DOI: 10.1016/j.atmosenv.2015.04.057
Scopus记录号: 2-s2.0-84928783182
论文题名: Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors
作者: Heimann I ; , Bright V ; B ; , McLeod M ; W ; , Mead M ; I ; , Popoola O ; A ; M ; , Stewart G ; B ; , Jones R ; L
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 113 起始页码: 10
结束页码: 19
语种: 英语
英文关键词: Air quality
; Baseline extraction
; Electrochemical sensors
; Emission scales
; Sensor networks
; Source attribution
Scopus关键词: Air quality
; Carbon monoxide
; Complex networks
; Costs
; Electrochemical sensors
; Gas dynamics
; Pollution
; Sensor networks
; Air quality assessment
; Air quality measurements
; High spatial density
; High spatial resolution
; Measurement-based approach
; Measurements of carbon monoxide
; Relative frequencies
; Source attribution
; Source separation
; carbon monoxide
; air quality
; anthropogenic source
; atmospheric pollution
; carbon monoxide
; electrochemistry
; emission inventory
; spatial analysis
; spatial resolution
; air pollution
; air quality
; Article
; boundary layer
; circadian rhythm
; cost
; exhaust gas
; meteorology
; priority journal
; quality control
; rural area
; sensor
; troposphere
; urban area
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: To carry out detailed source attribution for air quality assessment it is necessary to distinguish pollutant contributions that arise from local emissions from those attributable to non-local or regional emission sources. Frequently this requires the use of complex models and inversion methods, prior knowledge or assumptions regarding the pollution environment. In this paper we demonstrate how high spatial density and fast response measurements from low-cost sensor networks may facilitate this separation. A purely measurement-based approach to extract underlying pollution levels (baselines) from the measurements is presented exploiting the different relative frequencies of local and background pollution variations. This paper shows that if high spatial and temporal coverage of air quality measurements are available, the different contributions to the total pollution levels, namely the regional signal as well as near and far field local sources, can be quantified. The advantage of using high spatial resolution observations, as can be provided by low-cost sensor networks, lies in the fact that no prior assumptions about pollution levels at individual deployment sites are required. The methodology we present here, utilising measurements of carbon monoxide (CO), has wide applicability, including additional gas phase species and measurements obtained using reference networks. While similar studies have been performed, this is the first study using networks at this density, or using low cost sensor networks. © 2015 The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81734
Appears in Collections: 气候变化事实与影响
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作者单位: University Chemical Laboratory, University of Cambridge, Lensfield Road, Cambridge, United Kingdom
Recommended Citation:
Heimann I,, Bright V,B,et al. Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors[J]. Atmospheric Environment,2015-01-01,113