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Improving emission inventories using direct flux measurements and modeling

Improving emission inventories using direct flux measurements and modeling. Gunnar Schade , PI Don Collins, Qi Ying (Co-PIs) Texas A&M University EPA STAR Meeting, 16 Nov. 2010. Overview. Brief Introduction The Yellow Cab tower challenges of an urban flux site

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Improving emission inventories using direct flux measurements and modeling

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  1. Improving emission inventories using direct flux measurements and modeling Gunnar Schade, PI Don Collins, Qi Ying (Co-PIs) Texas A&M University EPA STAR Meeting, 16 Nov. 2010

  2. Overview • Brief Introduction • The Yellow Cab tower • challenges of an urban flux site • Selected results from previous measurements • Energy exchange fluxes • CO2 and criteria pollutants • VOCs • EPA STAR fund activities and measurements

  3. Introduction, I • Regional Air Quality (AQ) modeling improved • uses submodels for • emissions distribution (“inventory”) • atmospheric transport and chemistry • Emissions Inventory (EI) often assumed as being known well • Ambient AQ measurements challenge some EI assumptions; inadequate? • Can the EI be improved?

  4. Introduction, II • Past efforts of EI improvement • multivariate source apportionment using ambient AQ (concentration) data • ‘real-world’ emission measurements (tunnel studies) • AQ model studies • Our approach • micrometeorological flux measurements • top-down – bottom-up comparison • EI model AND AQ model testing

  5. Site Description, I

  6. Site Description, IIland use land cover

  7. Hardy / Elysian Roads

  8. Traffic Counts Hardy (south bound) Elysian (north bound) Quitman Road (east/west bound)

  9. How it looks like

  10. Tower Measurement Setup WS/WD aspirated T/RH Tower N PAR pyranometer w net radiation 60 m 50 m 40 m 20 m 13 m Sonic PC 20-m gradient DL Wind data (10 Hz) 3/8’’ and 1/4“ OD PFA Tubes Relaxed Eddy Accumulation Base Building GC- FID EC CO2 / H2O gradient Lag time ≈ 9 s slow: CO, NOx, O3

  11. Tower installations

  12. The challenge ‘Ordinary’ flux site Urban flux site heterogeneous land cover ill-defined footprint roughness sublayer ill-defined flux contributors high variability limited access private property undocumented activities ‘chaos’ studies attention to averages/medians • homogeneous land cover • well-defined footprint (MO theory) • well-defined flux contributors • limited variability • access to surface sites • upscaling / downscaling • targeted manipulations • process studies • attention to detail

  13. Energy exchange fluxes, I

  14. Energy exchange fluxes, II summer winter • delayed sensible heat flux • significant latent cooling • large heat storage and release (with hysteresis)

  15. Carbon dioxide (CO2) fluxes, I winter summer

  16. Carbon dioxide (CO2) fluxes, II weekdays weekends

  17. Carbon dioxide (CO2) fluxes, III

  18. Criteria Pollutant Fluxes, I Summertime (multi-month) medians

  19. Criteria Pollutant Fluxes, II

  20. Criteria Pollutant Fluxes, III CO-Flux ≈ ∆CO/∆CO2 x FCO2 rush-hour only

  21. Criteria Pollutant Fluxes, IV TexAQS 2006

  22. VOC fluxes, I

  23. VOC fluxes, II

  24. VOC fluxes, II

  25. STAR grant activities • continued (improved) measurements (G. Schade) • criteria pollutants (ongoing) and VOCs (2011+2012) • gradient (CP, ongoing) and REA flux (VOCs, 2011+2012) • potentially EC CO fluxes (loaned instrument; 2011) • additional aerosol (flux) measurements (D. Collins) • particle number fluxes (2011+2012) • modeling (G. Schade, Qi Ying)(ongoing) • (more detailed) ground survey • GIS improvements (ongoing) • roadside measurements (2011 or 2012) • ‘undocumented’ emissions (2011)

  26. Aerosol flux measurements, I Novel REA particle flux setup

  27. Aerosol flux measurements, II • approx. 80 m SS tubing, laminar flow • insulated • size-dependent line loss tests • one or two instruments • Initial measurement with DMA • accumulate density measurements over 30 min • APS installed and to be used if losses not excessive • particle flux per size range per half hour

  28. Modeling, I • GIS data • footprint models overlay • ground survey of sources • tracer release experiment

  29. Modeling, II • Source apportionment • concentration AND flux data • CMB and PMF methods • MOBILE6 vs. MOVES • CMAQ episode modeling • alternate input based on measurements • hindcast optimization

  30. MOBILE6 versus MOVES: Population normalized emission factors with vehicle speed (2-axle vehicles)

  31. Roadside measurements • chemistry? depositional loss? • A&M trailer; line power from pole • subset of instruments • simultaneous traffic counts • QUIC plume modeling

  32. Expected Results • Identify (and characterize) EI short-falls • example: missing isoprene and MACR emissions • Temporal and spatial characterization of emissions, including CP and VOCs • example: road versus non-road • Improve modeling hindcasts • characterize needed EI changes • Improve forecasts

  33. Acknowledgements • Greater Houston Transportation Company (Yellow Cab) • Texas Air Research Center (TARC) • EPA • Bernhard Rappenglück, UH • TCEQ

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