DOI: 10.1016/j.atmosenv.2015.10.010
Scopus记录号: 2-s2.0-84945162344
论文题名: Development and transferability of a nitrogen dioxide land use regression model within the Veneto region of Italy
作者: Marcon A ; , de Hoogh K ; , Gulliver J ; , Beelen R ; , Hansell A ; L
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 122 起始页码: 696
结束页码: 704
语种: 英语
英文关键词: Air pollution
; Ambient air
; Environmental exposure
; Exposure assessment
; Geographic information systems
; Land use regression
Scopus关键词: Atmospheric movements
; Geographic information systems
; Land use
; Nitrogen
; Nitrogen oxides
; Pollution
; Regression analysis
; Urban growth
; Ambient air
; Background concentration
; Environmental exposure
; Exposure assessment
; Land use regression
; Land-use regression models
; Large scale air pollution
; Model transferabilities
; Air pollution
; nitrogen dioxide
; ambient air
; atmospheric pollution
; concentration (composition)
; GIS
; land use change
; nitrogen oxides
; performance assessment
; spatial variation
; urban site
; air pollution
; Article
; building
; city
; Italy
; land use
; priority journal
; surface area
; Italy
; Veneto
; Verona
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: When measurements or other exposure models are unavailable, air pollution concentrations could be estimated by transferring land-use regression (LUR) models from other areas. No studies have looked at transferability of LUR models from regions to cities. We investigated model transferability issues. We developed a LUR model for 2010 using annual average nitrogen dioxide (NO2) concentrations retrieved from 47 regulatory stations of the Veneto region, Northern Italy. We applied this model to 40 independent sites in Verona, a city inside the region, where NO2 had been monitored in the European Study of Cohorts for Air Pollution Effects (ESCAPE) during 2010. We also used this model to estimate average NO2 concentrations at the regulatory network in 2008, 2009 and 2011. Of 33 predictor variables offered, five were retained in the LUR model (R2 = 0.75). The number of buildings in 5000 m buffers, industry surface area in 1000 m buffers and altitude, mainly representing large-scale air pollution dispersion patterns, explained most of the spatial variability in NO2 concentrations (R2 = 0.68), while two local traffic proxy indicators explained little of the variability (R2 = 0.07). The performance of this model transferred to urban sites was poor overall (R2 = 0.18), but it improved when only predicting inner-city background concentrations (R2 = 0.52). Recalibration of LUR coefficients improved model performance when predicting NO2 concentrations at the regulatory sites in 2008, 2009 and 2011 (R2 between 0.67 and 0.80). Models developed for a region using NO2 regulatory data are unable to capture small-scale variability in NO2 concentrations in urban traffic areas. Our study documents limitations in transferring a regional model to a city, even if it is nested within that region. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81395
Appears in Collections: 气候变化事实与影响
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作者单位: Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands; National Institute for Public Health and the Environment, Bilthoven, Netherlands; Imperial College Healthcare NHS Trust, London, United Kingdom
Recommended Citation:
Marcon A,, de Hoogh K,, Gulliver J,et al. Development and transferability of a nitrogen dioxide land use regression model within the Veneto region of Italy[J]. Atmospheric Environment,2015-01-01,122