Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models
Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/599 |
| _version_ | 1797485038346960896 |
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| author | Peilong Ma Fei Tao Lina Gao Shaijie Leng Ke Yang Tong Zhou |
| author_facet | Peilong Ma Fei Tao Lina Gao Shaijie Leng Ke Yang Tong Zhou |
| author_sort | Peilong Ma |
| collection | DOAJ |
| description | Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, <i>R</i><sup>2</sup>. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated <i>R</i><sup>2</sup> of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. |
| first_indexed | 2024-03-09T23:14:04Z |
| format | Article |
| id | doaj.art-cdfd3a7d2ee14472b061be809cc056bd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:14:04Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cdfd3a7d2ee14472b061be809cc056bd2023-11-23T17:40:24ZengMDPI AGRemote Sensing2072-42922022-01-0114359910.3390/rs14030599Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning ModelsPeilong Ma0Fei Tao1Lina Gao2Shaijie Leng3Ke Yang4Tong Zhou5School of Geographical Sciences, Nantong University, Nantong 226007, ChinaSchool of Geographical Sciences, Nantong University, Nantong 226007, ChinaSchool of Geographical Sciences, Nantong University, Nantong 226007, ChinaSchool of Geographical Sciences, Nantong University, Nantong 226007, ChinaSchool of Geographical Sciences, Nantong University, Nantong 226007, ChinaSchool of Geographical Sciences, Nantong University, Nantong 226007, ChinaDue to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, <i>R</i><sup>2</sup>. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated <i>R</i><sup>2</sup> of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city.https://www.mdpi.com/2072-4292/14/3/599air pollutionPM2.5 retrievalfine-grained spatiotemporal resolutionmachine learning algorithmsremote sensing |
| spellingShingle | Peilong Ma Fei Tao Lina Gao Shaijie Leng Ke Yang Tong Zhou Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models Remote Sensing air pollution PM2.5 retrieval fine-grained spatiotemporal resolution machine learning algorithms remote sensing |
| title | Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models |
| title_full | Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models |
| title_fullStr | Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models |
| title_full_unstemmed | Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models |
| title_short | Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models |
| title_sort | retrieval of fine grained pm2 5 spatiotemporal resolution based on multiple machine learning models |
| topic | air pollution PM2.5 retrieval fine-grained spatiotemporal resolution machine learning algorithms remote sensing |
| url | https://www.mdpi.com/2072-4292/14/3/599 |
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