1. Recent Advances in Chemical Weather Forecasting in Support of Atmospheric Chemistry Field Experiments Gregory R. Carmichael Department of Chemical & Biochemical Engineering Center for Global & Regional Environmental Research and the University of Iowa

2. TRACE-P and ACE-Asia EXPERIMENTS Emissions -Fossil fuel -Biomass burning -Biosphere, dust Long-range transport from Europe, N. America, Africa ASIA PACIFIC P-3 Satellite data in near-real time: MOPITT TOMS SEAWIFS AVHRR DC-8 3D chemical model forecasts: - ECHAM - GEOS-CHEM - Iowa/Kyushu - Meso-NH FLIGHT PLANNING Boundary layer chemical/aerosol processing ASIAN OUTFLOW Stratospheric intrusions PACIFIC C-130

3. Models are an Integral Part of Field Experiments Flight planning Provide 4-Dimensional context of the observations Facilitate the integration of the different measurement platforms Evaluate processes (e.g., role of biomass burning, heterogeneous chemistry ….) Evaluate emission estimates (bottom-up as well as top-down)

5. http://www.cgrer.uiowa.edu/ACESS/acess_index.htm

6. Model Overview Regional Transport Model: STEM Modular  Structure: Modular (on-line and off-line mode) RAMSMM5ECMWFNCEP  Meteorology: RAMS - MM5 - ECMWF - NCEP  Emissions  Emissions: Anthropogenic, biogenic and natural SAPRC’99  Chemical mechanism: SAPRC’99 (Carter,2000)  93 Species, 225 reactions, explicit VOC treatment NCAR-TUV 4.1  Photolysis: NCAR-TUV 4.1 (30 reactions) Flexible  Resolution: Flexible 80km x 80km for regional and 16km x 16km for urban

7. Photochemistry : STEM-TUV Y. Tang (CGRER), 2002

8. CFORS/STEM Model Data Flow Chart Meteorological Outputs from RAMS or MM5 Meteorological Preprocessor CFORS Forecast Model with on-line TUV Normal meteorological variables: wind velocities, temperature, pressure, water vapor content, cloud water content, rain water content and PV et al Dust and Sea Salt emissions Emission Preprocessor Biomass Emissions Volcanic SO 2 Emissions Anthropogenic Area Emissions Biogenic Emissions Large Point Sources Satellite Observed total O 3 (Dobson Unit) Post Analysis

9. CFORS/STEM Model Data Flow Chart Meteorological Outputs from RAMS or MM5 Meteorological Preprocessor CFORS Forecast Model with on-line TUV Normal meteorological variables: wind velocities, temperature, pressure, water vapor content, cloud water content, rain water content and PV et al Dust and Sea Salt emissions Emission Preprocessor Biomass Emissions Volcanic SO 2 Emissions Anthropogenic Area Emissions Biogenic Emissions Large Point Sources Satellite Observed total O 3 (Dobson Unit) Post Analysis Tracers/Markers: SO2/SulfateDMS BCOC VolcanicMegacities CO fossilCO-Biomass EthaneEthene Sea SaltRadon Lightning NOx Dust 12 size bins

10. Regional Emission Estimates: Anthropogenic Sources Industrial and Power Sector Coal, Fuel Oil, NG SO 2, NO x, VOC, and Toxics Domestic Sector Coal, Biofuels, NG/LPG SO 2, CO, and VOC Transportation Sector Gasoline, Diesel, CNG/LPG NO x, and VOC

11. Regional Emission Estimates: Natural Sources Biomass Burning In-field and Out-field combustion CO, NO x, VOC, and SPM Volcanoes SO 2, and SPM Dust Outbreaks SPM

12. Regional Emission Estimates: Sectoral Contributions CO NO x SO 2 VOC SO 2 = 34.8 Tg NO x = 25.6 Tg CO = 244.8 Tg VOC = 52.7 Tg Annual Asian Emissions for Year 2000 PP = Power Sector BB = Biomass Burning IND = Industries TRAN = transport DOM = Domestic

13. Regional Emission Estimates: % by Economic Sector : SO 2 Emissions IndustrialDomestic TransportPower

14. Regional Emission Estimates: % by Economic Sector : NO x Emissions IndustrialDomestic TransportPower

15. For Southeast Asia and Indian Sub-Continent Original Fire Count(FC) data(AVHRR) “Fill-up” Zero Fire Counts using Moving Average(MA) “Fill-up” Zero Fire Count using TOMS AI Satellite Coverage Cloudiness Mask Grid (Landcover) Precipitation(NCEP) “Extinguish” Fire Count using Mask Grids Mask Grid (Never Fire) Moving Averaged Fire Count data (Level 2) AI Adjusted Fire Count data (Level 3) 5-day Fire Count Regress. Coeff.(AI/FC) Regional Emission Estimates: Biomass Burning Emissions

16. Open Burning Emissions of CO – Based on AVHRR Fire-count Data

17. The Importance of Fossil, Biofuels and Open Burning Varies by Region

18. Uncertainty analysis has revealed wide differences in our knowledge of the emissions of particular species in particular parts of Asia …

19. 3/9 March 9 --forecast

21. Frontal outflow of biomass burning plumes E of Hong Kong Observed CO (G.W. Sachse, NASA/LaRC) Observed aerosol potassium (R. Weber, Georgia Tech) Biomass burning CO forecast (G.R. Carmichael, U. Iowa)

22. Using Measurements and Model – We Estimate Contributions of Fossil, Biofuel and Open Burning Sources

25. Contribution of Asian Fuel Burning to Tropospheric Ozone Yienger, et al, JGR 2000

26. DC8P3  Two aircrafts – DC8 and P3 urban plumes  Chemical evolution during continental outflow, biomass burning, dust outbreaks, and urban plumes  22  22 flights out of Hong Kong, Okinawa and Tokyo  O 3, CO, SOx, NOx, HOx, RH and J  100m to 12000m China NASA GTE TRACE-P Mar’01- Apr’01

27. Monthly Average March’01 Between 0-500m % Urban Contribution to Regional Photochemistry Monthly Average March’01 Between 0-500m

28.  1000 ppbv of CO, 10 ppbv of HCHO, 100 ppbv of O 3 Shanghai  Fresh plumes out of Shanghai, < 0.5 day in age % Urban HCHO Flight Path Back Traj. Characterization of Urban Pollution Flight DC8-13 : 03/21/2001

29.  Sunrise experiment 300 ppbv of CO, 60 ppbv of O 3  Pollution entrainment in the high pressure system ShanghaiBeijing  Fresh plumes out of Shanghai, aged plumes from Beijing % Urban HNO 3 Flight Path Back Traj. Characterization of Urban Pollution Flight DC8-16 : 03/29/2001

30.  200-350 ppbv of CO, 60 ppbv of O 3, 5-6 ppbv of NO y, 700-1500 pptv of NO and 3 ppbv of C 2 H 6 Seoul Pusan Beijing Coastal China  Fresh plumes out of Seoul and Pusan in one leg, aged plumes from Beijing and Coastal China in the other Flight Path Back Traj. Characterization of Urban Pollution Flight P3-18 : 03/30/2001

31. Color code indicates plume age in days from that city 984 out of 2238 No. of Points Characterization of Urban Pollution Back Trajectory Analysis

32. Urban Photochemistry OH Radical Cycle Air Toxics Ozone Acid Rain Visibility PM2.5 WaterQuality. OH NOx + VOC + OH + hv ---> O 3 SOx [or NOx] + NH 3 + OH ---> (NH 4 ) 2 SO 4 [or NH 4 NO 3 ] SO 2 + OH ---> H 2 SO 4 NO 2 + OH ---> HNO 3 VOC + OH ---> Orgainic PM OH Air Toxics (POPs, Hg(II), etc.) Fine PM (Nitrate, Sulfate, Organic PM) NOx + SOx + OH (Lake Acidification, Eutrophication)

33.  Tropospheric chemistry is characterized by reaction cycles  OH  OH plays a key role in tropospheric chemistry removalgeneration  Reactions lead to removal as well as generation of pollutants  NO x to VOC ratio  NO x to VOC ratio governs Ozone production Urban Photochemistry

34. Urban Photochemistry NO x -VOC-Ozone Cycle  Organic radical production and photolysis of NO 2  VOC’s and N-species compete for OH radical

35. Urban Photochemistry NO x -VOC-Ozone Cycle  In polluted environment, CO contributes to O 3 production

36. Urban Photochemistry NO x -VOC-Ozone Cycle  HCHO – primary intermediate in VOC-HO x chemistry  Short lived and indicator of primary VOC emissions

37. Urban Photochemistry NO x -VOC Emission Ratio Units: g NO 2 to g C In 2000 CityEmission Ratio Dhaka0.2 New Delhi0.4 Calcutta0.3 Mumbai0.4 Karachi0.6 Tokyo0.7 Beijing0.5 Shanghai0.6 Chongqing0.4 Hong Kong0.8 Seoul1.4 Manila0.2 Singapore1.4 Bangkok0.2

38. Urban Photochemistry NO x -VOC-Ozone Cycle O 3 Cycle STEM Box Model Calculations For City of Seoul, O 3 Cycle STEM Box Model Calculations For City of Shanghai Units: ppbv/hr

39. CO Vs VOC: Megacity points from back trajectories  CO produced due to photolysis of HCHO, a short lived intermediate from reactions between VOC and HO x  High O 3 and CO concentrations are linked with high VOC concentrations, especially with urban plume age < 1.0 day Urban Photochemistry Species to Species Comparison

40. Age in days calculated from back trajectories along the flight path Units: ppbv-HCHO/ ppbv-CO Urban Photochemistry HCHO to CO Ratios City Plume Age (days) Ratio (Obs.) Ratio (Mod.) All Points< 1 day0.01020.0079 1 to 2 days0.00690.0068 2 to 3 days0.00610.0066 3 to 4 days0.00610.0069 4 to 6 days0.0070 Shanghai< 1 day0.01140.0079 1 to 2 days0.00740.0066 2 to 4 days0.00390.0047 4 to 6 days 0.0043 Beijing0.00650.0071 Seoul< 1 day 0.0120 1 to 6 days0.0078 Pusan< 1 day0.0116 1 to 6 days0.0077 Hong Kong0.00630.0062 Tokyo0.0102 Manila0.0192

41. O 3 Vs Species: Megacity points from back trajectories Urban Photochemistry Species to Species Comparison

42. Urban Photochemistry NO x -VOC Sensitivity to O 3 Production VOC sensitive NOx sensitive Loss(N)/(Loss(N)+Loss(R)) Model NOx (ppbv) Model results along the flight path Megacity points from back trajectories Klienman et al., 2000 Less than 2 day old plumes

43. Urban Photochemistry NO x -VOC Sensitivity Implications VOC limited  Ozone production in the urban plumes is VOC limited  Decrease in NO x may actually increase local O 3 production criteria pollutant  Though at present, NO x is contributing less to local O 3 mixing ratios, it is contributing to local NO 2 mixing ratios (health criteria pollutant) and to O 3 production at downwind sites.

44. Emissions AmbientConcentration Exposure Air Quality Management System Policy Issues Technical Options Environmental Integrated Assessment

45. Trends in Urban Asia Sulfur Pollution Model Overview RAINS-Asia Developed by IIASA, Austria SO 2, PM, NO x Energy, Emissions, Controls, Costs and Optimization modules ATMOS Dispersion Model SO 2, PM, NO x Lagrangian Puff Transport Linear Chemistry NCEP Winds (1975-2000)

46. Shanghai Province Shanghai 30 o 36’ 120 o 36’ 32 o 122 o East China Sea Emissions for 1995 PM 10 : 166 ktons PM/year PM 2.5 : 68 ktons PM/year Sulfur: 458 ktons SO 2 /year Population: 19 Million Source: Li and Guttikunda et al., 2002 Environmental Integrated Assessment Case Study of Shanghai, China

47. 2020 BAU Units: Gg/year Economic Sector PM 10 (C ) PM 10 (M) PM 2.5 ( C) PM 2.5 (M) SO 2 NO x Power11.25.1394.3112.7 Industry52.118.619.65.3214.273.2 Domestic5.23.616.85.4 Transport31.116.732.0276.6 Other0.036.40.09.30.0 Total99.655.045.014.6657.2468.0 Economic Sector PM 10 (C ) PM 10 (M) PM 2.5 ( C) PM 2.5 (M) SO 2 NO x Power40.618.1214.180.4 Industry49.231.518.39.0199.971.1 Domestic10.46.831.95.9 Transport10.16.011.6125.8 Other7.018.05.94.61.02.5 Total117.249.555.113.7458.4285.8 1995 Shanghai Urban Air Quality Management Emission Estimates

48. in 1995 2020 BAU Units:  g/m 3 PM 10 Shanghai Urban Air Quality Management Annual Average PM 10 Concentrations

49. Shanghai Urban Air Quality Management Health Benefit Analysis Dose-response function coefficients Health EndpointCoefficientSource Mortality0.84Lvovsky et al., 2000 Hospital Visit0.18Xu et al., 1995 Emergency Room Visit0.10Xu et al., 1995 Hospital Admission0.80Dockery and Pope, 1994 Chronic Bronchitis0.10Xu and Wang, 1993 Coefficient: % change in endpoint per 10  g/m 3 change in annual PM 10 levels Incidence rate: rate of occurrence of an endpoint among the population

50. Shanghai Urban Air Quality Management Health Benefit Analysis No. of cases avoided Health Endpoint Power Scenario (no. of cases) Industrial Scenario (no. of cases) Mortality2,8081,790 Hospital Visit96,29361,379 Emergency Room Visit 48,50630,918 Hospital Admission43,48227,716 Chronic Bronchitis1,7531,117

51. Shanghai Urban Air Quality Management Health Benefit Analysis Units: US$ millions in 1998 dollars Economic Evaluation Health Benefits Power Scenario Industrial Scenario Mortality Low13988 Medium347221 High1,030656 Morbidity Low3824 Medium5736 High11976 Work Day Lossess138 Total Benefits190 – 1,162121 – 741 (Median Case)(417)(266)

52. Emissions&CostsEmissions&Costs DispersionModelingDispersionModeling Depositions&ConcentrationsDepositions&Concentrations EnergyTechnologyFuelSectorsScalesEnergyTechnologyFuelSectorsScales Exposure&ImpactsExposure&Impacts Days & Weeks Source Receptor Matrix Seconds IAMS Integrated Assessment Modeling System (IAMS)

53. Central Heating Plants Central Heating Plants Transfer Matrix for Area Sources Transfer Matrix for Area Sources Domestic Sources Domestic Sources Industrial Boilers Industrial Boilers Transportation Sources Large Point Sources Large Point Sources Emission Sources (PM and SO 2 ) Transfer Matrix for LPS Sources Transfer Matrix for LPS Sources PM and Sulfur Concentrations PM and Sulfur Concentrations IAMS Model Schematics Atmospheric Dispersion Calculations

54. IAMS Software Tracks Concentration Changes. Tracks Emission Changes.

55. IAMS Software Tracks Health Benefits to Costs Ratio. Calculates Health Damages for Mortality, Chronic Bronchitis, Hospital Visits, Work Day Losses.

56. U. Iowa/Kyushu/Argonne/GFDL With support from NSF, NASA (ACMAP,GTE), NOAA, DOE