Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models
Abstract Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high ac...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2024-02-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023EA003346 |
| _version_ | 1827572709159075840 |
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| author | Zheng Luo Peilan Lu Zhen Chen Run Liu |
| author_facet | Zheng Luo Peilan Lu Zhen Chen Run Liu |
| author_sort | Zheng Luo |
| collection | DOAJ |
| description | Abstract Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use. In this study, we utilized meteorological parameters obtained from european center for medium—range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8‐hr average (MDA8) O3 concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission‐related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015–2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O3 concentration, with an average contribution of 9.4 μg m−3. |
| first_indexed | 2024-03-07T19:16:38Z |
| format | Article |
| id | doaj.art-eab942648eeb4ea4992a819edbe08c26 |
| institution | Directory Open Access Journal |
| issn | 2333-5084 |
| language | English |
| last_indexed | 2024-03-07T19:16:38Z |
| publishDate | 2024-02-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj.art-eab942648eeb4ea4992a819edbe08c262024-02-29T13:15:57ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842024-02-01112n/an/a10.1029/2023EA003346Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning ModelsZheng Luo0Peilan Lu1Zhen Chen2Run Liu3Institute for Environmental and Climate Research Jinan University Guangzhou ChinaInstitute for Environmental and Climate Research Jinan University Guangzhou ChinaInstitute for Environmental and Climate Research Jinan University Guangzhou ChinaInstitute for Environmental and Climate Research Jinan University Guangzhou ChinaAbstract Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use. In this study, we utilized meteorological parameters obtained from european center for medium—range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8‐hr average (MDA8) O3 concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission‐related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015–2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O3 concentration, with an average contribution of 9.4 μg m−3.https://doi.org/10.1029/2023EA003346ozone estimationmachine learningmeteorological normalizationSHAP modelBeijing‐Tianjin‐Hebei region |
| spellingShingle | Zheng Luo Peilan Lu Zhen Chen Run Liu Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models Earth and Space Science ozone estimation machine learning meteorological normalization SHAP model Beijing‐Tianjin‐Hebei region |
| title | Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models |
| title_full | Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models |
| title_fullStr | Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models |
| title_full_unstemmed | Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models |
| title_short | Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models |
| title_sort | ozone concentration estimation and meteorological impact quantification in the beijing tianjin hebei region based on machine learning models |
| topic | ozone estimation machine learning meteorological normalization SHAP model Beijing‐Tianjin‐Hebei region |
| url | https://doi.org/10.1029/2023EA003346 |
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