Development and comparison of regression models for the uptake of metals into various field crops
Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
26448504
DOI
10.1016/j.envpol.2015.09.043
PII: S0269-7491(15)30090-7
Knihovny.cz E-zdroje
- Klíčová slova
- Field crops, Heavy metals, Linear regression, Plant uptake, Prediction models,
- MeSH
- Brassica rapa chemie MeSH
- kontaminace potravin MeSH
- kukuřice setá chemie MeSH
- látky znečišťující půdu analýza MeSH
- monitorování životního prostředí metody statistika a číselné údaje MeSH
- multivariační analýza MeSH
- pšenice chemie MeSH
- půda chemie MeSH
- regresní analýza MeSH
- teoretické modely * MeSH
- těžké kovy analýza MeSH
- zemědělské plodiny chemie MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Česká republika MeSH
- Názvy látek
- látky znečišťující půdu MeSH
- půda MeSH
- těžké kovy MeSH
Field crops represent one of the highest contributions to dietary metal exposure. The aim of this study was to develop specific regression models for the uptake of metals into various field crops and to compare the usability of other available models. We analysed samples of potato, hop, maize, barley, wheat, rape seed, and grass from 66 agricultural sites. The influence of measured soil concentrations and soil factors (pH, organic carbon, content of silt and clay) on the plant concentrations of Cd, Cr, Cu, Mo, Ni, Pb and Zn was evaluated. Bioconcentration factors (BCF) and plant-specific metal models (PSMM) developed from multivariate regressions were calculated. The explained variability of the models was from 19 to 64% and correlations between measured and predicted concentrations were between 0.43 and 0.90. The developed hop and rapeseed models are new in this field. Available models from literature showed inaccurate results, except for Cd; the modelling efficiency was mostly around zero. The use of interaction terms between parameters can significantly improve plant-specific models.
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