Articles | Volume 15, issue 5
https://doi.org/10.5194/amt-15-1511-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-1511-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2022
Research article |
| 16 Mar 2022
| 16 Mar 2022
Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species
Junlei Zhan
Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Wei Ma
Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Xin Zhang
State Key Laboratory of Environmental Criteria and Risk Assessment,
Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Xuezhong Wang
State Key Laboratory of Environmental Criteria and Risk Assessment,
Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Fang Bi
State Key Laboratory of Environmental Criteria and Risk Assessment,
Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Yujie Zhang
State Key Laboratory of Environmental Criteria and Risk Assessment,
Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Zhenhai Wu
State Key Laboratory of Environmental Criteria and Risk Assessment,
Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Hong Li
CORRESPONDING AUTHOR
State Key Laboratory of Environmental Criteria and Risk Assessment,
Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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Cited
14 citations as recorded by crossref.
- Slower than expected reduction in annual PM2.5 in Xi'an revealed by machine learning-based meteorological normalization M. Wang et al. 10.1016/j.scitotenv.2022.156740
- The role of NOx in Co-occurrence of O3 and PM2.5 pollution driven by wintertime east Asian monsoon in Hainan J. Zhan et al. 10.1016/j.jenvman.2023.118645
- Low- and Medium-Cost Sensors for Tropospheric Ozone Monitoring—Results of an Evaluation Study in Wrocław, Poland M. Badura et al. 10.3390/atmos13040542
- Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations A. Samad et al. 10.1016/j.atmosenv.2023.119987
- Research on ozone formation sensitivity based on observational methods: Development history, methodology, and application and prospects in China W. Chu et al. 10.1016/j.jes.2023.02.052
- The contribution of industrial emissions to ozone pollution: identified using ozone formation path tracing approach J. Zhan et al. 10.1038/s41612-023-00366-7
- A machine learning approach to investigate the build-up of surface ozone in Mexico-City M. Ahmad et al. 10.1016/j.jclepro.2022.134638
- Slower than Expected Reduction in Annual Pm2.5 in Northwest China Revealed by Machine Learning-Based Meteorological Normalization M. Wang et al. 10.2139/ssrn.4096148
- Machine learning coupled structure mining method visualizes the impact of multiple drivers on ambient ozone H. Xu et al. 10.1038/s43247-023-00932-0
- Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning L. Wang et al. 10.1016/j.envpol.2022.120798
- Prediction of Air Quality Index using genetic programming Q. Chu Thi et al. 10.54939/1859-1043.j.mst.91.2023.85-95
- Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China L. Zhang et al. 10.1016/j.scitotenv.2024.171295
- Machine Learning Reveals the Parameters Affecting the Gaseous Sulfuric Acid Distribution in a Coastal City: Model Construction and Interpretation C. Yang et al. 10.1021/acs.estlett.3c00170
- Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species J. Zhan et al. 10.5194/amt-15-1511-2022
13 citations as recorded by crossref.
- Slower than expected reduction in annual PM2.5 in Xi'an revealed by machine learning-based meteorological normalization M. Wang et al. 10.1016/j.scitotenv.2022.156740
- The role of NOx in Co-occurrence of O3 and PM2.5 pollution driven by wintertime east Asian monsoon in Hainan J. Zhan et al. 10.1016/j.jenvman.2023.118645
- Low- and Medium-Cost Sensors for Tropospheric Ozone Monitoring—Results of an Evaluation Study in Wrocław, Poland M. Badura et al. 10.3390/atmos13040542
- Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations A. Samad et al. 10.1016/j.atmosenv.2023.119987
- Research on ozone formation sensitivity based on observational methods: Development history, methodology, and application and prospects in China W. Chu et al. 10.1016/j.jes.2023.02.052
- The contribution of industrial emissions to ozone pollution: identified using ozone formation path tracing approach J. Zhan et al. 10.1038/s41612-023-00366-7
- A machine learning approach to investigate the build-up of surface ozone in Mexico-City M. Ahmad et al. 10.1016/j.jclepro.2022.134638
- Slower than Expected Reduction in Annual Pm2.5 in Northwest China Revealed by Machine Learning-Based Meteorological Normalization M. Wang et al. 10.2139/ssrn.4096148
- Machine learning coupled structure mining method visualizes the impact of multiple drivers on ambient ozone H. Xu et al. 10.1038/s43247-023-00932-0
- Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning L. Wang et al. 10.1016/j.envpol.2022.120798
- Prediction of Air Quality Index using genetic programming Q. Chu Thi et al. 10.54939/1859-1043.j.mst.91.2023.85-95
- Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China L. Zhang et al. 10.1016/j.scitotenv.2024.171295
- Machine Learning Reveals the Parameters Affecting the Gaseous Sulfuric Acid Distribution in a Coastal City: Model Construction and Interpretation C. Yang et al. 10.1021/acs.estlett.3c00170
Latest update: 19 Apr 2024
Short summary
Our study investigated the O3 formation sensitivity in Beijing using a random forest model coupled with the reactivity of volatile organic
compound (VOC) species. Results found that random forest accurately predicted O3 concentration when initial VOCs were considered, and relative importance correlated well with O3 formation potential. The O3 isopleth curves calculated by the random forest model were generally comparable with those calculated by the box model.
Our study investigated the O3 formation sensitivity in Beijing using a random forest model...
