Predicting the Concentration Levels of PM 2.5 and O 3 for Highly Urbanized Areas Based on Machine Learning Models

Chao Wei, Chen Zhao (), Yuanan Hu () and Yutai Tian
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Chao Wei: China National Environmental Monitoring Center, Beijing 100012, China
Chen Zhao: State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Yuanan Hu: MOE Laboratory of Groundwater Circulation and Evolution, School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, China
Yutai Tian: State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

Sustainability, 2025, vol. 17, issue 20, 1-22

Abstract: The accurate real-time forecasting and impact factor identification of air pollutant levels are critical for effective pollution control and management. In this study, we implemented three machine learning algorithms, namely, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Fully Connected Neural Network (FCNN), to predict PM 2.5 and O 3 concentrations in the Beijing–Tianjin–Hebei region from 2019 to 2023. XGBoost outperformed the other algorithms and was further utilized to predict PM 2.5 and O 3 concentrations and identify their controlling factors. The models could efficiently capture the spatial and temporal variations in the pollutants in the study area, and it was found that both anthropogenic sources and weather conditions can have significant impacts on air pollutant levels. PM 10 and CO were significantly correlated to PM 2.5 levels, which could be attributed to their similar emission sources and dispersion characteristics in air. O 3 concentrations were greatly influenced by temperature and NO 2 due to their significant impacts on O 3 generation. This study demonstrates that XGBoost-based models are cost-effective tools for predicting PM 2.5 and O 3 levels and identifying their controlling factors. These findings provide valuable insights for formulating effective air pollution prevention policies.

Keywords: air pollution; PM 2.5; O 3; machine learning; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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