Effect of salinity on heavy metal mobility and availability in intertidal sediments of the Scheldt estuary

Laing, Gijs Du; Vos, R. De; Vandecasteele, Bart; Lesage, Els; Verloo, Marc

doi:10.1016/j.ecss.2007.10.017

Cited by 220 publications

(102 citation statements)

References 30 publications

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“…6). Current studies have indicated that salinity could affect the mobility and availability of trace metals (Du Laing et al 2008. Since chlorocomplexation decreases the activity of free ion in the sediment solution and increases desorption, trace metal (especially Cd) can be mobilized as soluble chloride complexes (Hahne and Kroontje 1973).…”

Section: Variations Of Heavy Metals In Plants and Sedimentsmentioning

confidence: 99%

Temporal variations and bioaccumulation of heavy metals in different Suaeda salsa marshes of the Yellow River estuary, China

Song

Sun

2014

Environ Sci Pollut Res

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To understand the temporal variations and bioaccumulation of heavy metals in the coastal marshes, the concentrations of heavy metals (Cr, Ni, Pb, and Cu) in the two Suaeda salsa marshes [middle S. salsa marsh (MM) and low S. salsa marsh (LM)] of the Yellow River estuary were determined from May to November in 2008 by in situ sampling and inductively coupled plasma mass spectrometry (ICP-MS) analysis. Results showed that heavy metal concentrations in S. salsa of MM and LM were generally in the order of Cu>Cr>Pb>Ni, while those in sediments fell in the order of Cr>Ni>Cu>Pb. Heavy metal concentrations of S. salsa in MM and LM were different, and significant differences were observed in stems (F = 4.797, p= 0.046) and litters (F=6.799, p=0.026) for Ni. Litter was the main stock of heavy metals, and the allocations of Cr, Ni, and Pb reached 31. .64 % (in LM), respectively. The ratios of roots/ leaves (R/L) and roots/stems (R/S) for Cr and Ni in MM were mostly greater than 1, while those ratios in LM were mostly less than 1, indicating that Cr and Ni in S. salsa at LM had greater mobility compared with those at MM. Moreover, the [accumulation factor, AF] plant of Cr, Ni, Cu, and Pb in LM, especially [AF] root and [AF] stem of Cr and [AF] litter of Ni, was also higher than that in MM. These indicated that S. salsa grown in LM was more suitable for potential biomonitor or phytoremediation of Cr, Ni, Cu, and Pb if intertidal sediments were seriously contaminated with an increase of pollutant loading (especially heavy metals) in the Yellow River estuary. The use of biomonitor (S. salsa) living and growing in LM could yield valuable information not only on the presence of anthropogenic stressors, but, more importantly, on the adverse influence the stressors are having on the environment.

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Section: Variations Of Heavy Metals In Plants and Sedimentsmentioning

confidence: 99%

Temporal variations and bioaccumulation of heavy metals in different Suaeda salsa marshes of the Yellow River estuary, China

Song

Sun

2014

Environ Sci Pollut Res

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“… EC: Du Laing et al, (2008Laing et al, ( , 2009 and Hatje et al, (2003) found that an increase in conductivity (salinity) led to an increase in the mobility of some metals such as cadmium and zinc, thereby increasing the availability and the bioavailability of these metals to the plant.…”

Section: Selection Of Soil Factorsmentioning

confidence: 99%

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

Hattab

Motelica-Heino

2014

Journal of Geochemical Exploration

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To cite this version:Nour Hattab, Mikael Motelica-Heino. Application of an inverse neural network model for the identification of optimal amendment to reduce Copper toxicity in phytoremediated contaminated soils. Journal of Geochemical Exploration, Elsevier, 2014, 136, pp.14-23 AbstractArtificial neural network ANN prediction approaches applied to the modeling of soil behavior are often solved in the forward direction, by measuring the response of the soil (outputs) to a given set of soil inputs. Conversely, one may be interested in the assessment of a given set of soil inputs that leads to given (target) soil outputs. This is the inverse of the former problem. In this study, we develop and test an inverse artificial neural network model for the prediction of the optimal soil treatment to reduce copper ( Cu) toxicity assessed by a given target concentration of Cu in dwarf bean leaves (BL) from selected soil inputs. In this study the inputs are the soil pH, electrical conductivity (EC), dissolved organic carbon (DOC) and a given target toxicity value of Cu, whereas the output is the best treatment to reduce the given toxicity level. It is shown that the proposed method can successfully identify the best soil treatment from the soil properties (inputs).Two important challenges for optimal treatment prediction using neural networks are the nonuniqueness of the solution of the inverse problem and the inaccuracies in the measurement of the soil properties (inputs). It is shown that the neural network prediction model proposed can overcome both these challenges. It is also shown that the proposed inverse neural network method 2 can potentially be applied with a high level of success to the phytoremediation of contaminated soils. Before large-scale application, further validation is needed by performing several experiments and investigations including additional factors and their combinations to capture the complex soil behavior.

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“…The third parameter was EC. Du Laing et al (2008Laing et al ( , 2009) and Hatje et al (2003) found that an increase in conductivity (salinity) led to increasing the mobility of some metals such as Cd and Zn, thereby increasing the availability and bioavailability of these metals to plants.…”

Section: Selection Of Soil Factorsmentioning

confidence: 99%

Application of neural network model for the prediction of chromium concentration in phytoremediated contaminated soils

Hattab

Hambli

Motelica-Heino

et al. 2013

Journal of Geochemical Exploration

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The assessment of chromium concentrations in plants requires the quantification of a large number of soil factors that affect their potential availability and subsequent toxicity and a mathematical model that predicts their relative concentrations. Many soil characteristics can change the availability of chromium (Cr) to plants in soils. However, accurate, rapid and simple predictive models of metal concentrations are still lacking in soil and plant analysis. In the present work a novel artificial neural network (ANN) model was developed as an alternative rapid and accurate tool for the prediction of Cr concentration in dwarf bean leaves grown in the laboratory on phytoremediated contaminated soils treated with different amendments. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in south-western France. Second, a series of measurements were performed on the soil samples.The inputs are the soil amendment, the soil pH, the soil electrical conductivity and the dissolved organic carbon of the soil and the output is the concentration of Cr in the dwarf bean 2 leaves. Third, an ANN model was developed and its performance was evaluated using a test data set and then applied to predict the exposition of the bean leaves to the Cr concentration versus the soil inputs. The performance of the ANN method was compared with the traditional multi linear regressions method using the training and test data sets. The results of this studyshow that the ANN model trained on experimental measurements can be successfully applied to the rapid prediction of plant exposition to Cr.

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Effect of salinity on heavy metal mobility and availability in intertidal sediments of the Scheldt estuary

Cited by 220 publications

References 30 publications

Temporal variations and bioaccumulation of heavy metals in different Suaeda salsa marshes of the Yellow River estuary, China

Temporal variations and bioaccumulation of heavy metals in different Suaeda salsa marshes of the Yellow River estuary, China

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

Application of neural network model for the prediction of chromium concentration in phytoremediated contaminated soils

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