JpGU-AGU Joint Meeting 2017

講演情報

[EE]Eveningポスター発表

セッション記号 A (大気水圏科学) » A-AS 大気科学・気象学・大気環境

[A-AS05] [EE] Contributions of local and long-range transport to air pollutants in mega-cities

2017年5月25日(木) 15:30 〜 16:45 ポスター会場 (国際展示場 7ホール)

[AAS05-P08] A Modeling Study of Emission Control Strategies in Urban Cities in the Yangtze River Delta, China

*Jingyi Li1Nan Li1Yuhao Mao1Zhenxin Liu1Jianlin Hu1Hong Liao1Hao Fang1 (1.Nanjing University of Information Science & Technology)

キーワード:Emission control, CMAQ, WRF, China

With fast advances in economy, most eastern Chinese cities are experiencing severe air pollution and in an urgent demand of stringent emission control strategies. The Community Multi-scale Air Quality model (CMAQ) and the Weather Research & Forecasting model (WRF) were applied to study the air quality and emission control strategies in two urban cities, i.e. Shanghai and Nanjing in the Yangtze River Delta (YRD), China. Multi-resolution Emission Inventory for China (MEIC) and the Model of Emissions of Gases and Aerosols from Nature (MEGAN) were used for anthropogenic and biogenic emissions, respectively. We evaluated model’s performances against seasonal observations of O3, NOx, SO2, PM2.5 and PM10 at 10 monitoring sites in Shanghai and 11 monitoring sites in Nanjing during 2015. We further compared detailed PM2.5 composition from the model and measured data at an urban monitoring site as an additional constraint. The model can well reproduce the spatial and temporal distribution of these chemical compounds. We then designed emission control strategies for PM2.5 in Shanghai and Nanjing based on the modeling results. Sensitivity tests showed that long-range transport is mainly responsible for PM2.5 pollution in both cities. Therefore, a collaborative emission control strategy in Nanjing/Shanghai and their surrounding regions is needed to effectively improve air quality. We also performed several sensitivity tests to study the response of PM2.5 to different total controlled emission reductions as well as major primary emitted PM2.5 precursors. This information is very useful for the government in policy making in the future.