Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method
Xinyu Zou, Xinlong Li, Dali Wang, Ju Wang
TL;DR
This study uses machine learning and air mass tracking to analyze ozone pollution in Liaoyuan City, finding that regional transport and local emissions contribute to high ozone levels.
Contribution
The study introduces a combined approach of machine learning models and HYSPLIT clustering to identify ozone pollution sources and transport patterns in Liaoyuan City.
Findings
Ozone pollution in Liaoyuan peaks in spring and summer, with afternoon maxima.
Random forest outperforms other models in predicting ozone concentrations (R2 = 0.9043).
NO2 is the primary driver of ozone pollution, followed by meteorological and land surface factors.
Abstract
Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O3) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O3 concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O3 pollution. The results indicate that O3 pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R2 = 0.9043). Feature importance identifies NO2 as the primary…
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Taxonomy
TopicsAir Quality Monitoring and Forecasting · Air Quality and Health Impacts · Atmospheric chemistry and aerosols