مقالات رزومه سمیه صدر

This research was conducted in order to investigate the quality of groundwater for agricultural use in the northern and central parts of Kerman Province. In this study, there were 534 water samples from 8 counties (Kerman, Zarand, Bam, Bardsir, Jiroft, Baft, Faryab, and Rafsanjan), which were collected from different sources, and concentration of Na+, Ca+2, Mg+2, Cl-, SO4-2, and HCO3-2 was determined. Also, indicators such as Total Dissolved Solids (TDS), Sodium Absorption Ratio (SAR), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC), Electrical Conductivity (EC), Salinity Potential (PS), Magnesium Hazard (MH), Water Hardness (TH), percentage of sodium soluble in water (SSP) were determined and evaluated. After determining the quality characteristics of water, the results were compared and interpreted with water standards for agriculture. AqQA software was used to draw graphs to investigate and compare water characteristics. Based on the results, more than 37% of the studied resources were classified as unsuitable (salinity above 2 dS/m) in terms of salinity. Zarand and Rafsanjan counties had unfavorable conditions in terms of groundwater quality indicators, with EC, SAR, PS, TH, SSP and MH in critical conditions in one or both counties, but, in terms of agricultural water standards, the other sites had good quality water. The combined type of underground water in all areas was Ca-Mg-SO4-Cl: while in the northern parts of the province, such as Rafsanjan, Zarand, Bardsir, as well as Bam and Kerman, the predominant anion of water was chlorine, in Faryab, Baft and Jiroft, the status of the predominant anions was slightly sulfate. In most of the counties, Na+>Mg+2>Ca+2 were the dominant anions of chlorine-waters, and little bicarbonate was observed. Also, the dominant water in all areas were Na-Cl and Mg-Cl . Therefore, it is necessary to manage water properly and plant crops suitable for the chemical conditions of waters.

Optimum soil water content (OSWC) for tillage is the moisture at which, if tillage is done, the maximum amount of crushing (friability) is created in the soil with minimum energy consumption. The purpose of this research was to determine the OSWC for tillage operations under different agricultural land uses in Jiroft Plain, Iran, and to evaluate the effectiveness of the decision tree model in this regard for the region. For this purpose, soil samples (90 samples) were taken intact from 0 to 20 cm depth and some of its physical and chemical characteristics were determined. To determine the soil water characteristics curve, the soil water contents at matric suctions of 10, 30, 50, 100, 300, 500, 1000 and 1500 kPa were measured using a pressure plate device. The results showed that the type of cultivation did not have a significant effect on the physical and chemical characteristics of the soil, including the optimum weighted humidity. OSWC prediction was done with decision tree. In this research, three prediction accuracy criteria, namely, coefficient of determination (R2), root mean square error (RMSE), and normalized root mean square error (NRSME) were used to evaluate the model performance. The results showed that use of decision tree for predicting the OSWC was appropriate due to having a lower NRMSE (0.017) and a high R2 (0.75). Also, the results of OSWC modeling showed organic matter as the most influencing variable, followed by the equivalent calcium carbonate percentage, electrical conductivity, apparent density, and percentage of sand and silt.

Heavy metals are known to be the most dangerous environmental pollutants because they do not decompose by physical processes and therefore remain for a long time and will be effective in biochemical cycles and ultimately in the human food chain, disrupting biological reactions and damaging organs. And they will even die. Therefore, in order to control the quality of the environment, it is important to study the heavy elements in the soil. So far, much research has been done to identify contaminated areas and implement sustainable land use policies with respect to the risk of contamination. In this study, the spatial distribution of nickel in surface soils (0-10 cm) in parts of Qom province with urban-industrial, agricultural and uncultivated land uses with an area of 883 Km2 was investigated. In this study, geostatistical tools (ordinary kriging) were used. Sampling was performed at 209 points on a grid with distances of about 1.5 × 1.5 km in agricultural and urban land use and about 2×2 km in uncultivated land use. Concentration of total nickel and absorbable soil was measured by atomic absorption spectrometer. Statistical and geostatistical studies were performed by SPSS, Variowin and WINGSLIB software and contamination map was drawn with Surfer16 software. The results showed that the concentration of nickel in any of the forms in the region did not show toxicity. But there were significant differences in land uses. Existing industries, parent materials, and nickel-rich basic and ultra-basic rocks, the use of phosphate fertilizers and sewage sludge may have increased nickel, and the direction of westerly winds has led to the transfer of nickel in the region.

Tensile strength is one of the most important indicators for soil physical quality, which is equivalent to the maximum stress applied to an aggregate without any disruption. The purpose of this study was to investigate the performance of different modeling methods for estimating soil tensile strength of some Rafsanjan pistachio orchards. For this purpose, soil samples (80 samples from 0- 30 cm depth mostly sandy loam) were taken and some soil physical and chemical properties were determined. Aggregate tensile strength was also measured in different sizes. The average EC, pH, and SAR indicated that soils of the study area were saline and sodic. Multiple regression between tensile strength and other soil properties were investigated. Tensile strength modeling was also performed using multilayer perceptron neural network and decision tree. The mean squares of error and coefficient of determination were used to evaluate different modeling models. The results of model evaluation showed that the use of regression decision tree for predicting tensile strength was better than the other modeling methods because of the lowest error (R2=0.88 and RRMSE = 14%) compared to the two methods of multiple regression and the multilayer perceptron neural network. Also, the results of different tensile strength modeling showed that the percentage of clay, percentage of dispersible clay, adsorption ratio of sodium, percentage of calcium carbonate equivalent, and the percentage of organic matter are the most influential variables on tensile strength. According to the results, it seems that the most effective way to increase soil tensile strength and reduce soil bulk density in pistachio orchards is to increase the percentage of soil organic matter.

The aim of this study was to prepare a map of the distribution of lead and cadmium in the topsoils of parts of Qom province to the center of Qom city, with different agricultural, urban and industrial uses of barren in two total and absorbable forms. The spatial distribution of these elements in surface soils with different uses with an area of ​​1054 square kilometers was investigated. Sampling was performed at 209 points by networking the study area and the total and absorbable amount of lead and cadmium was determined using an atomic absorption spectrometer. All-round variograms of these elements were drawn by Variowin software. Due to the lack of a proper structure, no changes were made to the cadmium, kriging and mapping. Intermediation was performed by point kriging method with WinGslib software. MEE and MSEE were used to determine the accuracy of the estimates. The range of changes in total lead with a minimum of 9.6 and a maximum of 123.4 mg / kg is reported to be 113.8. In this study, the minimum and maximum total cadmium concentrations were 0.2 and 5.5, respectively. The average concentration of absorbable lead in the study area is 4.1 mg / kg with a minimum of 0.5 and a maximum of 34.8 mg / kg. Based on the lead distribution maps, it seems that the effective factor on the increasing trend of this element is the type of use. Lands with urban and industrial use had the highest amount of total and absorbable lead, which indicates the high potential of the area for pollution. Therefore, it is necessary to take the necessary management measures to prevent the occurrence of destructive environmental accidents.

In recent decades, climate variation during the pistachio growing have significantly reduced the quantity nd quslity of yield of this strategic crop of Iran. The purpose of this study is to determine the most important climatic factors affecting pistachio crop yield using C&R decision tree model in Rafsanjan region south of Iran. during the period of 1380-1399. Modeling was performed using climatic variables including wind speed, number of frost days,rainfall,total evaporation,mean relative humidity, sunshine hours, mean temperature, mean of Tmax and Tmin as input variables and pistachio yield as the target variable. Correlation coefficient, root mean square error, relative error and bias metrics were used to evaluate the model. The decision tree model was run separately for 6 months before and sfter harvest. The obtained correlation coefficients were of 0.88 and 0.86, respectively, which is acceptable for prediction of yield. The RMSE values for pre- and post-harvest periods were 521 and 558 Kg ha-1, respectively. According to the results of the decision tree, during the preharvest period, the most significant attributes were the relative humidity and the number of sunny hours, respectively and the for the second half of the year the wind speed, minimum temperature, rainfall and relative humidity were the most affecting variables, respectively.

The present study aimed at investigating the transport of lead by colloids in two types of soils with sandy loam and clay loam textures under vermicompost and organic acid treatments. The experiment was conducted in a complete randomized design. Treatments consisted of three factors including soil texture at two levels (sandy loam and clay loam), additive at five levels (50 and 1000 μM levels of oxalic acid, 1% and 2% weight basis for vermicompost, and a zero level as a control), and the amount of leaching water at 10 levels (10 pore volume). PVC soil columns with an inner diameter of 15 cm and a height of 25 cm were prepared and the treatments were applied, then, leaching was performed based on soil suction measurements at different pore volumes. The results indicated that, after the application of vermicompost to the clay loam soil, leaching colloid from the soil increased and the discharged lead decreased. Adding organic acid increased leaching colloid from the soil; however, treating 50 µM organic acids significantly decreased discharged colloid compared to the control and 1000 µM organic acids treatment. The lowest rates of lead discharge were in the control treatment and the more the concentration of the acid, the more was the discharged lead. However, regarding lead discharge, there was no significant difference among the three levels. In the sandy loam soil, the greatest level of colloid transport belonged to the control, and adding vermicompost significantly prevented colloid transport from the soil, which was compatible with the process of lead discharge. Moreover, adding organic acid decreased colloid transport and the greatest rates of the transportation was recorded in the control treatment that was also compatible with the process of lead discharge. Furthermore, the relation between lead and the discharged colloid was only significant in the sandy loam soil. The results of this study showed that the movement of soluble lead in fine textured soils was negligible and insignificant; however, a substantial amount of lead discharge was detected through colloidal adsorption transport in soil columns. Therefore, it may be concluded that such mechanism would result in lead contamination of groundwater in these types of soils.

Salinity is a highly important problem in arid and semi- arid region. In Iran، about 235،000 km 2 (or 14. 2% of the total area of the country) area is salt-affected، which is equivalent to about 50% of irrigated lands in Iran (Pazira 1999). Irrigating of these lands causes to transfer salts to the area of root growth and thus increases the osmotic pressure and reduces the absorption of the nutrient elements and product. Some of researchers believe that this process is one of the disasters that threats development of human societies. Esfahan province is exposed to the danger of salinity. Esfahan province is located in the central arid region of Iran. Of 105،000 km 2 total area، an area of 5000 km 2 is used for crop and fruit production. Soil and water salinity is the major limitation to achieve optimum crop yields. However، the classical soil survey methods of field sampling، laboratory analysis and interpolation of these field data for mapping، especially in large areas، are relatively expensive and time-consuming but to get informed about distribution of salinity in soil is very important for recognizing critical threshold، planning، management، operation of source، and suitable distribution of water to correct the soil saline. Soil chemical properties commonly have spatial dependence at regional scale (Yost et al.، 1982). Regional assessment of soil properties requires evaluation of their spatial distribution. In recent years، environmental scientists have come to appreciate the merits of geostatistics and kriging for investigating and mapping Soil chemical properties in un-sampled areas. There are a large number of reports to natural resource distributions. Geostatistic method such as Ordinary Kriging is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. Many geostatistical tools were originally developed as a practical means to describe spatial patterns and interpolate values for locations where samples were not taken. Three functions are used in geostatistics for describing the spatial or the temporal correlation of observations: these are the correlogram، the covariance and the semivariogram (hasani pak، 1999). The last is also more simply called variogram. The following parameters are often used to describe variograms: nugget: The height of the jump of the semivariogram at the discontinuity at the origin، sill: Limit of the variogram tending to infinity lag distances and range: The distance in which the difference of the variogram from the sill becomes negligible. In applied geostatistics the empirical variograms are often approximated by model function ensuring validity (Chiles and Delfiner 1999). Some important models are (Chiles and Delfiner، 1999; Cressie، 1993): The exponential variogram model، the spherical variogram model and The Gaussian variogram model. In assessments of some variables، spatial correlation structure is the same in all directions، or isotropic. In this case the variogram depend only on the magnitude of the lag vector، h = h، and not the direction، and the empirical variogram can be computed by pooling data pairs separated by the appropriate distances، regardless of direction. Such a variogram is described as omnidirectional. In many cases، however، a property shows different autocorrelation structures in different directions، and an anisotropic variogram model should be developed to reflect these differences. The most commonly employed model for anisotropy is geometric anisotropy، with the variogram reaching the same sill in all directions، but at different ranges. However some Geostatistical methods such as kriging need valid variograms. Cross validation is used to find the best model among the competitors. “Cross Validation” allows us to compare estimated and true values using the information available in our sample data set (Houlding، 2000). Study area: This research was conducted in Isfahan province (Fig1). It is located in the center of Iran in a predominantly arid or semiarid climate condition and is about 6800 km2 around Zayandehroud River. Mean annual precipitation and temperature are 120 mm and 14. 5 Co and Annual evapotranspiration is 1500 mm. The soils are classified as Aridisols. The area covers different land uses including agricultural، industrial، urban and uncultivated lands. In this study، soil sampling strategy was random stratify. In this method، the region was stratified in to regular- sized grid cells of 4 × 4 km and within each cell a sampling location was chosen randomly. A total of 255 soil samples (0-20 cm) were collected (Fig 2). At each sampling point the coordinates were obtained using a portable GPS and its land use was recorded. After calculation، 46. 5% of the sampling locations occurred in agricultural lands، 43. 5% in uncultivated lands and 10% in industrial and urban area (Fig2). Soil samples were air dried and ground to pass through a 2 mm sieve، Electrical conductivity was measured in a 1:2. 5 soil-water ratio suspension. Statistics including mean، variance، maximum، minimum، coefficient of variation (CV) and comparison average EC in different land use were calculated. The results of factor analysis were used to calculate the autocorrelation value between observed points and produce a minimum unbiased variance estimate. This variance is calculated as a function of variogram model. Variogram is calculated using the relative location of the samples (Soderstrom، 1998). The experimental variogram is calculated for several lag distances this is then generally fitted to a theoretical model and the parameters in suitable model are then used in the kriging procedure (Mohamadi، 2007). The next step is cross validation of the prediction models. The cross-validation technique is used to choose the best variogram model among candidate models and to select the search radius and lag distance that minimizes the kriging variance. Cross-validation is achieved by eliminating information، generally one observation at a time، estimating the value at that location with the remaining data and then computing the difference between the actual and estimated value for each data location. To compare different interpolation techniques، we examined the difference between the known data and the predicted data using the Mean Square Reduced Error (MSE). Correlation coefficient (Pearson) computed between real and estimated data with ordinary kriging. Distribution map of EC was produced using the ordinary kriging and use from maximum 16 point and minimum 3 point in estimation. The descriptive statistical parameters were calculated with Microsoft EXELE and SPSS (version 11). Maps were produce with Surfer (version 8) and ILWIS (version 3. 0) and geostatistics analyses were carried out with VARIOWIN and WINGSLIB. The average EC was 6. 9 dSm-1 with range of 1-74 dSm-1 in Isfahan surface soils (Table 1). EC values don’t follow a normal distribution and had a strongly skewed distribution (Fig 4a) therefore these values were transformed to logarithm and the log-transformed data fit an approximately normal distribution (Fig 4b). The average EC between different land uses was compared by using one-way ANOVA. The results showed that there is a significant difference in the soil salinity between uncultivated and agricultural area، but there is no significant difference between uncultivated and urban-industrial area. Soil salinity mean in the uncultivated area is at least three times higher than other land uses (Fig 3). The study of the spatial variability of EC is begun with the computation of directional variograms for EC in different directions. The best variograms was fitted in directions of 45 degrees، and a spherical theoretical covariance model fitted on variogram (Fig 6). The next step، cross validation of the prediction models computed with MSE and Pearson coefficient. MSE was minimum and Pearson coefficient was high (77%) and this parameter is identifier. Therefore valid variogram parameters (sill= 21000، rang= 0. 6 and nugget effect =0. 127) use for kriging and mapping (Table 2). According to the map of salt distribution in the top soil of study area (Fig 7)، all lands are saline (Ec> 4dS. m-1) but critical accumulation of salt is in the eastern region، especial Segzi plain. Segzi plain is located in the Eastern part of Isfahan province in the center of Iran and is about 40 kms from Isfahan center. The climate of the area according to the Gowsen method is found to be dry and semi-desert، respectively (Mojiri et al.، 2011). In Segzi plain are gypsum mines and wind can transport particles of chalk and sand that cause erosion and air pollution in Esfahan. Rainfall in east of Esfahan in comparison with center and west are lower and temperature is higher so there are more salt in compare to central and west parts. Moreover comparison of maps (salt distribution and land use) delineate، agricultural lands have lower salinity. Because irrigation and leaching are continued and transfer salt to deeper part. Moisture regime in study area is aridic. In this regime evapotranspiration is high and suction gradient transfer salinity solution to soil surface (Richards، 1954). The important result of this research was obtain concentration map of salt with this hope that this survey can be effective step in chose best decision in management of salinity lands to intention improvement and reformation this land. According result of this research، to be near with Lut deserts، low average rainfall، high annual temperature and high evaporation in eastern parts than western parts، are the most important parameters in accumulation of salt in this part of study area.