Overview of methods and challenges for

emission measurement from buildings

and fields

Gary J. Lanigan

Teagasc, Environment, Soils & Land-Use,

Johnstown Castle,

Co. Wexford,

Introduction

• Measurement of emissions needs to either a) detect differences

between treatments or (preferably) give accurate absolute

estimates

• Ultimately there are three goals:

• Refine emission factors

• Quantify the most effective mitigation strategies

• Parameterise process models that can be used as a decision

making tool for both of the above ….and as a predictive tool as

to the effects of climate change on the above

• Abatement measures need to be Measurable, Real and

Verifiable.

Background

• Grassland comprises 90% of utilisable agricultural

area in Ireland

• Agriculture constitutes 29.1% of total emissions

• Methane from livestock and Nitrous oxide from

agricultural soils are key contributors

• C sequestration offsets by 2.5Mt CO2-eq

50,000.00

55,000.00

60,000.00

65,000.00

70,000.00

75,000.00

1990 1995 2000 2005 2010

GH

G E

mis

sio

ns (

Kt

CO

2e

q y

r-1)

Methane

Ammonia/N2O

CO2

Uncertainties - Methane

• Enteric Methane – Variation caused by differences in dry

matter intake, feed residence time in the rumen and

efficiency of energy conversion. Directly influenced by feed

type and variation in age/size/type of livestock….also

differences in rumen microfauna

• Manure Methane – Variation in livestock and diet influences

the methane production potential – variation in temperature

and redox potential of manure controls acetate fermentation

to CO2 and methane

Measurement of enteric methane

• Via methane collars - animals fed with SP6 bolus

• Methane emissions from various cattle types and dietary strategies can be

assessed

• Advantages: Easy to assess a large variety of treatments

• Disadvantages: More inherent variation than respiration chambers uncertainty

(15-30%)

• Good for large-scale diet manipulation experiments and assessing country-

specific Tier 2 EF’s

• Bad for selecting animals high genetic merit animals

Tier 2 Emission Factors for methane derived from EF and MM from

cattle

• Respiration chambers – Advantages:

• measurements more accurate 10-15%

• Disadvantages: Artificial environments for

animals , low throughput

• Allows for the selection of high genetic merit

(EBI) animals

Measurement of enteric methane

Figure X. A comparison of published analyses of GHG emissions from dairy production systems

using LCA (red) and systems analysis (blue).

0

0.5

1

1.5

2

2.5

3

3.5

Willi

ams et

al. (2

006)

- Eng

land

, con

vent

iona

l

Willi

ams et

al. (2

006)

- Eng

land

, hig

h m

aize

Willi

ams et

al. (2

006)

- Eng

land

, split-

calvin

g

Cas

ey a

nd H

olde

n (2

006b

) - Ir

elan

d, a

vera

ge

Cas

ey a

nd H

olde

n (2

005a

) - Ir

elan

d, c

onve

ntiona

l

Thom

asse

n et

al. (2

008)

- Net

herla

nds or

ganic

Haa

s et

al.

(200

1) - G

erm

any

exte

nsive

Bas

set-M

ens et

al.

(200

9) - N

ew Z

eala

nd n

ationa

l

Bas

set-M

ens et

al.

(200

9) - N

ew Z

eala

nd in

tens

ive

N

Ger

ber et

al. (2

010)

- G

loba

l ave

rage

Ger

ber et

al. (2

010)

- Nor

th A

mer

ica

Love

tt et

al. (2

006)

- Ire

land

low g

enet

ic m

erit

Love

tt et

al. (2

006)

- Ire

land

hig

h ge

netic

mer

it

Love

tt et

al. (2

006)

- Ire

land

med

ium

con

cent

rate

Love

tt et

al. (2

008)

- Ire

land

free

dra

ining

soils

Ole

sen

et a

l. (2

006)

- E

urop

ean

conv

entio

nal

Sch

ils e

t al.

(200

5) - N

ethe

rland

s gr

ass/fe

rt N

Beu

kes et

al.

(201

0) - N

ew Z

eala

nd

O'B

rien

et a

l. (2

010)

- Ire

land

hig

h fe

rtility

O'B

rien

et a

l. (2

010)

- Ire

land

mod

erat

e stoc

king

rate

O'B

rien

et a

l. (2

010)

- Ire

land

hig

h co

ncen

trate

GH

G e

mis

sio

ns

(k

g C

O2

e/k

g m

ilk

)

Housing Emissions

• Treat the building as a chamber

• The concentration difference of a gas between the outside and inside of the building

• Has to be scale with respect to the mass flow of air through the building

• For a force ventilated building – just need to know the air flow of the circulation system

• For a naturally ventilated building – its more difficult.

• Need a tracer (SF6) which is released at a given rate – can measure its dispersion throughout the building

• Measure at various points around the building and

sum

• Measure at various points at increasing distance from

the buildings and use a dispersion model to back-

calculate emissions to the source.

Ammonia and methane from cattle sheds & OWP’s

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

Shed OWP

Housing Type

Mean

Em

issio

n R

ate

(g N

H3 5

00kg

-1 d

-1) Ammonia

Me

tha

ne

(g C

H4

LU

d-1

)

0

5

10

15

20

25

30

35

40

45

Shed OWP

Shed

OWP

Methane

Uncertainties – Nitrous Oxide

• Considerable uncertainty both spatially and temporally (>100% for N2O)

• N Direct sources – Urine/dung, manures, mineral fertiliser, crop

residues

• N Indirect sources – ammonia volatilisation and leached N

• Spatial – Soil type, N input type and amount, land-use type

• Temporal – Climate – particularly rainfall and temperature

• Local climatic and soil conditions promote greater emissions and justify

regional emission factors in inventory calculations

• Measurement - Background levels very low (350 ppb)

– Point measurements (circa 50%)

– Micromet. measurements (30-40%)

Uncertainties – CO2

• Also large spatial and temporal uncertainty (>100% for

N2O)

• Spatial – land-use type, land management, soil type

(%clay)

• Temporal – Climate – particularly temperature and

moisture – also diurnal variations

• Current Tier 1land-use factors are primarily based on US

data

• Measurement – Point measurements (circa 50%)

– Micromet. measurements (30-35%)

How to Measure: A Question of Scale

Chamber measurements:

Technically easier

Gives some indication of spatial variability

Micrometeorological techniques:

Integrate spatially over a larger area

Plot scale: Chamber measurements

– N2O/ Methane/ CO2

• Static closed chambers – prevents pressure changes

• Requires collars permanently inserted - reduces

disturbance

• Flux measured as conc. accumulation per unit time…with

either

• In situ with gas analyser

• Stored in gas-tight vials and

analysed with GC

• Temperature must be kept

constant

Applicability of the plot approach

• Most appropriate for looking at factorial-designed experiments (eg.the effects of soil type, mitigation options, management, etc)

• Is very effective if a lysimeter approach is taken – all losses to both atmosphere and water can be assessed.

• If used in conjunction with isotopic tracers, the fate of all applied N can be followed.

NH3 N2O CO2/CH4

NO3 DOC

C or N

N2O Fluxes

• UV stabilised transparent chambers (218 litres)

• Internal cooling system

• gas samples drawn from chamber headspace into

10 ml gas-tight syringes

• N2O fluxes determined using GC within 24 hours of

sampling chamber headspace

Overview of New Field

Lysimeters at Johnstown Castle

• 72 field monolith lysimeters (0.8 x 1.0m)

• 3 soil types (heavy, medium and free-draining)

• Urine, mineral fertiliser and N inhibitors

Losses out

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

23/04/05

27/04/05

01/05/05

05/05/05

09/05/05

13/05/05

17/05/05

21/05/05

25/05/05

29/05/05

02/06/05

06/06/05

10/06/05

14/06/05

18/06/05

22/06/05

26/06/05

30/06/05

04/07/05

08/07/05

12/07/05

Sampling date

N2O

em

issio

ns (

µg

m-2

hr

-1 N

2O

-N)

Rathangan Control

Rathangan Fertiliser

Rathangan Fertiliser & Urine

Elton Control

Elton Fertiliser

Elton Fertiliser & Urine

Clonakilty Control

Clonakilty Fertiliser

Clonakilty Fertiliser & Urine

25/04

↓ f & u

23/05

↓ f

20/06

↓ f

Effect of diet and inhibitors on N cycling

y = -0.0002x2 + 0.3501x + 8.8332

R2 = 0.9934

0

50

100

150

200

0 200 400 600 800 1000

Urine application rate (kg N ha-1

)

To

tal N

O3- -N

le

ac

he

d (

kg

N h

a-1)

Urine N

DCD

Field-scale measurements

Integrated Horizontal flux

Measurements made over 7 days

Shuttles changed at 1, 3, 6, 24, 48, 96, 168 hours

6m

Mast with shuttles @ 0.2, 0.4, 0.8, 1.2, 2.2 & 3.3 m

Meade et al (2011) Ag. Ecosys. Env. 140: 208-217

Ammonia Losses

0

10

20

30

40

50

60

0 24 48 72 96 120 144 168

Time (hr)

Am

monia

loss

TA

N (

%)

Splashplate

Trailing shoe

49.2%

29.9%

59%

0

10

20

30

40

50

60

70

80

90

April June

Am

mo

nia

(%

TA

N)

TS

SP

Timing % application technique on N2O emissions

0

100

200

300

400

June April June TS April TS

CH4

N2O (direct)

N2O (indirect)

GH

G e

mis

sio

ns (

kg C

O2-e

q h

a-1

)

Indirect N2O – Assumes 98% ammonia is redeposited within

2km & 1% of deposited N is re-emitted as N2O

Mitigating N loss: Timing and spreading technique effects on

Ammonia loss and N fertilizer replacement value (NFRV)

Cattle Slurry on grassland

• Typical slurry: 6.9% DM total N content = 3.6 kg/t

NH4+-N content = 1.8 kg/t

0

20

40

60

80

100

120

April June

Date

% T

AN

lo

st

0

5

10

15

20

25

30

35

40

45

% N

FR

V

Ammonia

Trailing Shoe

Broadcast

NFRV

Trailing Shoe

Broadcast

If performed in conjunction with 15N tracing……

Hoekstra et al 2010 Plant & Soil 330, 357–368

At low N application and 20% clover, clover

reduced nitrous oxide by 41%

Effect of replacing fertiliser with clover

GHG Fluxes

• Relates the co-variation

of gas concentration

with net upward

/downward movement of

turbulent eddys in the

atmosphere

• F = u*[DC]

-1500

-1000

-500

0

500

1000

1500

-70

-50

-30

-10

10

30

50

70

19/05/2009 08/06/2009 28/06/2009 18/07/2009 07/08/2009 27/08/2009 16/09/2009

Cu

mu

lati

ve C

arb

on

Flu

x (g

C m

-2)

NE

E (µ

mo

l CO

2 m

-2s-1

)

ΣNEE = +102 g C m-2

Reco

GPP

NEE

emission

uptake

-80

-60

-40

-20

0

20

40

0 10 20 30 40 50 60

Pasture Net C Balance

Uptake

Loss

C flu

x (

gC

m-2

)

Davis & Lanigan (2009) Ag. For. Meterol.

150: 564-574

-80

-60

-40

-20

0

20

40

0 10 20 30 40 50 60

Pasture Net C Balance

Uptake

Loss

C flu

x (

gC

m-2

)

Davis & Lanigan (2009) Ag. For. Meterol.

150: 564-574

Pasture/Maize Net C Balance

-80

-60

-40

-20

0

20

40

0 10 20 30 40 50 60

C flu

x (

gC

m-2

)

-80

-60

-40

-20

0

20

40

0 10 20 30 40 50 60

Pasture/OSR Net C BalanceC

flu

x (

gC

m-2

)

-80

-60

-40

-20

0

20

40

0 10 20 30 40 50 60

Pasture/Maize/Miscanthus Net C Balance

Miscanthus has a long growing season and little

disturbance

C flu

x (

gC

m-2

)

Comparison of Land-Use GHG Budgets

0

10

20

30

40

50

60

70

Peatland Afforested Deforested

GH

G f

lux

(kg

CO

2-e

q h

a-1

yr-

1)

N2O

CH4

Modelling Emissions

•Empirical

•Semi-mechanistic (eg. RothC, ECOSSE)

•Mechanistic process models

• Allows a region to move to Tier 3 accounting

• Can be incorporated into farms systems models

and used as a predictive tool

The Effect of Arable and Biomass Cultivation on SOC

• Conversion of grassland or forest to arable reduces

SOC by 1tC/ha/yr

• Conversion of arable to biomass increases C sink by 1.8

tC/ha/yr

• Fossil fuel substitution using biomass/forestry thinnings

can yield even larger savings

Temporal Emissions Profile – Grazed plots

0

50

100

150

200

250

300

GG+FN

GWC+FN

GWC-FN

Modelled

Measured

0

100

200

300

400

500

600

N2O

(g

N2O

-N h

a-1

d-1

)

0

50

100

150

25-Aug 03-Dec 13-Mar 21-Jun 29-Sep

Results

N2O

(g

N2O

-N h

a-1

d-1

)0

1000

2000

3000

4000

5000

6000

0

1000

2000

3000

4000

5000

6000

0

1000

2000

3000

4000

5000

6000

0

200

400

600

800

1000

0

200

400

600

800

1000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Modelled

Measured

GG+FN

GWC+FN

GWC-FN

G-B

WC-B

Measured/simulated emissions & milk production

Milk

pro

du

ction

(ton

ha

-1 y

r-1)

0

2

4

6

8

10

12

14

16

GG+FN GWC+FN GWC-FN G-B WC-B

0

2

4

6

8

10

12

14

16

Measured

Simulated

Milk productionN

2O

(kg N

ha

-1yr-

1)

Lanigan & Humphries (2011) Ecosystems (in press)

The Rate of Forestry Sequestration is dependent on the

afforestation rate

Conclusions

• Large uncertainties around GHG’s, particularly N2O

• Crucial for verification of EF’s and mitigation

• Measurements should constrain models

• These can be used to generate spatial and temporal

specific EF’s