A new approach for optimizing air pollutant emissions using Newtonian relaxation and the coupled WRF-CAMx model: a case study in Xuzhou city, China

摘要

Air pollution remains a very serious problem in China. Simulating and forecasting the air quality have become important tools for analyzing the temporal and spatial variations in air pollution and diagnosing the sources and transport pathways of air pollutants. However, the forecasting accuracy is greatly influenced by the quality of air pollutant emission inventory data, which are difficult to obtain. In this study, we used Newtonian relaxation and the coupled WRF (Weather Research and Forecasting)-CAMx (Comprehensive Air Quality Model with Extensions) model to improve the simulation accuracy to correct the inventory data. By utilizing the corrected inventory data, the correlation coefficients of the simulated PM(2.5)emissions in April 2016 and April 2017 are greatly improved (with correlation coefficientR= 0.619 vs. 0.409, respectively, and root mean square errors RMSE = 0.0364 vs. 0.0404 mg/m(3), respectively). The estimated emission flux in the inner domain of the model is 41106.43 Mg/month in April, which is much higher (by 9.77%) than the emission flux predicted by the Multi-resolution Emission Inventory for China (MEIC) (37446.85 Mg/month). This paper is of great significance for the study of air quality early warning forecast on the mesoscale scale.