مجله محیط زیست و مهندسی آب
سال یکم شماره 1 (زمستان 1394)

In this study, MCM-41 was prepared via hydrothermal method in presence of ethanol as a co-solvent and co-surfactant in alkaline media. Inorganic sorbent ZnCl2-MCM-41 was synthesized via covalent grafting method by dispersion of samples into pure toluene for solid state interaction between silanol groups of MCM-41 surface and ZnCl2 particles. Samples were characterized by nitrogen adsorption analysis, XRD, BET surface area, and FTIR spectrometry. MCM-41 BET surface area measured was 1099 m2/g, which was later reduced to 602 m2/g after incorporation of ZnCl2 particles onto MCM-41 surface. Effect of different experimental conditions such as solution temperature, pH, contact time, and initial concentration of Ni(II) ions were investigated on the amount of nickel adsorption. For experimental data, the Langmuir isotherm showed a better fit than Freundlich isotherm, which indicates that nickel adsorption onto inorganic sorbent was homogeneous and monolayer. At optimum conditions, maximum adsorption capacity of Ni(II) by sorbent obtained was 303 mg/g according to Langmuir isotherm. Pseudo-second order model predicted kinetic of nickel adsorption onto synthesized sorbent better than other models. Free energy was 9.8 kJ/mol determined by Dubinin-Radushkevich model, which confirms chemical nature of nickel adsorption onto synthesized sorbent.

Investigating the soil hydraulic properties such as wilting point and field capacity is very important in studying and modeling water and solute in soil. Due to the spatial and temporal changes of these characteristics, recent studies have led to the development of indirect methods to estimate the soil properties. In this study, 10 models were used in order to estimate the soil water characteristics curve (SWCC) in soils of Clay, Clay loam, and Sandy loam; hence, to choose the best model. For this purpose, first the SWCC for the studied soils was calculated using pressure plate and then, models were fitted to the experimental data. Saturated hydraulic conductivity was measured in the laboratory and it was estimated by Rosetta model. The results showed that except for Campbell and Russo, all models presented the appropriate performance in estimation of SWCC. Moreover, Biexponential model fitted well with the best performance compared with other models in soils of Clay, Clay loam, and Sandy loam with the sum of the squared residuals (SSR) of 5×10-4, 2×10-4, 1×10-4 respectively, RMSE: 0.013, 0.015, and 0.015 cm3/cm3 respectively and R2: 0.997, 0.995, and 0.998 respectively. SSCBDTH331500 method in Rosetta model predicted the amount of saturated hydraulic conductivity with high accuracy. The results of this research will help to provide the optimum method for estimating SWCC.

Flow sediment load is a useful indicator in predicting soil erosion in watersheds. As a result, developing a model to estimate sediment load can be useful in the management and operations of watersheds and river engineering projects. In this study, the data classification was studied as a way to increase the accuracy of Artificial Neural Network (ANN) model for estimating suspended sediment. For this purpose, the amount of suspended sediments in the Khalifa-Tarkhãn and Chehelgazi Rivers in Gheshlagh watershed, Kurdistan, Iran was predicted in three modes using an ANN with multilayer configurations. The measured data were also modeled without such classification. Then the runoff data were classified as high and low flows and the sediment data based on sediment concentration were classified as high and low concentrations. The observed runoff and sediment data were used to calibrate the models. Then the calculated values were compared with observed data and the models efficiency was examined using statistical tests. The findings indicate the effective role of data classification in improving the ANN efficiency in sediment estimation. So that classification based on sediment concentration promoted the model efficiency in Chehelgazi and Khalifa-Tarkhān by 16.6 and 30.5% respectively. The comparison of classifications showed that in comparison with flow, the sediment classification has more affective role on models estimations. The results of this study can be used to improve the management of the basin by more accurately estimating suspended sediments transporting in the rivers leading to Gheshlagh Dam Reservoir.

Irrigation and drainage networks are having numerous problems in various stages such as design, implementation and utilization usually that reduce efficiency and destroy its structures. Also its reform and restructuring is difficult and unprofitable. This subject is main concern for water planners in the country and so far several activities for management of irrigation and drainage networks have been taken but most of these measures have not had significant success due to the lack of holistically. The main purpose of this study was to assess the irrigation and drainage networks performance (Gavoshan B2 unit (from the viewpoint of water user cooperatives in Kamyaran Township. To prepare the questionnaire, preliminary data were collected by PRA techniques and analysed using SWAT model and 12 point of strength, 10 weakness, 8 opportunity, 11 threats were ranked and 25 strategy have been presented. Findings showed that internal factors (strength and weakness points) had higher score weight than external factors (treatments and opportunities). Strength points with mean score weight of 3.97 had maximum and opportunities with mean score weight of 3.36 had minimum score. Score weight of strength points are more than score weight of weakness, that it shows its related Criteria has positive effect of on project. Also score weight of threats points are more than score weight of opportunity, that it shows its related Criteria has negative effect of on project. Hence, based on results is determined that diversification strategy should be considered as priority for optimization development of irrigation networks performance.

In recent years, newly-developed data mining and machine learning techniques have been applied in various fields to build intelligent information systems. However, few of these approaches offer online support or are flexibleto be adapted to large and complex datasets. Therefore, the present research work adopts Particle Swarm Optimization (PSO) techniques to obtain appropriate parameter settings for membership function and integrates the Adaptive-Network-based Fuzzy Inference System (ANFIS) model to make the model fit for predicting scour depth. A dataset of 188 scour depths for single piers presented by the USGS was used. Results of the model prediction show that the derived model is best fitted to the field data. The proposed one-order momentum method is able to learn quickly through one-pass training and provides high-accuracy short-term predictions. Moreover, this method is suitable for online learning but the two-order momentum method is appropriate for incremental learning. The PSO-ANFIS approach could provide better results in predicting scour depths compared with other models.

In this research, the impact of meteorological parameters on SO2 accumulation between 2009 to 2015 years over Urmia City was investigated. Due to the polluted days and no remarkable effects relating to increasing industrial activities and transport services as well as the number of lorries and cars around the Urmia City, the parameter of meteorology plays a very crucial role in this issue. Therefore, some meteorological parameters considered were wind speed, wind direction, nebulosity, and temperature and SO2 pollutant as air pollution index. This study revealed that SO2 accumulation over Urmia City happens when the wind speed is 2-3 m/s, prevailing wind direction is Eastern, South-Eastern, nebulosity is none or low and the temperature is between -5 to °C. February month is the most polluted month in year and the highest pollution occurs at 15-20 h.

Iran is located in arid and semiarid areas; hence, it is essential to optimize the use of water resources, conserve, and reduce the loss of these resources. The use of mulch is one way to reduce water loss, especially in agriculture. This research was conducted in a completely randomized design in 2014. In order to investigate the effect of the amount and types of mulch on reducing soil surface moisture, nine treatments with three replications experiments were conducted in University of Birjand. The treatments were gravel mulch types (3 and 4), straw (10 and 20 g), coco peat (5 and 10 g), and zeolite (5 and 20 g). The treatments were saturated then weighed. The moisture loss per day was calculated for each treatment. The research showed that during testing the treatment, effect was significant at 1%. In addition, moisture content reduction did not change on a regular basis in none of the treatments. Moreover, it was found that the greatest effect in reducing evaporation from the soil surface obtained when coco peat, stubble, and straw were applied at 10, 10, and 20 g respectively). Comparing total evaporation after the completion of the experiment indicated that all treatments had positive and significant effect on reducing evaporation. The reduction of evaporation rate in treatments of straw 10 g, coco peat 10, gravel 4, and especially in straw 20 g was more than other treatments.