基于PM2.5来源解析的减排方案制定

为定量解析PM_2.5浓度与排放源削减比例之间的关系，利用WRF-NAQPMS/OSAM模式对2017年12月京津冀及周边地区“2+26”城市的PM_2.5浓度变化和来源解析进行了模拟，并基于来源解析结果对各城市进行了迭代减排实验.结果表明，各城市削减本地排放源的效果最为显著，由于受化学生成影响引起的排放源和PM_2.5浓度之间的高度非线性关系，使得线性减排方案具有较大的局限性.各城市排放源削减引起的PM_2.5浓度变化主要由排放源的一次贡献和化学生成的二次贡献组成，其中化学生成的二次贡献浓度与行业解析结果的函数之间存在显著的线性关系.随着排放源的削减，清洁期间PM_2.5浓度中各组分的浓度随之下降，污染期间硝酸盐、二次有机气溶胶、铵盐等浓度不降反升，这为迭代减排方案中物种的选择提供了指导意义.

Emission reduction scheme based on source apportionment analysis

In order to quantify the relationship between variability of mass concentration of ambient PM_2.5 and the reduction in emission sources scenarios, a WRF-NAQPMS/OSAM model was used in this study to simulate the variations in mass concentration of PM_2.5 in Beijing-Tianjin-Hebei and surrounding "2+26" cities in December 2017, and various iterative emission reduction experiments were carried out for cities based on the PM_2.5 source apportionment. The results showed that reducing local emission sources in each city had the most significant effect on overall decrease in PM_2.5 concentrations. Due to the influence of chemical formation, there was a highly non-linear relationship between the emission sources and the PM_2.5 concentrations, which resulted in large uncertainties for the linear-reduction scheme. The changes in PM_2.5 concentrations caused by the emission regulation in each city were mainly composed of the contributions of primary emissions and the secondary chemical generation. A significant linear relationship was observed between the PM_2.5 concentration contributed by chemical formation and the sector source apportionment. With the reduction of emission sources, most of PM_2.5 compositions decreased during the clean periods. However, mass concentration of nitrate, secondary organic compounds, ammonium increased during the pollution periods, which indicated that selection of emission reduction species was vitally important in the iterative emission reduction experiments.