A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China

Chao Liu; Barron H. Henderson; Dongfang Wang; Xinyuan Yang; Zhong Ren Peng

doi:10.1016/j.scitotenv.2016.03.189

A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations in City of Shanghai, China

Chao Liu
, Barron H. Henderson
, Dongfang Wang
, Xinyuan Yang
, Zhong Ren Peng

University of Florida
University of Florida
Shanghai Environmental Monitoring Center
Shanghai Jiao Tong University

Research output: Contribution to journal › Article › peer-review

198 Scopus citations

Abstract

Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM_2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM_2.5 and NO₂ concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM_2.5 and NO₂ models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM_2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO₂ model. Respectively, both PM_2.5 and NO₂ showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO₂ also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM_2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM_2.5 (0.88) being higher than NO₂ (0.62). The LUR was then used to produce continuous concentration fields for NO₂ and PM_2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM_2.5 studies in New York and Beijing show that Shanghai PM_2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions.

Original language	English
Pages (from-to)	607-615
Number of pages	9
Journal	Science of the Total Environment
Volume	565
DOIs	https://doi.org/10.1016/j.scitotenv.2016.03.189
State	Published - 15 Sep 2016
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities
SDG 14 Life Below Water

Keywords

China
Intra-urban air pollution
Land Use Regression (LUR)
NO
PM
Spatial analysis

Access to Document

10.1016/j.scitotenv.2016.03.189

Cite this

@article{76a936de297443e78acd939d25930b1c,

title = "A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China",

abstract = "Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM2.5 and NO2 concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM2.5 and NO2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO2 model. Respectively, both PM2.5 and NO2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM2.5 (0.88) being higher than NO2 (0.62). The LUR was then used to produce continuous concentration fields for NO2 and PM2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM2.5 studies in New York and Beijing show that Shanghai PM2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions.",

keywords = "China, Intra-urban air pollution, Land Use Regression (LUR), NO, PM, Spatial analysis",

author = "Chao Liu and Henderson, \{Barron H.\} and Dongfang Wang and Xinyuan Yang and Peng, \{Zhong Ren\}",

note = "Publisher Copyright: {\textcopyright} 2015.",

year = "2016",

month = sep,

day = "15",

doi = "10.1016/j.scitotenv.2016.03.189",

language = "English",

volume = "565",

pages = "607--615",

journal = "Science of the Total Environment",

issn = "0048-9697",

publisher = "Elsevier B.V.",

}

A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations in City of Shanghai, China. / Liu, Chao; Henderson, Barron H.; Wang, Dongfang et al.
In: Science of the Total Environment, Vol. 565, 15.09.2016, p. 607-615.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China

AU - Liu, Chao

AU - Henderson, Barron H.

AU - Wang, Dongfang

AU - Yang, Xinyuan

AU - Peng, Zhong Ren

PY - 2016/9/15

Y1 - 2016/9/15

N2 - Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM2.5 and NO2 concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM2.5 and NO2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO2 model. Respectively, both PM2.5 and NO2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM2.5 (0.88) being higher than NO2 (0.62). The LUR was then used to produce continuous concentration fields for NO2 and PM2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM2.5 studies in New York and Beijing show that Shanghai PM2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions.

AB - Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM2.5 and NO2 concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM2.5 and NO2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO2 model. Respectively, both PM2.5 and NO2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM2.5 (0.88) being higher than NO2 (0.62). The LUR was then used to produce continuous concentration fields for NO2 and PM2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM2.5 studies in New York and Beijing show that Shanghai PM2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions.

KW - China

KW - Intra-urban air pollution

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KW - NO

KW - PM

KW - Spatial analysis

UR - https://www.scopus.com/pages/publications/84969180412

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DO - 10.1016/j.scitotenv.2016.03.189

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