DOI: 10.1016/j.atmosenv.2015.01.053
Scopus记录号: 2-s2.0-84921880351
论文题名: Satellite NO2 data improve national land use regression models for ambient NO2 in a small densely populated country
作者: Hoek G ; , Eeftens M ; , Beelen R ; , Fischer P ; , Brunekreef B ; , Boersma K ; F ; , Veefkind P
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 105 起始页码: 173
结束页码: 180
语种: 英语
英文关键词: Land use regression
; Nitrogen dioxide
; OMI
; Satellite
; Spatial variation
Scopus关键词: Air pollution
; Geographic information systems
; Land use
; Nitrogen
; Regression analysis
; Satellites
; Troposphere
; Ultraviolet spectrometers
; Urban growth
; Geographical coordinates
; Land use regression
; Land-use regression models
; Nitrogen dioxides
; OMI
; Ozone monitoring instruments
; Satellite observations
; Spatial variations
; Nitrogen oxides
; nitrogen dioxide
; atmospheric pollution
; data set
; land use change
; nitrogen dioxide
; numerical model
; ozone
; population density
; regression analysis
; satellite data
; spatial variation
; troposphere
; Article
; geographic information system
; land use
; Netherlands
; priority journal
; surface property
; urban rural difference
; Netherlands
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) modelling has increasingly been applied to model fine scale spatial variation of outdoor air pollutants including nitrogen dioxide (NO2). Satellite observations of tropospheric NO2 improved LUR model in very large study areas, including Canada, United States and Australia. The aim of our study was to assess the value of satellite observations of NO2 in modelling the spatial variation of annual average NO2 concentrations in a small densely populated country. We used surface level annual average NO2 concentration and geographic information system data from 144 monitoring sites spread over the Netherlands: 26 regional background, 78 urban background and 40 traffic sites for developing land use regression models. For the 144 monitoring sites we obtained the annual average tropospheric NO2 concentration for 2007 from the Ozone Monitoring Instrument (OMI) satellite sensor. These OMI data reflect a spatial scale of about 10×10km. We calculated the correlation between satellite and surface level NO2 concentrations for all sites and for background sites only. We next evaluated whether adding satellite observations improved land use regression models.Annual average satellite observations of tropospheric NO2 correlated well spatially with annual average urban plus regional background (R=0.74, n=104 sites) and especially regional background NO2 concentrations (R=0.88, n=26). The correlation was moderate for all sites, including traffic locations (R=0.51, n=144). A LUR model including satellite NO2 observations performed better (overall R2=0.84) than LUR models including geographical coordinates or indicator variables (overall R2 65-74%) in modeling concentrations at the 104 background sites across the Netherlands.Satellite NO2 observations agreed well with measured surface concentrations at background locations and improved land use regression models, even in a small densely populated country. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81977
Appears in Collections: 气候变化事实与影响
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作者单位: Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80178, Utrecht, Netherlands; Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; Center for Centre for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, Bilthoven, Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, P.O. Box 85500, Utrecht, Netherlands; Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt, Netherlands; Wageningen University, Meteorology and Air Quality Group, P.O. Box 47, Wageningen, Netherlands; Department of Geoscience and Remote Sensing, Delft University of Technology, PO-box 5048, Delft, Netherlands
Recommended Citation:
Hoek G,, Eeftens M,, Beelen R,et al. Satellite NO2 data improve national land use regression models for ambient NO2 in a small densely populated country[J]. Atmospheric Environment,2015-01-01,105