Fine particulate matter (PM2.5), one of the main components of haze, is of wide concern for its potential negative health effects. In order to further improve ambient air quality, it is essential to conclude the spatial variability of pollutants by investigating air pollution exposure. We divide China into two parts, north and south, and use a Land Use Regression (LUR) model to extract data including meteorological data, land use factors, and AOD retrievals, and use the machine learning algorithm to optimize the model to achieve predictions of the spatial distribution of near-surface PM2.5 mass concentrations in southern and northern China. We evaluated the seasonal consistency of the models in southern and northern China, and in northern China, we found a better fit with better seasonal consistency for the heating season and annual average model, while in southern China, we did not find a more fitted seasonal phase. The study illustrates that it is feasible to simulate the spatial distribution of PM2.5 mass concentration in large-scale areas based on the LUR model, and the seasonal consistency of the LUR model has been done to some extent.
Numerous studies and monitoring data indicate that fine particle ( PM 2.5 ) pollution in China is still comparatively severe. Given the sparse and uneven distribution of air quality monitoring base stations established in China and the limitation of geographical conditions, inversion of aerosol optical depth by satellite remote sensing can achieve low-cost air quality monitoring in global areas. In this study, we use the machine learning algorithm XGBoost to build a prediction model to achieve nationwide average PM 2.5 concentration prediction. Meanwhile, we used aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS) in a specific band, combined with a land use regression (LUR) model as predictors of surface PM 2.5 concentrations in China, for the period Dec. 2019-Nov. 2021. In order to provide more accurate PM 2.5 concentration prediction, the correspondence between PM 2.5 and aerosol optical depth (AOD) under different seasons was studied. The coefficients of determination (R2) for different seasons are 0.86 (spring), 0.80 (summer), 0.90 (autumn), and 0.88 (winter), indicating that the fit is best for autumn and worse for summer. The study shows the potential usefulness of using the LUR model with the XGBoost algorithm for predictive assessment of PM 2.5 spatial distribution.
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