Performance Analysis and Application of Ensemble Air Quality Forecast System in Shanghai

Transcription

1 Performance Analysis and Application of Ensemble Air Quality Forecast System in Shanghai Qian Wang 1, Qingyan Fu 1, Ping Liu 2, Zifa Wang 3, Tijian Wang 4 1.Shanghai environmental monitoring center 2.Shanghai Jiao Tong University 3.The Institute of Atmospheric Physics, Chinese Academy of Sciences 4.Nanjing University AWMA Conference-International Specialty Conference May 10-14,2010 Xi'an, Shaanxi Province, China

2 Outline Goals Background Model performance Sensitivity analysis of emission in major pollutants Summary

3 Year API distribution during the same period with 2010 EXPO Attain ment ratio API Concentration of PM 10 (mg/m 3 ) >150 >110 > >

4 Outline Goal Background Model performance Sensitivity analysis of emission in major pollutants Summary

5 Workflow of air quality daily report and forecast Numerical Model Assistant tools statistic model 24hr~48hr Forecasting Monthly backward looking Weekly backward looking Trend Analysis Information Publication

6 Air quality daily reporting and forecast platform based on AIRNow-I Daily Report for Whole Shanghai and All County Forecasting Publication (Web Fax Short message ) AIRNow-Shanghai Regional Cooperation during EXPO2010 Assisstant Forecasting Tools AIRNow-I QA/QC Web Resource Download Map Producing ( Distribution of Concentration) Data Processing

7 Ensemble Air Quality Forecast System MM5 CMAQ-4.6 Emission Reduction Calculation MM5 NAQPMS CMAQ-4.4 Model Output Process Observation NCEP RA Data CAMx Assisstant Tools Airnow-I system WRF WRF- Chem PM10-CART Multiple Regression Least square support vector machine model for ozone Output of the Statistical Models Air Quality Forecasting and Early Warning for EXPO2010

8 Outline Goal Background Model Performance Sensitivity analysis Summary

9 Simulation domains and Periods Domain Grid Resolution Forestin g Periods D1 81km 96h D2 27km 72h D3 9km 72h D4 3km 72h 4 nested domains East Asia East China YRD Surrounding Shanghai

10 Emission distribution in the surface layer

11 Performance of models(sep,2009- Feb,2010) Autumn: Sep Nov R PM 10 SO 2 NO 2 CMAQ CMAQ CAMx NAQPMS Chem Winter: Nov-Feb R PM 10 SO 2 NO 2 CMAQ CMAQ CAMx NAQPMS WRF- WRF- Chem

12 Autumn Model Pollutant mean_sim mean_obs MB NMB(%) NME(%) RMSE CMAQ4.6 PM SO NO CMAQ4.4 PM SO NO CAMx PM SO NO NAQPMS PM SO NO WRF-Chem PM SO NO The NMB and NME of PM 10 simulation was much lower than that of SO 2 and NO 2. The SO 2 simulation was greatly overestimated in all of the models. Emission overestimated

13 Winter Model Pollutant mean_sim mean_obs MB NMB NME RMSE CMAQ4.6 PM % 31.0% 29 SO % 29.6% 17 NO % 31.3% 16 CMAQ4.4 PM % 38.5% 40 SO % 54.9% 29 NO % 23.7% 13 CAMx PM % 35.5% 32 SO % 27.1% 15 NO % 36.1% 18 NAQPMS PM % 34.4% 32 SO % 38.5% 21 NO % 37.2% 18 WRF- Chem PM % 29.9% 26 SO % 26.6% 14 NO % 24.8% 14 Overestimation for SO 2 simulating was decreased in the winter. Transportation of SO 2 might offset the overestimation of local SO 2 emission

14 Outline Goal Background Model performance Sensitivity analysis Summary

15 Emission sources contribution Which emission source is the most important factor to specific pollutant? Do the pollution of other cities which are around shanghai influence the air quality of Shanghai? How big is the influence of the desulfurization measures on ambient air quality?

16 Emission inventory in 2007 Shanghai YRD sources Power plant Industrial Furnace Point Industrial emission boiler Industrial process Energy balance PM 10 PM 25 SO 2 Nox CO VOCs NH Line emission Area emission Sum Power plant PM 10 PM 25 SO 2 Nox CO VOCs NH 3 Jiangsu zhejiang Shanghai (Unit: ton*10000/year)

17 Model: WRF-CMAQ4.6 WRF Vertical: 24 sigma level 100hpa at model top Microphysics : 4 (WRF Single-Moment 5-class scheme) Surface layer : 7(Pleim-Xiu surface layer) land surface: 7 (Pleim-Xiu land surface model ) Planetary Boundary layer : 7 (ACM2 PBL) Cumulus Parameterization:2(Betts-Miller-Janjic scheme.)

18 CMAQ Vertical: 15 sigma level Mechanism:CB05 Emis: David Streets 2006(0.5 o 0.5 o ),Shanghai emission inventory in 2007 (1km 1km)

19 Weather condition-1 April 1 st April 2 nd April 3 rd simulation Simulation period: March 25 th - April 7 th 2009 observation

20 Weather condition-2 April 4 th April 5 th April 6 th April 7 th

21 Simulation performance

22 Contribution of main emission sources in Shanghai (%) Emission sources PM 10 PM 25 SO 2 NOx Power plant Point emissions except power plant Line Area

23 Concentration simulation of major pollutants in seven cases Sensitive cases Average Concentration Contribution ratio (%) PM1 0 PM PSO PSO4 O3 PN PNH 4 SO2 NO2 PM1 0 PM O3 PSO PN PNH 4 SO2 NO2 All emissions Cut off area emissions Cut off line emissions Cut off power plant emissions Cut off other point emissions Cut off power plant emissions of Jiangsu Cut off power plant emissions of Zhejiang

24 Emission of power plant of shanghai in 2007 and 2010 (unit :ton*10 4 /year) Power plant PM PM 10 PM 25 SO 2 NOx CO VOC s NH

25 Concentration simulation of SO 2 before and after desulphurization 浓 度 (mg/m 3 ) 脱 硫 前 脱 硫 后 浓 度 下 降 百 分 比 浓 度 下 降 百 分 比 (%) 0-2 SO2 PM10 PM25 SO4 NO3 NH4 After taking the flue gas desulphurization measures,the concentration of SO 2 decreased about 14%,while the concentration of sulfate decreased 0.5%.

26 Outline Goals background Model performance sensitivity analysis of emission in major pollutants Summary

27 Summary Basically EMS can simulate the daily change of PM 10,SO 2 and NO 2, and EMS showed the different performance in different season. The concentration of PM 10,PM 2.5 in shanghai was mainly influenced by local area emission(65.03%). The point emission(63.8%) was the major factor caused the continual high concentration of SO 2.The line emission(56.9%)was the major factor caused the high concentration of NO 2. The SO 2 emission of Jiangsu might contribute to SO 2 concentration of Shanghai, especially in cold season.

28 Next step Improve the performance of numerical models Continuous Evaluation of the model operational performance Adjustment of the model simulation by observation Evaluation of emission inventory uncertainty Ozone and aerosol formation study Policy making

29

30

31