Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China

Elevated O3 concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses on investigating the key factors affecting O3 formation in the ecologically sensitive Dongtan Wetland (Chongming District, Shanghai, China) area. By comparing the performance of O3 concentration prediction of multiple machine learning models, this study found that the random forest model achieved the highest accuracy (R2 = 0.9, RMSE = 11.5). Feature importance and structure mining showed that peroxyacetyl nitrate (PAN), nitrogen oxides (NOx), temperature, wind direction, and relative humidity were the main drivers of O3 formation. Specifically, PAN concentrations exceeding 0.1 ppb and temperatures above 3 °C were found to have a significant impact on O3 levels, especially in spring, summer, and autumn. Trajectory analysis showed that westward urban pollution and emissions transported from the ocean were the main factors in O3 formation in the area. This study highlights the need for targeted emission control strategies, especially for PAN precursors generated by ships and NOx generated by urban industries, providing important insights for improving air quality in ecologically sensitive coastal areas.