Water and waste load allocation in rivers with emphasis on agricultural return flows: application of fractional factorial analysis

In this paper, a new methodology is developed to handle parameter and input uncertainties in water and waste load allocation (WWLA) in rivers by using factorial interval optimization and the Soil, Water, Atmosphere, and Plant (SWAP) simulation model. A fractional factorial analysis is utilized to provide detailed effects of uncertain parameters and their interaction on the optimization model outputs. The number of required optimizations in a fractional factorial analysis can be much less than a complete sensitivity analysis. The most important uncertain inputs and parameters can be also selected using a fractional factorial analysis. The uncertainty of the selected inputs and parameters should be incorporated real time water and waste load allocation. The proposed methodology utilizes the SWAP simulation model to estimate the quantity and quality of each agricultural return flow based on the allocated water quantity and quality. In order to control the pollution loads of agricultural dischargers, it is assumed that a part of their return flows can be diverted to evaporation ponds. Results of applying the methodology to the Dez River system in the southwestern part of Iran show its effectiveness and applicability for simultaneous water and waste load allocation in rivers. It is shown that in our case study, the number of required optimizations in the fractional factorial analysis can be reduced from 64 to 16. Analysis of the interactive effects of uncertainties indicates that in a low flow condition, the upstream water quality would have a significant effect on the total benefit of the system.     

Monitoring of trace amounts of heavy metals in different food and water samples by flame atomic absorption spectrophotometer after preconcentration by amine-functionalized graphene nanosheet

We are introducing graphene oxide modified with amine groups as a new solid phase for extraction of heavy metal ions including cadmium(II), copper(II), nickel(II), zinc(II), and lead(II). Effects of pH value, flow rates, type, concentration, and volume of the eluent, breakthrough volume, and the effect of potentially interfering ions were studied. Under optimized conditions, the extraction efficiency is >97 %, the limit of detections are 0.03, 0.05, 0.2, 0.1, and 1 μg L^?1 for the ions of cadmium, copper, nickel, zinc, and lead, respectively, and the adsorption capacities for these ions are 178, 142, 110, 125, and 210 mg g^?1. The amino-functionalized graphene oxide was characterized by thermogravimetric analysis, transmission electron microscopy, scanning electron microscopy, and Fourier transform infrared spectrometry. The proposed method was successfully applied in the analysis of environmental water and food samples. Good spiked recoveries over the range of 95.8–100.0 % were obtained. This work not only proposes a useful method for sample preconcentration but also reveals the great potential of modified graphene as an excellent sorbent material in analytical processes.     

Rapid spectrophotometric determination of trace amounts of palladium in water samples after dispersive liquid–liquid microextraction

A simple, rapid, and efficient dispersive liquid–liquid microextraction method, followed by UV–Vis spectrophotometry was developed for the preconcentration and determination of Pd ions in water samples. Pd ions react with α-furildioxime (chelating agent) to form a hydrophobic complex. Various parameters were altered to study and optimize their effects on the extraction efficiency, such as pH, ligand concentration, the type and volume of extraction and dispersive solvents, extraction time, and salt concentration. Under optimized conditions, the method exhibited an enrichment factor (C _org/C _aq) of 25 and recovery more than 98 % within a very short extraction time. The linearity of the method ranged from 10 to 200 μg?L^?1. The limit of detection was 1.1 μg?L^?1. The relative standard deviation for the concentration of 100 μg?L^?1 of Pd was 2.3 % (n?=?10). Finally, the developed method was successfully applied to the extraction and determination of Pd in tap, river, mineral, and sea water samples.     

Identification of effective best management practices in sediment yield diminution using GeoWEPP: the Kasilian watershed case study

Identifying areas that are susceptible to soil erosion is crucial for water resource planning and management efforts. Furthermore, modeling has proven helpful in recognizing and monitoring high-risk areas at the watershed scale. The Water Erosion Prediction Project (WEPP) geospatial interface (GeoWEPP) software integrates GIS with the WEPP to analyze the spatial variation in soil loss, and it has been used as a modeling tool to determine the areas that are most prone to soil erosion and to evaluate best management practices for the Kasilian watershed in Iran. As much as 62.4 % of the agronomic land in the Kasilian watershed is affected by a high magnitude of erosion (>5 t/ha). On the basis of this study, by using soybeans, high fertilization levels, and the drill-no-tillage system, reductions of erosion by almost 32.68–34.02 % are perceivable in three critical subwatersheds that are located in the cultivated lands. Also, it is projected that reductions in the production of sediment in the range of about 36.7–47.1 % are achievable by structural management within two critical, upland subwatersheds. So, by utilizing the best management strategies, sediment yield can be lowered and the conservation of soil and water is feasible at the watershed scale. These results objectively indicate that GeoWEPP can be efficaciously used for evaluating effective management practices for developing watershed conservation.     

Changes in urinary catecholamines in response to noise exposure in workers at Sarcheshmeh Copper Complex, Kerman, Iran

Noise is one of the most harmful agents in the workplace. In addition to the adverse effects of noise on the auditory system, as a stressor it may cause increased blood pressure, cardiovascular disease, anxiety, and impaired secretion of hormones. The purpose of this study was to evaluate changes in urinary catecholamines in workers exposed to industrial noise. This is an experimental study of the workers at the smelter section of Sarcheshmeh Copper Industries done on two separate days. During the first day, urine samples from 20 workers who did not use any hearing protection device, were collected during an 8-h work shift and on the second day the same was done but they were asked to use earplugs. Also 20 people were selected as a control group from people who were not exposed to noise at work. Urinary catecholamine levels were measured with ELISA kits. The mean urinary epinephrine and norepinephrine levels in the workers (without earplugs) was respectively 8.69 and 35.56 μg/8h on the first day and on the second day (with earplugs) dropped to 6.45 and 30.95 μg/8h. Noise reduction by earplugs led to almost significant reductions in urinary epinephrine (p?=?0.05) and significant reductions in norepinephrine (p?=?0.02). The results showed that with noise reduction the urinary excretion of stress hormones, especially norepinephrine significantly decreases and workers are probably less prone to stress-related disorders.     

A stochastic conflict resolution model for trading pollutant discharge permits in river systems

This paper presents an efficient methodology for developing pollutant discharge permit trading in river systems considering the conflict of interests of involving decision-makers and the stakeholders. In this methodology, a trade-off curve between objectives is developed using a powerful and recently developed multi-objective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II (NSGA-II). The best non-dominated solution on the trade-off curve is defined using the Young conflict resolution theory, which considers the utility functions of decision makers and stakeholders of the system. These utility functions are related to the total treatment cost and a fuzzy risk of violating the water quality standards. The fuzzy risk is evaluated using the Monte Carlo analysis. Finally, an optimization model provides the trading discharge permit policies. The practical utility of the proposed methodology in decision-making is illustrated through a realistic example of the Zarjub River in the northern part of Iran.     

Contaminants of emerging concern: a review of new approach in AOP technologies

The presence of contaminants of emerging concern (CECs) such as pharmaceuticals and personal care products (PPCPs), endocrine-disrupting compounds (EDCs), flame retardants (FRs), pesticides, and artificial sweeteners (ASWs) in the aquatic environments remains a major challenge to the environment and human health. In this review, the classification and occurrence of emerging contaminants in aquatic environments were discussed in detail. It is well documented that CECs are susceptible to poor removal during the conventional wastewater treatment plants, which introduce them back to the environment ranging from nanogram per liter (e.g., carbamazepine) up to milligram per liter (e.g., acesulfame) concentration level. Meanwhile, a deep insight into the application of advanced oxidation processes (AOPs) on mitigation of the CECs from aquatic environment was presented. In this regard, the utilization of various treatment technologies based on AOPs including ozonation, Fenton processes, sonochemical, and TiO₂ heterogeneous photocatalysis was reviewed. Additionally, some innovations (e.g., visible light heterogeneous photocatalysis, electro-Fenton) concerning the AOPs and the combined utilization of AOPs (e.g., sono-Fenton) were documented.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

Assessing the effects of dam building on land degradation in central Iran with Landsat LST and LULC time series

This study aimed to analyze the impact of Zayandehrood Dam on desertification using the spatio-temporal dynamics of land use/land cover (LULC) and land surface temperature (LST) in an arid environment in central Iran from 1987 to 2014. The LULC and LST images were calculated from Landsat TM, ETM+, and OLI data, and their accuracies were assessed against reference data using error matrix and linear regression analysis. Results showed that salty and bare lands increased up to 57,302 ha, while agricultural lands declined substantially (28,275.58 ha) in the region. The changes in LULC classes resulted in dramatic variations in LST values. The average temperature showed a 5.03 °C increase, and the minimum temperature increased by 5.66 °C. LST had an increasing trend in bare lands (8.74 °C), poor rangelands (6.8 °C), agricultural lands (9.46 °C), salty lands (9.6 °C), and residential areas (3.18 °C) in this 27-year period. Rainfall and temperature trend analysis revealed that the main cause of these extreme changes in LULC and LST was largely attributed to the drying up of Zayandehrood River due to dam construction and allocating water mainly for industrial sectors. Results indicate that in addition to LULC changes, the spatio-temporal variations of LST can be used as an effective index in desertification assessment and monitoring in arid environments.