Ali Asghar Besalatpour

Germination and Growth of Selected Plants in a Petroleum Contaminated Calcareous Soil

Difference in the ability of various crops to germinate and grow in contaminated soils should be better explored to choose the most appropriate plant species in the development of any phytoremediation process. Germination and subsequent growth of seven plants including tall fescue, agropyron, puccinellia, white clover, canola, safflower, and sunflower were tested in a soil with three petroleum contamination levels. Treatments consisted of C0 (uncontaminated soil), C1 (1:1 w/w, uncontaminated: contaminated soil), and C2 (1:3 w/w, uncontaminated: contaminated soil). Presence of total petroleum hydrocarbons (TPHs) in the soil had no effect on seed germination of agropyron, white clover, sunflower, and safflower, although canola and white clover seedlings were sensitive to these contaminants and failed to produce dry matter yield (DMY) at the end of the trial period. There were 52 and 56% decrease in germination of tall fescue and puccinellia seeds, respectively, in C2 treatment as compared to C0 treatment. No reduction was found in DMY of puccinellia in contaminated soils (C1 and C2), while the presence of TPHs in C2 decreased DMY of sunflower and safflower by about 50 and 73%, respectively. The results showed that germination did not predict the crop differences in subsequent growth and cannot be used as an approach to find suitable crops for field trials.

Landfarming Process Effects on Biochemical Properties of Petroleum-Contaminated Soils

The presence of petroleum contaminants in soil may be toxic to humans, plants, and soil microorganisms. Therefore, remediation of these compounds from the environment is vital. In this study, bioremediation of two petroleum-contaminated soils (S1 and S2) using a landfarming technique was evaluated. Investigation of the effect of this technique on biological and chemical properties of contaminated soil was also part of the goal. The results showed that about 50 and 57% of hydrocarbon contents were eliminated from soils S1 and S2 at the end of the experiment, respectively. Landfarming processes enhanced microbial respiration rate in both soils S1 and S2. Microbial biomass-nitrogen values in the landfarming plots were significantly (P < 0.05) higher than in the control plots (without landfarming operations). Urease activity increased by 21, 45, 26, and 23% in the landfarming plots as compared to the control plots for soil S2 at the end of first to the 4th month of the experiment, respectively. There was also significant difference (P < 0.05) in soil pH values between the landfarming treatment and control. Soil electrical conductivity in the landfarming plots was lower than in the controls. Total organic matter and total nitrogen contents in the landfarming plots were significantly lower in comparison to the control plots. It appears that improving soil aeration and exposing new layers of soil to sunlight and air as a result of landfarming operations facilitated the degradation of petroleum hydrocarbons.

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Surface soil shear strength can be a useful dynamic index for soil erodibility and thus a measure of soil resistance to water erosion. In this study, we evaluated the predictive capabilities of artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) in estimating soil shear strength from measured particle size distribution (clay and fine sand), calcium carbonate equivalent (CCE), soil organic matter (SOM), and normalized difference vegetation index (NDVI). The results showed that the ANN model was more feasible in predicting the soil shear strength than the ANFIS model. The root mean square error (RMSE), mean estimation error (MEE), and correlation coefficient (R) between the measured soil shear strength and the estimated values using the ANN model were 0.05, 0.01, and 0.86, respectively. In ANFIS analysis, the RMSE was 0.08 and a lower correlation coefficient of 0.60 was obtained in comparison with the ANN model. Furthermore, the ANN and ANFIS models were more accurate in predicting the soil shear strength than was the conventional regression model. Results indicate that the ANN model might be superior in determining the relationships between index properties and soil shear strength.

Reclamation of a Petroleum-Contaminated Calcareous Soil Using Phytostimulation

Soil contamination by hydrocarbons poses a threat to groundwater and food chains. Hence, the elimination of these compounds from contaminated soil is vital. In this study, we investigated the degradation of total petroleum hydrocarbons (TPHs) in the rhizosphere of tall fescue (Festuca arundinacea L.), agropyron (Agropyron smithii L.), safflower (Carthamus tinctorius L.), and sunflower (Helianthus annus L.) at three soil contamination levels, denoted as C0 (<50 mg kg-1 TPH), C1 (40360 mg kg-1 TPH), and C2 (69760 mg kg-1 TPH). The dry matter yield decreased with increasing contamination level in all four plant species. Safflower seedlings grew poorly and died within 10 to 11 weeks at the highest contamination level. Soil microbial respiration rate increased by 77 and 80% in the rhizosphere soil of tall fescue and agropyron, respectively, in the C1 treatment as compared to the control. The TPH concentration decreased by 71 and 69% in the C1 treatment and by 45 and 42% in the C2 treatment in the rhizosphere soil of agropyron and tall fescue, respectively. Sunflower had no significant effect on the degradation of the contaminating petroleum hydrocarbons in comparison to plant-free control. According to these results, agropyron and tall fescue appear to be suitable choices for the phytoremediation of investigated petroleum-contaminated soils.

Feature Selection Using Parallel Genetic Algorithm for the Prediction of Geometric Mean Diameter of Soil Aggregates by Machine Learning Methods

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.

Prediction of soil physical properties by optimized support vector machines

Prediction of soil physical properties by optimized support vector machines The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiple-linear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods.

Modeling Blue and Green Water Resources Availability in an Iranian Data Scarce Watershed Using SWAT

Knowledge of the renewable water resources of a watershed is strategic information which is vital for the long term planning of water and food security. In thi…

Identifying Soil and Plant Nutrition Factors Affecting Yield in Irrigated Mature Pistachio Orchards

ABSTRACT The main objective of this study was to evaluate the potential use of a hybrid Genetic Algorithm-Artificial Neural Network (GA–ANN) method for predicting pistachio yield and for identifying the determinant factors affecting pistachio yield in Rafsanjan region, Iran. A total of 142 pistachio orchards were selected randomly and soil samples were taken at three depths. Besides, water samples and leaves from branches without fruit were taken in each sampling point. Management information and pistachio yields were achieved by completing a questionnaire. Primarily, 58 variables affecting pistachio yield were measured, and then 26 out of them were selected by minimizing mean square error (MSE) using a feature selection (FS) method. The results showed that the accuracy of the method was acceptable. Furthermore, the sensitivity analysis showed that the main determinant features affecting the pistachio yield were the irrigation water amount, leaf phosphorus, soil soluble magnesium, electrical conductivity (EC), and leaf nitrogen.

Soil Particulate Organic Matter (POM) Prediction in a Mountainous Watershed using Artificial Neural Networks

Soil organic matter is a very important component of soil that supports the sustainability and quality in all ecosystems, especially in arid and semi-arid regions. A comparison study was carried out to verify when the artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for the prediction of soil organic matter (SOM) and particulate organic matter (POM). Discussions of advantages and disadvantages are given for both methods. Three different sets of easily available properties (soil properties alone, topographic and vegetation index, a combination of soil and topographic data) were used as inputs and the one affecting the model the most was determined. The smallest prediction errors were obtained by the ANN method; however, the prediction accuracies of the constructed MLR models using different data sets were closed to the ANN models in many cases.