Showing 27 results for Moghaddam
M. Esfahani Moghaddam, A. Fotovat, Gh. Haghnia,
Volume 16, Issue 59 (spring 2012)
Abstract
Silver toxicity and its fate in the environment are currently being debated and are important as challenging research topics. Even though there are several studies on its total content in soils, fractionation of Ag especially in calcareous soils has not been investigated. Therefore, to provide fundamental information on the chemical behavior of Ag in calcareous and noncalcareous soils, we studied 8-step chemical fractions of Ag (i.e., EXCH, CARB, Me-Org, re-MeOx, H2O2-Org, am-MeOx, cr-FeOx, and RES) after 30 and 60 days of incubation in soils amended with Ag (0 and 15 mg kg-1), sewage sludge (0 and 20 t ha-1) and EDTA (0 and 0.5%). Experimental results showed that redistribution of Ag in spiked noncalcareous soils was EXCH (34%), H2O2-Org (33%) and RES (17%). In calcareous soils, after 30 days, EXCH- and RES-Ag increased but at the end of 60 days H2O2-Org-Ag increased. Based on our data, we could conclude that addition of Ag results in an increase of Ag mobility in soils but incubation and sewage sludge may have adverse effect on its mobility. In contrast to noncalcareous soil, EDTA in calcareous soil resulted in higher Ag mobility. This may have environmental implications in Ag polluted calcareous soils.
Sh. Ghorbani Dashtaki, S. Dehghani Baniani, H. Khodaverdiloo, J. Mohammadi, B. Khalilmoghaddam,
Volume 16, Issue 60 (Summer 2012)
Abstract
Saturated hydraulic conductivity (Kfs) and macroscopic capillary length of soil pores are important hydraulic properties for water flow and solute transport modeling. Measuring these parameters is tedious, time consuming and expensive. One way is using indirect methods such as Pedotransfer functions (PTFs). The objective of this research was to develop some PTFs for estimating saturated hydraulic conductivity and inverse of macroscopic capillary length parameters (*). Therefore, the coefficients, Kfs and * from 60 points of Azadegan plain in Shahrekord were measured using single ring and multiple constant head method. Also, some of the readily available soil parameters from the two first pedogenic layers of the soils were obtained. Then, the desired PTFs were developed using stepwise multiple linear regression. The accuracy and reliability of the derived PTFs were evaluated using root mean square error (RMSE), mean error (ME), relative error (RE) and Pearson correlation coefficient (r). The highest correlation coefficients of 0.92 and 0.72 were found between Kfs-bulk density and *-bulk density, respectively. There was no significant correlation between soil particle size distribution and Kfs and *. This can be related to the fact that most of the soil samples were similar in texture and macro pores. The most efficient PTFs in predicting Kfs and * could explain 85 and 66 percent of the variability of these parameters, respectively. All the derived PTFs underestimated the Kfs and * parameters.
M. Ebrahimi, M. Jafari, E. Rouhimoghaddam,
Volume 19, Issue 72 (summer 2015)
Abstract
The present study was conducted to increase phytoextraction efficiency of Festuca ovina L. in lead contaminated soil in the EDTA-assisted (0, 1.5, 3, 1.5+1.5, 3+3, 6 mmol kg−1), assess the best time of plant harvesting to increase Pb uptake and method of EDTA application to reduce Pb leaching risk. The results revealed that the greatest Pb uptake was observed in 3EDTA treatment. Therefore, 3mmolkg-1 was used in the second step for assessing harvest time for 15, 30 and 45 days. Results showed that the concentration of Pb in plant tissues was increased with the passage of time and the best harvest time to achieve maximum removal of Pb was 60 days of the first harvest. In the third step to reduce leaching of Pb-chelate, 3mmolkg-1 EDTA in five ways of single, double, triple, quadruplet, quintuplet were added to the soil. The results indicated that under quintuplet application, Pb content reached its minimum concentration in the soil and in the plant organs, the Pb concentration was maximum and metal concentration in the plant organs did not vary significantly when triple, quadruplet and quintuplet dosages were added (p<5%). Overall, optimum phytoextraction of F. ovina L. and Pb leaching reduction were achieved when 3mmol kg−1 EDTA was added in quintuplet dosage and the plant was harvested at the end of growth stage.
Z. Sorkheh, B. Khalili Moghaddam,
Volume 22, Issue 1 (Spring 2018)
Abstract
The purpose of this research was to study the effects kerosene by a factorial experiment in the nested design in three replications. The factors included region (Shush, Dezful and Bavi), plant (parsley, dill, coriander and carrot), and management practice (control, contaminated field with kerosene 1, contaminated field with kerosene 2). Heavy metals concentration (Pb, Zn, Cu and Cd) was measured in soil (DTPA extraction method) and plants samples. The results indicated that the average values of the heavy metals concentration in both soil and plants samples subjected to kerosene contaminated treatments were greater than those of the control treatment in all of the regions. The Bavi region had the highest Cd (14.29 in soil; 11.9 in Dill) and Pb (40.46 in soil; 35.53 in Coriander) and the lowest Zn (34.75 in soil ; 28.44 in Carrot) and Cu(22.30 in soil; 16.96 in Carrot) concentration values in both soil and plants subjected to kerosene contaminated treatments. Also, the lowest concentration values of Cd (9.33 in soil; 8.01 in Carrot) and Pb (30.36 in soil; 23.54 in Carrot) and the highest values of Zn (109.08 in soil; 86.33 in Dill) and Cu (47.71 in soil; 38.57 in Dill) were recorded in Shush and Dezful regions, respectively. Based on these findings, kerosene usage could lead to a significant increase in the heavy metals (Cd, Cu and Pb) uptake, exceeding the critical level for the vegetables. This might increase the transformation risk of the mentioned heavy metals in the food chain
F. Farsadnia, B. Ghahreman, R. Modarres, A. Moghaddam Nia,
Volume 22, Issue 3 (Fall 2018)
Abstract
In recent years, the joint distribution properties of drought including severity and duration have been widely evaluated using copula. Few studies, however, have worked on drought modeling based on stream flow, especially in semi-arid regions such as the southern regions of Iran. This study followed two purposes. The first purpose was to find the appropriate marginal distribution function for hydrologic drought duration and severity, and the other one was to find appropriate copulas. First, the severity and duration of hydrological droughts in the hydrometery stations in the Karkhe basin were extracted by the run’s theorem, and the absence of trends was tested using the modified Mann-Kendall trend test. Appropriate marginal distribution functions for duration and severity drought were derived by using the linear moment's method. In addition, copula’s parameters for Frank, Clyton and Gumbel families were calculated by both direct and indirect methods. The best copulas were selected by the goodness of fit tests. Finally, the joint and conditional return periods for duration and severity drought were derived for each station. The results showed that drought severity and duration for all hydrometery stations in Karkhe basin followed both the generalized extreme value marginal distribution function and Gumbel copulas family, which could be used for regional copulas modeling.
S. Ghobadi Alamdari, A. Asghari Moghaddam, A. Shahsavari,
Volume 23, Issue 4 (Special Issue of Flood and Soil Erosion, Winter 2019)
Abstract
Lack of the proper conjunctive use of surface and groundwater resources causes large water stresses in one of these resources. Conjunctive use of surface and groundwater, especially in arid and semi-arid regions, is a scientific and practical solution for sustainable water resources management. The aim of this research was to prepare some mathematical modeling to apply the conjunctive use of surface and groundwater in the Dehloran plain aquifer. In this study, the mathematical model of the Dehloran plain aquifer was developed using GMS 9.1 and the river data were entered. For the steady state condition, the time series data in the average year 2010-2011 were utilized. In the next step, the time series data from October, 2010, to September, 2011, were used for the unsteady state analysis. In the unsteady state, four stress periods were taken; then the model calibration was carried out in three steps for each stress period; after the optimization of the hydrogeological parameters of the model, its verification was done for the period of 2011-2012 period. After the calibration of the model in the unsteady state, the values of the mean error (ME), the mean absolute error (MAE) and the root mean squared (RMS) errors measured in piezometers were obtained to be -0.24, 0.46 and 0.65, respectively. The results of verification confirmed the ability of the model in simulating the natural conditions of the aquifer. Finally, applying different scenarios to the model showed that the proper conjunctive use of surface and groundwater could increase the volume of water at a rate of 2.23 million cubic meters per year.
V. Habibi Arbatani, M. Akbari, Z. Moghaddam, A.m. Bayat,
Volume 26, Issue 4 (Winiter 2023)
Abstract
In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R2, MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.
