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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]

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]

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]