Socioeconomic Drivers of PM2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China

Cai-rong, Lou; Liu, Hongyu; Li, Yufeng; Li, Yuling

doi:10.3390/ijerph13100928

Cited by 96 publications

(37 citation statements)

References 45 publications

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“…This suggests that human activities might be the dominant factor affecting PM 2.5 concentrations in Jinjiang, China, which is consistent with previous research [50]. Of the anthropogenic factors, we found that population density was most responsible for enhancing the effects of elevation and dominant tree species on PM 2.5 concentrations.…”

Section: Interactive Functionssupporting

confidence: 81%

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

et al. 2018

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Abstract:Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM 2.5 ). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM 2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM 2.5 ), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM 2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM 2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM 2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM 2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM 2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.

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Section: Interactive Functionssupporting

confidence: 81%

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

et al. 2018

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“…The Yangtze River Delta (YRD) is the region of eastern China with the most developed economy, the greatest degree of urbanization and high coal consumption [8]. The region has suffered frequent extreme haze episodes in recent years [9]. Therefore, understanding the characteristics and its driving factors of PM 2.5 concentrations will be of benefit in the task of regional pollution prevention and control.…”

Section: Introductionmentioning

confidence: 99%

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

Yun

Jiang

et al. 2019

Atmosphere

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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.

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“…It can detect the spatial stratified heterogeneity of dependent variables and reveal the driving force behind it with its great flexibility [18]. It has been widely used in various fields, such as natural and social sciences [19][20][21], environmental pollution [22,23] and disease risk detection [16,24], etc., but it is rarely used in the field of LE research.Therefore, we analyzed the spatial distribution characteristics of LE by descriptive methods and spatial autocorrelation analysis. Then we used the Geographical Detector technique to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level, providing reference for the research on LE, economic and educational development, utilization of medical resources, environmental protection as well as population management policy-making in China.…”

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confidence: 99%

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confidence: 99%

Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

et al. 2020

IJERPH

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Life expectancy (LE) is a comprehensive and important index for measuring population health. Research on LE and its influencing factors is helpful for health improvement. Previous studies have neither considered the spatial stratified heterogeneity of LE nor explored the interactions between its influencing factors. Our study was based on the latest available LE and social and environmental factors data of 31 provinces in 2010 in China. Descriptive and spatial autocorrelation analyses were performed to explore the spatial characteristics of LE. Furthermore, the Geographical Detector (GeoDetector) technique was used to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results show that there existed obvious spatial stratified heterogeneity of LE, and LE mainly presented two clustering types (high-high and low-low) with positive autocorrelation. The results of GeoDetector showed that the number of college students per 100,000 persons (NOCS) could mainly explained the spatial stratified heterogeneity of LE (Power of Determinant (PD) = 0.89, p < 0.001). With the discretization of social and environmental factors, we found that LE reached the highest level with birth rate, total dependency ratio, number of residents per household and water resource per capita at their minimum range; conversely, LE reached the highest level with consumption level, GDP per capita, number of college students per 100,000 persons, medical care expenditure and urbanization rate at their maximum range. In addition, the interaction of any two factors on LE was stronger than the effect of a single factor. Our study suggests that there existed obvious spatial stratified heterogeneity of LE in China, which could mainly be explained by NOCS. Int. J. Environ. Res. Public Health 2020, 17, 906 2 of 15 2010 [3]. Therefore, it is an urgent task to explore the spatial characteristics and influencing factors of LE in order to deal with the regional inequality of LE in China.There have been many studies concerning the various influencing factors of LE. These factors can mainly be divided into two categories: biological and social and environmental factors [4]. Biological factors involve the individual's genetic factors, living habits, etc. Differences in LE between men and women are mainly due to differences in gene expression intensity. In addition, a study also found that people who smoked, drank and exercised less were vulnerable to cancer, cardiovascular and cerebrovascular diseases, which caused a reduction of LE [5]. However, social and environmental factors were the most frequently considered and important influencing factors in LE research [6]. Many studies found that economy and demographic composition were key influencing factors of LE [7-10]. Studies have found that health care and services had a positive effect on disease prevention and treatment, so the LE level was often higher in areas with good medical conditions [11]. People with a higher educat...

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Socioeconomic Drivers of PM2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China

Cited by 96 publications

References 45 publications

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

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

Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

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