DOI: 10.1016/j.atmosenv.2015.01.024
Scopus记录号: 2-s2.0-84921318155
论文题名: Land use regression modeling with vertical distribution measurements for fine particulate matter and elements in an urban area
作者: Ho C ; -C ; , Chan C ; -C ; , Cho C ; -W ; , Lin H ; -I ; , Lee J ; -H ; , Wu C ; -F
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 104 起始页码: 256
结束页码: 263
语种: 英语
英文关键词: Elemental composition
; Fine particulate matter
; Land use regression
; Vertical variability
Scopus关键词: Floors
; Manganese
; Nickel
; Pollution
; Regression analysis
; Silicon
; Titanium
; Zinc
; Distributed measurements
; Elemental compositions
; Epidemiological studies
; Fine particulate matter
; Land use regression
; Land-use regression models
; Vertical distributions
; Vertical variability
; Land use
; copper
; iron
; manganese
; nickel
; potassium
; silicon
; sulfur
; titanium
; trace element
; zinc
; atmospheric pollution
; land use change
; long-term change
; particulate matter
; pollution exposure
; regression analysis
; spatial variation
; urban area
; vertical distribution
; ambient air
; Article
; controlled study
; environmental exposure
; industrial area
; land use
; land use regression
; linear regression analysis
; long term exposure
; particulate matter
; pollution monitoring
; priority journal
; urban area
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Land use regression (LUR) models have been developed and applied to evaluate long-term exposure to air pollutants in residential area. However, adopting LUR models for vertical distributions of PM2.5 elemental composition has not been studied extensively. Developing this type of LUR model in various urban areas is essential to examine the influence of sampling height from ground level on the modeling prediction of these pollutants. The purpose of this study was to examine spatial variations of exposures to PM2.5 and PM2.5 composition in an urban area and build LUR models with vertical distribution measurements. PM2.5 samples were collected at twenty low-level sites (first to third floors), five mid-level sites (fourth to sixth floors), and five high-level sites (seventh to ninth floors). LUR models considering local land use data and traffic information were developed for PM2.5 and elements (i.e., silicon (Si), sulfur (S), potassium (K), titanium (Ti), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), and zinc (Zn)). The results demonstrated that the vertical ratios were higher than 1 (i.e., highest concentrations at low-level sites) for PM2.5, Si, Ti, and Fe. Their median ratios ranged from 1.05 to 1.18. The explained variances (R2) of LUR models ranged from 0.46 to 0.80. Traffic and industrial land were major variables in most models, and the floor level was identified as a significant predictor in the PM2.5, Si, and Fe models. This indicated the necessity of collecting vertically distributed measurements in future LUR studies for reducing the exposure bias in epidemiological studies. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81951
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, National Taiwan University, Taipei, Taiwan
Recommended Citation:
Ho C,-C,, Chan C,et al. Land use regression modeling with vertical distribution measurements for fine particulate matter and elements in an urban area[J]. Atmospheric Environment,2015-01-01,104