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Soil parameters affecting the levels of potentially harmful metals in Thessaly area, Greece: a robust quadratic regression approach of soil pollution prediction
- Source :
- Environmental Science and Pollution Research. 29:29544-29561
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Summarization: The behavior and possible contamination risk due to the presence of potentially harmful metals (PHM) were studied based on 2250 soil samples that were collected in a 5-year period (2013–2017) from the plain of Thessaly (prefectures of Karditsa, Trikala, and Larissa). The vertical distribution of metals was also investigated from sample profiles at three depths 0–30, 30–60, and 60–90cm. The soils of the sampling belong to four taxonomy soil orders that are dominant in the studied area (Alfisols, Inceptisols, Endisols, and Vertisols). In a novel approach, robust quadratic regression analysis on multiple variables was used to define prediction models of the concentrations of two metals: Fe which is an essential metal and the toxic Cd. Linear and quadratic regression formulae were estimated based on the iteratively reweighted least squares robust regression approach in an effort to eliminate the impact of the outliers. These formulae define how several soil properties affect the distribution of the considered metals in each soil order. The evaluation of the estimated regression equations based on the R2 metric indicates that they constitute a useful, reliable, and valuable tool for managing, describing, and predicting the pollution in the studied area. Presented on: Environmental Science and Pollution Research
- Subjects :
- Pollution
Soil test
Health, Toxicology and Mutagenesis
media_common.quotation_subject
Soil science
Endisols
010501 environmental sciences
01 natural sciences
Vertisols
Robust regression
Iteratively reweighted least squares
Quadratic regression
Environmental Chemistry
0105 earth and related environmental sciences
media_common
Polynomial regression
General Medicine
Iteratively reweighted least squares robust regression
Inceptisols
Soil contamination
Alfisols
Heavy metals
Soil water
Environmental science
Predictive modelling
Subjects
Details
- ISSN :
- 16147499 and 09441344
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- Environmental Science and Pollution Research
- Accession number :
- edsair.doi.dedup.....bf43610105ad901d0a4afb72a8332df4