Appleton, Don; Rawlins, Barry; Thornton, Ian. 2008 National-scale estimation of potentially harmful element ambient background concentrations in topsoil using parent material classified soil : stream-sediment relationships. Applied Geochemistry, 23 (9). 2596-2611. 10.1016/j.apgeochem.2008.05.010
Abstract
Regulatory authorities require estimates of ambient background concentrations (ABCs) of
potentially harmful elements (PHEs) in topsoil; such data are currently not available in
many countries. High resolution soil geochemical data exist for only part of England and
Wales, whilst stream sediment data cover the entire landscape. A novel methodology is
presented for estimating soil equivalent ABCs for PHEs from high-resolution (HR) stream
sediment geochemical data grouped by common parent materials (PM), using arsenic
(As) as an example. Geometric mean (GM) values for local PM groups are used to investigate
different approaches for transforming sediment to soil equivalent concentrations.
Holdout validation is used to assess: (i) the optimum number of samples for calculating
local GM values, and (ii) the optimum scale at which to group data when using linear
regression analysis to estimate GM soil ABCs from local sediment geochemical values.
Holdout validation showed that the smallest differences were generally observed when
five observations were used to calculate the GM and that these should be grouped over
the smallest possible area in order to encompass soils over PMs with elevated GM As concentrations.
Geometric mean ABCs are estimated and mapped for As in mineral soil across
all of England and Wales within delineations of PM polygons. Errors for the estimation of
soil equivalent GM As ABCs based on sediment data for an independent validation set were
of a similar magnitude to those from holdout validation applied to the original data suggesting
the approach is robust. The estimates of soil equivalent ABCs suggest that As
exceeds the regulatory threshold used in risk assessments for residential land use
(20 mg kg1) across 16% of the landscape of England and Wales. The applicability of the
method for cognate landscapes, and potential refinements is discussed.
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