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Concentrations of potentially toxic elements in soils and vegetables from the macroregion of São Paulo, Brazil: availability for plant uptake

The occurrence and accumulation of heavy metals or so-called potentially toxic elements (PTEs) in soils and plants have driven long-standing concerns about the adverse effects such metals have on the environment and human health. Furthermore, contaminated food products are known to be a leading sour...

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Bibliographic Details
Published in:Environmental monitoring and assessment 2016-02, Vol.188 (2), p.92, Article 92
Main Authors: dos Santos-Araujo, Sabrina Novaes, Alleoni, Luís Reynaldo Ferracciú
Format: Article
Language:English
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Summary:The occurrence and accumulation of heavy metals or so-called potentially toxic elements (PTEs) in soils and plants have driven long-standing concerns about the adverse effects such metals have on the environment and human health. Furthermore, contaminated food products are known to be a leading source of exposure to heavy metals for the general population. It is crucial to accurately assess the concentrations of metals in crops and the bioavailable contents of these elements in the soil. The state of São Paulo is the largest consumer market of horticultural products in Brazil with production focused essentially on urban and industrial areas, which greatly increases the degree of exposure to contaminants. The objective of the authors in this study was to evaluate the soil-plant relationships between concentrations of Cd, Cu, Ni, Pb and Zn in vegetable and garden soils in the state of São Paulo, Brazil. To accomplish this, 200 soil (0–20 cm) and plant samples were collected from 25 species in the production areas. With the exception of Cd, there was positive correlation between pseudototals (USEPA 3051a) and bioavailable contents (extracted with DTPA) of heavy metals. However, the Cd and Pb contents in plants were not significantly correlated with any of the variables studied. All random forest and tree models proved to be good predictors of results generated from a regression model and provided useful information including covariates that were important for specifically forecasting Zn concentration in plants.
ISSN:0167-6369
1573-2959
DOI:10.1007/s10661-016-5100-2