Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM2.5 concentration is inverted. The results show that the PM2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM2.5 concentration estimation and prediction based on satellite remote sensing technology.
Keywords: PM2.5 concentration estimation; machine learning; multisource data; partial least squares.