Predictive Modeling of Zinc Fractions in Zinc Chloride-Contaminated Soils Using Soil Properties

Edyta Nartowska (), Anna Podlasek, Magdalena Daria Vaverková, L’ubica Kozáková and Eugeniusz Koda
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Edyta Nartowska: Faculty of Environmental Engineering, Geomatics and Renewable Energy, Kielce University of Technology, 25-314 Kielce, Poland
Anna Podlasek: Department of Sustainable Construction and Geodesy, Institute of Civil Engineering, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland
Magdalena Daria Vaverková: Department of Sustainable Construction and Geodesy, Institute of Civil Engineering, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland
L’ubica Kozáková: Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia
Eugeniusz Koda: Department of Sustainable Construction and Geodesy, Institute of Civil Engineering, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland

Land, 2025, vol. 14, issue 9, 1-22

Abstract: The combined effects of soil properties, zinc (Zn), and chloride ion (Cl − ) concentrations on Zn distribution across soil fractions are poorly understood, even though zinc chloride (ZnCl 2 ) contamination in industrial soils is a major source of mobile Zn and poses significant environmental risks. This study aimed to (1) assess how the soil type, physicochemical properties, and Zn concentration affect Zn distribution in Community Bureau of Reference (BCR)-extracted fractions; (2) evaluate the impact of Cl − on Zn mobility; and (3) develop predictive models for mobile and stable Zn fractions based on soil characteristics. Zn mobility was analyzed in 18 soils differing in Zn and Cl − , pH, specific surface area (SSA), organic matter (OM), and texture (sand, silt, clay (CLY)), using a modified BCR method. Zn fractions were measured by atomic absorption spectroscopy (AAS). Analysis of Covariance was used to assess Zn distribution across soil types, while Zn fractions were modeled using non-linear regression (NLR). The results showed that mobile Zn increased with the total Zn, and that the soil type and Zn levels influenced Zn distribution in soils contaminated with ZnCl 2 (Zn 304–2136 mg·kg −1 d.m.; Cl − 567–2552 mg·kg −1 ; pH 3.5–7.5; CLY 11–22%; SSA 96–196 m 2 ·g −1 ; OM 0–4.8%). Although Cl − enhanced Zn mobility, its effect was weaker than that of Zn. Predictive models based on the total Zn, SSA, and CLY accurately estimated Zn in mobile and stable fractions (R > 0.92), whereas the effects of the pH and OM, although noticeable, were not statistically significant.

Keywords: soil pollution; predictive models; salinity; potentially toxic metals; zinc; chloride ions; sequential extraction; clay fraction; specific surface area (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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