ArticleEnvironmental science and pollution research international2022
Soil parameters affecting the levels of potentially harmful metals in Thessaly area, Greece: a robust quadratic regression approach of soil pollution prediction.
Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Article
- Bismuth Film along with dsDNA-Modified Electrode Surfaces as Promising (bio)Sensors in the Analysis of Heavy Metals in Soils.Biosensors · 2024Article
- Multivariate analysis applied to X-ray fluorescence to assess soil contamination pathways: case studies of mass magnetic susceptibility in soils near abandoned coal and W/Sn mines.Environmental geochemistry and health · 2024Article
- Assessing environmental impacts through innovative solutions.Environmental science and pollution research international · 2022Article
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2 authors.
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Abstract
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 R
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