مجله تحقیقات آب و خاک ایران
سال پنجاه و سوم شماره 12 (پیاپی 84، اسفند 1401)

The remote sensing technique is a suitable method for estimating actual evapotranspiration (ETa) at the large-scale due to spatial and temporal resolution. The present study aims to assess the ETa using the SEBAL and different algorithms to survey the effect of the LST and their impact assessment on the ETa fluctuation. Field measurement, including canopy temperature and the volume of inflow and outflow of water consumption was done based on lysimeters during 2018-2019. After the necessary pre-processing on the satellite images, the Land Surface Temperature (LST) was estimated using Planck's and split window algorithms. The result showed that the performance of Split window was better than to the Planck algorithm. Also, ETa was estimated by the SEBAL algorithm based on two temperature scenarios including the Planck and split window. The results showed, the cold pixel of SEBAL algorithm had compliance with the Lysimetric measurement. Moreover, the cold pixel of the split window algorithm with RMSE=0.56, NRMSE=0.084 and NS=0.992 (mm/day) had the highest consistency with the lysimeter data. Also, the rMBE index of the split window algorithm was associated with underestimation in the range of -4.07 to -3.22%, while the Planck function algorithm fluctuated with overestimation in the range of 4.76 to 12.65%. This research has been verified to the cold pixel of satellite for crop with no stress conditions and for better investigation at crop stress condition, precise instruments are needed.

Soil enzymes are involved in processes such as decomposition of organic matter, food chain cycle, and degradation of contaminants. Therefore, it is very important to protect activity and stability of soil enzymes. This research was conducted at Ferdowsi University of Mashhad in 2017. In the present study, two types of organo-montmorillonites were produced by intercalating montmorillonite with magnetite nanoparticles and with an exopolysaccharide. The properties of the produced organoclays were studied using XRD and scanning electron microscopy (SEM). Effects of application of organo-montmorillonites to the soil on activities of urease, phosphatase, and dehydrogenase were investigated. The experiments were carried out using the completely randomized design with factorial arrangement employing four clay types (montmorillonite, NMM or montmorillonite intercalated with magnetite nanoparticles, ENMM or montmorillonite intercalated with both the exopolysaccharide and magnetite nanoparticles, and the control) at five durations (1, 3, 7, 14, and 21 days) with three replications. SEM images revealed that the best morphological changes happened in ENMM. Morphology images of this organoclay showed that it had small layers with abundant pores, the exopolysaccharide surfactant completely separated the clay layers and created abundant pores thus preventing nanoparticle aggregation. Results of the statistical analysis indicated that adding MM, NMM, and ENMM to the soil increased urease activity by 1.4-, 1.5-, and 3-fold, respectively. Moreover, activity levels of phosphatase enzyme increased by 1.1-, 1.3-, and 1.5-fold when MM, NMM, and ENMM were added to the soil, respectively and dehydrogenase activity increased by 1.1-, 1.2-, and 1.3-fold when MM, NMM, and ENMM were applied to the soil, respectively. Results indicated that the change in the soil environment surrounding the enzymes with the exopolysaccharide surfactant and magnetite nanoparticles increased activity and stability of enzymes in soil during the 21-day incubation period.

Introduction:

Reference evapotranspiration (ET0), a complex hydrological variable affecting crop water requirements and irrigation scheduling, is defined by a number of climatic factors that have an impact on water and energy balances. On the basis of accurate climatic data, conventional methods for calculating ET0 include a variety of empirical approaches. But there are a lots of locations where different climatic information might not be available for ET0 estimation.

The objective of this study is to evaluate different data mining methods to estimate ET0 with limited meteorological data. This study aims to answer the question: can reference evapotranspiration be estimated without reducing accuracy, regardless of the availability of all variables? In this research, the accuracy of data mining methods in estimating ET0 with respect to the plant water demand system (FAO Penman-Monteith standard method) was evaluated

 Data such as sunshine hour, air temperature, wind speed, and relative humidity from thirteen climatology stations in the Zanjan province over a ten-year period (2010-2021) were collected. The ET0 was calculated using the FAO56 Penman-Mantith method on a daily time scale (as refrence method) and the estimated values obtained by data mining methods (Artificial Neural Network (ANNs), Random Forest (RF) and Support Vector Machine (SVM)) were evaluated. The data from each station were divided into two sets: training (two-thirds of the data) and testing (one-third of the data) in order to calibrate and validate the proposed methods. Finally, based on NRMSE, RMSE, MBE, and EF criteria, the generalizability of the aforementioned methods for estimating ET0 was examined.

 According to the results, ANNs performed better than SVM and RF methods. The mean values of, RMSE, EF and NRMSE criteria for the ANNs method in the training and testing steps were 0.49, 0.94 and 0.14, respectively. The mean values of these criteria for RF method in the training step were 0.49, 0.94 and 0.14 and in the testing step was 0.52, 0.94 and 0.15, respectively. The mean values of these criteria for the SVM method for both (training and testing) steps were 0.52, 0.94 and 0.15, respectively.The average air temperature is the most significant and effective parameter to estimate ET0, according to more than 92 percent (12 stations) of the results obtained from two ANNs and RF methods. The sunshine hours is the second-most crucial and useful input in estimating ET0, according to more than 84 percent (11 stations) of the results. As a result, using four meteorological variables such as average air temperature, average relative humidity, wind speed, and sunshine hours as input, excellent performance can be achieved. The NRMSE values obtained from ET0 estimation did not exhibit regular variations with the average values of parameters (temperature, humidity, wind speed, sunshine hours, slope percentage).

It was found that the average air temperature was the most crucial and useful parameter as a result of the sensitivity analysis of the ANNs method and the Predictor Importance of the RF method. According to the current study, Pari and Zanjan stations outperformed than the other stations in Zanjan province, probably due to their plainer conditions. The results of the current study will help to estimate ET0 for semi-arid climates where ET0 is critical for agricultural water resource management.

Saving, optimizing water consumption, and improving productivity and efficiency indicators in agricultural sector are important and necessary. Miandoab region is of particular importance as a model of reducing consumption in order to conserve real water for the restoration of Lake Urmia. This research was conducted to evaluate water consumption reduction scenarios at the farm level using AquaCrop plant model in Miandoab region. For this purpose, the information on farms related to the sustainable agriculture project of Iran's Wetlands Protection Plan in Miandoab region was used. The data from the first year (the crop year 2016-2017) and the second year (the crop year 2017-2018) were used for model calibration and validation, respectively. The evaluation of statistical indicators shows the accuracy and high ability of the model in simulating grain yield. After the calibration and validation of the model, the irrigation planning scenarios were analyzed and evaluated in the form of reducing irrigation depth by 10, 20, and 30% for the conditions of the second year. The results of field measurements in 2017-2018 showed that the average irrigation depth and the applied irrigation (Irrigation + effective precipitation) for wheat were 405.44 and 580.44 mm respectively and mean grain yield was 8340 kg/ha, then irrigation water productivity and applied irrigation productivity were 2.27 and 1.52 kg/ , respectively. Also, the results of the study of deficit irrigation scenarios showed that the S3 scenario (30% irrigation water reduction) will reduce only 10% crop yield and water productivity will increase from 1.53 to 1.72 kg/ . The values of water productivity indices were calculated using balance components and performance simulated by the model for each farm. In general, the results showed that it is possible to increase the grain yield of wheat and water productivity using AquaCrop model for proper and accurate irrigation planning and crop management improvement, in order to save and conserve water on a farm scale.

Monitoring land surface temperature (LST) using remote sensing data and examining its temporal and spatial variation is important in various studies such as environmental research, land use change, water resources management, and drought monitoring, especially at local scales. Remote sensing data can provide a long-term and continuous spatial coverage of LST. However, LST data retrieved from thermal infrared (TIR) band imagery have a coarser spatial resolution than surface reflectance (SR) data collected from shortwave bands on the same instrument. LST products with high spatial and temporal resolutions are not yet available. Therefore, several downscaling algorithms to produce high-resolution LST maps from sensors with appropriate temporal resolution have been developed recently. In this study, thermal sharpening of land surface temperature obtained from Sentinel-3 satellite images with a spatial resolution of 1 km and temporal resolution of less than 1 day was carried out using the PyDMS model and Sentinel-2 images to produce LST maps with a spatial resolution of 20 meters for Isfahan, Iran. PyDMS is a machine learning algorithm based on decision tree regression that relates the reflectance of high-resolution bands to the LST of the corresponding low-resolution image. The results of this model have been compared against the LST measurements at a depth of 5 cm in three meteorological stations including Isfahan Airport, Isfahan, and Kaboutarbad. Moreover, LST products of MODIS and Landsat-8 have been used to assess the consistency of the sharpened LSTs. The results show that the correlation coefficient is higher than 0.74 in all 3 stations and the RMSE error is equal to 6.7, 4.0 and 15.5 °C in Isfahan, Kabutrabad and the airport, respectively. Moreover, the spatial pattern of the sharpened LST is compatible with the spatial pattern of the LST products of Landsat 8, Sentinel 3 and MODIS. The findings of this study indicate the promising application of the PyDMS algorithm for producing LST maps with a spatial resolution of 20 meters and temporal resolution of fewer than 7 days, though bias correction using in situ LST can improve results. Production of LST maps with both high spatial and temporal resolutions is extremely useful for many practical applications such as soil and crop management practices, evapotranspiration estimation, and irrigation water management.

Short-term prediction of daily plant evapotranspiration (ET) is of great importance in precision agriculture and irrigation management. In this paper, a method for short-term prediction of daily ET maps of rice is presented using satellite images and machine learning algorithms. After merging the bands of Landsat-8 and MODIS images using the STARFM method, daily ET images were produced using the METRIC algorithm and used to predict the ET maps of the following days as input to the relation vector machine (RVM) and long short-term memory (LSTM). Two scenarios were considered for prediction. In the first scenario, model is trained using image of nth day of the growth period as input, and the n+6th day's image as target. Using this configuration, the model can predict ET images at a six-day timestep. In the second scenario, the forecast was made for consecutive days up to six days.The correlation coefficient between the values obtained by RVM and the values calculated by METRIC for the first and second scenario were 0.89 and 0.84, respectively, which indicates the acceptable accuracy of these two scenarios in predicting ET. In the first scenario, R2 values for RVM and LSTM methods were 0.8 and 0.59, respectively, which shows that RVM is more accurate for evapotranspiration prediction compared to LSTM. The values of RMSE for RVM in the first and second scenarios were 0.56 and 0.82, respectively, and the values of MAE were 0.43 and 0.66, respectively, which indicates a lower error in the configuration of the first scenario.

Prioritizing critical source areas and using the best management practices, including agricultural and conservation management, are effective methods to reduce erosion in catchments.The main objective of this research was to evaluate the impact of agricultural and conservation management on surface runoff and sediment yields in Dashte Bezorg catchment, Khuzestan, Iran using the ArcSWAT model.The data was collected in 2021. The Sequential Uncertainty Fitting was applied for Calibration and validation. The model was calibrated from 2004 to 2015 and validated from 2016 to 2021 for surface runoff. Furthermore, calibration and validation of sediment yields were performed for the statistical periods of 2004-2013 and 2014-2019, respectively. The performance of the model was evaluated by four objective functions (NS, R2, BIAS, and RSR). The model was then applied to predict critical source areas for sediment yields and surface runoff. Agricultural management practices in four crop rotation scenarios (“wheat-wheat-wheat”, “wheat-rice-wheat-mung bean-wheat”, “rice-mung bean-wheat” and “wheat-potato-tomato”), residue management scenarios (No residue and 50 percent of the residue) and three tillage scenarios (conservation tillage, no-tillage, and conventional tillage) were evaluated. Conservation management scenarios were focused on contouring, strip cropping, terracing, vegetated filter strip, and the grassed waterway scenarios (width of 5 and 10.4 meters).The sensitivity analysis showed that ALPHA_BF (Baseflow alpha factor) and RCHRG_DP (Deep aquifer percolation fraction) parameters were identified as the most effective base flow parameters. The objective function values (NS, R2, BIAS, and RSR) were 0.7, 0.72, 3.7, and 0.55 for surface runoff during calibration, and 0.74, 0.75, 2.1, and 0.51 during the validation period, respectively. These results indicated that the ArcSWAT model performed well in estimating surface runoff but was not satisfactory for sediment yields.Collecting sediment data only during floods resulted in large uncertainty in the input data, and the uncertainty in the inputs produced a large uncertainty in the 95 Percent Prediction Uncertainty (95PPU) bands. Subcatchments 5 and 17 were critical source areas for surface runoff and subcatchments 4, 9, 14, and 16 were also identified as critical source areas for sediment in the catchment. The application of agricultural management practices showed that the cultivation of wheat for three consecutive years increased surface runoff and sediment loss under no-tillage and Conservation tillage. The result of conservation management scenarios indicated that the difference in the width of the grassed waterway had no significant effect on reducing the sediment load. The terracing and vegetated filter strip scenarios were more effective than the other conservation scenarios on sediment reduction.The findings of this study showed that the application of conservation management scenarios can significantly reduce sediment yields compared to agricultural management. It is also recommended to avoid continuous cultivation of the same crop as much as possible.

Wetlands play an essential role in the ecosystem and have a significant impact on people's lives, so the restoration of destroyed wetlands is vital. With the lack of water sources, municipal and industrial wastewater can be used as alternative water sources for various purposes. Due to the presence of contamination in wastewater, the use of wastewater carries risks depending on which purpose it is used for. In this study, the risk of using Alborz Industrial Town treated wastewater in Qazvin province to restore Allahabad Wetland has been investigated. First, by visiting the project's origin, route, and destination and holding numerous meetings with experts involved in the project, influential factors were identified within the scope of the project. Then, an integrated hierarchical structure was created to express these factors. This structure starts from the initial nodes which are the quality of the effluent and leads to the final node. Using the water quality data of the Alborz Industrial Town treatment plant that were collected in the year of study and collecting the opinion of 20 experts in the form of a questionnaire in order to evaluate each node, the risk of the wastewater transfer project to Allahabad Wetland was calculated using the Bayesian network method. The academic version of GeNIe software was used to calculate the risk using the Bayesian network method. The results showed that among the middle nodes of the risk structure, the lowest and highest values are related to the nodes of heavy metals in the health environment risk subgroup and the increase of implementation costs in the economic subgroup. Despite the fact that the effluent quality is within standard limits in most parameters, environmental, cultural, social, economic and technical health risks are equal to 44, 47, 50 and 49%, respectively. The total risk of the project, which is the result of four risks, was calculated as 49%. According to the acceptable quality of wastewater and the calculated risks, the nodes and weights of other economic, technical and social sectors have influenced the final risk. Therefore, to reduce the risk of the project, these parts should be examined. The technical and economic part of the project has more potential for project failure. Before implementing the project, various environmental, social, technical, and economic dimensions should be carefully examined and the possibility of creating risk should be minimized. To reduce the risk of the wastewater transfer project to the wetland, pollutants such as heavy metals must first be removed from the wastewater, and social tensions should be prevented by increasing the awareness of the people of the region and also by allocating water quotas. Allahabad Wetland plays an important role in the ecosystem and people livelihood of the region, and with a more detailed examination of the dimensions of the plan and risk reduction in the four environmental, social, economic, and technical sectors, the implementation of the wastewater transfer project to Allah Abad Wetland can be effective in its restoration.

available water resources and their management are very important in preserving and sustaining the development plans of a country, along with providing foodstuffs. Meanwhile, the main tributaries and flooded rivers of a country play a vital role. There are also very important rivers in Iran, one of which is Ghezel Ozan. This river originates in Kurdistan province and it reaches Gilan province after it passes through downstream provinces and joins Sefid-Rud river after which it is called Qezel Ozen of Sefid-Rud river and enters the great dam of Sefid-Rud. In the meantime, considering small details of the area, modeling will provide a faster and more accurate investigation so that managers can use it. In this regard, there are two views, dynamic and linear. In this research, the mechanistic view was used as a decision support system due to the available feedback, holistic thinking, and the possibility of using the model. Then the problems of the region were evaluated by identifying generic archetypes. The current research has dealt with the dynamic simulation of the surface water resources allocation in upstream Sefid-Rud basin during the years 1990 to 2019 in two annual and monthly scales. During 1990 to 2011 when the dams had not yet started to operate, simulation was conducted by considering about 30 intervals in the mainstream of the rivers and tributaries of Kurdistan province, part of Zanjan province, and a small area of Hamadan province precipitation, discharge and ground waters as well as all agricultural products, domestic and industrial amounts as input. The annual scale of the model structure, with MAE, RMSE, and R2 scales of 0.098, 0.087, and 0.980, respectively, confirmed the accuracy and precision of the model along with the test of the limit conditions that are specific to the dynamic system. Due to the construction of dams, since 2010, monthly simulations were carried out in two sections, before and after the dams' operation. Before the operation of the dams, the values of MAE, RMSE, and R2 were equal to 0.052, 0.103, and 0.940, respectively, confirming the high efficiency of the model. From 2011 to 2019, modeling was defined so as during the first 6 months of water year, water withdrawal was done from the river and during the second 6 months it was done from the dam. All the specifications of the dams along with the needs of the region were simulated in all intervals on a monthly scale. In the meantime, according to the dynamic simulation, the feedback and causal loops formed by them formed patterns that corresponded to the generic archetypes of the region. With the start of the dams, the escalation archetype, limits to growth archetype, and fixes that fail archetype were activated and the increase in water withdrawal was observed in all intervals, whether with the construction of the dam or not.The results showed that despite the drop of aquifers to an average amount of 30 meters in the whole period and 6.35 meters after the construction of dams, in addition to a 90% reduction in discharges, there was an increase in water withdrawal and despite the severe reduction of water resources, the area under cultivation in dealing with escalation archetype has increased by four times, and following the limits to growth archetype, there has been a very sharp decrease to about 3 times. On average, in the region, the area under cultivation has increased by 2.8 times, at the highest value by 4 times and at the lowest value by 1.6 times. The results indicate that, dam construction led to inappropriate exploitation in the region.

The Hargreaves-Samani method of estimating reference evapotranspiration (ETo) has been recommended by irrigation and drainage paper No.56 of the Food and Agricultural Organization of the United Nations (FAO)  in the situations that weather data are missing. since the method requires just measured temperature, the it has been used in several studies over the past decades. FAO paper No. 56 and other investigations has mentioned that Hargreaves-Samani method is an overestimating method in humid cliamtes. Hence, the method has been calibrated for different site and situations. This issue made the purpose of the current study. In this study, the history of the development of Hargreaves-Samani method has been reviewed. Thus, an equation for adjusting humidity condition is discussed. To compare the goodness of models,  ETo was calculated based on 3 different methods 1) Hargreaves-Samani method 2) Allen modified method of Hargreaves-Samani, and 3) the modified method of Hargreaves-Samani based on relative humidity. Also ETo was calculated according to FAO Penman-Montith method as a control estimation. The values were calculated for monthly weather reports of 4,169 stations from CLIMWAT v2.0 all around the world. The results showed that the Hargreaves-Samani’s method has a weakness of estimation under extreme conditions. Interestingly, the corrected method improved precision of the estimations especially in arid and semi-arid locations.

In order to investigate the effect of using treated wastewater in agriculture on the chemical quality of groundwater at the northern region of Isfahan, an area of 6000 hectares was studied during the years 2018 and 2019 by sampling 46 wells. The location of the studied wells was in two areas under long-term irrigation with effluent for at least 10 years and the control area. Chemical parameters of the samples such as electrical conductivity (EC), chemical reaction (pH), anions including bicarbonate, sulfate, chloride, nitrate and cations including calcium, magnesium and sodium were measured. The results showed that the effect of using treated wastewater on groundwater quality of the region, in shallow and deep wells has a different behavior. Due to the long-term use of treated wastewater for irrigation of agricultural lands, none of the chemical parameters of the deep wells were significant in compared to the control area. But, in area with shallow groundwater, the chemical parameters of EC, bicarbonate (HCO3) and chlorine (Cl) were significant at 5% level as compared to the control. Most of the wells in this area are deep and only 8% of the wells are shallow. Therefore, according to the results, the use of treated wastewater for agriculture in this region has a negligible effect on the chemical quality of deep groundwater and therefore is recommended for use in agriculture.

The accurate separation of evapotranspiration components is one of the key gaps in evapotranspiration research. Knowing this variable as well as the mechanism of separating its components to determine the exact value of the components of the water balance equation in relation to planning and managing water resources, optimizing crop production, designing irrigation systems, evaluating crop performance, identifying plant stresses and the impact of drought, and also evaluating the effects of climate change is very important on the efficiency of water consumption. In this research, the efficiency of Two-Source Energy Balance (TSEB) model was evaluated to separate the components of this parameter. In this regard, the outputs of TSEB model were compared and evaluated with the outputs of the standard FAO-56 dual crop coefficient method in the corn field located in the agricultural research station of Ferdowsi University of Mashhad. For this purpose, four Landsat 8 satellite images were used between planting and harvesting corn plants in the spring and summer planting seasons of 2021. The results of this research showed that despite the closeness of two methods (TSEB and FAO-56 dual crop coefficient method with R2=0.94) in terms of total values of evapotranspiration, there is a big difference between the two methods in terms of detail components (R2=0.46 for transpiration and R2=0.75 for evaporation). This difference can be due to the overestimation of the transpiration amount and underestimation of the evaporation amount in the dual crop coefficient method, and because the FAO-56 dual crop coefficient method estimates transpiration and evaporation values and it can be associated with some error, it cannot be said for sure that the TSEB model is not accurate enough. Also, comparing the ratio of transpiration to evapotranspiration in this research (0.77) with the results of other researchers (0.75-0.88) showed that the outputs of the TSEB model are within the permissible range and provide reliable outputs.

In this study, a new approach was used to reduce the solubility of chemical fertilizers. This research was conducted in Karaj Biotechnology Research Institute in 2020. Granular urea and fertilizers were coated with starch-based polymer nanocomposite latex with three different formulations A (without nanoparticle), B (containing nanoparticle), and C (commercial latex) using a rotary drum machine. The effects of different factors such as temperature, number of coating layers and particle size on the release rate of nutrients were investigated. The results of the field emission scanning electron microscope (FESEM) analysis showed that the starch-based polymer nanocomposite formed a completely uniform and smooth coating film on the surface of urea granules. Fertilizers coated with formulation B (containing nanoparticles) showed higher efficiency. Therefore, the presence of natural char nanoparticles in the structure of the polymer coating controlled the release rate of nutrients by improving the coating properties. After 120 min, the release rate of urea from three formulations A, B and C was estimated to be 63.3, 48.41 and 66.85%, respectively which were significantly different from each other. In addition, the results showed that with increasing the number of coating layer, the release time of urea and phosphorus was increased while the release of potassium did not show a definite trend with increasing the coating layer and was unpredictable. With increasing temperature urea release was increased. Investigation of the effect of particle size on urea release showed that coated granules larger than 2 mm had less release rate than granules smaller than 2 mm.

Water is one of the most important physical factors to provide the food security of the world’s growing population. The limitation of Iran’s water resources, as well as the increasing competition of different sectors for the use of water make the optimal management of water resources necessary. Different strategies are used in irrigation networks for water resources management. One of these strategies is optimal allocation of water and land. In this research, an optimization model for water and land allocation with the aim of maximizing economic benefit is presented based on genetic algorithm and using the AquaCrop plug-in model. For this purpose, #C coding was done in Visual Studio. In order to measure the model performance, the lands covered by one of the Moghan irrigation network channels were investigated. In this model, the agricultural year was divided into 36 periods of ten days. The irrigation water depth in each period and the cultivated area were considered as decision variables. The results show that the highest increase in percentage of economic benefit is related to the first-cultivation maize, alfalfa and wheat by 9, 7.3 and 7 percent respectively. Although the lowest increase in economic benefit is related to the second-cultivation seed maize and soybeans. The optimal allocated water volume was decreased by 14.7 percent, meanwhile the economic benefit was increased by 5.7 percent. Therefore, the optimal water allocation in this region encourages saving water consumption more than increasing economic benefit.

Water and soil play an important role in all human environmental activities and controling physical, chemical and biological processes in the environment. The existence of limitations of water resources, especially the fresh water of rivers and dams, makes it necessary to pay more attention to them against pollution. Contamination of water and soil with heavy metals are serious environmental pollutants that enter the environment in many different ways with natural and human origin and cause serious damage. Karaj Dam is one of the most vital sources of drinking water supply in the metropolis of Tehran, which faces a serious risk of pollution due to the development of human activities. This study investigated the concentrations of heavy metals (Zn, V, Ti, Pb, Ni, Mn, Cu, Cr, and As) in 40 soil samples and sediments, as well as five suspended sediment samples, in the river of the Laniz watershed in Karaj, Iran. Due to the construction activities in the watershed including the construction of the Tehran-North Freeway, the natural condition has been affected over the past decade and therefore need to use and new inventory method to separate the effects of natural geochemical from humane effects. The single-element indices of contamination factor (CF), geoaccumulation index (Igeo), normalized enrichment factor (EF), and ecological risk potential factor (ER), as well as two multi-element indices of pollution load index (PLI) and ecological risk index (RI), were employed. At first the elements and samples were grouped by principal component analysis (PCA) and cluster analysis. FA distinguished two groups of elements; called natural and human-natural origins. The cluster analysis divided 50% of the freeway-affected samples into two distinct clusters. Based on the CF index, the As contamination was extreme in the suspended sediment samples and soil samples related to the Freeway while the Pb and Mn carried out the next rank. Also, the single Igeo index revealed the high As contamination. Moreover, based on the Igeo, most suspended sediment samples related to the Freeway put in the extreme contamination class for all of the elements. However, the Mn-modified enrichment factor showed only one freeway-affected and two freeway-unaffected samples exhibited extreme enrichment, and the enrichment of other samples was low or moderate. Unlike the single-element indices, PLI and RI showed that the suspended sediment and soil samples related to the freeway had low pollution load and ecological risk. Compare of single and multiple contamination indices revealed an inconsistency of pollution risk that can be addressed into the background value (in single indices) and the toxicity response factor of the metals (in multiple indices). Results indicated that due to spatial-temporal changes of human effects, it would be better to use different values for both background and elements toxicity response. Therefore, to reach a reality image of contamination risk, more researches are needed to elucidate the mentioned standard value based on the regional variations.