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Article

Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models

1
School of Geographical Sciences, Nantong University, Nantong 226007, China
2
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
*
Author to whom correspondence should be addressed.
Academic Editor: Luke Knibbs
Remote Sens. 2022, 14(3), 599; https://doi.org/10.3390/rs14030599
Received: 20 December 2021 / Revised: 21 January 2022 / Accepted: 24 January 2022 / Published: 26 January 2022
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, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 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. View Full-Text
Keywords: air pollution; PM2.5 retrieval; fine-grained spatiotemporal resolution; machine learning algorithms; remote sensing air pollution; PM2.5 retrieval; fine-grained spatiotemporal resolution; machine learning algorithms; remote sensing
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MDPI and ACS Style

Ma, P.; Tao, F.; Gao, L.; Leng, S.; Yang, K.; Zhou, T. Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models. Remote Sens. 2022, 14, 599. https://doi.org/10.3390/rs14030599

AMA Style

Ma P, Tao F, Gao L, Leng S, Yang K, Zhou T. Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models. Remote Sensing. 2022; 14(3):599. https://doi.org/10.3390/rs14030599

Chicago/Turabian Style

Ma, Peilong, Fei Tao, Lina Gao, Shaijie Leng, Ke Yang, and Tong Zhou. 2022. "Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models" Remote Sensing 14, no. 3: 599. https://doi.org/10.3390/rs14030599

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