Search

This study aims to contribute to understanding the processes influencing the spatial distributions of trace metal elements in surface sediments of Brenne’s ponds (France) and assess potential pollution levels in the area. It was motivated by the lack of knowledge regarding the hydrogeochemical dynamics of limnic entities, which are ponds. To achieve this, 25 sediment samples were collected from Pond Thomas, considered as a ‘sentinel pond’, and underwent analysis for grain size, total organic carbon (TOC) concentrations, and trace metal elements (TMEs) (specifically Cu, Ni, Pb, As, Zn, Cr, and Cd). The results revealed that trace metal elements are primarily concentrated in two areas: the northwest section and the upstream region in the southern part of the pond. Applying various pollution indicators such as the enrichment factor (EF), geo-accumulation index (Igeo), and potential ecological risk index (RI), it was found that Pb and As are the most enriched elements, with respective EFs of 3.5 and 4.9. Their average concentrations exceed geochemical background values by 4 and 5.68 times in certain samples. The sediments in the southern part of the pond showed moderate contamination levels for As and Pb, with moderate to severe pollution (2 < Igeo < 3), reaching a maximum Igeo of 2.26 for As. Although the RI values across the pond are relatively low, Cd and As still pose moderate potential ecological risks. Additionally, principal component analysis and Pearson correlation analysis indicated two sources of TMEs in the area: Zn, As, Ni, Cd, Pb, and Cr are primarily from natural sources, while Cu appears to have a local anthropogenic origin. [ABSTRACT FROM AUTHOR]

The statistical variation of soil properties and their stochastic combinations may affect the extent of soil contamination by metals. This paper describes a method for the stochastic analysis of the effects of the variation in some selected soil factors (pH, DOC and EC) on the concentration of copper in dwarf bean leaves (phytoavailability) grown in the laboratory on contaminated soils treated with different amendments. The method is based on a hybrid modeling technique that combines an artificial neural network (ANN) and Monte Carlo Simulations (MCS). Because the repeated analyses required by MCS are time-consuming, the ANN is employed to predict the copper concentration in dwarf bean leaves in response to stochastic (random) combinations of soil inputs. The input data for the ANN are a set of selected soil parameters generated randomly according to a Gaussian distribution to represent the parameter variabilities. The output is the copper concentration in bean leaves. The results obtained by the stochastic (hybrid) ANN-MCS method show that the proposed approach may be applied (i) to perform a sensitivity analysis of soil factors in order to quantify the most important soil parameters including soil properties and amendments on a given metal concentration, (ii) to contribute toward the development of decision-making processes at a large field scale such as the delineation of contaminated sites., (Copyright © 2013 Elsevier Ltd. All rights reserved.)