DOI: 10.1016/j.atmosenv.2016.11.014
Scopus记录号: 2-s2.0-84999006650
论文题名: Effect of monitoring network design on land use regression models for estimating residential NO2 concentration
作者: Wu H ; , Reis S ; , Lin C ; , Heal M ; R
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 149 起始页码: 24
结束页码: 33
语种: 英语
英文关键词: Dispersion model
; Exposure assessment
; Land-use regression model
Scopus关键词: Dispersions
; Housing
; Location
; Monitoring
; Nitrogen oxides
; Regression analysis
; Dispersion modeling
; Dispersion Modelling
; Exposure assessment
; Land-use regression models
; Monitoring network designs
; Predictive capabilities
; Predictive performance
; Residential locations
; Land use
; nitrogen dioxide
; atmospheric pollution
; concentration (composition)
; dispersion
; environmental assessment
; environmental monitoring
; land use change
; model validation
; network design
; nitrogen dioxide
; pollution exposure
; regression analysis
; roadside environment
; urban pollution
; air pollution
; Article
; controlled study
; land use
; predictor variable
; priority journal
; United Kingdom
; urban area
; Edinburgh [Scotland]
; Scotland
; United Kingdom
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 are increasingly used to estimate exposure to air pollution in urban areas. An appropriate monitoring network is an important component in the development of a robust LUR model. In this study concentrations of NO2 were simulated by a dispersion model at ‘virtual’ monitoring sites in 54 network designs of varying numbers and types of site, using a 25 km2 area in Edinburgh, UK, as an example location. Separate LUR models were developed for each network. The LUR models were then used to estimate NO2 concentration at all residential addresses, which were evaluated against the dispersion-modelled concentration at these addresses. The improvement in predictive capability of the LUR models was insignificant above ∼30 monitoring sites, although more sites tended to yield more precise LUR models. Monitoring networks containing sites located within highly populated areas better estimated NO2 concentrations across all residential locations. LUR models constructed from networks containing more roadside sites better characterised the high end of residential NO2 concentrations but had increased errors when considering the whole range of concentrations. No particular composition of monitoring network resulted in good estimation simultaneously across all residential NO2 concentration and of the highest NO2 levels. This evaluation with dispersion modelling has shown that previous LUR model validation methods may have been optimistic in their assessment of the model's predictive performance at residential locations. © 2016 The Authors Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82621
Appears in Collections: 气候变化事实与影响
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作者单位: University of Edinburgh, School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, United Kingdom; Centre for Ecology & Hydrology, Bush Estate, Penicuik, Edinburgh, Midlothian, United Kingdom; University of Exeter Medical School, Knowledge Spa, Truro, United Kingdom
Recommended Citation:
Wu H,, Reis S,, Lin C,et al. Effect of monitoring network design on land use regression models for estimating residential NO2 concentration[J]. Atmospheric Environment,2017-01-01,149