%0 Dataset
%T Statistical Chart of Sandstorm Frequency and Air Quality (2015-2020)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/b745c3a0-16e3-4b52-8646-558a48e8c9ce
%W NCDC
%R 10.1371/journal.pone.0285610.g009
%A Xia Nan
%K Sandstorms;aerosols;air quality
%X A geographic and time weighted regression model (GTWR) was established for this data, and the PM2.5 concentration in Xinjiang from 2015 to 2020 was estimated using MCD19A2 (MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1-kilometer SIN Grid V006) data with a resolution of 1 kilometer and 9 auxiliary variables. The research results indicate that compared with simple linear regression (SLR) and geographically weighted regression (GWR) models, the GTWR model performs better in the accuracy and feasibility of PM2.5 concentration inversion in Xinjiang. Meanwhile, by combining the GTWR model with MCD19A2 data, a higher spatial resolution PM2.5 spatial distribution map can be obtained. The regional distribution of annual PM2.5 concentrations in Xinjiang from 2015 to 2020 is consistent with the terrain features. Low value areas are mainly distributed in high-altitude mountains, while high-value areas are mainly located in low altitude basins. Overall, the concentration is higher in the southwest and lower in the northeast. From the perspective of temporal changes, the PM2.5 concentration over the past six years has shown a unimodal distribution, with 2016 being a turning point. Finally, there were significant differences in the seasonal average PM2.5 concentration in Xinjiang from 2015 to 2020, showing the order of winter (66.15 μ g/m ³)>spring (52.28 μ g/m ³)>autumn (40.51 μ g/m ³)>summer (38.63 μ g/m ³). Research has shown that the combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration.