Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information - Wang, Xiangkai; Xue, Yong; Jin, Chunlin; Sun, Yuxin; Li, Na (Frontiers in Environmental Science, 2022)
Accurate near surface ozone concentration with high spatial resolution data is very important to solve the problem of serious ozone pollution and health impact assessment. However, the existing remotely-sensed ozone products cannot meet the requirements of high spatial resolution monitoring. In this paper, the surface O3 concentration (at 30km spatial resolution) was extracted from the daily TROPOMI O3 profile products. Meanwhile, this paper improved the downscaling algorithm based on the mutual information and applied it to the mapping of surface O3 concentration in China. Combined with the surface O3 concentration data (with 5km spatial resolution) obtained by the Light Gradient Boosting Machine (LightGBM) algorithm and AOD data (at 1km resolution) from MODIS, the downscaling of TROPOMI ground O3 concentration data from 30km to 1km has been achieved in this study. The downscaled ground O3 concentration data were subsequently validated using independent ground O3 concentration dataset. The main conclusion of this study is that the mutual information entropy between the bottom layer data of TROPOMI ozone profile (at 30km resolution), LightGBM surface O3 concentration data (at 5km resolution) and MCD19A2 AOD data (at 1km resolution) can accurately reduce the spatial resolution of ozone concentration in the ground layer. The downscaling procedure not only resulted in increase of the spatial resolution over the whole area, but also significant improvements in precision with coefficient of determination (R2) increased from 0.733 to 0.823, mean biased error decreased from 7.905μg/m3 to 3.887μg/m3 and root mean square error decreased from 14.395μg/m3 to 8.920μg/m3 for ground O3 concentration.