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 PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 55 ◽  
Author(s):  
Guoliang Yun ◽  
Yuanrong He ◽  
Yuantong Jiang ◽  
Panfeng Dou ◽  
Shaoqing Dai
Keyword(s):  
Spatial Distribution ◽  
Socioeconomic Factors ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Total Carbon ◽  
The Yangtze River ◽  
Natural Factors ◽  
The Yangtze River Delta ◽  
Pm2.5 Concentration

High concentrations of PM2.5 are a primary cause of haze in the lower atmosphere. A better understanding of the spatial heterogeneity and driving factors of PM2.5 concentrations is important for effective regional prevention and control. In this study, we carried out remote sensing inversion of PM2.5 concentration data over a long time series and used spatial statistical analyses and a geographical detector model to reveal the spatial distribution and variation characteristics of PM2.5 and the main influencing factors in the Yangtze River Delta from 2005 to 2015. Our results show that (1) The average annual PM2.5 concentration in the Yangtze River Delta prior to 2007 displayed an increasing trend, followed by a decreasing trend after 2007 which eventually stabilized; and (2) climate regionalization and geomorphology were the dominant natural factors driving PM2.5 concentration diffusion, while total carbon dioxide emissions and population density were the dominant socioeconomic factors affecting the formation of PM2.5. Natural factors and socioeconomic factors together lead to PM2.5 pollution. These findings provide an interpretation of PM2.5 spatial distribution and the mechanisms influencing PM2.5 pollution, which can help the Chinese government develop effective abatement strategies.

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.

scholarly journals High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

2019 ◽  
Vol 11 (23) ◽  
pp. 2724 ◽  
Author(s):  
Wang ◽  
Li ◽  
Gao ◽  
Yim ◽  
Shen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Spatial Resolution ◽  
Yangtze River ◽  
High Spatial Resolution ◽  
Yangtze River Delta ◽  
Population Exposure ◽  
Satellite Retrievals ◽  
The Yangtze River Delta ◽  
Pm2.5 Concentration

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

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