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Introduction

Assessment of impact of fire emissions during the Second Texas Air Quality Study in summer 2006 with satellite fire observations.

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  1. Assessment of impact of fire emissions during the Second Texas Air Quality Study in summer 2006 with satellite fire observations Hyun Cheol Kim 1,2,3, Daewon W. Byun1,2, Fong Ngan1,2, Daegyun Lee 1,4, Soontae Kim 1,5, Warren E. Heilman6, Jesoph J. Charney6, Xindi Bian6, Bryan Lambeth7, ShobhaKondragunta8, and Robert Griffin9 1. University of Houston, Houston, TX 2. Now in NOAA Air Resources Laboratory, Silver Spring, MD 3. Earth Resources Technology, Inc 4. Now in National Institute of Environmental Research, Korea 5. Now in Ajou University, Korea 6. USDA Forest Service, North Central Research Station, East Lansing, MI 7. Texas Commission on Environmental Quality (TCEQ) 8. NOAA NESDIS, Camp Springs, MD 9. Rice University, Houston, TX

  2. Introduction • Causes of high PM2.5 are • Regional and long-range transport (dust, regional/continental haze, wildfires) • Local primary and secondary sources • Wildfire emissions are important missing sources for accurate simulation of local PM events, especially for OC and EC • Historical wildfire emissions in NEI cannot characterize high temporal variation of real wildfire events • Satellite-based fire detection (i.e. HMS) can provide near real-time information of wildfire events

  3. PM events in Houston (Aug/Sep 2006) • Saharan dust (Liu et al, 2008) • Regional haze +wildfire + local sources CAMS Houston 14 sites Regional haze Saharan Dust + wildfire

  4. Continental haze + wildfires 10 day average (Aug. 31 – Sep. 9, 2006) Wildfires detected Continental Haze

  5. Houston PM event (Regional haze) (1) Hysplit Backward trajectory Sep 3 Sep 4 Sep 5 Sep 6 Sep 7 Sep 8

  6. AQS & UH Moody tower observations A (✚) B (✖) C () D () Upwind sites A B C, D Moody tower sulfate & organic component A (✚) B (✖) C () D () Downwind sites

  7. PM species CMAQ PM other EC OM ANH4 ANO3 ASO4 CAMS Sulfate and Nitrate are dominant

  8. Overestimated • Observed (AQS) and modeled (E12 domain) PM speciation • Sulfate and ammonium are slightly overestimated • High overestimation of nitrate • Black circles are from downwind states (TX, AR, LA)

  9. Underestimated OC & EC underestimated HMS fire detections Need wildfire emissions

  10. Fire emission estimation (1) • Emissioni = A·B·CE·ei • where A is the area burned, • B is the fuel loading, • CE is the combustion efficiency, and • ei is an emission factor for species i • (Wiedinmyer et al.,2006) NESDIS HMS : Wildfire information analyzed form GOES, AVHRR, MODIS, DMSP/OLS, and etc • Global land cover dataset 2000 (from SPOT4) Fuel characteristic classification system (FCCS)

  11. Fire emission estimation (2) Fire location/time information Fire emission calculation (CO, CO2, PM10, PM2.5, Nox, NH3, SO2, VOC) Source classification (ALD2, ETH, NR, OLE, PAR, CO, NO, NO2, NH3, SO2, PEC, PMFINE, PNO3, POA) Merging into SMOKE emission outputs Run CMAQ • From NESDIS HMS • Using GLC/FCCS • Using EPA SCC • Plume distribution options • OPT1 – 80% of emission mixes within PBL, and 20% of emission goes over PBL up to 5 km • OPT2 – 60% / 40% / 6km (stronger plume rise)

  12. Simulation options Domain : CONUS 36 km and Texas 12km Period : Aug. 26 ~ Sep. 9

  13. OC/EC improvement by fire emission AQS in 12km domain Black circles are downwind states (TX, AR, LA) With fire emission, EC and OC are much improved, but still underestimated

  14. Mass budget estimation (1) Total mass flux integrated (through cross section A-B) A B ASO4 Base (89.9 Gg) ASO4 Fire (90.8 Gg) 10 km 10 km 5 km 5 km 1.5 km 1.5 km 1 km 1 km 500 m 500 m 200 m 200 m A B B A

  15. Mass budget estimation (2) OC Primary OC Secondary OC Base 22.3 Gg 7.6 Gg 14.7 Gg Fire 10 km 10 km 16.3 Gg 30.3 Gg 14.0 Gg 5 km 5 km 1.5 km 1.5 km 1 km 1 km 500 m 500 m 200 m 200 m

  16. Mass budget estimation (3) EC Base EC Fire 10 km 5 km 1.5 km EC shows similar transport pattern with POC 1 km 500 m 200 m

  17. Plume distribution sensitivity (1) OC 80/20 (30.3 Gg) OC 60/40 (30.0 Gg) Fire Base 10 km 10 km 10 km 5 km 5 km 5 km 1.5 km 1.5 km 1.5 km 1 km 1 km 1 km Fire (60%/40%/6km) 500 m 500 m 500 m 200 m 200 m 200 m

  18. Transport or local source ?(Sensitivity to wind fields) Default Stagnant condition caused high nitrate Reduced northerly wind Sulfate reduced

  19. Conclusion • We have simulated high PM event in Houston, with additional satellite derived wildfire emissions. Results show improvement, especially in OC & EC. Impact of regional haze, wildfire, and local sources are analyzed with detailed PM speciation • Even with additional wildfire emission, OC and EC are still underestimated, implying missing sources (EC & POC) and/or insufficient SOA formation (SOC) • Plume rise sensitivity was tested with different vertical distribution options. Difference in total mass transported was small • Proper simulation of meteorological condition is crucial. Case study showed that strong wind filed enhances long/mid-range transport (Sulfate), and weak/stagnant wind caused increased contribution from local sources (nitrate) • PM speciation analysis is needed for better understanding of local high PM event. Need more data with better temporal frequency and coverage

  20. ASO4

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