Abstract
Estimating ground-level PM2.5/10 based on satellite aerosol optical depth (AOD) products is a research hotspot at home and abroad. It has large area and high-density coverage characteristics, making up for the lack of ground monitoring stations. The AOD products are usually retrieved from top-of-atmosphere (TOA) reflectance via an atmospheric radiative transfer model. However, the strict surface assumptions in the AOD retrieval process make it impossible to retrieve AOD effectively in specific regions or periods. Therefore, this paper proposes a method based on machine learning to estimate ground-level PM2.5/10 concentration using TOA reflectance, observation angles and meteorological data, called TOA-PM2.5/10 model, and compares it with the AOD-PM2.5/10 model, whose inputs are AOD data and meteorological data. The comparative results show that the R2, RMSE, and MAE of PM2.5/10 concentration estimated using the TOA-PM2.5/10 model can reach 0.888, 6.158, 3.580 for PM2.5 and 0.889, 13.887, 8.141 for PM10 respectively, which is superior to that of the AOD-PM2.5/10 model.
This work is supported in part by the National Key R&D Program of China under Grant 2019YFB2102002, and in part by the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Zhang, L., Hao, J., Xu, W. (2021). PM2.5 and PM10 Concentration Estimation Based on the Top-of-Atmosphere Reflectance. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_61
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