Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning
<p>Ozone (O<span class="inline-formula"><sub>3</sub></span>) pollution is posing significant challenges to urban air quality improvement in China. The formation of surface O<span class="inline-formula"><sub>3</sub></span> is i...
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Copernicus Publications
2025-02-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/1749/2025/acp-25-1749-2025.pdf |
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| author | Y. Qiu X. Li X. Li W. Chai Y. Liu M. Song X. Tian Q. Zou W. Lou W. Zhang J. Li Y. Zhang |
| author_facet | Y. Qiu X. Li X. Li W. Chai Y. Liu M. Song X. Tian Q. Zou W. Lou W. Zhang J. Li Y. Zhang |
| author_sort | Y. Qiu |
| collection | DOAJ |
| description | <p>Ozone (O<span class="inline-formula"><sub>3</sub></span>) pollution is posing significant challenges to urban air quality improvement in China. The formation of surface O<span class="inline-formula"><sub>3</sub></span> is intricately linked to chemical reactions which are influenced by both meteorological conditions and local emissions of precursors (i.e., NO<span class="inline-formula"><sub><i>x</i></sub></span> and volatile organic compounds, VOCs). When meteorological conditions deteriorate, the atmosphere's capacity to cleanse pollutants decreases, leading to the accumulation of air pollutants. Although a series of emission reduction measures have been implemented in urban areas, the effectiveness of O<span class="inline-formula"><sub>3</sub></span> pollution control proves inadequate. Primarily due to adverse changes in meteorological conditions, the effects of emission reduction are masked. In this study, we integrated a machine learning model, an observation-based model, and a positive matrix factorization model based on 4 years of continuous observation data from a typical urban site. We found that transport and dispersion impact the distribution of O<span class="inline-formula"><sub>3</sub></span> concentration. During the warm season, positive contributions of dispersion and transport to O<span class="inline-formula"><sub>3</sub></span> concentration ranged from 12.9 % to 24.0 %. After meteorological normalization, the sensitivity of O<span class="inline-formula"><sub>3</sub></span> formation and the source apportionment of VOCs changed. The sensitivity of O<span class="inline-formula"><sub>3</sub></span> formation shifted towards the transition regime between VOC- and NO<span class="inline-formula"><sub><i>x</i></sub></span>-limited regimes during the O<span class="inline-formula"><sub>3</sub></span> pollution event. Vehicle exhaust became the primary source of VOC emissions after “removing” the effect of dispersion, contributing 41.8 % to VOCs during the pollution periods. On the contrary, the contribution of combustion to VOCs decreased from 33.7 % to 25.1 %. Our results provided new recommendations and insights for implementing O<span class="inline-formula"><sub>3</sub></span> pollution control measures and evaluating the effectiveness of emission reduction in urban areas.</p> |
| format | Article |
| id | doaj-art-f787f1945a474efdae6207d9cf2fc2ef |
| institution | Kabale University |
| issn | 1680-7316 1680-7324 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj-art-f787f1945a474efdae6207d9cf2fc2ef2025-02-07T05:43:16ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-02-01251749176310.5194/acp-25-1749-2025Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learningY. Qiu0X. Li1X. Li2W. Chai3Y. Liu4M. Song5X. Tian6Q. Zou7W. Lou8W. Zhang9J. Li10Y. Zhang11College of Environmental Sciences and Engineering, Peking University, Beijing 100871, ChinaCollege of Environmental Sciences and Engineering, Peking University, Beijing 100871, ChinaInstitute of Carbon Neutrality, Peking University, Beijing 100871, ChinaChina National Environmental Monitoring Center, Beijing 100012, ChinaCollege of Environmental Sciences and Engineering, Peking University, Beijing 100871, ChinaCollege of Environmental Sciences and Engineering, Peking University, Beijing 100871, ChinaZhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, ChinaZhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, ChinaJinhua Ecological and Environmental Monitoring Center, Jinhua 321015, ChinaJinhua Ecological and Environmental Monitoring Center, Jinhua 321015, ChinaJinhua Ecological and Environmental Monitoring Center, Jinhua 321015, ChinaCollege of Environmental Sciences and Engineering, Peking University, Beijing 100871, China<p>Ozone (O<span class="inline-formula"><sub>3</sub></span>) pollution is posing significant challenges to urban air quality improvement in China. The formation of surface O<span class="inline-formula"><sub>3</sub></span> is intricately linked to chemical reactions which are influenced by both meteorological conditions and local emissions of precursors (i.e., NO<span class="inline-formula"><sub><i>x</i></sub></span> and volatile organic compounds, VOCs). When meteorological conditions deteriorate, the atmosphere's capacity to cleanse pollutants decreases, leading to the accumulation of air pollutants. Although a series of emission reduction measures have been implemented in urban areas, the effectiveness of O<span class="inline-formula"><sub>3</sub></span> pollution control proves inadequate. Primarily due to adverse changes in meteorological conditions, the effects of emission reduction are masked. In this study, we integrated a machine learning model, an observation-based model, and a positive matrix factorization model based on 4 years of continuous observation data from a typical urban site. We found that transport and dispersion impact the distribution of O<span class="inline-formula"><sub>3</sub></span> concentration. During the warm season, positive contributions of dispersion and transport to O<span class="inline-formula"><sub>3</sub></span> concentration ranged from 12.9 % to 24.0 %. After meteorological normalization, the sensitivity of O<span class="inline-formula"><sub>3</sub></span> formation and the source apportionment of VOCs changed. The sensitivity of O<span class="inline-formula"><sub>3</sub></span> formation shifted towards the transition regime between VOC- and NO<span class="inline-formula"><sub><i>x</i></sub></span>-limited regimes during the O<span class="inline-formula"><sub>3</sub></span> pollution event. Vehicle exhaust became the primary source of VOC emissions after “removing” the effect of dispersion, contributing 41.8 % to VOCs during the pollution periods. On the contrary, the contribution of combustion to VOCs decreased from 33.7 % to 25.1 %. Our results provided new recommendations and insights for implementing O<span class="inline-formula"><sub>3</sub></span> pollution control measures and evaluating the effectiveness of emission reduction in urban areas.</p>https://acp.copernicus.org/articles/25/1749/2025/acp-25-1749-2025.pdf |
| spellingShingle | Y. Qiu X. Li X. Li W. Chai Y. Liu M. Song X. Tian Q. Zou W. Lou W. Zhang J. Li Y. Zhang Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning Atmospheric Chemistry and Physics |
| title | Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning |
| title_full | Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning |
| title_fullStr | Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning |
| title_full_unstemmed | Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning |
| title_short | Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning |
| title_sort | insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning |
| url | https://acp.copernicus.org/articles/25/1749/2025/acp-25-1749-2025.pdf |
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