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Data-driven machine learning quantifies ozone transport in the Hangzhou Bay urban cluster
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DOI：10.1007/s11783-025-2089-1.png)
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Abstract
En 中文
Severe ozone (O3) pollution has always been a serious problem faced by areas with rapid economic development, and the regional O3 transport between cities is a major cause of this problem. Therefore, we used a bidirectional long short-term memory (Bi-LSTM) model to quantitatively identify the regional O3 transport in Hangzhou Bay, China. Combined with the meteorological removal method, we were able to model O3 concentrations that were not affected by transport. The contribution of regional transport to Shanghai’s O3 was quantified and validated using two different simulation schemes, which yielded highly consistent results of 18.41 μg/m3 (24% contribution) and 20.52 μg/m3 (27% contribution). According to the model simulation results, we found that approximately 24% of the O3 pollution in Shanghai originates from other cities in the summer when the O3 pollution is high. In addition, the regional O3 transport was mainly concentrated during the high-value weather of O3 pollution in Shanghai, and transport on non-pollution days was not apparent. Therefore, the regional O3 transport from other cities is an important source of O3 pollution in Shanghai. Overall, our study demonstrates the potential of machine-learning models coupled with meteorological removal for quantifying the inter-city influence of atmospheric pollutants.
Keywords:
Ozone transport
Ozone pollution
Machine learning
Meteorological removal
Bi-LSTM
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