Application of Statistical Inference for Analysis of Heavy Metal Variability in Roadside Soil
- Publication Type:
- Journal Article
- Citation:
- Water, Air, and Soil Pollution, 2018, 229 (1)
- Issue Date:
- 2018-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| WSA pollution 2018 Ball.pdf | Published Version | 2.07 MB |
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© 2018, Springer International Publishing AG, part of Springer Nature. Previous studies have found there are a variety of factors that influence heavy metal concentrations and Pb isotope ratios in roadside soil. One issue in assessing these factors is the need to distinguish between the natural sample variability at a single site and the variability between different sites. Data constraint often results in the lack of an adequate number of samples and hence is often a constraint on statistical reliability. Presented herein is a regionalisation approach that can be used to overcome the data constraint. This approach was used to analyse data collected at Miranda Park, Sydney, for assessment of the influence of rainfall, distance, depth and soil types. Application of the regionalisation approach enabled discrimination between natural sample variability and that from changes in the factors being considered. The regionalisation approach mitigates the data constraint and may assist researchers in their analysis of constrained data sets enabling more efficient monitoring of potential environmental issues. Additionally, it was found that the primary factors for heavy metal concentrations were rainfall, distance and soil types while depth was a secondary factor. A similar result was determined for the anthropogenic Pb component but not for the natural Pb component.
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