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

Yilin Chen, Yuanjian Yang*, Meng Gao

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)

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 in the air quality index (AQI) and PM2.5, PM10, SO2, NO2 and O3 concentrations are 0.84 (14.88), 0.86 (10.31 µg m−3), 0.84 (17.03 µg m−3), 0.51 (8.13 µg m−3), 0.80 (13.64 µg m−3) and 0.89 (22.43 µg m−3), 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 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.
Original languageEnglish
Pages (from-to)1279-1294
Number of pages16
JournalAtmospheric Measurement Techniques
Volume16
Issue number5
DOIs
Publication statusPublished - 10 Mar 2023

Scopus Subject Areas

  • Atmospheric Science

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