The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

Wang, Wenting; Zhang, Lijun; Zhao, Jun; Qi, Mengge; Chen, Fengrui

doi:10.3390/ijerph17093014

Cited by 10 publications

(6 citation statements)

References 55 publications

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“…Natural factors, such as temperature, wind speed, air humidity, topography, and the underlying surface, are notable examples. Moreover, socioeconomic factors include population density [32], GDP per capita [33], industrial structure [26], energy consumption [34], and other issues such as use of fireworks and firecrackers [22,23,35,36]. Land use patterns can also be critical.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In reviewing the literature, focusing on the scalability of research, recent scholarship seems to fall into three categories: the national scale [34,52], urban agglomerations [32,33], and provincial or city scale [50,53]. It is now understood that relevant research on PM 2.5 pollution characteristics and source analyses in China is mainly concentrated on the regions of Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta and Central Plains urban agglomerations, and other complex and severely polluted areas [32,33,51,53,54]. Some Chinese regions maintain or enhance their competitiveness in attracting FDI at the expense of the natural environment [55].…”

Section: Literature Reviewmentioning

confidence: 99%

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How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

Bao

Shan

Wang

et al. 2021

Land

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Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between real estate investment and PM2.5 concentrations using multi-source panel data from 30 provinces in China between 1987 and 2017. The results demonstrate that compared with static spatial panel modelling, using a dynamic spatial Durbin lag model (DSDLM) more accurately reflects the influences of real estate investment on PM2.5 concentrations in China, and that PM2.5 concentrations show significant superposition effects and spillover effects. Moreover, there is an inverted U-shaped relationship between real estate investment and PM2.5 concentrations in the Eastern and Central Regions of China. At the national level, the impacts of real estate investment on land urbanization and PM2.5 concentrations first increased and then decreased over time. The key implications of this analysis are as follows. (1) it highlights the need for a unified PM2.5 monitoring platform among Chinese regions; (2) the quality of population urbanization rather than land urbanization should be given more attention; and (3) the speed of construction of green cities and building of green transportation systems and green town systems should be increased.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

Bao

Shan

Wang

et al. 2021

Land

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“…PM 2.5 pollution is not only a natural phenomenon influenced by meteorological conditions, but is also influenced by severe anthropogenic emissions [ 8 , 22 , 40 , 41 , 47 , 48 ]. Thus, the relationship between PM 2.5 meteorological variables is not constant but is changing over time [ 24 ]. Therefore, we assumed that the developed regression model trained based on the PM 2.5 and meteorological data collected from a specific period represents the relationship between them under the emission scenario of the specific period.…”

Section: Methodsmentioning

confidence: 99%

“…These studies demonstrated that on an economic development level, urbanization level, coal consumption, motor vehicles, and population size are key influencing factors of PM 2.5 . Wang et al used a spatial panel Dubin model to investigate the relationship between PM 2.5 and six socioeconomic factors during 2015~2017 in Beijing–Tianjin–Hebei (BTH) [ 24 ]. It was found that the urbanization rate has a negative effect on PM 2.5 , which may be due to the stricter environmental regulations than before.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution

Yang

Fan

Zhao

2020

IJERPH

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Aiming at improving the air quality and protecting public health, policies such as restricting factories, motor vehicles, and fireworks have been widely implemented. However, fine-grained spatiotemporal analysis of these policies’ effectiveness is lacking. This paper collected the hourly meteorological and PM2.5 data for three typical emission scenarios in Hubei, Beijing–Tianjin–Hebei (BTH), and Yangtze River Delta (YRD). Then, this study simulated the PM2.5 concentration under the same meteorological conditions and different emission scenarios based on a reliable hourly spatiotemporal random forest model ( R 2 exceeded 0.84). Finally, we investigated the fine-grained spatiotemporal impact of restricting factories, vehicles, and fireworks on PM2.5 concentrations from the perspective of hours, days, regions, and land uses, excluding meteorological interference. On average, restricting factories and vehicles reduced the PM2.5 concentration at 02:00, 08:00, 14:00, and 20:00 by 18.57, 16.22, 25.00, and 19.07 μ g / m 3 , respectively. Spatially, it had the highest and quickest impact on Hubei, with a 27.05 μ g / m 3 decrease of PM2.5 concentration and 17 day lag to begin to show significant decline. This was followed by YRD, which experienced a 23.52 μ g / m 3 decrease on average and a 23 day lag. BTH was the least susceptible; the PM2.5 concentration decreased by only 8.2 μ g / m 3 . In addition, influenced by intensive human activities, the cultivated, urban, and rural lands experienced a larger decrease in PM2.5 concentration. These empirical results revealed that restricting factories, vehicles, and fireworks is effective in alleviating air pollution and the effect showed significant spatiotemporal heterogeneity. The policymakers should further investigate influential factors of hourly PM2.5 concentrations, combining with local geographical and social environment, and implement more effective and targeted policies to improve local air quality, especially for BTH and the air quality at morning and night.

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“…For this local variation GWR model, local variable parameters can be more accurately used to reflect the spatial variation and difference, and a continuous parameter value surface is generated (Engel-Cox et al, 2004;Hu et al, 2013). GWR is a regression model for testing continuous surface spatial variation and non-stationary problems of regression parameter values on a regional scale (Wang et al, 2020). Although AOD is the most significant indicator of PM2.5 concentration, PM2.5 concentration is also significantly affected by temperature, precipitation and other climatic factors (Lv and Li, 2018).…”

Section: Geographically Weighted Regression Modelmentioning

confidence: 99%

Analysis of the temporal and spatial pattern of air pollution and the heterogeneity of its influencing factors in central Inner Mongolia from 2016 to 2018

Wang

Li²,

Xu³

et al. 2022

Front. Environ. Sci.

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The central region of Inner Mongolia is the northern ecological safety barrier of Beijing and even the whole country. It is one of the main sources of dust in North China, and air pollution control is the top priority in this region. In this study, the central region of Inner Mongolia was selected as the study area, multiple auxiliary variables were used to estimate the spatial distribution of PM2.5 concentration from 2016 to 2018 by geographically weighted regression, and the socioeconomic determinants of PM2.5 concentration were analyzed by geographic detectors. The results show that: 1) the established model can better estimate the spatial distribution of PM2.5 concentration in the study area, and the monthly mean correlation coefficient R of the verification parameters is stable, ranging from 0.58 to 0.66. 2) PM2.5 concentration in central Inner Mongolia showed significant temporal and spatial variation. The mean annual PM2.5 concentration along the Yellow River basin is the highest in the study area. PM2.5 concentration first increased and then decreased from 2016 to 2018. 3) Urban built-up area, permanent population and per capita GDP are the key factors affecting the spatial and temporal distribution of PM2.5 concentration in the study area. The results of this study provide theoretical basis and technical support for air pollution monitoring, management and prevention in central Inner Mongolia.

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The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

Cited by 10 publications

References 55 publications

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution

Analysis of the temporal and spatial pattern of air pollution and the heterogeneity of its influencing factors in central Inner Mongolia from 2016 to 2018

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