DOI: 10.1016/j.atmosenv.2015.10.002
Scopus记录号: 2-s2.0-84944451107
论文题名: Modeling particulate matter concentrations measured through mobile monitoring in a deletion/substitution/addition approach
作者: Su J ; G ; , Hopke P ; K ; , Tian Y ; , Baldwin N ; , Thurston S ; W ; , Evans K ; , Rich D ; Q
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 122 起始页码: 477
结束页码: 483
语种: 英语
英文关键词: Aethalometer
; D/S/A
; Land use regression
; Mobile air pollution monitoring
; Woodsmoke
Scopus关键词: Air pollution
; Forecasting
; Land use
; Monitoring
; Nitrogen oxides
; Pollution
; Aethalometer
; Air pollution monitoring
; Cross-validation technique
; Fine particulate matter
; Land use regression
; Land-use regression models
; Traffic-related air pollution
; Woodsmoke
; Pollution detection
; black carbon
; algorithm
; atmospheric pollution
; black carbon
; concentration (composition)
; land use change
; nitrous oxide
; particulate matter
; pollution monitoring
; regression analysis
; traffic congestion
; addition reaction
; air monitoring
; air pollutant
; air pollution
; air sampling
; Article
; concentration (parameters)
; mobile application
; particulate matter
; priority journal
; substitution reaction
; ultraviolet radiation
; United States
; Monroe County [New York]
; New York [United States]
; United States
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 modeling (LUR) through local scale circular modeling domains has been used to predict traffic-related air pollution such as nitrogen oxides (NOX). LUR modeling for fine particulate matters (PM), which generally have smaller spatial gradients than NOX, has been typically applied for studies involving multiple study regions. To increase the spatial coverage for fine PM and key constituent concentrations, we designed a mobile monitoring network in Monroe County, New York to measure pollutant concentrations of black carbon (BC, wavelength at 880 nm), ultraviolet black carbon (UVBC, wavelength at 3700 nm) and Delta-C (the difference between the UVBC and BC concentrations) using the Clarkson University Mobile Air Pollution Monitoring Laboratory (MAPL). A Deletion/Substitution/Addition (D/S/A) algorithm was conducted, which used circular buffers as a basis for statistics. The algorithm maximizes the prediction accuracy for locations without measurements using the V-fold cross-validation technique, and it reduces overfitting compared to other approaches. We found that the D/S/A LUR modeling approach could achieve good results, with prediction powers of 60%, 63%, and 61%, respectively, for BC, UVBC, and Delta-C. The advantage of mobile monitoring is that it can monitor pollutant concentrations at hundreds of spatial points in a region, rather than the typical less than 100 points from a fixed site saturation monitoring network. This research indicates that a mobile saturation sampling network, when combined with proper modeling techniques, can uncover small area variations (e.g., 10 m) in particulate matter concentrations. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81367
Appears in Collections: 气候变化事实与影响
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作者单位: Environmental Health Sciences, School of Public Health, University of California at Berkeley, Berkeley, CA, United States; Institute for a Sustainable Environment and Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, United States; Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States; Public Health Sciences, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States
Recommended Citation:
Su J,G,, Hopke P,et al. Modeling particulate matter concentrations measured through mobile monitoring in a deletion/substitution/addition approach[J]. Atmospheric Environment,2015-01-01,122