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Air quality and haze weather forecast for 2010 EXPO,2014 YOG, 2016 G20 in China

Air quality and haze weather forecast for 2010 EXPO,2014 YOG, 2016 G20 in China. Wang Tijian, Jiang Ziqiang, Wang Shekou, Zhu Jialei ， Jiang Fei, Deng Junjun, Shen Yi , Tian Jun, School of Atmospheric Science ， Nanjing University ， China. Jan.10-12, Toronto. Outline.

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Air quality and haze weather forecast for 2010 EXPO,2014 YOG, 2016 G20 in China

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  1. Air quality and haze weather forecast for 2010 EXPO,2014 YOG, 2016 G20 in China Wang Tijian, Jiang Ziqiang, Wang Shekou, Zhu Jialei， Jiang Fei, Deng Junjun, Shen Yi , Tian Jun, School of Atmospheric Science，Nanjing University， China Jan.10-12, Toronto

  2. Outline Air pollution and haze weather forecast in China AQHWF for 2010 EXPO in Shanghai AQHWF for 2014 YOG in Nanjing AQHWF for 2016 G20 in Hangzhou Summary

  3. Fine particles become the main air pollutants in China in recent years

  4. PM2.5 decrease in last three years

  5. Tools for air quality forecast and source apportionment in China Air quality model: WRF-Chem, WRF-CMAQ,CAMX, CALGRID, TAPM, RegAEMS, CUACE,NAQMPS…… Source apportionment model: CAMX-PSAT/OSAT, RegAEMS-CMB/PMF…… Chemical data assimilation: OA,3DVAR, EnkF….. Model output statistics: Kalman filter, Regression

  6. Urban air quality forecast system Automatic Download GFS Data Emission Inventory WRF Preprocessing System Emission Preprocessing Model Weather processes simulation Meteorological initial and Boundary conditions WRF-Chem Model WRF-CMAQ Model WRF-CAMx Model Transport and diffusion Dry and wet deposition Chemical initial and Boundary conditions Chemical reactions and aerosol process Hourly concentrations at selected sites Previous forecast results 3-D grid chemical concentrations Products Output Hourly surface concentrations distribution, SO2, NO2, O3, PM10 Display and Release to Website Air Pollution Index

  7. Regional haze weather forecast system RegAEMS

  8. Meteorology data Topography data Landuse data Source data Landuse data Background concentration data Chemical dynamics data Initial and boundary condition Area source emission SO2、Nox,,CO,VOC,PM10 Point source emission SO2、Nox,CO,VOC,PM10 Regional emission NH3 3D advection 3D diffusion Mesoscale Model (MM5,WRF, TAPM) Atmospheric Environment Model (AEM) 3 layer resistance model Engineer Model (EM) Gaseous conversion rates of SO2、NOx 20 species 36 reactions QSSA, Hybrid SO2,NOx concentration in air SO42-,NO3-concentration in precipitation S,N dry,wet,total deposition In cloud （rain out） （aqueous chemistry） （entrainment redistribution Below cloud （wash out） （aqueous chemistry） Concentration of gaseous species Concentration of aqueous species Dry deposition Wet deposition Total deposition Aqueous conversion rate and wet scavenging coefficient Regional Atmospheric Environment Modeling System (RegAEMS) developed by NJU Aerosol (sulfate/nitrate, BC/OC, Seasalt)

  9. Haze level classification (K is 3.912) PM2.5>75μg/m3 RH<0.8 Visibility<10km Super heavy haze：Visibility<2km Heavy haze：2km≤Visibility<4km Medium haze：4km≤Visibility<7km Light haze：7km≤Visibility<10km

  10. Air quality and haze weather forecast step Spin up Day one forecast Day two forecast …… 08:00 0:00 0:00 0:00 0:00 …… …… Today Tomorrow Yesterday 16 24 24 24 The day after tomorrow 72-120 hours forecast with 16 hours spin up

  11. Air quality standard in China

  12. Air quality index in China

  13. Air quality forecast information system

  14. Outline Air pollution and haze weather forecast in China AQHWF for 2010 EXPO in Shanghai AQHWF for 2014 YOG in Nanjing AQHWF for 2016 G20 in Hangzhou Summary

  15. Air pollution forecast for 2010 EXPO , Shanghai Emission reduction effect CMAQ-4.6 MM5 Numerical model output NAQPMS Obs data MM5 CMAQ-4.4 CAMx Air quality forecast platform NCEP GFS Other tools WRF WRF-Chem NO2 PM10 Statistics model output O3 PM2.5 Early planning for air pollution control CO SO2

  16. Model settings for Shanghai forecast • Horizontal grid: 4 nested domains • Domain 1: 88*75, 81km • Domain 2: 85*70, 27km • Domain 3: 76*67, 9 km • Domain 4: 88*73, 3 km • Vertical level: 24 sigma level Model top: 100hpa • Gas phase mechanism: RACM • Aerosol: MADE/SORGAM • Anthro. Emi.: INTEX-B + Shanghai emission inventory • Natrual Emi.: calculated online by Guenther scheme

  17. Point source in Shanghai CO NOx SO2 PM10 VOC PM2.5

  18. Area source in Shanghai SO2 CO NOx VOC PM2.5 PM10

  19. Line source in Shanghai CO NOx SO2 VOC PM2.5 PM10

  20. SO2 ,NO2 ,PM10,O3hourly concentration

  21. Statistics on accuracy, correlation, difference on APIprediction 24 hours API forecast

  22. Outline Air pollution and haze weather forecast in China AQHWF for 2010 EXPO in Shanghai AQHWF for 2014 YOG in Nanjing AQHWF for 2016 G20 in Hangzhou Summary

  23. GFSforecast GFSforecast Monitoring and forecast of yesterday Monitoring and forecast of yesterday Emission inventory Emission inventory Topography, landuse, soil Topography, landuse, soil WRF-CHEM WRF-CHEM CMAQforecast CMAQforecast Assemble:3D concentration Assemble:3D concentration RegAEMS forecast RegAEMS forecast Air quality monitoring data Air quality monitoring data Forecast from statistical Statistical forecast Forecast assessment Forecast assessment SO2，NO2，PM10，O3,CO,PM2.5 ,visibility SO2，NO2，PM10，O3,CO,PM2.5 ,visibility AQI and haze level Forecast products distribution Forecast products distribution Technical route for air quality and haze weather forecast for 2014 YOG,Nanjing Data assimilation Potential forecast

  24. Air quality forecast for 2nd Youth Olympic game in Nanjing in 2014 DOMAIN5: 1KM DOMAIN 1: 88×75, 81 km DOMAIN 2: 85×70, 27 km DOMAIN 3: 70×64, 9 km DOMAIN 4: 55×61, 3 km

  25. Emission distribution Emission distribution VOC NOx CO SO2 SO2 PM P’M2.5 PM10

  26. Soil dust Biomass burning dust Paved road dust Construction dust

  27. Emission from vehicle

  28. Emission from ships and ports

  29. Air pollutants &AQIprediction vs observation

  30. Data Assimilation in prediction • Directly analyze 3D aerosol mass concentration with a one-step procedure of variational minimization within the GSI • Do NOT apply any assumption about vertical shape and relative weight of individual species. • 14 WRF/Chem-GOCART 3D aerosol mass concentration as analysis variables • need background error covariance statistics for each aerosol species • Use CRTM as the AOD observation operator, including both forward and Jacobian models • In short, no much difference from 3DVAR DA for meteorological obs. 30

  31. AOD data assimilation

  32. 33 Liu, J.G.R.,2011

  33. OMB/OMA of MODIS AOD 34

  34. PM2.5 and AOD assimilation

  35. PM2.5 forecast verification Schwartz, 2012, J.G.R.

  36. PM10 data assimilation 261x222 @ 27 km 45L @ top 50 hPa WSM 5-class microphysics scheme; RRTM longwave and Goddard shortwave radiation schemes; MYJ boundary layer scheme; Noah land surface model; Grell-3D cumulus scheme; GOCART aerosol scheme coupled with RACM-KPP; “Streets” anthropogenic + GOCART dust and sea salt emissions; 6-hr cycling DA/FC experiment: 01~28 June, 2011 The East Asia domain and the observation network for PM10 with model topography

  37. Impacts on aerosol ICs

  38. Impacts on aerosol ICs difference = assimilation minus control Jiang. 2013, J.G.R.

  39. Forecast verification

  40. Outline Air pollution and haze weather forecast in China AQHWF for 2010 EXPO in Shanghai AQHWF for 2014 YOG in Nanjing AQHWF for 2016 G20 in Hangzhou Summary

  41. Ozone Source Appointment based on WRF/CAMx-OSAT WRF/CAMx-OSAT • WRF ( Weather Research Forecast ) is a new generation of mesoscale weather forecast model. • CAMx (Comprehensive Air Quality Model with Extension) is a third-generation Eulerian (gridded) regional photochemical dispersion model developed by ENVIRON. • OSAT （Ozone Source Apportionment Technology）can provide modeled contributions of emission from different regions and source groups to modeled ozone concentrations.

  42. Model configuration Domain settings Receptors Source categories: agricultural source, domestic source, industrial source, transportation source and power plant source Regional division: Hangzhou, Zhejiang province without Hangzhou, Shanghai, Jiangsu province, Anhui province and other regions

  43. Contributions to O3 from different regions categories

  44. Contributions to O3 from different source categories

  45. Numerical model RegAEMS combined receptor model CMB/PMF The numerical model RegAEMS was used to predict PM2.5 and its chemical composition. The receptor model uses the chemical composition of RegAEMS to estimate the relative contributions of different source categories. CMB model PMF model

  46. Model configuration for Hangzhou

  47. PM2.5 industrial source PM2.5 power plant source PM2.5 transportation source PM2.5 domestic source

  48. The main chemical components in PM2.5

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