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Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

Hou, Deyi ; O'Connor, David ; Nathanail, Paul ; Tian, Li ; Ma, Yan

Environmental pollution (1987), 2017-12, Vol.231 (Pt 1), p.1188-1200 [Periódico revisado por pares]

England: Elsevier Ltd

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  • Título:
    Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review
  • Autor: Hou, Deyi ; O'Connor, David ; Nathanail, Paul ; Tian, Li ; Ma, Yan
  • Assuntos: Cluster Analysis ; Environmental Monitoring - methods ; Environmental Monitoring - statistics & numerical data ; Geographic Information Systems ; GIS ; Humans ; Kriging ; Metals, Heavy - analysis ; Multivariate Analysis ; Multivariate statistical analysis ; Principal Component Analysis ; Soil - chemistry ; Soil Pollutants - analysis ; Spatial Analysis
  • É parte de: Environmental pollution (1987), 2017-12, Vol.231 (Pt 1), p.1188-1200
  • Descrição: Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km2, with a median of 0.4 samples per km2. The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). [Display omitted] •Critically reviewed field studies of regional distribution of heavy metal in soil.•Reviewed studies combined GIS with multivariate statistical analysis.•Summarized the most common sampling strategies, GIS method, and multivariate technique.•Identified determining and correlating factors for soil heavy metals.•Discussed major natural and anthropogenic sources of soil heavy metals. The integration of GIS and multivariate statistical analysis is a valuable tool in studying the regional distribution of heavy metals in soil.
  • Editor: England: Elsevier Ltd
  • Idioma: Inglês
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