DOI: 10.1016/j.atmosenv.2017.10.017
Scopus记录号: 2-s2.0-85031800041
论文题名: Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass and association to type two diabetes mellitus
作者: Hellack B ; , Sugiri D ; , Schins R ; P ; F ; , Schikowski T ; , Krämer U ; , Kuhlbusch T ; A ; J ; , Hoffmann B
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 171 起始页码: 181
结束页码: 190
语种: 英语
英文关键词: EPR
; Land use regression modeling
; LUR
; Oxidative potential
; PM2.5
; ROS
; Type 2 diabetes mellitus
Scopus关键词: Health
; Land use
; Paramagnetic resonance
; Particles (particulate matter)
; Pollution
; Regression analysis
; Rural areas
; Statistical methods
; Inter quartile ranges
; Land-use regression models
; Leave-one-out cross-validation (LOOCV)
; Oxidative potential
; PM2.5
; PM2.5 concentration
; Type 2 diabetes mellitus
; Urban and rural areas
; Nitrogen oxides
; nitrogen dioxide
; concentration (composition)
; diabetes
; health impact
; health risk
; land use
; model validation
; nitrous oxide
; oxidative stress
; particle size
; particulate matter
; performance assessment
; regression analysis
; aged
; Article
; carbon footprint
; combustion
; environmental exposure
; female
; follow up
; Germany
; health hazard
; human
; jackknife test
; land use
; major clinical study
; non insulin dependent diabetes mellitus
; oxidation reduction potential
; particulate matter
; priority journal
; proportional hazards model
; regression analysis
; rural area
; sensitivity analysis
; urban area
; Germany
; North Rhine-Westphalia
; Ruhr
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: While land use regression models (LUR) are commonly used, e.g. for the prediction of spatially variable air pollutant mass concentrations, they are scarcely used for predicting the oxidative potential (OP), a suggested unifying predictor of health effects. Therefore a LUR model was developed to examine if long-term OP of fine particulate exposure can be reasonably predicted by LUR modeling and whether it is related to health effects in a study region comprised of urban and rural areas. Four 14-day sampling periods over 1 year at 40 sites in the western Ruhr Area and adjacent northern rural area, Germany, in 2002/2003 were conducted and annual Nitrogen Dioxide (NO2), fine particles (PM2.5), and OP were calculated. LUR models were developed to estimate spatially-resolved annual OP, NO2 and PM2.5 concentrations. The model performance was checked by leave-one-out cross validation (LOOCV) and cox regression was used to analyze the association of modeled residential OP and NO2 with incident type 2 diabetes mellitus (T2DM) in 1784 elderly women during a mean follow-up of 16 years (baseline 1985–1994). The measured OP and NO2 concentrations were moderately correlated (rSpearman 0.57). The LUR models explained 62% and 92% of the OP and NO2 variance (adjusted LOOCV R2 57% and 90%). PM10 emission from combustion in a 5000 m buffer was the most important predictor for OP and NO2. Modeled pollutants were highly correlated (rSpearman 0.87). Model quality for OP was sensitive to the inclusion of a single influential measurement site. For PM2.5 mass only an insufficient model with a low explained variance of 22% (adjusted R2) was developed so no health effects analyses were conducted with estimated PM2.5. Increases in OP and NO2 were associated with an increase in risk of T2DM by a hazard ratio of 1.38 (95% CI 1.06–1.80) and 1.39 (95% CI 1.07–1.81) per interquartile range of OP and NO2, respectively. We conclude that spatially-resolved OP can be predicted by LUR modeling, but future work is needed to investigate the possibility to increase OP model quality with refined predictors. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82176
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Institute of Energy and Environmental Technology e.V. (IUTA), Bliersheimerstraße 58-60, Duisburg, Germany; Leibniz Research Institute for Environmental Medicine (IUF), Auf´m Hennekamp 50, Düsseldorf, Germany; Federal Office for Occupational Safety and Occupational Medicine, Friedrich-Henkel-Weg 1-25, Dortmund, Germany; Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich-Heine-University of Düsseldorf, Universitätsstraße 1, Düsseldorf, Germany
Recommended Citation:
Hellack B,, Sugiri D,, Schins R,et al. Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass and association to type two diabetes mellitus[J]. Atmospheric Environment,2017-01-01,171