Download - Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
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)
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