Evaluation of Regional Atmospheric Models for Air Quality Simulations in the Winter Season in China

This study conducted an intensive air quality model evaluation as a response to the urgent need to understand the reliability, consistency, and uncertainty of air quality models supporting the implementation of the PM2.5 Air Pollution Control Action Plan in China. Five regional air quality models of CMAQ version 5.02, CMAQ version 5.3.2, CAMx version 6.2, CAMx version 7.1, and NAQPMS have been evaluated for the CO, SO2, NO2, O3, PM10, and PM2.5 concentration and components. A unified statistical method and the same observational data set of 2017, comprising 17 air pollution episodes collected from four super monitoring stations in the regions of Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing in China, have been used for the evaluation. All the participating models performed well in simulating the mean PM2.5 concentrations, with an NMB ranging from −0.29 to −0.04, showing that the participating models are basically suitable for simulation and as evaluation tools for PM2.5 in regulatory applications. However, the participating models showed a great variability for PM2.5 components, with the NME ranging from 0.48 to 0.53. The models performed reasonably well in simulating the mean sulfate, nitrate, BC, and NH4+ concentration in PM2.5, while they were diversified in simulating the mean OC concentrations. The participating models also consistently performed well in simulating the concentration of NO2, CO, and O3. However, the models generally overestimated SO2 concentrations, and to some extent underestimated PM10 concentrations, which is likely attributable to uncertainties in emission sources and the rapid implementation of strict control policies for SO2. The evaluation work of this study shows that there remains significant potential for further enhancement. Updating and improving the emission inventory should be prioritized to achieve better results, and further investigations into the uncertainties associated with the meteorological simulations, chemical mechanisms, and physical parameterization options of air quality models should also be conducted in future work.