Spatial Distribution Patterns and Influencing Factors of PM2.5 Pollution in the Yangtze River Delta: Empirical Analysis Based on a GWR Model

2020 ◽  
Author(s):  
Meixia Wang ◽  
Huiping Wang
Keyword(s):  
Spatial Distribution ◽  
Empirical Analysis ◽  
Influencing Factors ◽  
Yangtze River ◽  
Distribution Patterns ◽  
Yangtze River Delta ◽  
The Yangtze River ◽  
Spatial Distribution Patterns ◽  
Gwr Model ◽  
The Yangtze River Delta

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

2021 ◽  
Vol 10 (6) ◽  
pp. 413
Author(s):  
Weihao Xuan ◽  
Feng Zhang ◽  
Hongye Zhou ◽  
Zhenhong Du ◽  
Renyi Liu
Keyword(s):  
Spatial Distribution ◽  
Geographically Weighted Regression ◽  
Yangtze River ◽  
Ground Level ◽  
Yangtze River Delta ◽  
Weighted Regression ◽  
The Yangtze River ◽  
Gwr Model ◽  
The Yangtze River Delta ◽  
Pm2.5 Concentration

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.

scholarly journals Spatial distribution and profile of atmospheric short-chain chlorinated paraffins in the Yangtze River Delta

2020 ◽  
Vol 259 ◽  
pp. 113958 ◽  
Author(s):  
Shan Niu ◽  
Ruiwen Chen ◽  
Yun Zou ◽  
Liang Dong ◽  
Reti Hai ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Short Chain ◽  
The Yangtze River ◽  
Chlorinated Paraffins ◽  
The Yangtze River Delta

scholarly journals Analysis of Spatial Wharf Pattern of the Yangtze River Delta Urban Agglomeration, China

2019 ◽  
Vol 8 (12) ◽  
pp. 541
Author(s):  
Penglin Zhang ◽  
Hongli Li ◽  
Junqiang Wang ◽  
Jiewen Hong
Keyword(s):  
Spatial Distribution ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Urban Agglomeration ◽  
Social Progress ◽  
Eastern Coast ◽  
The Yangtze River ◽  
Water Transportation ◽  
The Yangtze River Delta

Wharves, which play a vital role in ensuring and promoting social progress and national economic development, are important in water transportation. At present, studies on related fields mainly focus on ports. A robust research system has been formed through the continuous development of port geography from the perspective of space. However, the number of relevant studies on wharves is limited. This study explores the spatial distribution characteristics of wharves in the Yangtze River Delta Urban Agglomeration by using spatial analysis methods, such as nearest neighbor index, multi-distance spatial clustering, kernel density estimation, and standard deviation ellipse. Moreover, it evaluates the allocation level of wharves from different scales by constructing an index system based on the location data of 1264 wharves in the Yangtze River Delta Urban Agglomeration. Results show that the spatial pattern of wharves exhibits evident aggregation and regional differences. The spatial distribution of wharves is characterized by a “band” structure, which is densely distributed along the Yangtze River and the eastern coast. The allocation level of wharves presents evident agglomeration at different scales. The relationship between the spatial wharf pattern and the economy shows that high gross domestic product and total imports and exports correspond to a considerable number of wharves.

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