%0 Dataset
%T Chinese sulfur dioxide 1km spatial distribution dataset (March 2018-2020)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/1343bed1-b71d-4906-841c-7c1a8eba3b2e
%W NCDC
%R 10.5281/zenodo.7580714
%A None
%K Multiple air pollutants;Machine learning model optimization;Spatial distribution products of air pollutants;SHAP
%X Currently, in the modeling of various atmospheric pollutants, the simulation of independent trace gases is constrained by the insufficient resolution of key remote sensing products, resulting in insufficient simulation reliability.In this study, spatial sampling and parameter convolution are combined to optimize LightGBM by utilizing ground observations, remote sensing products, meteorological data, assistance data, and random ID.Through the above techniques and an sequentialsimulation of air pollutants, we produce seamless daily 1-km-resolution products of SO2for most parts of China from 2018 to 2020.Through random sampling, random site sampling, area-specific validation, comparisons of different models, and a cross-sectional comparison of different studies, we verified that our simulations of the spatial distribution of multiple atmospheric pollutants are reliable and effective.