou1, F. Amato1, C. Vasconcelos1, A. Alastuey1, M. Viana1, T. Moreno1, F. Plana1, N. Perez1, M. Cabañas1, R. Bartoli1, S. Martinez1, M. Sosa1,

F. Lucarelli2, S. Nava2, G. Calzolai2, R. Udisti2, S. Becagli2, R. Traversi2, M. Severi2, S. Borselli2, C. Alves3, C. Pio3, T. Nunes3, L. Tarelho3, M. Duarte3, M. Cerqueira3, E. Vicente3, D. Custódio3, H. Pinto3,

E. Montfort4, I.Celades4, A. Escrig4, V. Sanfelix4, S. Gomar4, M. Padilla4, L. Ortiz4, B. Ferrer4, Y. Reig4, K. Eleftheriadis5, L. Diapouli5, S. Vratolis5, M. Gini5, E. Bairaktari5, S. Dalaina5, V. Galifianakis5,

V.L. Gianelle6, C. Colombi6 1 2 3

4 5 6

african dust over greece (acept-air life+) and over airuse...

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