JWSS - Journal of Water and Soil Science

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Accumulation of heavy metals (HMs) in cultivated soils is an important environmental problem in many parts of the world. In recent years, HM leaching through preferential paths and also in the form of metal-organic acids complexes has received much attention. For this reason, the effects of plants on creating preferential flow through the soil is important. The objective of this study was to assess the mobility of Cd, Cu, Pb and Zn in a calcareous soil (Typic Haplocalcids) planted with safflower (Carthamus tinctorious). The study was conducted on 12 undisturbed soil columns (22.5 cm in diameter and 50 cm in depth) in greenhouse. The top 10 cm of soil in half of the columns were contaminated with Cd, Cu, Pb, and Zn at the rates of 19.5, 750, 150 and 1400 kg ha^-1, respectively. Half of the contaminated and uncontaminated columns were planted with safflower at a rate of 20 seeds m^-2. Leachate was collected continuously and analyzed for these four heavy metals. After the crop harvest, soil samples were taken at 10 cm intervals and analyzed for DTPA-extractable and water-soluble HMs concentration. Results showed that heavy metal concentrations (DTPA and soluble) of the subsoil in planted columns were more than in fallow columns. The DTPA-extractable Cd, Cu and Zn concentrations in contaminated planted columns were 3.3-, 1.5- and 1.5-times more than in contaminated fallow columns, respectively. The water-soluble Cd, Cu and Zn in planted treatments increased 2.4, 1.2- and 1.1 times more than the fallow treatment. Lead concentrations in both planted and fallow treatments were similar. Metal uptake by safflower increased such that Cd and Zn uptake was more than Cu and Pb. Cd, Cu, Pb and Zn concentrations in the leachate of planted columns increased 32.0-, 2.5-, 6.0- and 2.7- time more than the uncontaminated planted columns. In summary, although topsoil contamination increased metal uptake by safflower, however the presence of safflower increased DTPA-extractable and also soluble metal concentrations in the soil profile and therefore enhanced metal mobility. The order of metal mobility was Cd > Zn >Cu >Pb.

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Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are two methods which are used to develop PTFs. In this study, the multi-layer perceptron (MLP) neural network and backward and stepwise regression models were used to estimate saturated hydraulic conductivity using some soil characteristics including the percentage of particle size distribution, porosity, and bulk density. Data of 125 soil profiles were collected from the reports of basic soil science and land reclamation studies conducted by Khuzestan Water and Power Organization. The results showed that MLP neural network having Bayesian training algorithm with the greater coefficient of determination (R2=0.65) and the lower error (RMSE =0.04) had better performance than multiple linear regression model in predicting saturated hydraulic conductivity.