Evaluation of Long-Term Modeling Fine Particulate Matter and Ozone in China During 2013–2019

Abstract

Air quality in China has been undergoing significant changes due to the 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) pollution 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 from 2013 to 2019 using the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling 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, that is , the North China Plain (NCP), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the Chengyu Basin (CY), and the Fenwei 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 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 all 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 all four seasons across the five regions. This study validates the ability of the CMAQ model in simulating air pollution in China over a long period and provides insights for designing effective emission control strategies across China.

Modelling PM2.5 during severe atmospheric pollution episode in Lagos, Nigeria: Spatiotemporal variations, source apportionment, and meteorological influences

“…With positive MFB (0.52), the CMAQ model over-predicted PM 2.5 concentrations during the study period. Overall, the CMAQ model in this study has exhibited a good performance when compared to previous studies around the world (Jiang et al, 2021;Kitagawa et al, 2021;Kota et al, 2018;Mao et al, 2022;Pedruzzi et al, 2021;Sulaymon et al, 2021a;Tao et al, 2020). Therefore, the predicted PM 2.5 concentrations were deemed acceptable for further analyses.…”

“…Meteorological factors play major roles in the formation, accumulation, and dispersion of air pollutants (Hu et al, 2016;Islam et al, 2015;Mao et al, 2022;Okimiji et al, 2021;Owoade et al, 2021;Sulaymon et al, 2021aSulaymon et al, , 2021bSulaymon et al, , 2021c. Previous studies have posited that the atmospheric pollution dispersion is greatly influenced by relative humidity (RH), wind speed (WS), and planetary boundary layer height (PBLH) (Li et al, 2019;Zhang et al, 2019;Zhang et al, 2018), and that low WS and PBLH hinder atmospheric pollutant dispersion and consequently lead to severe air pollution episodes (Dai et al, 2020;Li et al, 2019;Sulaymon et al, 2021a;Wang et al, 2020;Zhang et al, 2019).…”

Variation and Trend of Nitrate radical reactivity towards volatile organic compounds in Beijing, China

“…3). The Factor was obtained by the regional model (WRF/CMAQ), more details of the model simulation setup can be found in Mao et al (2022). We used the Factor to estimate monoterpenes level rather than modelled monoterpene concentrations is due to the modelled isoprene is systematically higher than that of observation (Fig.…”

Supplementary material to "High frequency of new particle formation events driven by summer monsoon in the central Tibetan Plateau, China"

“…The physical parameterizations used in this study are the Thompson microphysical process, RRTMG longwave/shortwave radiation scheme; Noah land-surface scheme; MYJ boundary layer scheme; and modified Tiedtke cumulus parameterization scheme. The detailed configuration settings could be found in the works of Hu et al (2016), Mao et al (2022), Wang et al (2021).…”

“…Previous studies have investigated the impacts of meteorological conditions on the formation, transportation, and dissipation of air pollutants (Hu et al, 2016;Hua et al, 2021;Mao et al, 2022;Sulaymon et al, 2021b;Sulaymon et al, 2021a). Therefore, the evaluation of the WRF model performance was carried out before the usage of its meteorological fields in the CMAQ simulations.…”