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Evaluation and Application of Air Quality Model System in Shanghai

Evaluation and Application of Air Quality Model System in Shanghai. Qian Wang 1 , Qingyan Fu 1 , Yufei Zou 1 , Yanmin Huang 1 , Huxiong Cui 1 , Junming Zhao 1 , Qi Chen 2 1.Shanghai environmental monitoring center 2.Shanghai Environmental Protection Bureau.

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Evaluation and Application of Air Quality Model System in Shanghai

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  1. Evaluation and Application of Air Quality Model System in Shanghai Qian Wang1, Qingyan Fu1, Yufei Zou1 , Yanmin Huang1, Huxiong Cui1 , Junming Zhao1 , Qi Chen2 1.Shanghai environmental monitoring center 2.Shanghai Environmental Protection Bureau The 3rd International Workshop on Air Quality Forecast Research Nov 29-Dec 1,2011

  2. Outline Model application 3 1 2 4 Introduction Model performance Future plan

  3. Background-air quality during Shanghai Expo • Attainment ratio is 98.4%, which is 2.7% higher than in 2009. • 181 days with excellent and good air quality • The average concentration of PM10，SO2and NO2 during the expo in 2010 is the lowest. • Comparing to the other 8 cities in YRD, the attainment ratio of air quality in Shanghai is the second highest.

  4. Continuous “Excellent” air quality for 17 days in July 7月12日(40) 7月13日(48) 7月14日(33) 7月15日(32) 7月16日(50) 7月17日(32) 7月18日(35) 7月19日(39) 7月20日(24) 7月21日(50) 7月22日(32) 7月26日(22) 7月27日(16) 7月28日(36) (Air Pollution Index)

  5. Air quality model system Same domain setting Same emission inventory Operational run

  6. Emission inventory in model Trace-P Asia emission inventory in 2006 Gridded emission inventory in model system Shanghai emission inventory in 2007 SMOKE Power plant emission inventory in YRD in 2007

  7. Update of point source EI in 2007 2006年 2007年

  8. Outline Model application 3 1 2 4 Introduction Model performance Future plan

  9. Meteorological simulation evaluation • the correlation coefficient of T, Rh ,Pre simulation and observation is above 0.7. • MM5 and WRF could well predict the hourly variation of major elements.

  10. Wind The predicted wind direction of MM5 is much accurate than that of WRF, especially when the wind speed is low.

  11. Air quality forecast evaluation-1 Model system well predicts the daily variation of PM10,SO2 andNO2 in shanghai.

  12. Air quality forecast evaluation-2 cvcv cvcvc PM10、NO2、SO2 -over predicted，PM2.5-under predicted——PM2.5、NH3emission sources is under predicted, SO2 emission sources is over predicted. cvcvc cvcvc cvcv cvcvc cvcv cvcvc cvcv

  13. Outline Model improvement 3 1 2 4 Introduction Model performance Future plan

  14. Difficulties in air quality model forecast Dust Storm Regional Haze & Biomass Burning dust storm observed from space (MODIS 2011/04/29 ) regional haze & biomass burning (MODIS 2011/10/07 ) Only anthropogenic emissions are quantified (with great uncertainty) in our operational EMS Dust & Biomass Burning emission are missing!

  15. Defect in current system framework Air quality forecasting subsystem Daily report operation and publishing subsystem Two Independent Subsystems ? DMS Ingest/import data QC automatically QC manually Calculate data Generate reports IMS Mapper Dispatcher Info services Meteorological info download Daily report operation platform No Interactions in Current Framework! Numerical forecasting interface Information release system AIRNow-I Air Quality Monitoring Network Ensemble Model System Data import and integration subsystem

  16. Initial Input Data assimilation scheme Initial Condition; Boundary Condition； Emissions Gain Matrix EnKF formula: (First Guess） Perturbed observation where, EnKF-PCTSystem Observations Quality Control National/Regional Monitoring Network CAMx Forward Model Ensemble Members PCT Analysis Forward Model Meteorology EnKF Update Offline Coupling MM5 （Assimilated） (Jia Li, Dongbin Xiu, JCP, 2009) Updated Chemical Field Analysis Ensemble Forecast Model Uncertainty Quantification/Apportionment Improved Forecast;

  17. Monitoring network National control sites City control sites NationalMonitoring Network AutomaticMonitoring Network in Shanghai

  18. Model uncertainty analysis Uncertainty sources: (Rao and Hosker, 1993)

  19. Application-----dust storm forecast 2011-04-30 12：00 (24h forecast) 2011-04-29 12：00 (24h forecast) Obs Analysis_A Analysis_B PM10 Concentration （mg/m3） ASSIMILATION PREDICTION 2011-05-01 12：00 (24h forecast) 2011-05-02 12：00 (24h forecast) 2011-05-03 12：00 (24h forecast)

  20. Application-----dust storm forecast 2011/04/30 11:00 -14:00 am (MODIS aqua & terra: NASA) Beijing 2011/04/30 12:00 am (CAMx simulation with assimilation) Shanghai

  21. Application-----dust storm forecast 2011/05/01 11:00 -14:00 am (MODIS aqua & terra: NASA) Beijing 2011/05/01 12:00 am (CAMx simulation with assimilation) Shanghai

  22. Application-----regional haze forecast 2011/05/31 11:00 am (MODIS terra: NASA) Beijing CB6 vs.CB05?! Fire Spots 2011/05/31 11:00 am (CAMx simulation with assimilation) Shanghai (Greg Yarwood et al., 2010)

  23. Outline Model application 3 1 4 2 Introduction Model performance Future plan

  24. Future plan

  25. Thanks for your attention!

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