Abstract
SummaryA reconnaissance survey was undertaken on soil near mine tailings to investigate variation in the content of copper, chromium and uranium. A nested sampling design was used. The data showed significant relations between the content of copper and uranium in the soil and its organic matter content, and a significant spatial trend in uranium content with distance from the tailings. Soil pH was not significantly related to any of the metals. The variance components associated with different scales of the sample design had large confidence intervals, but it was possible to show that the random variation was spatially dependent for all spatial models, whether for variation around a constant mean, or with a mean given by a linear effect of organic matter or distance to the tailings. For copper, we showed that a fractal or multifractal random model, with equal variance components for scales in a logarithmic progression, could be rejected for the model of variation around the fixed mean. The inclusion of organic matter as an explanatory factor meant that the fractal model could no longer be rejected, suggesting that the effect of organic matter results in spatial variation that is not scale invariant. It was shown, taking uranium as a case study, that further spatially nested sampling to estimate scale‐dependent variance components, or to test a non‐fractal model with adequate power, would require in the order of 200–250 samples in total.Highlights Sampling was undertaken to investigate spatial variation of metal content in soil near mine tailings. Chromium and uranium were related to soil organic matter content; uranium showed a spatial trend. Spatial variation was scale dependent, variation of copper was not scale‐invariant. Characterizing random spatial variation requires substantial sample effort.
Highlights
Reconnaissance investigations of the soil may be undertaken prior to detailed sampling for tasks such as spatial mapping, or the design of field experiments in which soil variation may be an important source of the residual variance of the observed response
Any changes in soil management that affect turnover of soil carbon might have an m m m effect on copper availability, for example if dissolved organic matter in the soil solution was increased (Temminghoff et al, 1998). These results suggest that a possible effect would be increased retention of potentially harmful elements in the soil, if the interaction with soil organic carbon reduces availability to plant roots this might reduce the risk of transfer to humans through consumption of food
One way to mitigate this problem, with respect to analytical laboratory costs, might be to use rapid in-field measurement (Hartemink & Minasny, 2014), to obtain more observations than conventional field sampling and laboratory analysis. This reconnaissance survey identified key factors that affect the spatial variation of the metals of interest in soil used for agriculture on a site next to mine tailings
Summary
Reconnaissance investigations of the soil may be undertaken prior to detailed sampling for tasks such as spatial mapping, or the design of field experiments in which soil variation may be an important source of the residual variance of the observed response. Appropriate reconnaissance sampling supports decisions on the design of subsequent sampling or experiments, and can enable initial testing of hypotheses about the sources of variation in soil properties of interest. In this paper we present a case study in which. The site of the reported study is land immediately adjacent to a large tailings dam. The land is used by a neighbouring village for agricultural production. This paper reports a reconnaissance survey of the soil at this site that was undertaken before more detailed
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