%0 Dataset
%T China 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 Ye Hong
%K Multiple air pollutants;Machine learning model optimization;Spatial distribution products of air pollutants;SHAP
%X At present, in the simulation 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. This study combines spatial sampling and parameter convolution to optimize LightGBM using ground observations, remote sensing products, meteorological data, aid data, and random IDs. Through the above techniques and atmospheric pollutant sequence simulation, we obtained seamless products with a daily resolution of 1 kilometer for SO2in most parts of China from 2018 to 2020. Through random sampling, random site sampling, specific area validation, comparison of different models, and horizontal comparison of different studies, we have verified that our simulation of the spatial distribution of various atmospheric pollutants is reliable and effective.