Nested sampling and spatial analysis for reconnaissance investigations of soil: an example from agricultural land near mine tailings in Zambia
| Autor: | Belinda Kaninga, Godfrey M. Sakala, Elliott M. Hamilton, R. M. Lark, Michael J. Watts, Moola Mutondo, Kakoma K. Maseka |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
chemistry.chemical_classification
Soil organic matter Soil Science Sampling (statistics) chemistry.chemical_element Soil science 04 agricultural and veterinary sciences 010501 environmental sciences Uranium 01 natural sciences Tailings chemistry Mining engineering Sampling design 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Spatial variability Organic matter Nested sampling algorithm 0105 earth and related environmental sciences |
| Zdroj: | European Journal of Soil Science. 68:605-620 |
| ISSN: | 1351-0754 |
| DOI: | 10.1111/ejss.12449 |
| Popis: | Summary A 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text