Overview of methods and challenges for emission measurement from buildings              and fields                  Gary J...
Introduction• Measurement of emissions needs to either a) detect differences  between treatments or (preferably) give accu...
Background                                   • Grassland comprises 90% of utilisable agricultural                         ...
Ammonia/N2O        MethaneCO2
Uncertainties - Methane• Enteric Methane – Variation caused by differences in dry  matter intake, feed residence time in t...
Measurement of enteric methane•   Via methane collars - animals fed with SP6 bolus•   Methane emissions from various cattl...
Tier 2 Emission Factors for methane derived from EF and MM fromcattle
Measurement of enteric methane• Respiration chambers – Advantages:• measurements more accurate 10-15%• Disadvantages: Arti...
W                                                                                                  GHG emissions (kg CO2e/...
Housing Emissions• Treat the building as a chamber• The concentration difference of a gas between the  outside and inside ...
• Measure at various points around the building and  sum• Measure at various points at increasing distance from  the build...
Ammonia and methane from cattle sheds & OWP’s                       70.000                       60.000   AmmoniaMean Emis...
Uncertainties – Nitrous Oxide• Considerable uncertainty both spatially and temporally (>100% for N2O)• N Direct sources – ...
Uncertainties – CO2• Also large spatial and temporal uncertainty (>100% for  N2O)• Spatial – land-use type, land managemen...
How to Measure: A Question of Scale          Chamber measurements:          Technically easier          Gives some indicat...
Plot scale: Chamber measurements                  – N2O/ Methane/ CO2 • Static closed chambers – prevents pressure changes...
Applicability of the plot approach                                     NH3 N2O   CO2/CH4• Most appropriate for looking  at...
N2O Fluxes•  UV stabilised transparent chambers (218 litres)•  Internal cooling system•  gas samples drawn from chamber he...
Overview of New FieldLysimeters at Johnstown Castle•   72 field monolith lysimeters (0.8 x 1.0m)•   3 soil types (heavy, m...
10000                                                                                      Rathangan Control              ...
Effect of diet and inhibitors on N cycling                                   200Total NO3--N leached (kg N ha-1)          ...
Field-scale measurements
Integrated Horizontal fluxMeade et al (2011) Ag. Ecosys. Env. 140: 208-217                                                ...
Ammonia Losses                             90                                            60                               ...
Timing % application technique on N2O emissions                                  400 GHG emissions (kg CO2-eq ha-1)       ...
Mitigating N loss: Timing and spreading technique effects on             Ammonia loss and N fertilizer replacement value (...
If performed in conjunction with 15N tracing……                       Hoekstra et al 2010 Plant & Soil 330, 357–368
Effect of replacing fertiliser with clover At low N application and 20% clover, clover   reduced nitrous oxide by 41%
GHG Fluxes             • Relates the co-variation               of gas concentration               with net upward        ...
70                                                                                                 1500                   ...
Pasture Net C Balance                                           Loss                  40                  20C flux (gC m-2...
Pasture Net C Balance                                           Loss                  40                  20C flux (gC m-2...
Pasture/Maize Net C Balance                  40C flux (gC m-2)                  20                   0                    ...
Pasture/OSR Net C Balance                  40C flux (gC m-2)                  20                   0                      ...
Pasture/Maize/Miscanthus Net C Balance                  40C flux (gC m-2)                  20                   0         ...
Comparison of Land-Use GHG Budgets  (kg CO2-eq ha-1 yr-1)                          70                                     ...
Modelling Emissions  • Allows a region to move to Tier 3 accounting  • Can be incorporated into farms systems models    an...
The Effect of Arable and Biomass Cultivation on SOC• Conversion of grassland or forest to arable reduces  SOC by 1tC/ha/yr...
Temporal Emissions Profile – Grazed plots                           600                           500                     ...
6000                              5000                                           GG+FNResults                       4000  ...
Measured/simulated emissions & milk production                             16                                             ...
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• Measurem...
Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan
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Methods and Challenges for Emission Measurement from Buildings and Fields | Gary J. Lanigan

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

  1. 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. 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. 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-eqGHG 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. 4. Ammonia/N2O MethaneCO2
  5. 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. 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. 7. Tier 2 Emission Factors for methane derived from EF and MM fromcattle
  8. 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
  9. 9. W GHG emissions (kg CO2e/kg milk) illi am s W et a illi am l. (2 W s 00 6) illi et am al -E .( C s 20 ng as et 06 la C ey al )- nd as .( ,c ey an 20 En on an d 06 gl Ho an ven d ld )- t io H d, 0 0.5 1 1.5 2 2.5 3 3.5 Th en En hi na om old en (2 gl an gh l as (2 00 d, m se 6b ai n 00 )- sp l ze et 5a I re it-ca Ba al )- ss H .( I re lan lvi et aa 20 la d, ng Ba -M s 08 nd av ss en et )- er et ,c -M s al .( Ne on ag en et 20 th ve e s al 01 er nt et .( la io 20 )- nd na al .( 09 G s l er or 20 )- N m 09 ew an gan G )- y ic er be N Ze ex te ew al ns re an d iv Lo G t a Z ea e ve er l. la na tt be (2 nd tio Lo et re 01 in na ve al .( ta 0) te l tt l. -G ns Lo 20 (2 lo iv ve et 06 e tt al .( )- 01 ba N et 0) la 20 Ire -N ve Lo al . 06 la or ra )- nd th ge ve (20 Ire lo tt 06 la w Am et )- ge e O al Ire nd ne rica le .( la hi se 20 g tic n 08 nd m h g m Sc et )- en er hi al ed et it ls .( Ire iu ic et 20 la m m al 06 nd c er .( )- fre onc it 20 Eu e en 05 ro dr tra O Be )- pe ain te B N in using LCA (red) and systems analysis (blue). rie O uk et an g n B es he c so et rie et rla onv ils al n al nd en .( et .( t io O 20 al .( 20 s g ra na B 10 20 10 ss l rie )- )- n Ire 10 )- N /fe et la ew rt N al Ire .( nd la Z 20 m nd eal 10 od an )- er hi gh d at Ire e fe la st rti nd oc lit hi ki y gh ng co ra teFigure X. A comparison of published analyses of GHG emissions from dairy production systems nc en tra te
  10. 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. 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.
  12. 12. Ammonia and methane from cattle sheds & OWP’s 70.000 60.000 AmmoniaMean 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
  13. 13. 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%)
  14. 14. 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%)
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. Overview of New FieldLysimeters 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
  20. 20. 10000 Rathangan Control 9000 Rathangan Fertiliser Rathangan Fertiliser & UrineN2 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
  21. 21. Effect of diet and inhibitors on N cycling 200Total 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 )
  22. 22. Field-scale measurements
  23. 23. Integrated Horizontal fluxMeade 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
  24. 24. 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
  25. 25. 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 TSIndirect N2O – Assumes 98% ammonia is redeposited within2km & 1% of deposited N is re-emitted as N2O
  26. 26. 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
  27. 27. If performed in conjunction with 15N tracing…… Hoekstra et al 2010 Plant & Soil 330, 357–368
  28. 28. Effect of replacing fertiliser with clover At low N application and 20% clover, clover reduced nitrous oxide by 41%
  29. 29. GHG Fluxes • Relates the co-variation of gas concentration with net upward /downward movement of turbulent eddys in the atmosphere • F = u*[DC]
  30. 30. 70 1500 50 1000 Cumulative Carbon Flux (g C m-2) 30 RecoNEE (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
  31. 31. Pasture Net C Balance Loss 40 20C flux (gC m-2) 0 0 10 20 30 40 50 60 -20 -40 -60 -80Davis & Lanigan (2009) Ag. For. Meterol.150: 564-574 Uptake
  32. 32. Pasture Net C Balance Loss 40 20C flux (gC m-2) 0 0 10 20 30 40 50 60 -20 -40 -60 -80Davis & Lanigan (2009) Ag. For. Meterol.150: 564-574 Uptake
  33. 33. Pasture/Maize Net C Balance 40C flux (gC m-2) 20 0 0 10 20 30 40 50 60 -20 -40 -60 -80
  34. 34. Pasture/OSR Net C Balance 40C flux (gC m-2) 20 0 0 10 20 30 40 50 60 -20 -40 -60 -80
  35. 35. Pasture/Maize/Miscanthus Net C Balance 40C 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
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. 6000 5000 GG+FNResults 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
  41. 41. 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)
  42. 42. The Rate of Forestry Sequestration is dependent on the afforestation rate
  43. 43. 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

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