1. Overview of methods and challenges for
emission measurement from buildings
and fields
Gary J. Lanigan
Teagasc, Environment, Soils & Land-Use,
Johnstown Castle,
Co. Wexford,

2. 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.

3. 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
GHG Emissions (Kt CO2eq yr-1)
75,000.00
70,000.00
65,000.00
60,000.00
55,000.00
50,000.00
1990 1995 2000 2005 2010

4. Ammonia/N2O
Methane
CO2

5. 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

6. 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

7. Tier 2 Emission Factors for methane derived from EF and MM from
cattle

8. Measurement of enteric methane
• 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

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Figure X. A comparison of published analyses of GHG emissions from dairy production systems
nc
en
tra
te

10. 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

11. • 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.

13. Ammonia and methane from cattle sheds & OWP’s
70.000
60.000 Ammonia
Mean Emission Rate
(g NH3 500kg-1 d-1)
50.000
40.000
30.000
20.000
10.000
0.000
Shed OWP
Housing Type
45
40
Methane
(g CH4 LU d-1)
35
30
Methane
25 Shed
20 OWP
15
10
5
0
Shed OWP

14. 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%)

15. 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%)

16. 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

17. 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

19. Applicability of the plot approach
NH3 N2O CO2/CH4
• 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. C or N
• If used in conjunction with
isotopic tracers, the fate of
all applied N can be
followed.
NO3 DOC

20. 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

21. 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

22. 10000
Rathangan Control
9000 Rathangan Fertiliser
Rathangan Fertiliser & Urine
N2 O emissions (µg m-2 hr-1 N2 O-N)
8000 25/04 23/05 Elton Control
7000
↓ f&u ↓f Elton Fertiliser
Elton Fertiliser & Urine
6000 Clonakilty Control
Clonakilty Fertiliser
5000 Clonakilty Fertiliser & Urine
4000
3000
2000
20/06
1000 ↓f
0
27 / 05
01 / 05
05 / 05
09 / 05
13 / 05
17 / 05
21 / 05
25 / 05
29 / 05
02 / 05
06 / 05
10 / 05
14 / 05
18 / 05
22 / 05
26 / 05
30 / 05
04 / 05
08 / 05
12 / 05
5
/0
/04
/04
/05
/05
/05
/05
/05
/05
/05
/05
/06
/06
/06
/06
/06
/06
/06
/06
/07
/07
/07
23
Sampling date

23. Effect of diet and inhibitors on N cycling
200
Total NO3--N leached (kg N ha-1)
y = -0.0002x 2 + 0.3501x + 8.8332
R2 = 0.9934
150
100
Urine N
50
DCD
0
0 200 400 600 800 1000
-1
Urine application rate (kg N ha )

24. Field-scale measurements

25. Integrated Horizontal flux
Meade et al (2011) Ag. Ecosys. Env. 140: 208-217
6m
Mast with shuttles
@ 0.2, 0.4, 0.8, 1.2,
2.2 & 3.3 m
Measurements made over 7 days
Shuttles changed at 1, 3, 6, 24, 48, 96, 168 hours

27. Ammonia Losses
90
60
49.2%
Ammonia (%TAN)
80 50
70
Ammonia loss TAN (%) 29.9%
40
60
50 30 TS
40 SP
30 20 59%
20 10 Splashplate
Trailing shoe
10
0 0
0 24 48 72 96 120 144 168
April Time (hr) June

28. Timing % application technique on N2O emissions
400
GHG emissions (kg CO2-eq ha-1)
CH4
N2O (direct)
300
N2O (indirect)
200
100
0
June April June TS April TS
Indirect N2O – Assumes 98% ammonia is redeposited within
2km & 1% of deposited N is re-emitted as N2O

29. 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
120 45 Ammonia
40 Broadcast
100
35
Trailing Shoe
% TAN lost
80 30
% NFRV
25 NFRV
60
20
Broadcast
40 15
10 Trailing Shoe
20
5
0 0
April June
Date

30. If performed in conjunction with 15N tracing……
Hoekstra et al 2010 Plant & Soil 330, 357–368

31. Effect of replacing fertiliser with clover
At low N application and 20% clover, clover
reduced nitrous oxide by 41%

32. GHG Fluxes
• Relates the co-variation
of gas concentration
with net upward
/downward movement of
turbulent eddys in the
atmosphere
• F = u*[DC]

33. 70 1500
50
1000
Cumulative Carbon Flux (g C m-2)
30
Reco
NEE (µmol CO2 m-2 s-1)
500
emission ΣNEE = +102 g C m-2
10
NEE 0
-10
uptake -500
-30 GPP
-1000
-50
-70 -1500
19/05/2009 08/06/2009 28/06/2009 18/07/2009 07/08/2009 27/08/2009 16/09/2009

34. Pasture Net C Balance
Loss
40
20
C flux (gC m-2)
0
0 10 20 30 40 50 60
-20
-40
-60
-80
Davis & Lanigan (2009) Ag. For. Meterol.
150: 564-574
Uptake

35. Pasture Net C Balance
Loss
40
20
C flux (gC m-2)
0
0 10 20 30 40 50 60
-20
-40
-60
-80
Davis & Lanigan (2009) Ag. For. Meterol.
150: 564-574
Uptake

36. Pasture/Maize Net C Balance
40
C flux (gC m-2)
20
0
0 10 20 30 40 50 60
-20
-40
-60
-80

37. Pasture/OSR Net C Balance
40
C flux (gC m-2)
20
0
0 10 20 30 40 50 60
-20
-40
-60
-80

38. Pasture/Maize/Miscanthus Net C Balance
40
C flux (gC m-2)
20
0
0 10 20 30 40 50 60
-20
-40
-60
-80
Miscanthus has a long growing season and little
disturbance

41. Comparison of Land-Use GHG Budgets
(kg CO2-eq ha-1 yr-1)
70
N2O
60
CH4
50
GHG flux
40
30
20
10
0
Peatland Afforested Deforested

42. Modelling Emissions
• Allows a region to move to Tier 3 accounting
• Can be incorporated into farms systems models
and used as a predictive tool
•Empirical
•Semi-mechanistic (eg. RothC, ECOSSE)
•Mechanistic process models

43. 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

46. Temporal Emissions Profile – Grazed plots
600
500
GG+FN
400
300
N2O (g N2O-N ha-1 d-1)
200
100
0
300
250
GWC+FN
200
150 Measured
100
50
Modelled
0
150
GWC-FN
100
50
0
25-Aug 03-Dec 13-Mar 21-Jun 29-Sep

47. 6000
5000
GG+FN
Results 4000
3000
2000
1000
0
6000
N2O (g N2O-N ha-1 d-1)
5000
GWC+FN
4000
3000
2000
1000
0
6000
5000
GWC-FN Measured
4000
3000
2000 Modelled
1000
0
1000
800
G-B
600
400
200
0
1000
800
WC-B
600
400
200
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

48. Measured/simulated emissions & milk production
16 16
Milk production (ton ha-1 yr-1)
Measured
N2O (kg N ha-1 yr-1)
14 14
Simulated
12 Milk production 12
10 10
8 8
6 6
4 4
2 2
0 0
GG+FN GWC+FN GWC-FN G-B WC-B
Lanigan & Humphries (2011) Ecosystems (in press)

49. The Rate of Forestry Sequestration is dependent on the
afforestation rate

50. 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