Benefit Analysis of Precursor Emission Reduction on PM2.5: Using CMAQ-RSM to Evaluate Control Strategies in Different Seasons

PM2.5 pollution has been a major problem that threatens the environment and human health. To implement more effective management of this problem, the sensitivity of ambient PM2.5 reduction to precursors needs to be clarified. In this study, a mature air quality model was used to simulate the contribution of precursors emission reduction to decreased PM2.5 concentration. To evaluate the benefits of emission reduction on PM2.5 and the changes in different seasons and regions, we used CMAQ to establish the Response Surface Model (RSM) and set an emission reduction scenario based on 2013 to reduce emissions by 10-100% for each species. The RSM model was used to calculate the decreased concentration of PM(2.5 )under the reduction of primary PM2.5, NOx, SOx, and NH3 emissions, and then to estimate the impact of emission reduction on PM2.5 concentration per ton of precursor. The primary PM2.5 emission reduction benefits ranged from 9.43-9.79 x 10(-5 )mu g m(-3) t(-1), NOx from 8.12-8.84 x 10(-6) mu g m(-3) t(-1), SOx from 6.15-7.45 x 10(-6) mu g m(-3) t(-1) and NH3 from 1.78-1.83 x 10(-6) mu g m(-3) t(-1). The reduction benefit of primary PM2.s was more than 11 times that of NOx, whereas the reduction benefit of NH3 was more than twice that of NOx and SOx. The simulation results show that PM2.5 concentration is highly sensitive to primary PM(2.5 )and NH3, and the reduction benefit of NH3 is superior to that of NOx and SOx. Through RSM calculation, the temporal and spatial variation of emission reduction benefits can be obtained, which is helpful to formulate flexible control strategies for different pollutants in different seasons.