DOI: 10.1016/j.atmosenv.2020.117267
论文题名: Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations
作者: Cai J. ; Ge Y. ; Li H. ; Yang C. ; Liu C. ; Meng X. ; Wang W. ; Niu C. ; Kan L. ; Schikowski T. ; Yan B. ; Chillrud S.N. ; Kan H. ; Jin L.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 223 语种: 英语
英文关键词: Air pollution
; Land use
; Regression analysis
; Application in China
; Exposure assessment
; Fine particulate matter (PM2.5)
; Land-use regression models
; Multivariate regression models
; Pollution concentration
; Spatial corrections
; Spatial variations
; Nitrogen oxides
; black carbon
; nitrogen dioxide
; atmospheric pollution
; black carbon
; chemical composition
; heterogeneity
; land use change
; nitrogen dioxide
; particulate matter
; pollution exposure
; regression analysis
; spatial variation
; agricultural land
; air pollutant
; air pollution
; Article
; China
; concentration (parameter)
; environmental exposure
; environmental monitoring
; exhaust gas
; industrial area
; land use
; particulate matter
; priority journal
; rural area
; traffic
; urban area
; China
; Taizhou
; Zhejiang
学科: Air pollution
; Exposure assessment
; Land use regression model
; Spatial variation
中文摘要: Background: Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data. Objective: Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM2.5), black carbon (BC) and nitrogen dioxide (NO2) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study. Method: Two one-week integrated samples were collected at 30 PM2.5 (BC) sites and 45 NO2 sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model. Results: LUR explained 65% of the spatial variability in PM2.5, 78% in BC and 73% in NO2. Mean (±Standard Deviation) of predicted PM2.5, BC and NO2 exposure levels were 48.3 (±6.3) μg/m3, 7.5 (±1.4) μg/m3 and 27.3 (±8.2) μg/m3, respectively. Weak spatial corrections (Pearson r = 0.05–0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM2.5, BC and NO2 levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO2 levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100–700m) were found for BC and NO2. Conclusion: We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM2.5, NO2 and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM2.5, NO2 and BC concentrations. © 2020 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160965
Appears in Collections: 气候变化与战略
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作者单位: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai meteorological service, shanghai, China; Key Laboratory of Medicinal Chemistry and Molecular Diagnosis, College of Public Health, Hebei University, Baoding, 071002, China; School of Public Health, University of California, Berkeley, United States; Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany; Division of Geochemistry, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, United States; State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; CMC Institute of Health Sciences, Taizhou, Jiangsu Province, China
Recommended Citation:
Cai J.,Ge Y.,Li H.,et al. Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations[J]. Atmospheric Environment,2020-01-01,223