DOI: 10.1016/j.atmosenv.2018.04.010
Scopus记录号: 2-s2.0-85045344476
论文题名: Characterizing the spatial variability of local and background concentration signals for air pollution at the neighbourhood scale
作者: Shairsingh K ; K ; , Jeong C ; -H ; , Wang J ; M ; , Evans G ; J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 183 起始页码: 57
结束页码: 68
语种: 英语
英文关键词: Black carbon
; Land use emissions
; Mobile sampling
; Neighbourhood background signal
; Nitrogen oxides
; Ultrafine particles
Scopus关键词: Air pollution
; Deconvolution
; Digital storage
; Nitric oxide
; Nitrogen oxides
; Background concentration
; Background signals
; Black carbon
; Exposure misclassification
; Mobile samplings
; Pollutant concentration
; Ultrafine particle
; Variable concentration
; Land use
; black carbon
; nitric oxide
; nitrogen oxide
; anthropogenic source
; atmospheric pollution
; black carbon
; concentration (composition)
; instrumentation
; land use
; model validation
; neighborhood
; oxide group
; particle size
; sampling
; spatial variation
; traffic emission
; urban area
; air monitoring
; air pollutant
; air pollution
; air quality
; air sampling
; Article
; climate change
; commercial phenomena
; comparative study
; concentration (parameters)
; environmental parameters
; exhaust gas
; highway
; industrial area
; land use
; minimum concentration
; neighborhood
; neighborhood background signal
; priority journal
; recreational park
; residential area
; seasonal variation
; spatiotemporal analysis
; summer
; wind
; Canada
; Ontario [Canada]
; Toronto
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: Vehicle emissions represent a major source of air pollution in urban districts, producing highly variable concentrations of some pollutants within cities. The main goal of this study was to identify a deconvolving method so as to characterize variability in local, neighbourhood and regional background concentration signals. This method was validated by examining how traffic-related and non-traffic-related sources influenced the different signals. Sampling with a mobile monitoring platform was conducted across the Greater Toronto Area over a seven-day period during summer 2015. This mobile monitoring platform was equipped with instruments for measuring a wide range of pollutants at time resolutions of 1 s (ultrafine particles, black carbon) to 20 s (nitric oxide, nitrogen oxides). The monitored neighbourhoods were selected based on their land use categories (e.g. industrial, commercial, parks and residential areas). The high time-resolution data allowed pollutant concentrations to be separated into signals representing background and local concentrations. The background signals were determined using a spline of minimums; local signals were derived by subtracting the background concentration from the total concentration. Our study showed that temporal scales of 500 s and 2400 s were associated with the neighbourhood and regional background signals respectively. The percent contribution of the pollutant concentration that was attributed to local signals was highest for nitric oxide (NO) (37–95%) and lowest for ultrafine particles (9–58%); the ultrafine particles were predominantly regional (32–87%) in origin on these days. Local concentrations showed stronger associations than total concentrations with traffic intensity in a 100 m buffer (ρ:0.21–0.44). The neighbourhood scale signal also showed stronger associations with industrial facilities than the total concentrations. Given that the signals show stronger associations with different land use suggests that resolving the ambient concentrations differentiates which emission sources drive the variability in each signal. The benefit of this deconvolution method is that it may reduce exposure misclassification when coupled with predictive models. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82854
Appears in Collections: 气候变化事实与影响
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作者单位: Southern Ontario Centre for Atmospheric Aerosol Research, Dept. of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Canada
Recommended Citation:
Shairsingh K,K,, Jeong C,et al. Characterizing the spatial variability of local and background concentration signals for air pollution at the neighbourhood scale[J]. Atmospheric Environment,2018-01-01,183