Geochemical indices and regression tree models for estimation of ambient background concentrations of copper, chromium, nickel and zinc in soil. (November 2018)

• Record Type: Journal Article
• Authors: Mikkonen, Hannah G.; van de Graaff, Robert; Clarke, Bradley O.; Dasika, Raghava; Wallis, Christian J.; Reichman, Suzie M.
• Is Part Of: Chemosphere. Volume 210(2018)
• Page Start: 193
• Page End: 203
• Publication Date: 2018-11
• Subjects: Background -- Prediction -- Australia -- Model -- Geochemical indices -- Correlation; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2018.06.138
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Geochemical ratios between elements of environmental concern and Fe have been recommended for estimation of "background" concentrations of Cr, Cu, Ni and Zn in soil. However, little research has occurred to assess the consistency of geochemical ratios across soils developed in different environments. Broad application of generic geochemical ratios could result in under or over estimation of anthropogenic impacts to soil and subsequent inaccurate assessment of risk to the environment. A soil survey was undertaken in Victoria, Australia, including collection of samples (n = 622) from surface (0–0.1 m below ground level) and sub-surface (0.3–0.6 m below ground level) soils, overlying Tertiary-Quaternary basalt, Tertiary sediments and Silurian siltstones and sandstones. Samples were analyzed for metals and soil physical and chemical properties (particle size, cation exchange capacity, organic matter and pH). Geochemical correlations between elements in soils from different parent materials and environments were compared against geochemical relationships reported in Australia and internationally. Ratios of Cr and Fe were relatively consistent across parent materials, and comparable to published models for estimation of background Cr. Conversely, ratios between Cu, Ni, and Zn with Fe, were variable between soils developed in different weathering environments and/or soil depths. Alternative regression equations and rule based regression tree models were developed as an improved means for prediction of ambient background Cu, Ni and Zn concentrations in soil. Ambient background concentrations of Ni and Cr were predictable across parent materials and depths, allowing these models to be extended to soils across Australia and potentially internationally. Graphical abstract: Image 1 Highlights: Estimation of ambient background Cr using normalization with Fe. Quantification of Cu contamination by normalization with Ni. Regression tree model provided for estimation of background Ni in soils. Geochemical indices not suitable for estimation of background Zn in urban surface soils.