Modeling metal uptake by selected vegetables from urban soils in Europe: uncovering key soil factors using partial least squares regression (PLS-R)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F25%3A101961" target="_blank" >RIV/60460709:41330/25:101961 - isvavai.cz</a>
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/10807039.2025.2464109" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/10807039.2025.2464109</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s42773-024-00409-z" target="_blank" >10.1007/s42773-024-00409-z</a>
Alternative languages
Result language
angličtina
Original language name
Modeling metal uptake by selected vegetables from urban soils in Europe: uncovering key soil factors using partial least squares regression (PLS-R)
Original language description
Partial Least Squares Regression (PLS-R) was introduced as a method for modeling the uptake of six potentially toxic elements (PTEs)- Ba, Cd, Cu, Ni, Pb, and Zn- by lettuce, chard, and carrot. Data were obtained from a pot experiment where these crops were cultivated in urban soils of various characteristics. The models consider soil concentrations of PTE, Al, Ca, Fe, K, Mg, Mn, Na, P, S and pH, SOM, CEC, and soil texture as predictors. Initially, eighteen metal- and crop-specific models with all predictors were developed, using selectivity ratios (SRi) to identify influential variables for predicting PTE soil-to-crop transfer. Reduced models were then created using only predictors with high SRi. Key variables for predicting PTE soil-to-crop transfer included soil PTE concentration, pH, Fe and Mn soil concentrations, and soil texture. Out of eighteen models, sixteen were suitable for predicting correlations and assessing PTE accumulation in crops, while eight were accurate for quantitative predictions. This study shows that PLS-R is a robust method for modeling soil-to-crop transfer of metal contaminants, even with multicollinear predictors. PLS-R also helps identify key variables, providing insights into the mechanisms of PTE accumulation in crops, which is crucial for effective risk assessments.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
HUMAN AND ECOLOGICAL RISK ASSESSMENT
ISSN
1080-7039
e-ISSN
1080-7039
Volume of the periodical
31
Issue of the periodical within the volume
3-4
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
25
Pages from-to
434-458
UT code for WoS article
001424105200001
EID of the result in the Scopus database
2-s2.0-105002263246