Preprints
https://doi.org/10.5194/amt-2022-225
https://doi.org/10.5194/amt-2022-225
 
12 Sep 2022
12 Sep 2022
Status: this preprint is currently under review for the journal AMT.

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

Yilin Chen1, Yuanjian Yang1, and Meng Gao2 Yilin Chen et al.
  • 1School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, 210044, China
  • 2Department of Geography, Hong Kong Baptist University, Hong Kong, China

Abstract. The summertime air pollution events endangering 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 of the air quality index (AQI) and PM2.5, PM10, SO2, NO2 and O3 concentrations are 0.84 (14.88), 0.86 (10.31 µg/m3), 0.84 (17.03 µg/m3), 0.51 (8.13 µg/m3), 0.80 (13.64 µg/m3) and 0.89 (22.43 µg/m3), respectively. Additionally, the prediction models for non-typhoon days are established. According to the feature importance output of the models, the differences in the meteorological 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 pollutants reduces significantly under stagnant weather, while 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.

Yilin Chen et al.

Status: open (until 17 Oct 2022)

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Yilin Chen et al.

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Short summary
The Guangdong–Hong Kong–Macao Greater Bay Area suffers from summertime air pollution events related to typhoons. The present study leverages machine learning to predict typhoon-associated air quality over the area. The model evaluation shows that the model performs excellently. Besides, the change in meteorological drivers of air quality on typhoon days and non-typhoon days suggests that the air pollution control strategies should have different focuses on typhoon days and non-typhoon days.