A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution
Abstract Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons. Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study...
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| Format: | Article |
| Language: | English |
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Springer
2022-12-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.220239 |
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| _version_ | 1823862845313384448 |
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| author | Yang Cao Xiaoli Zhao Debin Su Xiang Cheng Hong Ren |
| author_facet | Yang Cao Xiaoli Zhao Debin Su Xiang Cheng Hong Ren |
| author_sort | Yang Cao |
| collection | DOAJ |
| description | Abstract Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons. Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study, a classification method for meteorological conditions of ozone pollution levels based on a back propagation (BP) neural network was proposed to reflect the impact of meteorological conditions on the occurrence of ozone pollution. Ozone pollution was divided into three levels according to surface hourly ozone (O3) concentrations and thus into three groups of meteorological conditions. The input physical parameters for the BP neural network were determined by evaluating the relationship between surface O3 concentrations and meteorological parameters and precursors, including relative humidity, temperature, mixing layer height, precipitation, and nitrogen dioxide (NO2) concentrations. The study area focused on 21 cities in Sichuan Province in southwestern China, which was divided into 12 BP classifiers according to the urban geographical location and sample number of each city, and a single BP classifier was trained for 21 cities. The classification results of the trained BP classifiers were verified by comparison to the observations. With 12 individual BP classifiers, the classification accuracy of all 21 cities was more than 60%, of which 18 cities were more than 70%, and 9 cities were more than 80%. With the single BP classifier, the classification accuracy of 20 cities was more than 60%, of which 18 cities were more than 70%, and 14 cities were more than 80%. Overall, the classification performance of the trained single model was better than trained 12 individual models. The classification method can comprehensively reflect the impact of meteorological conditions on the occurrence of ozone pollution. |
| format | Article |
| id | doaj-art-cd4880f9040b4e31b537563766707802 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-cd4880f9040b4e31b5375637667078022025-02-09T12:22:39ZengSpringerAerosol and Air Quality Research1680-85842071-14092022-12-0123111710.4209/aaqr.220239A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone PollutionYang Cao0Xiaoli Zhao1Debin Su2Xiang Cheng3Hong Ren4Sichuan Meteorological Disaster Prevention Technology CenterSichuan Meteorological Disaster Prevention Technology CenterChina Meteorological Administration Key Laboratory of Atmospheric Sounding, Chengdu University of Information TechnologySichuan Meteorological Disaster Prevention Technology CenterCollege of Resources and Environment, Chengdu University of Information TechnologyAbstract Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons. Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study, a classification method for meteorological conditions of ozone pollution levels based on a back propagation (BP) neural network was proposed to reflect the impact of meteorological conditions on the occurrence of ozone pollution. Ozone pollution was divided into three levels according to surface hourly ozone (O3) concentrations and thus into three groups of meteorological conditions. The input physical parameters for the BP neural network were determined by evaluating the relationship between surface O3 concentrations and meteorological parameters and precursors, including relative humidity, temperature, mixing layer height, precipitation, and nitrogen dioxide (NO2) concentrations. The study area focused on 21 cities in Sichuan Province in southwestern China, which was divided into 12 BP classifiers according to the urban geographical location and sample number of each city, and a single BP classifier was trained for 21 cities. The classification results of the trained BP classifiers were verified by comparison to the observations. With 12 individual BP classifiers, the classification accuracy of all 21 cities was more than 60%, of which 18 cities were more than 70%, and 9 cities were more than 80%. With the single BP classifier, the classification accuracy of 20 cities was more than 60%, of which 18 cities were more than 70%, and 14 cities were more than 80%. Overall, the classification performance of the trained single model was better than trained 12 individual models. The classification method can comprehensively reflect the impact of meteorological conditions on the occurrence of ozone pollution.https://doi.org/10.4209/aaqr.220239Ozone pollutionMeteorological conditionsBack propagation (BP) neural networkClassification method |
| spellingShingle | Yang Cao Xiaoli Zhao Debin Su Xiang Cheng Hong Ren A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution Aerosol and Air Quality Research Ozone pollution Meteorological conditions Back propagation (BP) neural network Classification method |
| title | A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution |
| title_full | A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution |
| title_fullStr | A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution |
| title_full_unstemmed | A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution |
| title_short | A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution |
| title_sort | machine learning based classification method for meteorological conditions of ozone pollution |
| topic | Ozone pollution Meteorological conditions Back propagation (BP) neural network Classification method |
| url | https://doi.org/10.4209/aaqr.220239 |
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