Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression
Ozone variation, excluding meteorological effects, is very important to assess the effects of air pollution control policies. In this study, the Kolmogorov-Zurbenko (KZ) filter method and multiple linear stepwise regression are combined to study the impact of meteorological parameters on ozone conce...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2022.1081453/full |
| _version_ | 1797959806197170176 |
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| author | Menghui Li Menghui Li Chunmei Geng Liming Li Zhensen Zheng Bo Xu Wen Yang Xinhua Wang |
| author_facet | Menghui Li Menghui Li Chunmei Geng Liming Li Zhensen Zheng Bo Xu Wen Yang Xinhua Wang |
| author_sort | Menghui Li |
| collection | DOAJ |
| description | Ozone variation, excluding meteorological effects, is very important to assess the effects of air pollution control policies. In this study, the Kolmogorov-Zurbenko (KZ) filter method and multiple linear stepwise regression are combined to study the impact of meteorological parameters on ozone concentration over the past 5 years (2016–2020) in a petrochemical industrial city in northern China. Monte Carlo simulations were used to evaluate the reliability for the potential quasi quantitative prediction of the baseline component. The average level of the city and the details of five stations in the city were studied. The results show that the short-term, seasonal, and long-term component variances of maximum daily running average 8 h (MDA8) ozone in Zibo city (City) decomposed by the KZ filter account for 32.06%, 61.67% and 1.15% of the total variance, for a specific station, the values were 32.37%–34.90%, 56.64%–62.00%, and .35%–3.14%, respectively. The average long-term component increase rate is 3.19 μg m−3 yr−1 on average for the city, while it is 1.52–5.95 μg m−3 yr−1 for a specific station. The overall meteorological impact was not stable and fluctuated between −2.60 μg m−3 and +3.77 μg m−3. This difference in trends between the city and specific stations implied that the O3 precursor’s mitigation strategy should be more precise to improve its practical effects. |
| first_indexed | 2024-04-11T00:36:53Z |
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| id | doaj.art-512feb61221a4c54a12a4a6a388c0e74 |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-04-11T00:36:53Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-512feb61221a4c54a12a4a6a388c0e742023-01-06T15:42:41ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2023-01-011010.3389/fenvs.2022.10814531081453Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regressionMenghui Li0Menghui Li1Chunmei Geng2Liming Li3Zhensen Zheng4Bo Xu5Wen Yang6Xinhua Wang7State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaState Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaShandong Zibo Eco-Environmental Monitoring Center, Zibo, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaOzone variation, excluding meteorological effects, is very important to assess the effects of air pollution control policies. In this study, the Kolmogorov-Zurbenko (KZ) filter method and multiple linear stepwise regression are combined to study the impact of meteorological parameters on ozone concentration over the past 5 years (2016–2020) in a petrochemical industrial city in northern China. Monte Carlo simulations were used to evaluate the reliability for the potential quasi quantitative prediction of the baseline component. The average level of the city and the details of five stations in the city were studied. The results show that the short-term, seasonal, and long-term component variances of maximum daily running average 8 h (MDA8) ozone in Zibo city (City) decomposed by the KZ filter account for 32.06%, 61.67% and 1.15% of the total variance, for a specific station, the values were 32.37%–34.90%, 56.64%–62.00%, and .35%–3.14%, respectively. The average long-term component increase rate is 3.19 μg m−3 yr−1 on average for the city, while it is 1.52–5.95 μg m−3 yr−1 for a specific station. The overall meteorological impact was not stable and fluctuated between −2.60 μg m−3 and +3.77 μg m−3. This difference in trends between the city and specific stations implied that the O3 precursor’s mitigation strategy should be more precise to improve its practical effects.https://www.frontiersin.org/articles/10.3389/fenvs.2022.1081453/fullozoneKZ filtermeteorological conditionsmultiple linear regressionMonte Carlo simulation |
| spellingShingle | Menghui Li Menghui Li Chunmei Geng Liming Li Zhensen Zheng Bo Xu Wen Yang Xinhua Wang Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression Frontiers in Environmental Science ozone KZ filter meteorological conditions multiple linear regression Monte Carlo simulation |
| title | Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression |
| title_full | Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression |
| title_fullStr | Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression |
| title_full_unstemmed | Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression |
| title_short | Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression |
| title_sort | meteorological impacts on surface ozone a case study based on kolmogorov zurbenko filtering and multiple linear regression |
| topic | ozone KZ filter meteorological conditions multiple linear regression Monte Carlo simulation |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2022.1081453/full |
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