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 Spatial-Temporal Patterns of Air Pollutant Emissions From Landing and Take-Off Cycles in the Yangtze River Delta of China During the COVID-19 Outbreak

2021 ◽  
Vol 9 ◽  
Author(s):  
Danwen Bao ◽  
Shijia Tian ◽  
Ziqian Zhang ◽  
Hao Cheng ◽  
Ting Zhu ◽  
...  
Keyword(s):  
Air Pollutants ◽  
Yangtze River ◽  
Ground Level ◽  
Yangtze River Delta ◽  
Air Pollutant ◽  
River Delta ◽  
The Yangtze River ◽  
Aircraft Emissions ◽  
Variation Pattern ◽  
The Yangtze River Delta

The global aviation industry has been experiencing catastrophic disruption since the beginning of 2020 due to the unprecedented impact of the COVID-19 pandemic on air traffic. Although the decline in regular commercial air travel has caused tremendous economic loss to aviation stakeholders, it has also led to the reduction in the amount of recorded air pollutants. Most of the aircraft emissions are released during the cruise phase of flight, however they have relatively small impact on humans due to the fact that those emissions are released directly into the upper troposphere and lower stratosphere. Therefore, the scope of this study is to investigate the ground-level aircraft emissions from landing and take-off (LTO) cycles, as they have a greater influence on the ambient environment of the airports in a specific region. In this paper, we study the variation of typical air pollutant concentrations (i.e., HC, CO, and NOx) from the LTO cycles during the outbreak of COVID-19 pandemic in both temporal and spatial scales. These ground-level emissions are estimated for the 22 airports in the Yangtze River Delta, China. The results indicate that the variation pattern of the three air pollutants were significantly influenced by the dramatic onset of the COVID-19 pandemic, as well as the pertinent policies to suppress the spread of the virus. The results also reveal non-uniform distribution of the emission quantified at different airports. It is noticeable that the emission quantity generally declined from the east coast to the central and western part of the research region. Furthermore, discrepancies in the target markets also create disparities in the variation pattern of the emissions at different airports under the context of COVID-19.

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