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Groundwater quality management is a priority in arid and semi-arid zones where water is scarce. Leachate from open dumping of municipal solid wastes may threaten groundwater quality. This research aimed at assessing groundwater quality of the aquifer of Shur river basin in Tehran province, Iran. The pollution potential of leachate from a landfill, located at the center of the basin, was estimated to assess its impact on the aquifer. Samples from 38 wells and 2 leachate ponds around the landfill were analyzed for their physico-chemical parameters and heavy metals. Leachate Pollution Index (LPI) and Water Quality Index (WQI) were calculated and multivariate statistical techniques were employed through geostatistical models to predict the spatial variability of groundwater quality and assess its contamination sources. The groundwater quality map was developed by GIS Interface. LPI indicated that leachate from the closed cell (LPI = 36) was more contaminating than that of the active site (LPI = 25). Kriging and cokriging geostatistical interpolation methods were applied to groundwater quality parameters. The best interpolation model was then identified through cross-validation with RMSE and GSD criteria. Cokriging yielded more accurate results than kriging. Spatial distribution maps showed high groundwater contamination and degraded water quality mainly in the central part of the basin, where the landfill was. Also, 293.7 ha of the study area possessed poor and very poor water quality, unsuitable for drinking. This study implicated multiple approaches for groundwater quality assessment and estimated its spatial structure as an effort toward effective groundwater quality management in Shur river basin.

Wetland hydrology in warm Mediterranean regions is strongly influenced by land use/cover change (LUCC) and accurate modeling plays an important role in the maintenance of these valuable ecosystems. Here, LUCC effect on hydrological changes in Ajigol, Almagol and Alago international wetlands in northern Iran, was investigated using Long-term hydrological impact assessment (L-THIA) model and satellite images (Landsat 5, 7 and 8) during 1987–2018. The results showed that the most LUCC was observed in agricultural lands and residential areas with an overall accuracy and kappa coefficient of 0.93 and 0.8, respectively. The L-THIA model showed that LUCCs have directly affected the three wetlands under different scenarios. The model showed a relatively high accuracy of changes in wetland water volume under LUCC. A correlation coefficient (R-squared) of 0.70, 0.64 and 0.60 was observed between modeled and actual water volume in Almago, Ajogol and Alagol wetlands, respectively, under LUCCs.

In this study, the impact of MSW landfill on the surface and groundwater resources pollution in Tehran Province was investigated using a multivariate statistical technique concerning multiple water quality indicators. To assess the role of the landfill on surface and groundwater pollution, a statistical approach was designed in a way that the distance from landfill was considered as the main factor while sub-factors were various physicochemical parameters of water samples collected from groundwater and surface water. Sampling took place for two years on a monthly basis. Results of groundwater samples indicated that SO42− (254 mg/l), PO43− (0.13 mg/l) and EC (1177.9 µs/cm) exceeded their acceptable limits of drinking water standard in near the landfill. For groundwater, all water quality parameters (except heavy metals, PO43−, SO42−, Na + , Mg2+, and pH) were higher adjacent to the landfill, than upstream and downstream locations (significance level P

Sulfide ions (S−2) derived from Na2S in black liquor, after the pulping process, is the source of production H2S during of black liquor recovery cycle in Kraft pulp and paper industries. Tow adsorbents (Cu-PAC, Cu-GAC) by loading Cu+2 on powder activated carbon (PAC) and granular activated carbon (GAC) were synthesized to created more adsorption sites and were improved their selectivity for removing S−2 from black liquor with 3 level of sulfidity (18, 20 and 22 %). The adsorbents were characterized by BET, FTIR, SEM, and EDX. Adsorption of S−2 for Cu-PAC and Cu-GAC were fitted well with Freundlich and Langmuir isotherm model respectively and both of the adsorbents followed pseudo-second-order kinetic. The results of thermodynamic parameters showed the endothermic process. The removal efficiency results showed Cu-PAC has better performance than Cu-GAC. This is due to the higher specific surface area of Cu-PAC, which has led to more adsorption sites for S−2. By considering appropriate temperature conditions and direct effect of the S−2 on the production of H2S during of black liquor recovery cycle, Cu-PAC will be able to prevent H2S production and odorous the black liquor recovery cycle by removing 79.89 % of the S−2.

This study aimed to forecast air concentrations of CO in Tehran. This research is an analytical–practical method using daily data from Tehran’s air quality monitoring stations, weather parameters, time parameters such as 1-day time delay and traffic parameters, the prediction model of air pollution caused by Tehran metropolitan transport. To apply in the decision support system, it was investigated. In the multi-criteria decision-making process, the importance of evaluation indicators is usually taken into account. Gray relationship analysis was used to rank the influencing parameters in air pollution. After estimating the effective parameters, an artificial neural network model was used to forecast the CO concentration using MATLAB software. In the end, the results of an artificial neural network model were compared with the linear regression model. Correlation coefficient and mean square error of the neural network model R = 0.72 and RMSE = 0.69 with linear regression model of R = 0.10 and RMSE = 11.747 were compared. The results of this study showed that the neural network model error is less than the linear regression model. Based on the results of sensitivity analysis, hot/cold season parameters, 1-day delay, 2-day delay, day of year, and month of the year have the most effect on the concentration of CO in Tehran.

The study and analysis of safety, health and environmental indicators in the risk assessment of landfill sites are essential to improving performance and reducing injuries. This study is essential in identifying effective criteria and providing useful solutions for proper waste control and management. In Gilan province, 2200 tons of waste enter landfills every day, and this has created a lot of pollution in the area. The methodology of this research was chosen using the opinions of experts and the Delphi technique. Factors affecting the risk assessment in the construction and operation phases were investigated in two phases. The risks of a landfill project were identified by combining (FANP) and (FTOPSIS) using a multi-criteria decision bow tie technique to rank and prioritize criteria. The results show that urban landfills are one of the environmental risk factors in cities. They often contaminate water sources. This has caused a disturbance in the stability of ecological systems. Using the bow tie model can control and prevent environmental health-safety risks caused by urban waste disposal. This can be a threat and a big factor in the destruction of ecosystem resources and services. The integrated approach used in this study provides a flexible tool for evaluating and developing municipal landfills. The risk assessment study proves that the most involved areas in the landfill are environment and health-safety, respectively. Socio-economic and cultural fields are in the next categories. Due to their nature and working process, lancets face many environmental, safety and health risks. The integrated approach (FANP) and (FTOPSIS) with bow ties are suitable methods for risk assessment in landfills. It is very important to use the bow tie technique in analyzing, examining and prioritizing risk sources for management and also control measures such as preventing and limiting high-risk sources.

Choosing an appropriate municipal waste management method is a very complicated environmental problem in cities. This research introduces an optimization model for waste management in the southwest region of Tehran province. It was developed by a metaheuristic algorithm that was used to minimize the economic and environmental costs. Incineration, composting, recycling and landfilling waste management methods were considered. Three scenarios were developed to determine the optimum allocation of waste to each method such to fulfill the objective of overall minimum of environmental burdens and costs. A multi-objective scenario selection model was implemented by the compromise programming method in MCAT software. Considering the budget limitation and available facilities on site, optimum allocations to recycling, composting, incineration and landfilling methods were obtained as 115,486, 132,094, 71,905 and 45,516 tons/year, respectively. The results of this study indicated that the metaheuristic algorithm in MCAT software was an efficient tool in decision making about waste management systems and thus, it was suggested to municipality managers and regional planning authorities.

Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

The Anzali Wetland, is located in south west of the Caspian Sea, is one of the most important aquatic ecosystems in Iran. In recent decades much domestic and industrial untreated wastewater has been deposited in the wetland. This study was done to obtain a preliminary assessment of heavy metals contamination in the wetland sediments. Tests were done to determine levels and distribution of the heavy metals Cd, Cr, Cu, Pb, and Zn in surface sediment of the wetland. Results showed that averages of metal concentrations (in g/g) were: Cd: 1.46 0.66; Cr: 6.12 0.27; Cu 18.12 4.11; Pb: 17.97 1.69 and Zn: 78.08 7.08. Contamination status was evaluated on the Geo-accumulation Index for each of these metals and ecological risk assessment was determined accordingly. The Geo-accumulation Index determined that levels of metals were not in the range indicating contamination (except for Cd that showed a level of moderate contamination), and in terms regional variation, levels of contamination were evaluated in the following order: Shyjan > Hendekhale > Siyahkeshim > Abkenar. Ecological risk assessment showed that Cd was the only metal posing a potentially high risk to the environment and contributed 77.5% of the total potential ecological risk in the Anzali Wetland.

Climate is a determinant factor in species distribution and climate change will affect the species abilities to occupy geographic regions. Prosopis juliflora is one of the most problematic invasive species and its biological invasion causes various negative effects in tropical, arid, and semi-arid regions of the world. As eradication efforts subsequent to the establishment of an alien invasive species are costly and time-consuming, assessing patterns of the introduction of an invasive species to new regions is among the most cost-effective means of monitoring and management of natural ecosystems. In this study by using the concept of species distribution modeling (SDM) and maximum entropy (MaxEnt) method, the effect of climate change on the current and future distribution of P. juliflora has been assessed at a global scale. Bioclimatic variables in current condition and 2050 regarding two global circulation models (GCM) and two climate change scenarios were considered as explanatory variables. Our results showed that annual mean temperature (BIO1), annual precipitation (BIO12), and temperature mean diurnal range (BIO2) represented more than 87% of the variations in the model, and with an AUC of 0.854 and TSS of 0.51, the model showed a good predictive performance. Our results indicate that on a global scale, suitable ranges for P. juliflora increase across all the GCM and RCP scenarios. In a global scale, Mediterranean Basin, Middle East, and North America are regions with the highest risk of range expansion in the future. Regarding the negative impacts of P. juliflora on structure and function of natural habitats in the invaded areas, findings of this study could be considered as a warning appliance for the environmental monitoring of the regions highly sensitive to the global invasion of the species. We suggest that assessing impacts of climate change on the global distribution of the invasive species could be used as an efficient tool to implement broad-scale and priority-setting monitoring programs in natural ecosystems.

Nowadays air quality is the main issue in urban areas that have been affecting human health, the environment, and the ecosystem. So, governmental authorities, environmental and health agencies usually need the prediction of daily air pollutants. This prediction is often based on statistical relations between various conditions and air pollution. This study aims to compare the performance of Multiple Linear Regression (MLR) and Multi-layer perceptron (MLP) for predicting SO2 concentration in the air of the Tehran. Different parameters namely meteorological parameters, urban traffic data, urban green space information, and time parameters were chosen for the prediction of SO2 daily concentration. Considering result, the correlation coefficient (R2), and root means square error (RMSE) of the MLR model are 0.708, and 6.025, respectively while these values for the MLP equal 0.9 and 0.42. According to the result of sensitivity analysis, the value of the one-day time delay, park indicator, season/year, and the total area parks were the main factors influencing SO2 concentration. MLP model suggested in this research could be applied to support, analysis, and improve predicting air pollution and air quality management. This study shows the importance of modeling and application of ANN in presenting management strategies to reduce urban pollution.

In this research diversity of plant species in ecosystem units in an oak habitat of Zagros forests was investigated. For this purpose data on vegetation in 60 sample plots were recorded. Multivariate statistical methods were used to determine ecosystem units. Combined methods of cluster analysis and two-way indicator species analysis were used for determining ecosystem units. To investigate the diversity gradient between ecosystem units, different methods of ordination including principal component analysis and detrended correspondence analysis were used. According to analysis 4 ecosystem units were determined. The diversity was calculated by using Simpson, Shanon, Macintosh, Margalof and Manhanic. Also the Manhanik index has created the most distinction among groups in comparison to other indexes.

Ecotourism, as a form of sustainable nature-based tourism, promotes conservation of ecological and scenic values. In this study, a Spatial Decision Support System, SDSS, was developed based upon Multi Criteria Evaluation, MCE, for ecotourism development in the Caspian Hyrcanian Mixed Forests ecoregion, northern Iran. For this, important criteria and constraints for ecotourism development were shortlisted using the Delphi Method. The criteria were weighted using Analytical Hierarchy Process, AHP. The obtained results indicated that "distance from water resources", "land use", "slope", "soil", "climate", "distance from roads", "land cover density", "erosion", and "distance from residential areas" were the most important criteria, respectively. The findings suggest that GIS-based SDSS is suitable to engage the various criteria affecting the development of ecotourism destinations. This empirical research develops a new method that can significantly facilitate planning forecotourism development with respect to ecological capability of ecotourism destinations. Ecoturismo, como forma de turismo sustentável baseado na natureza promove conservação de valores ecológicos e cênicos. Neste estudo, o Sistema Espacial de Suporte a Decisões, SDSS, foi desenvolvido com base na Avaliação Multi-Critério, MCE, para o desenvolvimento de ecoturismo na ecoregião de florestas mistas caspian hyrcanian, no norte do Irã. Para isto, critérios importantes e restrições para o desenvolvimento do ecoturismo foram listados usando o Método Delphi. Os critérios foram analisados ponderadamente usando o Processo de Hierarquia Analítica, AHP. Os resultados obtidos indicaram que: "distância de fontes de água", "uso da terra", inclinação do terreno", "solo", "clima", "distância das estradas", "densidade de cobertura vegetal", "erosão", e "distância de áreas residenciais", foram os critérios, respectivamente, mais importantes. Os resultados sugerem que o SIG baseado no SDSS é adequado para engajar-se a vários critérios afetando o desenvolvimento dos destinos do ecoturismo. Esta pesquisa empírica desenvolve um novo método que pode facilitar o planejamento para o desenvolvimento do ecoturismo que diz respeito à capacidade ecológica dos destinos ecoturísticos.

We present information on movement patterns and habitat selection of the endangered Euphrates softshell turtle Rafetus euphraticus (Daudin 1802) from Karkheh Regulating Dam Lake in southwestern Iran. Twelve adult turtles were trapped, fitted with radio-tracking transmitters, and relocated 21 to 51 times between May 2011 and July 2012. The mean linear range size was 2.54 ± 0.83 km, the mean river channel area was 55.35 ± 17.98 ha, the mean minimum convex polygon (MCP) size was 47.49 ± 23.36 ha, and the mean 95% kernel density estimator (KDE 95%) measured 21.75 ± 9.44 ha with a core area (KDE 50%) of 5.74 ± 2.87 ha. Range overlap was generally high; on average, individual MCPs overlapped with those of 7.5 other turtles, individual KDEs with those of 7.3 other turtles, and core areas with those of 5.5 other turtles. Selection of habitat types was not proportional to availability. Study animals preferred shallow-water edge habitats covered with Phragmites australis over all other habitat types.

The present study deals with application of the weighted linear combination method for zoning of forest fire risk in Dohezar and Sehezar region of Mazandaran province in northern Iran. In this study, the effective criteria for fires were identified by the Delphi method, and these included ecological and socioeconomic parameters. In this regard, the first step comprised of digital layers; the required data were provided from databases, related centers, and field data collected in the region. Then, the map of criteria was digitized in a geographic information system, and all criteria and indexes were normalized by fuzzy logic. After that, the geographic information system (GIS 10.3) was integrated with the Weighted Linear Combination and the Analytical Network Process, to produce zonation of the forest fire risk map in the Dohezar and Sehezar region. In order to analyze accuracy of the evaluation, the results obtained from the study were compared to records of former fire incidents in the region. This was done using the Kappa coefficient test and a receiver operating characteristic curve. The model showing estimations for forest fire risk explained that the prepared map had accuracy of 90% determined by the Kappa coefficient test and the value of 0.924 by receiver operating characteristic. These results showed that the prepared map had high accuracy and efficacy.

Unplanned growth of cities is a matter of concern these days. Lack of attention to proper patterns of urban development has left so many harmful effects on human health and the environment. One of the most effective methods that can be used to measure the efficiency of urban development is data envelopment analysis (DEA). The present study is an attempt to evaluate the performance and efficiency of development of Yazd City using the DEA over the years 1983–2013. In this regard, the ecological factors, affecting the growth of the city of Yazd in the study period, were identified initially. The factors include elevation, slope, aspect, geology, morphology, soil, water quantity, climatic features, and land cover. Next, using variable returns to scale (BCC) based on the output-oriented approach, the efficiency of development of Yazd City was calculated by GAMS software to recognize efficient and inefficient units. Then, Anderson-Peterson (AP) ranking method was used to rank the most efficient units in the development of Yazd City over the study years. The obtained results indicated that the DMUs 2 (1984), 3 (1986), 12 (1994), 15 (1997), 21 (2004), up to 30 (2013) were efficient and introduced as units with proper performance in terms of ecological indicators affecting the urban growth. According to the Anderson-Peterson method, DMU 3 (1986) was recognized as the most efficient unit, ranked the highest (with a score of 1.20319) among the other 30 units in terms of ecological indicators affecting development of the urban growth. The research findings could clarify the strength and weak points of the ecological characteristics of the city. According to which, a comprehensive understanding of the performance of the city could be given to relevant authorities in order to amend inefficient units of urban development or direct the orientation of the city growth toward the most efficient directions.

This study surveyed the behavior of diazinon pesticide in the water column of the Tajan River and its potential effects on river biota. The Tajan River is located on the southern basin of the Caspian Sea in Iran and diazinon enters the river from rice fields almost every year between the months of June and October. The sampling plan of flowing water was plotted in a 3-week period on five sites along the river and also at the end of tributaries. Samples were collected seven times during a 6-month period; they were analyzed for diazinon residues and some physicochemical characteristics. Total diazinon concentration range was between 0.01 and 46.99 μg/L in sampling stations. The calibrated AQUATOX model was applied to simulate fate processes and ecotoxicity of diazinon in the Tajan River. The model demonstrated that the diazinon residues in the Tajan River reacted to three major degradation processes (microbial, hydrolysis, and photolysis) and sedimentation; however, the river discharge played the m...

Selection of landfill site is a complex process and needs many diverse criteria. The purpose of this paper is to evaluate the suitability of the studied site as landfill for MSW in Karaj. Using weighted linear combination (WLC) method and spatial cluster analysis (SCA), suitable sites for allocation of landfill for a 20-year period were identified. For analyzing spatial auto-correlation of the land suitability map layer (LSML), Maron's I was used. Finally, using the analytical hierarchy process (AHP), the most preferred alternative for the landfill siting was identified. Main advantages of AHP are: relative ease of handling multiple criteria, easy to understand and effective handling of both qualitative and quantitative data. As a result, 6% of the study area is suitable for landfill siting and third alternative was identified as the most preferred for siting MSW landfill by AHP. The ranking of alternatives were obtained only by applying the WLC approach showed different results from the AHP. The WLC should be used only for the identification of alternatives and the AHP is used for prioritization. We suggest the employed procedure for other similar regions.

Environmental impact prediction is an important step in many environmental studies. A wide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental impact prediction in Robatkarim area, Iran, during the years of 2005∼2007. It was assumed that environmental impact could be predicted using time series satellite imageries. Natural vegetation cover was chosen as a main environmental element and a case study. Environmental impacts of the regional development on natural vegetation of the area were investigated considering the changes occurred on the extent of natural vegetation cover and the amount of biomass. Vegetation data, land use and land cover classes (as activity factors) within several years were prepared using satellite images. The amount of biomass was measured by Soil-adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) based on satellite images. The resulted biomass estimates were tested by the paired samples t-test method. No significant difference was observed between the average biomass of estimated and control samples at the 5% significance level. Finally, regression models were used for the environmental impacts prediction. All obtained regression models for prediction of impacts on natural vegetation cover show values over 0.9 for both correlation coefficient and R-squared. According to the resulted methodology, the prediction models of projects and plans impacts can also be developed for other environmental elements which may be derived using time series remote sensing images.

Following the marked increase of perylene concentration in southern coast of Caspian Sea, waterbird eggs were used as biomonitoring agents. Surface sediments and eggs of five bird species were collected from colonies in Anzali Wetland in the above coast for perylene analysis. The perylene concentrations in sediment and egg samples ranged within 70.6-204.4 and 25.5-43.2 ng/g dw, respectively. Diagnostic perylene ratios showed that the perylene found in all samples was of biogenic origin, possibly developing from terrestrial materials. The combination pattern of perylene was found to be similar in all samples. Conclusively, perylene observed in the area was transmitted from sediments in breeding areas into the eggs, so the eggs are biomonitoring agents and the prevalence of oxic conditions in surface sediments limits formation of perylene, reflecting perylene formation in the catchment area. We found that perylene distribution in surface sediments follows irregular patterns, representing significant effects from local inputs.

Although Iran is of founders of the Ramsar Convention, there is no comprehensive information available in the country on the status of wetlands in the past or at present. There is also no specific guideline for assessing the status of wetlands in the basin of the Caspian Sea as an ecosystem with unique ecological features. The main aim of this study was to develop a new procedure called “Caspian Rapid Assessment Method” (CRAM) for assessment of wetlands at southern fringe of the Caspian Sea. To this end, 16 rapid assessment methods analyzed by US EPA in 2003 were reviewed to provide an inventory of rapid assessment indices. Excluding less important indices, the inventory was short-listed based on Delphi panelists’ consensus. The CRAM was developed with 6 main criteria and 12 sub-criteria. The modified method was used to assess three important wetlands of Anzali, Boojagh and Miyankaleh at the southern border of the Caspian Sea. According to the obtained results, the highest score of 60 was assigned to the Anzali Wetland. Obtaining the scores of 56 and 47, Miyankaleh and Boojagh wetlands were ranked in the next priorities, respectively. At final stage, the accuracy of CRAM prioritization values was confirmed using the Friedman test. All of the wetlands were classified into category II, which indicates destroyed wetlands with rehabilitation potentials. In recent years, serious threats have deteriorated the wetlands from class III (normal condition) to the class II.

Having a very many number of pipelines all over the country, Iran is one of the countries consists of various ecosystems with variable degrees of fragility and robusticity as well as geographical conditions. This study presents a state-of-the-art method to estimate environmental risks of pipelines by recommending rational equations including FES, URAS, SRS, RRS, DRS, LURS and IRS as well as FRS to calculate the risks. This study was carried out by a relative semi-quantitative approach based on land uses and HVAs (High-Value Areas). GIS as a tool was used to create proper maps regarding the environmental risks, land uses and distances. The main logic for using the formulas was the distance-based approaches and ESI as well as intersections. Summarizing the results of the study, a risk geographical map based on the ESIs and final risk score (FRS) was created. The study results showed that the most sensitive and so of high risk area would be an area comprising of mangrove forests located in the pipeline neighborhood. Also, salty lands were the most robust land use units in the case of pipeline failure circumstances. Besides, using a state-of-the-art method, it showed that mapping the risks of pipelines out with the applied method is of more reliability and convenience as well as relative comprehensiveness in comparison to present non-holistic methods for assessing the environmental risks of pipelines. The focus of the present study is "assessment" than that of "management". It is suggested that new policies are to be implemented to reduce the negative effects of the pipeline that has not yet been constructed completely, {"references":["http://en.wikipedia.org/wiki/List_of_pipeline_accidents.","1. Anonymous. 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Background Being established in 1988 in the vicinity of Isfahan city, Mubarakeh Steel complex has imposed adverse environmental and health effects within the area. The study area is covered by lots of farms through which major crops like wheat and rice are provided. Methods Considering the imposed pollution load of the complex, the current study has monitored the concentration of metals Fe, Al, Cd, Cr, Ni, Pb, Cu, Zn, Mn, Co, Mo, As in 14 soil samples within the study area. Furthermore, human health hazards of mentioned metals due to consumption of domestic rice and wheat have also been evaluated through different scenarios. In order to evaluate the mobility of metals in soil samples the sequential chemical analysis is performed. Results Regarding the accumulation of metals in loose phases the order of metals bioavailability risk level is estimated to be as follows: Co > Cd > Mo > Ba > As > Pb > Mn > Cu > V > Zn > Cr > Ni Discussion An index approach is also considered to evaluate the severity of metal contamination. Regarding geochemical accumulation index, only cadmium is detected to be in a moderately contaminated status while other metals declare an unpolluted condition. Index of pollution pays more attention to mobility potential of metals and accordingly detects metals Co, Mn, As, Pb, Cd, Ba and Mo to be in a moderately contaminated level. On the other hand, enrichment factor declares all toxic metals except for Co, Ba and V to be enriched. Conclusions Considering human health hazard assessment, except for Fe, Ba, Cu and Zn, all metals intakes in different scenarios are considered as hazardous while their CDI values are much more than the respective oral reference doses.