DOI: 10.1016/j.atmosenv.2017.07.017
Scopus记录号: 2-s2.0-85024900309
论文题名: Ensemble classification for identifying neighbourhood sources of fugitive dust and associations with observed PM10
作者: Khuluse-Makhanya S ; , Stein A ; , Breytenbach A ; , Gxumisa A ; , Dudeni-Tlhone N ; , Debba P
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 166 起始页码: 151
结束页码: 165
语种: 英语
英文关键词: Ensemble classifier
; Fugitive dust
; K-means clustering
; Land cover
; Particulate matter
; Varying intercepts regression model
Scopus关键词: Air quality
; Cluster analysis
; Dust
; Dust control
; Industrial emissions
; Maximum likelihood
; Photomapping
; Regression analysis
; Reservoirs (water)
; Soils
; Spectroscopy
; Vegetation
; Ensemble classifiers
; Fugitive dust
; K-means clustering
; Land cover
; Particulate Matter
; Regression model
; Mapping
; water
; air quality
; concentration (composition)
; ensemble forecasting
; geological mapping
; industrial emission
; land cover
; maximum likelihood analysis
; model validation
; particulate matter
; pollutant source
; regression analysis
; satellite imagery
; SPOT
; traffic emission
; urban area
; accuracy
; air monitoring
; air quality
; ambient air
; Article
; carbon footprint
; classifier
; cluster analysis
; controlled study
; dust
; land use
; maximum likelihood method
; neighborhood
; particulate matter
; prediction
; priority journal
; regression analysis
; reservoir
; soil
; urban area
; vegetation
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: In urban areas the deterioration of air quality as a result of fugitive dust receives less attention than the more prominent traffic and industrial emissions. We assessed whether fugitive dust emission sources in the neighbourhood of an air quality monitor are predictors of ambient PM10 concentrations on days characterized by strong local winds. An ensemble maximum likelihood method is developed for land cover mapping in the vicinity of an air quality station using SPOT 6 multi-spectral images. The ensemble maximum likelihood classifier is developed through multiple training iterations for improved accuracy of the bare soil class. Five primary land cover classes are considered, namely built-up areas, vegetation, bare soil, water and ‘mixed bare soil’ which denotes areas where soil is mixed with either vegetation or synthetic materials. Preliminary validation of the ensemble classifier for the bare soil class results in an accuracy range of 65–98%. Final validation of all classes results in an overall accuracy of 78%. Next, cluster analysis and a varying intercepts regression model are used to assess the statistical association between land cover, a fugitive dust emissions proxy and observed PM10. We found that land cover patterns in the neighbourhood of an air quality station are significant predictors of observed average PM10 concentrations on days when wind speeds are conducive for dust emissions. This study concludes that in the absence of an emissions inventory for ambient particulate matter, PM10 emitted from dust reservoirs can be statistically accounted for by land cover characteristics. This supports the use of land cover data for improved prediction of PM10 at locations without air quality monitoring stations. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82369
Appears in Collections: 气候变化事实与影响
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作者单位: CSIR Built Environment, PO Box 395, Pretoria, South Africa; Faculty of Geo-Information and Earth Sciences (ITC), University of Twente, PO Box 217, Enschede, Netherlands; School of Statistics and Actuarial Sciences, University of Witwatersrand, Johannesburg, South Africa
Recommended Citation:
Khuluse-Makhanya S,, Stein A,, Breytenbach A,et al. Ensemble classification for identifying neighbourhood sources of fugitive dust and associations with observed PM10[J]. Atmospheric Environment,2017-01-01,166