Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 3; p. 467
• Main Authors: Yang, Yujie, Wang, Zhige, Cao, Chunxiang, Xu, Min, Yang, Xinwei, Wang, Kaimin, Guo, Heyi, Gao, Xiaotong, Li, Jingbo, Shi, Zhou
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2024
• Subjects: Accuracy; aerosol optical depth; Aerosols; Air pollution; Air quality; Air quality control; Correlation coefficient; Correlation coefficients; Deep learning; Environmental monitoring; Environmental quality; fine particular matter; GeoDetector; Ground stations; Long short-term memory; Machine learning; Neural networks; Outdoor air quality; Particulate emissions; Particulate matter; Pollution abatement; Pollution prevention; Prediction models; Quality control; random forest; Regression analysis; Regression models; Remote control; Remote sensing; Sensors; Spatial analysis; Statistical analysis; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16030467

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.