Hydrogeochemistry and water quality assessment of the Kor–Sivand Basin, Fars province, Iran

In order to assess the quality and suitability of waters in the Kor-Sivand river basin, 60 water samples from the Kor river and 90 water samples from wells in the basin were studied. Assessments were based on Piper's and Gibbs' diagrams for water quality, Food and Agricultural Organization's (FAO) guidelines, and US Salinity Laboratory diagram for water suitability. The results showed that the river water is of Ca-HCO(3) type, while well water is of Ca-Cl and Na-Cl type. Based on Gibbs' diagram, the source of soluble ions in the river water samples is the weathering of stones over which water flows, while evaporation was found to be the dominant process in the ion concentration of the well samples. According to the FAO Guidelines, the salinity of surface water for irrigation did not cause great restrictions; however, many of these waters could create potential permeability problems. In the groundwater samples, a high salt concentration is more important than the infiltration problem. Mg hazard values at some sites limit its use for agricultural purposes. One third of the river water samples and two thirds of well waters had more than 50% magnesium. Saturation indices showed that 94% of the analyzed water samples are supersaturated with calcite, aragonite, and dolomite. Based on the US Salinity Laboratory diagram, river water samples were classified as C(2)S(1) and C(3)S(1), while C(4)S(3), C(4)S(4), C(2)S(1), and C(3)S(1) were the most dominant classes in well samples. Some management practices necessary for sustainable development of water resources in the study area were discussed briefly, including appropriate selection of crops, adequate drainage, leaching, blending and cyclic use of saline water, proper irrigation method, and addition of soil amendment.     

Limitations of habitats as biodiversity surrogates for conservation planning in estuaries

Increasing pressures on global biodiversity and lack of data on the number and abundance of species have motivated conservation planners and researchers to use more readily available information as proxies or surrogates for biodiversity. “Habitat” is one of the most frequently used surrogates but its assumed value in marine conservation planning is not often tested. The present study developed and tested three alternative habitat classification schemes of increasing complexity for a large estuary in south-east Australia and tested their effectiveness in predicting spatial variation in macroinvertebrate biodiversity and selecting estuarine protected areas to represent species. The three habitat classification schemes were: (1) broad-scale habitats (e.g., mangroves and seagrass), (2) subdivision of each broad-scale habitat by a suite of environmental variables that varied significantly throughout the estuary, and (3) subdivision of each broad-scale habitat by the subset of environmental variables that best explained spatial variation in macroinvertebrate biodiversity. Macroinvertebrate assemblages differed significantly among the habitats in each classification scheme. For each classification scheme, habitat richness was significantly correlated with species richness, total density of macroinvertebrates, assemblage dissimilarity, and summed irreplaceability. However, in a reserve selection process designed to represent examples of each habitat, no habitat classification scheme represented species significantly better than a random selection of sites. Habitat classification schemes may represent variation in estuarine biodiversity; however, the results of this study suggest they are inefficient in designing representative networks of estuarine protected areas.     

Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study

In this paper, a new methodology is developed for integrated allocation of water and waste-loads in river basins utilizing a fuzzy transformation method (FTM). The fuzzy transformation method is used to incorporate the existing uncertainties in model inputs. In the proposed methodology, the FTM, as a simulation model, is utilized in an optimization framework for constructing a fuzzy water and waste-loads allocation model. In addition, economic as well as environmental impacts of water allocation to different water users are considered. For equitable water and waste load allocation, all possible coalition of water users are considered and total benefit of each coalition, which is a fuzzy number, is reallocated to water users who are participating in the coalition. The fuzzy cost savings are reallocated using a fuzzy nucleolus cooperative game and the FTM. As a case study, the Dez River system in south-west of Iran is modeled and analyzed using the methodology developed here. The results show the effectiveness of the methodology in optimal water and waste-loads allocations under uncertainty.     

Colloid-Associated Groundwater Contaminant Transport in Homogeneous Saturated Porous Media: Mathematical and Numerical Modeling

During the past two decades, significant efforts have been made to study contaminant transport in the presence of colloids. Several researchers reported that colloidal particles could enhance the migration of contaminants in groundwater by reducing retardation factor. When the colloidal particles are present in the aquifer, the subsurface system can be considered as a three-phase system with two solid phases and an aqueous phase. The interaction between contaminants, colloids, and solid matrix should be considered in assessing the fate and transport of the contaminant in the groundwater flow system. In this study, a one-dimensional numerical model is developed by employing a fully implicit finite difference method. This model is based on mass balance equations and mass partition mechanisms between the carriers and solid matrix, as well as between the carriers and contaminants in a saturated homogeneous porous medium. This phenomenon is presented by two approaches: equilibrium approach and fully kinetic first-order approach. The formulation of the model can be simplified by employing equilibrium partitioning of particles. However, contaminant transport can be predicted more accurately in realistic situations by kinetic modeling. To test the sensitivity of the model, the effect of the various chemical and physical coefficients on the migration of contaminant was investigated. The results of numerical modeling matched favorably with experimental data reported in the literature.     

Heavy metal bioaccumulation and risk assessment for wild and farmed beluga sturgeon caviar

Because of over-exploitation of sturgeon for caviar production, they have been listed worldwide in annex II of the Convention on International Trade in Endangered Species regulations; so caviar production using aquaculture is increasingly seen as a feasible way to reduce overfishing. The accumulation of the nonessential metals As, Ba, Cd, Hg, Pb, and Sn was determined in the caviar of farmed and wild Beluga sturgeon (Huso huso). The levels of As in both and Cd in wild samples were less than 0.01 mg kg^?1 wet weight, and the comparison for all of the metals studied did not show large fluctuations in metal concentrations between farmed and wild caviar samples. The average for each toxic metal was below the permissible limits proposed by the UK’s Ministry of Agriculture, Fisheries and Foods (2000). The maximum allowable daily consumption rate of caviar was calculated; however, the health risks from caviar consumption are uncertain because the amount of caviar consumed by heavy users is not known.     

Comparison of different static methods for assessment of AMD generation potential in mining waste dumps in the Muteh Gold Mines,Iran

Acid mine drainage (AMD) gives rise to several problems in sulfide-bearing mineral deposits whether in an ore body or in the mining wastes and tailings. Hence, several methods and parameters have been proposed to evaluate the acid-producing and acid-neutralizing potential of a material. This research compares common static methods for evaluation of acid-production potential of mining wastes in the Muteh gold mines by using 62 samples taken from six waste dumps around Senjedeh and Chah-Khatoun mines. According to a detailed mineralogical study, the waste materials are composed of mica-schist and quartz veins with a high amount of pyrite and are supposed to be susceptible to acid production, and upon a rainfall, they release acid drainage. All parameters introduced in different methods were calculated and compared in this research in order to predict the acid-generating and neutralization potential, including APP, NNP, MPA, NPR, and NAGpH. Based on the analytical results and calculation of different parameters, all methods are in a general consensus that DWS-02 and DWS-03 waste dumps are acid-forming which is clearly attributed to high content of pyrite in samples. DWS-04 is considered as non-acid forming in all methods except method 8 which is uncertain about its acid-forming potential and method 7 which considers a low potential for it. DWC-01 is acid-forming based on all methods except 8, 9, 10, and 11 which are also uncertain about its potential. The methods used are not reached to a compromise on DWS-01 and DWC-02 waste dumps. It is supposed that method 7 gives the conservationist results in all cases. Method 8 is unable to decide on some cases. It is recommended to use and rely on results provided by methods 1, 2, 3, and 12 for taking decisions for further studies. Therefore, according to the static tests used, the aforementioned criteria in selected methods can be used with much confidence as a rule of thumb estimation.     

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.