Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014–2017

Wang, Yazhu; Duan, Xuejun; Wang, Lei

doi:10.3390/ijerph16060985

Cited by 43 publications

(31 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, there is sufficient time for most pollutants to gradually diffuse. This finding is partly in agreement with previous studies of Wang et al [32], who showed that the daily average value of PM 2.5 concentration peaked at 10:00 and the valley occurred at 16:00. In contrast, the characteristics of O 3 are opposite to those of the AQI, PM 10 , and PM 2.5 .…”

Section: Resultssupporting

confidence: 93%

Spatiotemporal Characteristics of Air Quality across Weifang from 2014–2018

Dai

Yang

et al. 2019

IJERPH

View full text Add to dashboard Cite

Air pollution has become a severe threat and challenge in China. Focusing on air quality in a heavily polluted city (Weifang Cty), this study aims to investigate spatial and temporal distribution characteristics of air pollution and identify the influence of weather factors on primary pollutants in Weifang over a long period from 2014–2018. The results indicate the annual Air quality Index (AQI) in Weifang has decreased since 2014 but is still far from the standard for excellent air quality. The primary pollutants are O3 (Ozone), PM10 (Particles with aerodynamic diameter ≤10 µm), and PM2.5 (Particles with aerodynamic diameter ≤10 µm); the annual concentrations of PM10 and PM2.5 show a significant reduction but that of O3 is basically unchanged. Seasonally, PM10 and PM2.5 show a U-shaped pattern, while O3 exhibits inverted U-shaped variations, and different pollutants also present different characteristics daily. Spatially, O3 exhibits a high level in the central region and a low level in the rural areas, while PM10 and PM2.5 are high in the northwest and low in the southeast. Additionally, the concentration of pollutants is greatly affected by meteorological factors, with PM2.5 being negatively correlated with temperature and wind speed, while O3 is positively correlated with the temperature. This research investigated the spatiotemporal characteristics of the air pollution and provided important policy advice based on the findings, which can be used to mitigate air pollution.

show abstract

Section: Resultssupporting

confidence: 93%

Spatiotemporal Characteristics of Air Quality across Weifang from 2014–2018

Dai

Yang

et al. 2019

IJERPH

View full text Add to dashboard Cite

show abstract

“…Compared with the local spatial autocorrelation, the Getis-Ord G i * is more sensitive to the identification of cold and hot spots, and can fully reflect the high or low value distribution relationship between a certain geographic element and other surrounding elements [ 34 ]. The formula is [ 34 , 35 , 36 , 37 ]:

where

, n is the number of spatial units (in this study, n = 59), and w ij is the spatial weight matrix, where w ij = 1 if spatial units I and j share a common border and w ij = 0 otherwise.…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

Zhou

Zhang

Ning

et al. 2020

IJERPH

View full text Add to dashboard Cite

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial–temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai’an, and was distributed stably at the border between Jining and Tai’an; (3) The global spatial autocorrelation experienced a changing process of “decline–growth–decline”, and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

show abstract

“…To highlight the environmental pressure caused by urban overcrowding, the green coverage per capita (GC) in builtup areas is used to measure the level of urban greening. (2) Within the spectrum of city sizes there are enormous differences in population size, built-up area, and industrial structure. Accordingly, real GDP per capita (PG) is chosen to measure the level of economic development of a region.…”

Section: Research Design Dependent Variable: Pm 25 Datamentioning

confidence: 99%

“…With the rapid urbanization and economic development, urban greening has become an urban livability standard and important symbol of residents' well-being ( 1 ). How to achieve a win-win situation for environmental protection and urban development has long been controversial for policymakers ( 2 ). Especially for rapidly-developing countries such as China and India, where rapid urbanization and deteriorating air quality is increasingly threatening the well-being of urban residents ( 3 ), also increasingly demanded for urban planning, policy, and management.…”

Section: Introductionmentioning

confidence: 99%

Effect of Urban Greening on Incremental PM2.5 Concentration During Peak Hours

Wang

Cheng

2020

Front. Public Health

View full text Add to dashboard Cite

In China, severe haze is a major public health concern affecting residents' health and well-being. This study used hourly air quality monitoring data from 285 cities in China to analyze the effect of green coverage (GC) and other economic variables on the incremental PM 2.5 concentration (ΔPM 2.5 ) during peak hours. To detect possible non-linear and interaction effect between predictive variables, a kernel-based regularized least squares (KRLS) model was used for empirical analysis. The results show that there was considerable heterogeneity between cities regarding marginal effect of GC on ΔPM 2.5 , which could potentially be explained by different seasons, latitude, urban maintenance expenditure (UE), real GDP per capita (PG), and population density (PD). Also described in this study, in cities with high UE, the growth of GC, PG, and PD always remain a positive impact on mitigation of haze pollution. This shows that government expenditure on urban maintenance can reduce or mitigate the environmental pollution from economic development. In addition, the influence of other urban elements on air quality had also been analyzed so that different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.

show abstract

Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014–2017

Cited by 43 publications

References 42 publications

Spatiotemporal Characteristics of Air Quality across Weifang from 2014–2018

Spatiotemporal Characteristics of Air Quality across Weifang from 2014–2018

Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

Effect of Urban Greening on Incremental PM2.5 Concentration During Peak Hours

Contact Info

Product

Resources

About