Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM2.5 Estimation

The increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm (PM2.5) has become one of the most serious environmental hazards worldwide. The geographically weighted regression (GWR) model is a vital method to estimate the spatial distribution of the ground-level PM2.5 concentration. Wind information reflects the directional dependence of the spatial distribution, which can be abstracted as a combination of spatial and directional non-stationarity components. In this paper, a GWR model considering directional non-stationarity (GDWR) is proposed. To assess the efficacy of our method, monthly PM2.5 concentration estimation was carried out as a case study from March 2015 to February 2016 in the Yangtze River Delta region. The results indicate that the GDWR model attained the best fitting effect (0.79) and the smallest error fluctuation, the ordinary least squares (OLS) (0.589) fitting effect was the worst, and the GWR (0.72) and directionally weighted regression (DWR) (0.74) fitting effects were moderate. A non-stationarity hypothesis test was performed to confirm directional non-stationarity. The distribution of the PM2.5 concentration in the Yangtze River Delta is also discussed here.