Vital contribution of residential emissions to atmospheric fine particles (PM_(2.5)) during the severe wintertime pollution episodes in Western China

摘要

To mitigate severe wintertime pollution events in Western China, identifying the source of atmospheric fine particles with an aerodynamic diameter of = 2.5 mu m (PM2.5) is a crucial step. In this study, we first analyzed the meteorological and emission factors that caused a considerable increase in the PM2.5 concentration in December 2016. This severe pollution episode was found to be related with unfavorable meteorological conditions and increased residential emissions. The WRF-Chem simulations were used to calculate the residential contribution to PM2.5 through a hybrid source apportionment method. From the validation that used grid data and in situ observations in terms of meteorological elements, PM2.5 and its compounds, the simulated results indicated that the residential sector was the largest single contributor to the PM2.5 concentration (60.2%), because of its predominant contributions to black carbon (BC, 62.1%) and primary organic aerosol (POA, 86.5%), with these two primary components accounting for 70.7% of the PM2.5 mass. Compared with the remote background (RB) region covering the central part of the Tibetan Plateau, the residential sector contributed 11.3% more to PM2.5 in the highly populated mega-city (HM) region, including the Sichuan and Guanzhong Basins, due to greater contribution to the concentrations of primary PM2.5 components. As the main emission source of sulfur dioxide (SO2), nitrogen oxides (NOx), and secondary organic aerosol (SOA), the industrial sector was the second largest contributor to the PM2.5 concentration in the HM region. However, in the RB region, the dominating emissions of NOx, SOA and BC were from the transport sector; thus, it was the next largest contributor to total PM2.5. An evaluation of the emission control experiment suggested that mitigation strategies that reduce emissions from residential sources can effectively reduce the PM2.5 concentration during heavy pollution periods. (C) 2018 Elsevier Ltd. All rights reserved.