Articles | Volume 25, issue 3
https://doi.org/10.5194/acp-25-1749-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-1749-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning
Yuqing Qiu
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Wenxuan Chai
CORRESPONDING AUTHOR
China National Environmental Monitoring Center, Beijing 100012, China
Yi Liu
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Mengdi Song
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Xudong Tian
Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
Qiaoli Zou
Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
Wenjun Lou
Jinhua Ecological and Environmental Monitoring Center, Jinhua 321015, China
Wangyao Zhang
Jinhua Ecological and Environmental Monitoring Center, Jinhua 321015, China
Juan Li
Jinhua Ecological and Environmental Monitoring Center, Jinhua 321015, China
Yuanhang Zhang
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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Cited
17 citations as recorded by crossref.
- Predicting distribution characteristics of PM2.5 concentrations across China combining land use regression model and spatiotemporally weighted stacking machine learning algorithms W. Wang et al. https://doi.org/10.1016/j.jece.2026.121780
- Dominant role of humidity thresholds in driving seasonal ozone production regimes in urban Guiyang, Yunnan-Guizhou Plateau Y. Yang et al. https://doi.org/10.3389/fenvs.2026.1791891
- Atmospheric Oxidation Capacity Dynamics Driven by Meteorology and Multiprecursor Interactions Modulate O3 Variation in Hangzhou, China W. Li et al. https://doi.org/10.1021/acsestair.5c00483
- Crucial VOCs influences on ozone formation in North China: A spatial perspective D. Liu et al. https://doi.org/10.1016/j.atmosenv.2025.121183
- Measurement report: Altitudinal shift of ozone regimes in a mountainous background region Y. Yang et al. https://doi.org/10.5194/acp-26-789-2026
- Characteristics and multi-indicator-oriented source apportionment of VOCs in an integrated oil and gas processing station and its surrounding area J. Huang et al. https://doi.org/10.1016/j.jes.2026.03.044
- Investigating the mechanism of typhoon tracks on ozone pollution episodes in Guangdong, China X. Chen et al. https://doi.org/10.5194/acp-26-879-2026
- Associations of light at night exposure and environmental pollutants with risk for thyroid cancer in Fujian, China: a spatio-temporal analysis H. Zhang et al. https://doi.org/10.1186/s12889-025-25148-z
- An Air-Quality-Based Analysis of NO, NO2, and O3 at a Suburban Mediterranean Site S. Chatoutsidou et al. https://doi.org/10.3390/atmos17010007
- Generalized Additive Model (GAM) Applied to the Analysis of Ozone Pollution in a City in Eastern China W. Li et al. https://doi.org/10.3390/su18042134
- A data-driven framework for optimized forecasting of non-methane hydrocarbon concentrations in urban air quality Y. Bai et al. https://doi.org/10.1177/18724981251358009
- A causal analysis of ground-level ozone, meteorological factors, and other air pollutants: an in-depth AI-based study applied to the climate of Craiova City, Romania Y. El Mghouchi & M. Udristioiu https://doi.org/10.1140/epjp/s13360-026-07495-x
- Site-Dependent Dynamic Life Cycle Assessment of Human Health Impacts from Industrial Air Pollutants: Inhalation Exposure to NOx, SO2, and PM2.5 in PVC Window Manufacturing P. Megange et al. https://doi.org/10.3390/toxics14010023
- Multi-scale analysis of ozone pollution Shanghai based on meteorological normalization: A case study of multi-model collaborative machine learning framework and MCM coupling mechanism M. Wu et al. https://doi.org/10.1016/j.atmosenv.2026.121943
- Characteristics and drivers of springtime ozone pollution in the Sichuan Basin, China, in May 2020 and 2024 J. Wei et al. https://doi.org/10.1016/j.atmosenv.2025.121476
- A dataset of vertical profiles of O3 and HONO from the hyperspectral vertical remote sensing network in China (2021–2024) T. Zou et al. https://doi.org/10.5194/essd-18-3559-2026
- Decoding ozone pollution in Beijing-Tianjin-Hebei: A machine learning approach to disentangling meteorological and anthropogenic drivers X. Liu et al. https://doi.org/10.1016/j.aeaoa.2026.100460
17 citations as recorded by crossref.
- Predicting distribution characteristics of PM2.5 concentrations across China combining land use regression model and spatiotemporally weighted stacking machine learning algorithms W. Wang et al. https://doi.org/10.1016/j.jece.2026.121780
- Dominant role of humidity thresholds in driving seasonal ozone production regimes in urban Guiyang, Yunnan-Guizhou Plateau Y. Yang et al. https://doi.org/10.3389/fenvs.2026.1791891
- Atmospheric Oxidation Capacity Dynamics Driven by Meteorology and Multiprecursor Interactions Modulate O3 Variation in Hangzhou, China W. Li et al. https://doi.org/10.1021/acsestair.5c00483
- Crucial VOCs influences on ozone formation in North China: A spatial perspective D. Liu et al. https://doi.org/10.1016/j.atmosenv.2025.121183
- Measurement report: Altitudinal shift of ozone regimes in a mountainous background region Y. Yang et al. https://doi.org/10.5194/acp-26-789-2026
- Characteristics and multi-indicator-oriented source apportionment of VOCs in an integrated oil and gas processing station and its surrounding area J. Huang et al. https://doi.org/10.1016/j.jes.2026.03.044
- Investigating the mechanism of typhoon tracks on ozone pollution episodes in Guangdong, China X. Chen et al. https://doi.org/10.5194/acp-26-879-2026
- Associations of light at night exposure and environmental pollutants with risk for thyroid cancer in Fujian, China: a spatio-temporal analysis H. Zhang et al. https://doi.org/10.1186/s12889-025-25148-z
- An Air-Quality-Based Analysis of NO, NO2, and O3 at a Suburban Mediterranean Site S. Chatoutsidou et al. https://doi.org/10.3390/atmos17010007
- Generalized Additive Model (GAM) Applied to the Analysis of Ozone Pollution in a City in Eastern China W. Li et al. https://doi.org/10.3390/su18042134
- A data-driven framework for optimized forecasting of non-methane hydrocarbon concentrations in urban air quality Y. Bai et al. https://doi.org/10.1177/18724981251358009
- A causal analysis of ground-level ozone, meteorological factors, and other air pollutants: an in-depth AI-based study applied to the climate of Craiova City, Romania Y. El Mghouchi & M. Udristioiu https://doi.org/10.1140/epjp/s13360-026-07495-x
- Site-Dependent Dynamic Life Cycle Assessment of Human Health Impacts from Industrial Air Pollutants: Inhalation Exposure to NOx, SO2, and PM2.5 in PVC Window Manufacturing P. Megange et al. https://doi.org/10.3390/toxics14010023
- Multi-scale analysis of ozone pollution Shanghai based on meteorological normalization: A case study of multi-model collaborative machine learning framework and MCM coupling mechanism M. Wu et al. https://doi.org/10.1016/j.atmosenv.2026.121943
- Characteristics and drivers of springtime ozone pollution in the Sichuan Basin, China, in May 2020 and 2024 J. Wei et al. https://doi.org/10.1016/j.atmosenv.2025.121476
- A dataset of vertical profiles of O3 and HONO from the hyperspectral vertical remote sensing network in China (2021–2024) T. Zou et al. https://doi.org/10.5194/essd-18-3559-2026
- Decoding ozone pollution in Beijing-Tianjin-Hebei: A machine learning approach to disentangling meteorological and anthropogenic drivers X. Liu et al. https://doi.org/10.1016/j.aeaoa.2026.100460
Saved (final revised paper)
Latest update: 08 Jun 2026
Short summary
The chemical reactions of ozone (O3) formation are related to meteorology and local emissions. Here, a random forest approach was used to eliminate the effects of meteorological factors (dispersion or transport) on O3 and its precursors. Variations in the sensitivity of O3 formation and the apportionment of emission sources were revealed after meteorological normalization. Our results suggest that meteorological variations should be considered when diagnosing O3 formation.
The chemical reactions of ozone (O3) formation are related to meteorology and local...
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