In this work, we built an integrated analysis framework (SI Appendix, Fig. S18) to evaluate the air quality improvements and health benefits of clean air actions in China (i.e., the 6 measures listed in Fig. 1) from 2013 to 2017. We first used the WRF-CMAQ model (20 , 21 ) to simulate the variations in PM2.5 concentrations from 2013 to 2017, during which period contributions from anthropogenic and meteorological factors were separated through scenario analysis. We then estimated the accumulated benefits of the 5-y implementation of each major control measure in 2017. Measure-specific emission abatements were quantified by applying the MEIC model (18 ) with data collected from the Ministry of Ecology and Environment of China (SI Appendix, Table S6) as inputs (19 ). Reductions in PM2.5 concentrations introduced by each measure were then evaluated using the WRF-CMAQ model, and the number of PM2.5-attributable excess deaths avoided by each measure was further quantified using the newly developed GEMM (4 (link)).
As shown in SI Appendix, Table S1, the WRF-CMAQ modeling system was utilized to simulate PM2.5 concentrations in 4 groups of scenarios. The BASE scenario group provided baseline simulations from 2013 to 2017, from which variations in PM2.5 concentrations could be derived. With additional information provided by the FixEmis scenarios (scenarios with fixed 2017 emissions and varying meteorological conditions from 2013 to 2017), the contributions of interannual meteorological variations and anthropogenic emission abatements to the 2013–2017 PM2.5 variations were separated. The air quality improvements in 2017 introduced by each measure were further derived based on the MEAS scenario and the NoCtrl scenario groups. Details of the methods and datasets are described in the SI Appendix. To evaluate CMAQ model performance, we compared simulated meteorological parameters, total PM2.5 concentrations, and PM2.5 chemical composition concentrations with ground observations (SI Appendix, sections S3 and S4).