Evaluation of Long-Term Modeling Fine Particulate Matter and Ozone in China During 2013–2019 - Mao, Jianjiong; Li, Lin; Li, Jingyi; Sulaymon, Ishaq Dimeji; Xiong, Kaili; Wang, Kang; Zhu, Jianlan; Chen, Ganyu; Ye, Fei; Zhang, Na; Qin, Yang; Qin, Momei; et al. (Frontiers in Environmental Science, 2022)
Air quality has been undergoing significant changes due to implementation of extensive emission control measures since 2013. Many observational and modeling studies investigated the formation mechanisms of fine particulate matter (PM2.5) and ozone (O3) pollutions in the major regions of China. To improve understanding of the driving forces for the changes in PM2.5 and O3 in China, a nationwide air quality modeling study was conducted for 2013-2019 using the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modelling system. In this study, the model predictions were evaluated using the observation data for the key pollutants, including O3, sulfur dioxide (SO2), nitrogen dioxide (NO2), and PM2.5 and its major components. The evaluation mainly focused on five major regions, i.e, the North China Plain (NCP), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the Cheng-Yu Basin (CY), and the Fen-Wei Plain (FW). The CMAQ model successfully reproduced the air pollutants in all the regions, with model performance indices meeting the suggested benchmarks. However, over-prediction of PM2.5 was noted in CY. NO2, O3, and PM2.5 were well-simulated in the north compared to the south. Nitrate (NO3-) and ammonium (NH4+) were the most important PM2.5 components in heavily-polluted regions. For the performance on different pollution levels, the model generally over-predicted the clean days but underpredicted the polluted days. O3 was found increasing in each year, while other pollutants gradually reduced during 2013-2019 across the five regions. In all of the regions except PRD (all seasons) and YRD (spring and summer), the correlations between PM2.5 and O3 were negative during the four seasons. Low to medium correlations were noted between the simulated PM2.5 and NO2, while strong and positive correlations were established between PM2.5 and SO2 during the four seasons across the five regions. This study improves the understanding of the ability of CMAQ model in simulating air pollution in China over a long period, and provides insights for designing effective emission control strategies across China.