Typhoon-associated air quality over the Guangdong-Hong Kong-Macao Greater Bay Area, China: machine-learning-based prediction and assessment

The summertime air pollution events endanger-ing public health in the Guangdong-Hong Kong-Macao Greater Bay Area are connected with typhoons. The wind of the typhoon periphery results in poor diffusion conditions and favorable conditions for transboundary air pollution. Random forest models are established to predict typhoon-associated air quality in the area. The correlation coefficients and the root mean square errors in the air quality index (AQI) and PM2.5, PM10, SO2, NO2 and O-3 concentrations are 0.84 (14.88), 0.86 (10.31 mu g m-3), 0.84 (17.03 mu g m(-3)), 0.51 (8.13 mu g m(-3)), 0.80 (13.64 mu g m(-3)) and 0.89 (22.43 mu g m(-3)), respectively. Additionally, the prediction models for non-typhoon days are established. According to the feature im-portance output of the models, the differences in the meteo-rological drivers of typhoon days and non-typhoon days are revealed. On typhoon days, the air quality is dominated by local source emission and accumulation as the sink of pollu-tants reduces significantly under stagnant weather, while it is dominated by the transportation and scavenging effect of sea breeze on non-typhoon days. Therefore, our findings suggest that different air pollution control strategies for typhoon days and non-typhoon days should be proposed.