NDLI: Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level $PM_{2.5}$ Estimation

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Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level $PM_{2.5}$ Estimation

Content Provider	MDPI
Author	Xuan, Weihao Zhang, Feng Zhou, Hongye Du, Zhenhong Liu, Renyi
Copyright Year	2021
Description	The increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm $(PM_{2.5}$) 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 $PM_{2.5}$ 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 $PM_{2.5}$ 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 $PM_{2.5}$ concentration in the Yangtze River Delta is also discussed here.
Starting Page	413
e-ISSN	22209964
DOI	10.3390/ijgi10060413
Journal	ISPRS International Journal of Geo-Information
Issue Number	6
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2021-06-15
Access Restriction	Open
Subject Keyword	ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Marine Engineering Gwr Non-stationarity Wind Pm2.5 Concentrations Locally Varying Anisotropy
Content Type	Text
Resource Type	Article

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