Urban air quality and regional haze weather forecast in Yangtze River Delta of China

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1 Urban air quality and regional haze weather forecast in Yangtze River Delta of China Wang Tijian Jiang Fei Deng Junjun Shen Yi School of Atmospheric Science,Nanjing University Fu Qinyan Wang Qian Shanghai Environmental Monitoring Center Zhang Danning Fu Yin Xu Jinahua Nanjing Environmental Monitoring Center Dec.12,2012, Geneva

2 Outline Air pollution in Yangtze River Delta Urban air quality and regional haze weather forecast model Air quality forecast in urban Haze weather forecast in YRD Summary

3 Located on the western coast of the Pacific Ocean and covers an area of about 99,600 km 2, with a population of 75 million. Yangtze River Delta(YRD) Regional haze Acid rain Photochemical smog Dust storm

4 Heavy haze case PM PM

5 Dust storm occurred in Mar.20,2010

6 Continuous air pollution for more than 10 days

7 chemical weather forecasting (CWF) models in the word On line model: SKIRON/Dust, WRF-Chem, ENVIROHIRLAM, TAPM Off line model:aladincamx, CAMx- AMWFG, EURAD-RIU, FARM, LOTOSEUROS, MATCH, MM5-CAMx, MM5- CHIMERE,MM5/WRF-CMAQ, MOCAGE,NAME, OPANA,RCG, SILAM, THOR RegAEMS, NAQPS, PATH,TAQM

WRF-CHEM WRF-Chem Model Products Output Display and Release to Website Weather processes simulation Transport and diffusion Dry and wet deposition

8 Urban air quality forecast system Automatic GFS Data Download Emission Inventory WRF Preprocessing System Emission Preprocessing Model Meteorological initial and Boundary conditions Chemical initial and Boundary conditions Previous forecast results 3-D grid chemical concentrations WRF-CHEM WRF-Chem Model Products Output Display and Release to Website Weather processes simulation Transport and diffusion Dry and wet deposition Chemical reactions and aerosol process Hourly concentrations at selected sites Hourly surface concentrations distribution, SO 2, NO 2, O 3, PM10 Air Pollution Index

9 Regional haze weather forecast system RegAEMS

10 Different aerosol schemes in RegAEMS SO 4 2-, NO 3- :ISORROPIA( Nenes,1998 ) EC,OC SOA: SORGAM(Schell,2001) Sea salt: Monahan(1986) Dust: Gillette(1988)

11 Visibility scheme ν = K b / ext (K is 3.912) b = b + b + b + b + b ext scat abs ag sp ap (( ) ) b = 3 f ( RH ) NH SO + NH NO + ext 4Organics + 10Soot + Soil Coarsemass NO

12 Haze level classfication PM2.5>75μg/m 3 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

13 Air quality and haze weather forecast step Spin up Day one forecast 00:00 20:00 12:00 12:00 Today Tomorrow The day after tomorrow 48 hours forecast with 16 hours spin up

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

14 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

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

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

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

SO 2 hourly concentration 40.0 0.40 平均偏差 35.0 30.0 25.0 20.0 Before emi updated 各月份预报的平均情况 0.35 偏差相关性 0.30 0.25 预报平均相关性平均偏差时段观测平均 15.0 24-hour 48-hour 24-hour 48-hour 24-hour 48-hour 0.20 5 月 34.

18 SO 2 hourly concentration 平均偏差 Before emi updated 各月份预报的平均情况 0.35 偏差相关性预报平均相关性平均偏差时段观测平均 hour 48-hour 24-hour 48-hour 24-hour 48-hour 月月月 After 0.08 emi updated 月月月月 5 月月 7 月月月 10 月月 12 月月月 3 月月月月月平均相关性

NO 2 hourly concentration 40.0 0.40 35.0 30.0 25.0 偏差相关性 0.35 0.30 平均偏差 20.0 15.0 10.0 5.0 0.0 5.0 10.0 各月份预报的平均情况时段观测平均预报平均相关性平均偏差 24-hour 48-hour 24-hour 48-hour 24-hour 0.20 48-hour 5 月 54.

19 NO 2 hourly concentration 偏差相关性平均偏差各月份预报的平均情况时段观测平均预报平均相关性平均偏差 24-hour 48-hour 24-hour 48-hour 24-hour hour 5 月月月月月月月 6 月 11 7 月 8 月月 10 月月月 1 月月月月月月月平均相关性

PM10 hourly concentration 35.0 0.40 25.0 偏差相关性 0.35 平均偏差 15.0 5.0 5.0 15.0 25.0 35.0 各月份预报的平均情况时段观测平均 0.30 预报平均相关性 0.25 平均偏差 24-hour 48-hour 24-hour 48-hour 24-hour 48-hour 5 月 81.91 60.69 65.73 0.

20 PM10 hourly concentration 偏差相关性 0.35 平均偏差各月份预报的平均情况时段观测平均 0.30 预报平均相关性 0.25 平均偏差 24-hour 48-hour 24-hour 48-hour 24-hour 48-hour 5 月月 6 月 7 月 6 月 8 月月 10 月月月 1 月月 3 月月月月月月月月月月平均相关性

22 Statistics on API prediction Accuracy Correlation Deviation Accuracy & Correlation Deviation May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr 15 Month

23 Air quality forecast for 2nd Youngth Olympic game in Nanjing in 2014 DOMAIN 1: 88 75, 81 km DOMAIN 2: 85 70, 27 km DOMAIN 3: 70 64, 9 km DOMAIN 4: 55 61, 3 km

24 SO2 NOx PM10 Area source Point source

25 Meteorological variables in Nanjing during Oct., 2007

26 Observed and predicted concentrations of PM10, SO2 and NO2 in Nanjing during Oct., 2007

27 Soil dust Biomass burning dust Construction dust Paved road dust

Haze weather forecast in YRD using RegAEMS Regional Center: (32.06 N, 119.

30 Haze weather forecast in YRD using RegAEMS Regional Center: (32.06 N, E) Vertical level: Vertical: 10 levels Horizontal Grid Size: DOMAIN 1: 88 75, 81 km DOMAIN 2: 85 70, 27 km DOMAIN 3: 70 64, 9 km Prediction Period:

31 Observed vs. predicted meteorological variables

32 Daily concentrations of PM10 and PM2.5 in YRD during 2009

33 Hourly visibility from observation and prediction in YRD during 2009

34 Daily RH, PM2.5, Visibility and Haze level forecast

35 Heavy haze case prediction

36 Backward trajectory, fire spot and biomass emission 34N 33N 32N Nanjing Hefei 31N Shanghai 30N Hangzhou 117E 118E 119E 120E 121E 122E

37 Biomass burning 10 月 28 日 18:00~20:00 Fire spot RegAEMS

38 Data Assimilation in dust 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. J(x) = 1 2 (x x b )T B 1 (x x b ) [y H(x)]T R 1 [y H(x)] 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. 38

40 OMB/OMA of MODIS AOD 40

41 41

42 To improve the model performance of forecast on heavy air pollution case using data assimilation and satellite spot, such as dust storm, and biomass burning. Summary WRF-CHEM and RegAEMS are useful tools for urban quality and regional haze forecast. To improve the spatial resolution of emission inventory, especially emissions from fugitive dust, transportation and biomass burning in Yangtze River Delta region, to improve the performance of model prediction. To include urban canopy model in the meteorological model to improve the performance of meteorology prediction.

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