Abstract
Based on satellite remote sensing AOD, we can estimate and monitor the continuous changes of PM2.5, which solved the disadvantages of traditional ground station discrete monitoring. Four-dimensional spatiotemporal heterogeneity is not considered in the construction of traditional empirical regression models, such as geographically weighted regression model (GWR) and spatiotemporal geographically weighted regression model (gtwr). To solve this four-dimensional spatiotemporal nonstationarity, this article proposes and constructs a spatiotemporal adaptive fine particulate matter (PM2.5) concentration estimation model: 4D-GTWR by introducing a DEM (Digital elevation model) and time effects into a GWR model. This method solves the heterogeneity between the three-dimensional space and one-dimensional time by constructing a four-dimensional space kernel function and obtaining its weight. Based on PM2.5 ground observation data and meteorological data collected from December 2017 to February 2018 in Zhengzhou City, Henan Province, PM2.5 estimations are obtained from MODIS MYD-3K AOD data using the GWR, TWR, GTWR and 4D-GTWR models. The results showed that the MAE (mean absolute error) of the 4D-GTWR model decreased by 54.13%, 54.06% and 37.90%, compared to those of the GWR, TWR and GTWR models, respectively, and that the PM2.5 concentrations predicted by the 4D-GTWR model were closest to the measured values. The R2 (the correlation coefficient) of the 4D-GTWR model was 0.9496, which was better than those of the GWR (R2 =0.7761), TWR (R2 =0.7763) and GTWR (R2=0.8811) models. The 4D-GTWR model can not only improve the precision of PM2.5 estimations but can also reveal the four-dimensional spatial heterogeneity of PM2.5 concentrations and the differentiation of the DEM's influence on the spatial dimensions.
Toward Determining the Spatio-Temporal Variability of Upper-Ocean Ecosystem Stoichiometry From Satellite Remote Sensing
