Modeling study of impacts on surface ozone of regional transport and emissions reductions over North China Plain in summer 2015

Tropospheric ozone (O-3) has replaced PM2.5 or PM10 as the primary pollutant in the North China Plain (NCP) during summer in recent years. A comprehensive understanding of O-3 production in response to the reduction of precursor emissions over the NCP is urgently demanded for effective control policy design. In this study, the air quality modeling system RAMS-CMAQ (Regional Atmospheric Modeling System-Community Multiscale Air Quality), coupled with the ISAM (Integrated Source Apportionment Method) module is applied to investigate the O-3 regional transport and source contribution features during a heavy O-3 pollution episode in June 2015 over the NCP. The results show that emissions sources in Shandong and Hebei were the major contributors to O-3 production in the NCP. Not only the highest local contribution of O-3 mass burden but also more than 30% contribution of O-3 mass burdens in Beijing and Tianjin came from the emissions sources in these two provinces, respectively. Conversely, the urban areas and most O-3-polluted regions of the NCP were mainly dominated by conditions sensitive to volatile organic compounds, while both control and NOx-sensitive conditions dominated the suburban and remote areas, respectively. Then, based on the sensitivity tests, the effects of several hypothetical scenarios of emissions control on reducing the O-3 pollution were compared and discussed. The results indicated that the emissions control of industry and residential sectors was the most efficient method if the emissions reduction percentage was higher than 40 %. However, when the emissions reduction percentage dropped below 30 %, the power plant sector could make significant contributions to the decrease in O-3. The control strategies should be promptly adjusted based on the emissions reduction, and the modeling system can provide valuable information for precisely choosing the emissions sector combination to achieve better efficiency.