Spatiotemporal variations and the driving factors of PM2.5 in Xi’an, China between 2004 and 2018

[Display omitted] •Spatial autocorrelation and clustering characteristics of city-level PM2.5 levels were observed.•The resonance cycles of PM2.5 concentrations with each influence factor were identified.•The influence of long-term driving elements on PM2.5 is quantitatively explored.•LUCC coupled w...

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Published inEcological indicators Vol. 146; p. 109802
Main Authors Tuheti, Abula, Deng, Shunxi, Li, Jianghao, Li, Guanghua, Lu, Pan, Lu, Zhenzhen, Liu, Jiayao, Du, Chenhui, Wang, Wei
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2023
Elsevier
Subjects
autocorrelation
autumn
China
Driving factors
Geo-detector
humans
land use
particulates
pollution
relative humidity
socioeconomics
Spatio-temporal variation
spring
summer
temperature
time series analysis
wavelet
Wavelet analysis
winter
China
Geo-detector
Wavelet analysis
Driving factors
Spatio-temporal variation
Online AccessGet full text
ISSN1470-160X
1872-7034
DOI10.1016/j.ecolind.2022.109802

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Abstract [Display omitted] •Spatial autocorrelation and clustering characteristics of city-level PM2.5 levels were observed.•The resonance cycles of PM2.5 concentrations with each influence factor were identified.•The influence of long-term driving elements on PM2.5 is quantitatively explored.•LUCC coupled with other factors had a large influence on PM2.5 concentrations. High-intensity human socioeconomic activities in Xi’an have caused fine particulate matter (PM2.5) pollution. Understanding the spatial and temporal patterns and key factors influencing PM2.5 concentration was the basic step for taking targeted measures. Thus, spatial analysis techniques are used to reveal the temporal and spatial distribution characteristics of PM2.5 in Xi’an over a long time series; wavelet analysis and Geo-detector models are applied to assess the strength of the association between meteorological and socio-economic conditions on PM2.5 concentrations. The results illustrated that the average PM2.5 concentration was 40.13 μg/m3 in 2004 and peaked at 62.06 μg/m3 in 2011, before failing to 38.77 μg/m3 by 2018. The PM2.5 concentration distribution had a characteristic of high in winter and autumn but low in spring and summer, presenting a U-shaped profile. The main distribution of PM2.5 concentrations was oriented in a northeast-southwest direction, with obvious spatial autocorrelation and spatial aggregation characteristics. The resonance cycles of the meteorological and socioeconomic elements and PM2.5 concentrations were synchronous and divergent at different scales. U-wind was the influencing factor on PM2.5 concentration with a positive correlation coefficient of 0.9. Before 2011, the interaction of temperature (Tem) and relative humidity (RH) had the greatest impact on PM2.5 concentrations. Additionally, the land use and cover change (LUCC) coupled with other factors had a large influence on PM2.5 concentrations. These relationships can shed new light on the underlying mechanisms of PM2.5 contamination at the city level, assisting relevant departments in developing effective PM2.5 pollution management strategies.
AbstractList High-intensity human socioeconomic activities in Xi’an have caused fine particulate matter (PM2.5) pollution. Understanding the spatial and temporal patterns and key factors influencing PM2.5 concentration was the basic step for taking targeted measures. Thus, spatial analysis techniques are used to reveal the temporal and spatial distribution characteristics of PM2.5 in Xi’an over a long time series; wavelet analysis and Geo-detector models are applied to assess the strength of the association between meteorological and socio-economic conditions on PM2.5 concentrations. The results illustrated that the average PM2.5 concentration was 40.13 μg/m3 in 2004 and peaked at 62.06 μg/m3 in 2011, before failing to 38.77 μg/m3 by 2018. The PM2.5 concentration distribution had a characteristic of high in winter and autumn but low in spring and summer, presenting a U-shaped profile. The main distribution of PM2.5 concentrations was oriented in a northeast-southwest direction, with obvious spatial autocorrelation and spatial aggregation characteristics. The resonance cycles of the meteorological and socioeconomic elements and PM2.5 concentrations were synchronous and divergent at different scales. U-wind was the influencing factor on PM2.5 concentration with a positive correlation coefficient of 0.9. Before 2011, the interaction of temperature (Tem) and relative humidity (RH) had the greatest impact on PM2.5 concentrations. Additionally, the land use and cover change (LUCC) coupled with other factors had a large influence on PM2.5 concentrations. These relationships can shed new light on the underlying mechanisms of PM2.5 contamination at the city level, assisting relevant departments in developing effective PM2.5 pollution management strategies.
High-intensity human socioeconomic activities in Xi’an have caused fine particulate matter (PM₂.₅) pollution. Understanding the spatial and temporal patterns and key factors influencing PM₂.₅ concentration was the basic step for taking targeted measures. Thus, spatial analysis techniques are used to reveal the temporal and spatial distribution characteristics of PM₂.₅ in Xi’an over a long time series; wavelet analysis and Geo-detector models are applied to assess the strength of the association between meteorological and socio-economic conditions on PM₂.₅ concentrations. The results illustrated that the average PM₂.₅ concentration was 40.13 μg/m³ in 2004 and peaked at 62.06 μg/m³ in 2011, before failing to 38.77 μg/m³ by 2018. The PM₂.₅ concentration distribution had a characteristic of high in winter and autumn but low in spring and summer, presenting a U-shaped profile. The main distribution of PM₂.₅ concentrations was oriented in a northeast-southwest direction, with obvious spatial autocorrelation and spatial aggregation characteristics. The resonance cycles of the meteorological and socioeconomic elements and PM₂.₅ concentrations were synchronous and divergent at different scales. U-wind was the influencing factor on PM₂.₅ concentration with a positive correlation coefficient of 0.9. Before 2011, the interaction of temperature (Tem) and relative humidity (RH) had the greatest impact on PM₂.₅ concentrations. Additionally, the land use and cover change (LUCC) coupled with other factors had a large influence on PM₂.₅ concentrations. These relationships can shed new light on the underlying mechanisms of PM₂.₅ contamination at the city level, assisting relevant departments in developing effective PM₂.₅ pollution management strategies.
[Display omitted] •Spatial autocorrelation and clustering characteristics of city-level PM2.5 levels were observed.•The resonance cycles of PM2.5 concentrations with each influence factor were identified.•The influence of long-term driving elements on PM2.5 is quantitatively explored.•LUCC coupled with other factors had a large influence on PM2.5 concentrations. High-intensity human socioeconomic activities in Xi’an have caused fine particulate matter (PM2.5) pollution. Understanding the spatial and temporal patterns and key factors influencing PM2.5 concentration was the basic step for taking targeted measures. Thus, spatial analysis techniques are used to reveal the temporal and spatial distribution characteristics of PM2.5 in Xi’an over a long time series; wavelet analysis and Geo-detector models are applied to assess the strength of the association between meteorological and socio-economic conditions on PM2.5 concentrations. The results illustrated that the average PM2.5 concentration was 40.13 μg/m3 in 2004 and peaked at 62.06 μg/m3 in 2011, before failing to 38.77 μg/m3 by 2018. The PM2.5 concentration distribution had a characteristic of high in winter and autumn but low in spring and summer, presenting a U-shaped profile. The main distribution of PM2.5 concentrations was oriented in a northeast-southwest direction, with obvious spatial autocorrelation and spatial aggregation characteristics. The resonance cycles of the meteorological and socioeconomic elements and PM2.5 concentrations were synchronous and divergent at different scales. U-wind was the influencing factor on PM2.5 concentration with a positive correlation coefficient of 0.9. Before 2011, the interaction of temperature (Tem) and relative humidity (RH) had the greatest impact on PM2.5 concentrations. Additionally, the land use and cover change (LUCC) coupled with other factors had a large influence on PM2.5 concentrations. These relationships can shed new light on the underlying mechanisms of PM2.5 contamination at the city level, assisting relevant departments in developing effective PM2.5 pollution management strategies.
ArticleNumber 109802
Author Li, Guanghua
Deng, Shunxi
Liu, Jiayao
Wang, Wei
Lu, Zhenzhen
Tuheti, Abula
Lu, Pan
Du, Chenhui
Li, Jianghao
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Snippet [Display omitted] •Spatial autocorrelation and clustering characteristics of city-level PM2.5 levels were observed.•The resonance cycles of PM2.5...
High-intensity human socioeconomic activities in Xi’an have caused fine particulate matter (PM₂.₅) pollution. Understanding the spatial and temporal patterns...
High-intensity human socioeconomic activities in Xi’an have caused fine particulate matter (PM2.5) pollution. Understanding the spatial and temporal patterns...
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StartPage 109802
SubjectTerms autocorrelation
autumn
China
Driving factors
Geo-detector
humans
land use
particulates
pollution
relative humidity
socioeconomics
Spatio-temporal variation
spring
summer
temperature
time series analysis
wavelet
Wavelet analysis
winter
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Title Spatiotemporal variations and the driving factors of PM2.5 in Xi’an, China between 2004 and 2018
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