A new statistical modeling and optimization framework for establishing high-resolution PM10 emission inventory – II. Integrated air quality simulation and optimization for performance improvement

Abstract

An integrated air quality simulation and optimization approach was proposed to improve the accuracy of the high-resolution PM10 emission inventories developed through regression models. A case study of Tangshan region in northern China was presented as an example. A linear programming optimization model was developed to minimize the mean error between simulated and monitored PM10 concentrations in different counties. A transfer coefficient matrix was used to represent source–receptor relationships and was developed through running a MM5-CMAQ air quality model. The results revealed that the proposed simulation–optimization approach could decrease the error of the PM10 emission inventory from 17.0 to 7.9% at the regional level, and from 31.44 to 14.17% at the county level. Accuracy improvement ranged from 0.39 to 61.44% for the study counties in Tangshan. The correlation coefficient between the estimated PM10 emissions and the monitored PM10 concentration in various counties was also increased from 0.82 to 0.91. Together with the regression models, the simulation–optimization method provides a promising and effective framework for developing high-accuracy and high-resolution air pollutant emission inventories.