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This paper presents a new framework for the adaptive reservoir operation considering water quantity and quality objectives. In this framework, using the European Centre for Medium-Range Weather Forecasts (ECMWF) database, the monthly precipitation forecasts, with up to 6-month lead time, are downscaled and bias corrected. The rainfall forecasts are used as inputs to a rainfall-runoff simulation model to predict sub-seasonal inflows to reservoir. The water storage at the end of a short-term planning horizon (e.g. 6 months) is obtained from some probabilistic optimal reservoir storage volume curves, which are developed using a long-term reservoir operation optimization model. The adaptive optimization model is linked with the CE-QUAL-W2 water quality simulation model to assess the quality of outflow from each gate as well as the in-reservoir water quality. At the first of each month, the inflow forecasts for the coming months are updated and operating policies for each gate are revised. To tackle the computational burden of the adaptive simulation-optimization model, it is run using Parallel Cellular Automata with Local Search (PCA-LS) optimization algorithm. To evaluate the applicability and efficiency of the framework, it is applied to the Karkheh dam, which is the largest reservoir in Iran. By comparing the run times of the PCA-LS and the Non-dominated Sorting Genetic Algorithms II (NSGA-II), it is shown that the computational time of PCA-LS is 95 % less than NSGA-II. According to the results, the difference between the objective function of the proposed adaptive optimization model and a perfect model, which uses the observed inflow data, is only 1.68 %. It shows the appropriate accuracy of the adaptive model and justifies using the proposed framework for the adaptive operation of reservoirs considering water quantity and quality objectives. • Adaptive reservoir operation model considering water quantity and quality objectives. • Using ECMWF-based rainfall forecasts with monthly time step and 6-month lead time. • Linking the optimization model with the CE-QUAL-W2 water quality simulation model. • Applying the real-time operation model to the Karkheh river-reservoir system in Iran. • Illustrating the efficiency of the proposed model in a real-world large-scale problem. [ABSTRACT FROM AUTHOR]

Groundwater in arid and semi-arid coastal aquifers is vulnerable to seawater intrusion and quality deterioration despite being one of the most reliable sources of water supply due to the increasing number of development plans and competition between water consumers. A multi-dimensional groundwater management framework is developed to trade-off between groundwater abstraction, allocation equity, groundwater quality, and energy considerations in the reverse osmosis (RO) filtration process in the fresh groundwater lens of Kish Island, Iran. An arid island confined in the Persian Gulf is modeled using 3D simulation and three well-occupied multi-objective evolutionary optimization algorithms. Four objectives include: maximizing the groundwater abstraction, minimizing the Gini coefficient (allocation inequity), minimizing the total energy required to pass saline water through the RO membrane to reach the standard total dissolved solids (TDS), and minimizing the average TDS concentration of water abstraction positions from 11 management zones have been considered over a 50-year management horizon. Solutions obtained in the simulation-based constrained multi-objective optimization framework allow managers to choose from 587 Pareto optimal solutions. They provide an abstraction scheme with a range of 1.44 to 4.53 MCM/yr, a Gini coefficient of 0 to 0.98, filtration energy of 988,562 to 1,935,760 kWh/yr, and an average TDS of 19,663 to 21,351 mg/L. The Pareto optimal solutions can help decision-makers decide on the multi-dimensional problems of sustainable coastal groundwater management and show patterns among different objectives. [Display omitted] • A simulation-based constrained multi-objective optimization framework is used to solve a real-world multi-dimensional coastal groundwater management problem. • A holistic management model for Kish Island lens with four objectives and their tradeoffs is developed. • The ideal solution maximizes groundwater abstraction and allocation equity while minimizing TDS concentration and RO filtration energy. • Non-dominated solutions combination of three multi-objective evolutionary algorithms resulted in obtaining the best solutions. • Desalination effluents are considered, and their effects on groundwater are investigated. [ABSTRACT FROM AUTHOR]

Occurrence of phthalates in water resources, bottled water, and tap water, and health risk of exposure to the phthalates through drinking water in Tehran, Iran, 2018 were studied. The six phthalates with the most health and environmental concerns, including di-(2-ethylhexyl) phthalate (DEHP), butyl benzyl phthalate (BBP), di-n-butyl phthalate (DBP), diethyl phthalate (DEP), dimethyl phthalate (DMP), and di-n-octyl phthalate (DNOP) were monitored in drinking water and water resources. The average levels (±standard deviation: SD) of the total phthalates in drinking water from the water distribution system, bottled water, surface waters, and ground waters were determined to be 0.76 ± 0.19, 0.96 ± 0.10, 1.06 ± 0.23, and 0.77 ± 0.06 μg/L, respectively. The dominant compounds in the phthalates were DMP and DEHP causing a contribution to the total phthalate levels higher than 60% in all the water sources. The phthalate levels of drinking water significantly increased by contact of hot water with disposable plastic and paper cups and by sunlight exposure of bottled water (p value < 0.05). The hazard quotients (HQs) of DEHP, BBP, DBP, and DEP for all ages both sexes combined were determined to be 1.56 × 10−4, 1.01 × 10−5, 1.80 × 10−5, and 1.29 × 10−6, respectively that were much lower than the boundary value of 1.0. The disability-adjusted life years (DALYs) and DALY rate (per 100,000 people) attributable to DEHP intake through drinking water for all ages both sexes combined were estimated to be 6.385 (uncertainty interval: UI 95% 1.892 to 22.133), and 0.073 (0.022–0.255), respectively. The proportion of mortality in the attributable DALYs was over 96%. The attributable DALY rate exhibited no significant difference by sex, but was considerably affected by age in a manner that the DALY rates ranged from 0.052 (0.015–0.175) in the age group 65 y plus to 0.099 (0.026–0.304) in the age group 5 to 9 y. Both the carcinogenic and non-carcinogenic health risks of the phthalates in drinking water were considered to be very low. The results can also be of importance in terms of developing frameworks to expand the domain of burden of disease study to the other environmental risks. • Health risk of phthalates in water environment in Tehran was evaluated. • The average level of the total phthalates in drinking water was 0.76 ± 0.19 μg/L. • The dominant phthalates were DMP and DEHP causing a share higher than 60%. • The HQs of all the phthalates were much lower than the boundary value of 1.0. • The attributable DALYs and DALY rate in Tehran were respectively 6.385 and 0.073. [ABSTRACT FROM AUTHOR]

The assessment of lake water quality is crucial for protecting and managing natural resources. Total suspended matter (TSM) and total dissolved solids (TDS) are significant parameters in evaluating lake water quality, and their concentration levels provide useful information about the health of the ecosystem. However, measuring TSM and TDS concentrations in remote arid areas can be challenging, necessitating the development of a simple, efficient, and cost-effective monitoring system. In this study, the aim was to retrieve TDS and TSM concentrations in the arid region using an empirical and physics-based method simultaneously. Measured data and remote sensing images for Chah-Nimeh Reservoirs (CNRs), Iran, were used to establish the TSM and TDS concentration model. Twenty-seven Sentinel-2 multispectral instrument (MSI) data acquired from 2018 to 2021 were employed to determine the spatiotemporal distribution of TDS and TSM concentrations. The results indicate that Sentinel-2 MSI data is a productive tool for retrieving TSM concentration using the Case-2 Regional/Coastal Color (C2RCC) processor, with a high level of performance in the dry ecosystem of CNRs (R2 = 0.92). Furthermore, the Bayesian regularization artificial neural network (BRANN) algorithm was used to evaluate TDS concentration, and the results demonstrated that the BRANN algorithm outperforms other models, with a high level of performance (R2 = 0.89). It is noteworthy that evaluating TDS concentration is generally more challenging than TSM. The study provides a valuable model for monitoring TSM and TDS concentrations in remote arid areas using Sentinel-2 MSI data. The proposed approach can facilitate effective spatiotemporal monitoring of TSM and TDS concentrations and aid in the evaluation of lake water quality, specifically in regions where in-situ measurements are not feasible. The findings have significant implications for the management and conservation of natural resources in arid and remote regions. [Display omitted] • C2RCC processor accurately maps TSM in arid regions, providing valuable water quality insights. • BRANN-based algorithm has potential for measuring TDS concentrations in arid ecosystems, improving water quality monitoring. • Low-cost remote sensing and Sentinel-2 MSI data enable reliable, cost-effective water quality monitoring in arid regions, with important implications for ecosystem management. [ABSTRACT FROM AUTHOR]

Groundwater is an important source for drinking water supply in the arid and semi-arid regions around the world. Eighty four groundwater samples collected from Kakhk and its all rural areas were evaluated for drinking quality using the water quality index (WQI). Additionally, the human health risks associated with nitrate and fluoride concentrations in drinking water were estimated for children and adults using the United States Environmental Protection Agency (USEPA) method in the investigated areas. Parameters including potassium, carbonate, bicarbonate, chloride, sulfate, nitrate, fluoride, calcium, magnesium, sodium, pH, total hardness and electrical conductivity were analyzed. Among these, levels of potassium, carbonate, nitrate and fluoride in all areas were within the permissible limits of World Health Organization (WHO) for drinking water. The mean concentrations of nitrate and fluoride in the study areas were 20.00 ± 14.81 mg/L and 0.32 ± 0.17 mg/L, respectively. The WQI classification showed that 16.6 %, 38 % and 45.2 % of water resources had excellent, good and poor water quality, respectively. 40.4 % of the areas had Hazard Quotient (HQ) values more than one (HQ ≥ 1), indicating the possibility of being non-carcinogenic risk of nitrate through water consumption for children. But values of HQ adults (for nitrate), HQ children (for fluoride) and HQ adults (for fluoride) all were less than 1 (HQ < 1), showing little or no risk from water consumption containing these elements in the areas. Regarding all of the measured parameters, higher CDI and HQ values were seen for children compared to adults. This study is useful, as it is the most comprehensive water quality assessment research conducted so far in Kakhk and its rural areas and will be a reference to future studies. • Quality of drinking water and health risk in Kakhk and its rural areas were assessed. • 16.6 %, 38 % and 45.2 % of the areas had excellent, good and poor water quality, respectively. • 40.4 % of the areas indicated the possibility of non-carcinogenic risk of nitrate for children. • Higher CDI and HQ values were seen for children compared to adults. [ABSTRACT FROM AUTHOR]

This study aims at investigating the quality of drinking water and evaluating the non-carcinogenic risk of fluoride and nitrate ions in drinking water, and fluoride in tea in Zarrin Dasht, Iran. We focus on tea since it is the most popular drink among Iranian people and in the study region. We collected and analyzed 23 drinking water samples and 23 tea samples from different locations in the study region. Based on the water quality index, the consumed drinking water does not have a good quality in most Zarrin Dasht areas. Accordingly, the water quality index (WQI) is poor and very poor in 70% and 13% of the water samples, respectively. The average fluoride concentration of the tea samples is 2.71 mg/L. The mean values of Fluoride Hazard Index (HI fluoride) are 3.77, 2.77, and 2.33 for children, teenagers, and adults, respectively, which are higher than the safe limit of 1. The Nitrate Hazard Index (HI nitrate) is higher than the safe limit of 1 in 8.7% of the samples. The results of the Monte Carlo simulation demonstrate that HI fluoride and HI nitrate are higher than 1 in all the groups, except for adults. According to the results of the sensitivity analysis, ingestion rate and body weight have a large effect on HI fluoride and HI nitrate , but body weight is inversely associated with sensitivity. According to the Piper diagram, saline water is the predominant type in Zarrin Dasht. Besides, the results of the principal component analysis (PCA) show a high correlation between fluoride and pH, which could be related to the effect of pH on fluoride dissolution and ion exchange. Therefore, appropriate measures are recommended to be taken in order to reduce the amount of fluoride in the drinking water resources of this region. Reduction of tea consumption can also be considered an important factor in decreasing the amount of fluoride intake. • WQI and multivariate methods are used to reveal drinking water quality issues. • Risk assessment of fluoride & nitrate in water and tea consumed are determined. • WQI is weak and very weak in 70% and 13% of the water samples, respectively. • Piper diagram shows that saline water is the dominant type in the water. • HI fluoride is greater than 1 for children, teenagers, and adults. [ABSTRACT FROM AUTHOR]

As one of the largest rivers in the southwest of Iran, the Kor River plays an important role in local economy and ecosystem. However, the rapid development of industry has caused significant pollution in this river in recent years. Despite of a number of studies reported on this river regarding water pollution, few have conducted a comprehensive investigation of a wide range of water quality parameters to map the current pollution status. This study focuses on 21 water quality parameters around the industrial centers of the Kor River basin with samples taken from 25 stations. With the measured parameters, the interpolation maps of each parameter were determined using the Kriging method, and the water quality was quantified using the Water Quality Index (WQI) method. The results showed that the WQI values were between 28 and 73, showing more pollution around the factories than in the upstream areas. The results of the principal component analysis (PCA) indicated that BOD, COD, NO 3 − , and coliforms were the most important parameters among the 21 parameters affecting the water quality. Linear regression results suggested that the best parameters for determining coliforms and WQI values were BOD, and Cr, P O 4 3 − , Hg and Zn levels, respectively, with R2 greater than 0.87. These results can also simplify the prediction of coliforms and WQI using only a few parameters. We further found that flatter regions generally had more pollution, primarily due to pollutant accumulation as a result of water stagnation. [ABSTRACT FROM AUTHOR]

In this paper, principal component analysis (PCA) and hierarchical cluster analysis (CA) methods have been used to investigate the water quality of Jajrood River (Iran) and to assess and discriminate the relative magnitude of anthropogenic and “natural” influences on the quality of river water. T, EC, pH, TDS, NH4, NO3, NO2, Turb., T.Hard., Ca, Mg, Na, K, Cl, SO4, SiO2 as physicochemical and TC, FC as biochemical variables have been analyzed in the water samples collected every month over a three-year period from 18 sampling stations along a 50 km section of Jajrood River that is under the influence of anthropogenic and natural changes. Exploratory analysis of experimental data has been carried out by means of PCA and CA in an attempt to discriminate sources of variation in water quality. PCA has allowed identification of a reduced number of mean 5 varifactors, pointing out 85% of both temporal and spatial changes. CA classified similar water quality stations and indicated Out-Meygoon as the most polluted one. Ahar, Baghgol, Rooteh, Befor Zaygan, Fasham, Roodak and Lashgarak were identified as affected by organic pollution. A Scree plot of stations in the first and second extracted components on PCA also gave us a classification of stations due to the similarity of pollution sources. CA and PCA led to similar results, though Out-Meygoon was identified as the most polluted station in both methods. Box-plots showed that PCA could approximately demonstrate temporal and spatial variations. CA gave us an overview of the problem and helped us to classify and better explain the PCA results. [Copyright &y& Elsevier]

The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO 4 −2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO 4 −2 data recorded at Tange-Takab station for the period 1980–2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO 4 −2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4−2, respectively. [Display omitted] • Complementary machine learning models were developed for surface water prediction. • Mg+2, and SiO 4 −2 were simulated based on EC, river discharge, Mg+2, and SiO 4 −2. • Wavelet pre-processing approach is used to identify the essential predictors. • The improved simulated annealing method is integrated to improve the accuracy. • The proposed complementary machine learning models were provided optimistic results. [ABSTRACT FROM AUTHOR]