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The total phosphorus (TP) concentration, as the primary limiting eutrophication factor in the Mahabad Dam reservoir in Iran, was studied, considering the combined impacts of climate change, as well as the scenarios on changes in upstream TP loadings and downstream dam water allocations. Downscaled daily projected climate data were obtained from the Beijing Normal University Earth System Model (BNU-ESM) under moderate (RCP4.5) and extreme (RCP8.5) scenarios. These data were used as inputs of a calibrated Soil and Water Assessment Tool (SWAT) model of the watershed in order to determine the effects of climate change on runoff yields in the watershed from 2020 to 2050. The SWAT model was calibrated/validated using the SUFI-2 algorithm in the SWAT Calibration Uncertainties Program (SWAT-CUP). Moreover, to model TP concentration in the reservoir and to investigate the effects of upstream/downstream scenarios, along with forecasted climate-induced changes in streamflow and evaporation rates, the System Dynamics (SD) model was implemented. The scenarios covered a combination of changes in population, agricultural and livestock farming activities, industrialization, water conservation, and pollution control. Relative to the year 2011 in which the water quality data were available, the SD results showed the highest TP concentrations in the reservoir under scenarios in which the inflow to the reservoir had decreased, while the upstream TP loadings and downstream dam water allocations had increased (+29.9%). On the other hand, the lowest TP concentration was observed under scenarios in which upstream TP loadings and dam water allocations had decreased (−18.5%).

Purpose Biofiltration is one of the most accepted technologies in odor control in wastewater facilities. A biofilter system consists of a bed of organic material providing both as the carrier for the active microorganisms and as nutrient supply. This study was aimed to evaluate and model a biofilter performance operated under real conditions of odor emission from a wastewater pump station located in Khorramabad, Iran. Methods The media was a mixture of compost and wood chips with a weight ratio of 5:1. The treatment performance of the biofilter was assessed during a 90-day operation period and the gathered data were utilized to develop and determine the best fit kinetic model based on Michaelis-Menten and Ottengraf models. The best fit model was used in the analysis of scenarios defined based on inlet H 2 S loading fluctuations. Also, the effectiveness of the main parameters in biofilter performance was evaluated using a dimensionless sensitivity coefficient. Results The best fit model was found the Ottengraf zero-order type limited by diffusion based on the values of R-square (0.98) and mean square error (MSE) (0.002). The results demonstrated a high H 2 S removal efficiency of about 98% in an EBRT (empty bed residence time) of 60 s. despite high fluctuations of inlet concentration under real conditions. The system was able to meet the effluent standard limit of 10 ppm even if the inlet H 2 S loading increases up to two times the base level. According to the results of the defined sensitivity coefficient, the system performance was more sensitive to the inlet concentration than EBRT with a ratio of 1.4. Conclusions In addition to the acceptable efficiencies of biofilter in odor removal, the results proved the worth of using a kinetic model in forecasting the system performance which is a useful tool in the design and operation of such systems.

Sludge management is one of the most costly and technically challenging components of municipal wastewater treatment, highlighting the need for sustainable and low-cost stabilization technologies. This study evaluated a pilot-scale vermifiltration system for municipal sewage sludge stabilization under varying hydraulic and organic loading conditions. Three vermifilter pilots incorporating Eisenia andrei earthworms were operated using lightweight expanded clay aggregate (LECA), high-density polyethylene (HDPE) plastic media, and mineral pumice. The systems were tested at hydraulic loading rates (HLRs) of 150, 300, and 450 L/m2·d. Performance was assessed using chemical oxygen demand (COD), total solids (TS), volatile solids (VS), VS/TS ratio, sludge volume index (SVI), and sludge dewaterability indicators, including specific resistance to filtration (SRF) and time to filtration (TTF). Optimal performance occurred at an HLR of 150 L/m2·d, achieving maximum reductions of 49% in COD, 30% in TS, and 40% in VS, along with an SVI reduction of up to 78%. Increasing HLR significantly reduced treatment efficiency due to shorter retention times and biofilm washout. A regression analysis showed the strongest association between COD removal and organic loading rate (R2 = 0.63) under the coupled HLR–OLR conditions tested, while weaker correlations were observed for SVI and VS/TS. Dewaterability improved markedly after vermifiltration, particularly in the LECA-based system. Although filter media type did not significantly affect COD or SVI removal, pumice and plastic media provided greater hydraulic stability at higher loadings. These results demonstrate that vermifiltration is an effective and environmentally sustainable option for municipal sludge stabilization when operated under controlled hydraulic conditions.

Due to the growing scarcity of water resources, wastewater reuse has become one of the most effective solutions for industrial consumption. However, various factors can detrimentally affect the performance of a wastewater treatment plant (WWTP), which is considered a risk of not fulfilling the effluent requirements. Thus, to ensure the quality of treated wastewater, it is essential to analyze system failure causes and their potential outcomes and mitigation measures through a systematic dynamic risk assessment approach. This work shows how a dynamic Bayesian network (DBN) can be effectively used in this context. Like the conventional Bayesian network (BN), the DBN can capture complex interactions between failure contributory factors. Additionally, it can forecast the upcoming failure likelihood using a prediction inference. This proposed methodology was applied to a WWTP of the Moorchekhort Industrial Complex (MIC), located in the center of Iran. A total of 15 years’ time frame (2016–2030) has been considered in this work. The first six years’ data have been used to develop the DBN model and to identify the crucial risk factors that are further used to reduce the risk in the remaining nine years. The risk increased from 21% to 42% in 2016–2021. Applying the proposed risk mitigation measures can decrease the failure risk from 33% to 9% in 2022–2030. The proposed model showed the capability of the DBN in risk management of a WWTP system which can help WWTPs’ managers and operators achieve better performance for higher reclaimed water quality.

Coagulation–flocculation is a process commonly applied to treat many types of industrial wastewater. However, chemicals used in a wastewater treatment plant, including coagulants/flocculants, are costly. This study evaluated the feasibility of recycling the chemical sludge of the Mobarakeh Steel Complex (MSC), and using it as a coagulant aid in the process of wastewater treatment. The experiments were conducted in a jar test apparatus in consecutive cycles by adding a fraction of the settled sludge to samples. The response surface methodology (RSM) was employed to design the experiments and optimize the process. The response in the RSM model was residual turbidity, and the factors affecting the response included the sludge recycle ratio and return sludge cycle number. The sludge volume index (SVI) of the chemical sludge was also examined in each cycle of the tests to assess the sludge quality. The results revealed that recycling the sludge reduced the use of chemicals up to 60%. Finally, it was concluded that flocculation sludge could be considered as an alternative to chemicals, without adversely affecting the removal efficiency. Furthermore, SVI results showed that an increase in the number of cycles led to a decrease in SVI in each cycle, resulting in sludge volume reduction, better dewatering, and decreased sludge treatment costs.

Water quality management of rivers is one of the challenges in the analysis of water resource systems. The optimal operation of the pollutant carrying capacity of these systems provides significant economic value and could reduce treatment costs. In this study, the application of the trading ratio system is investigated to control the cost of pollutants in a river and make a fair deal. In this regard, transfer coefficients between pollution sources, along with the trade coefficients, are determined, considering the system limitations and each pollutant’s contaminant impact. To provide allowable limits of river water quality concentrations, the total cost of all sources and the system is minimized, using the linear programming method. Finally, the new trading discharge permits are calculated for each source. The proposed method is successfully applied to Dez River as a case study. Results show that using a trading ratio system could maintain water quality at a standard level containing economic benefits for the participants of this program.

The steel industry is known for its high water consumption and wastewater production, making it crucial to evaluate the environmental sustainability of water use in this sector. A product's water footprint (WF) represents the total freshwater volume used to produce consumer goods. In this study, the WF concept was used to evaluate the sustainability of water consumption in a steel plant in southeastern Iran. The WF components, including blue and gray water, were calculated for the steel production process from extraction to pelletizing. The findings revealed that indirect WF, such as water used in energy production, was nearly equal to direct WF, representing water used in the production process. To reduce the WF, seven scenarios based on the 3Rs (Reduction, Reuse, Recycling) strategies were proposed. The M-TOPSIS method, a multi-attribute decision-making technique, was used to prioritize the scenarios based on their effectiveness in gray and blue WF reduction, financial worth (income–expense balance), and the priority of the 3Rs strategy. The results indicated that the scenario of wastewater reuse from nearby cities achieved the highest priority due to its significant impact on reducing blue WF (by 38%) at a reasonable cost. The findings demonstrate that the proposed methodology can be applied to similar production systems.

The reliability of a wastewater treatment plant is a critical issue when the effluent is reused or discharged to water resources. Main factors affecting the performance of the wastewater treatment plant are the variation of the influent, inherent variability in the treatment processes, deficiencies in design, mechanical equipment, and operational failures. Thus, meeting the established reuse/discharge criteria requires assessment of plant reliability. Among many techniques developed in system reliability analysis, fault tree analysis (FTA) is one of the popular and efficient methods. FTA is a top down, deductive failure analysis in which an undesired state of a system is analyzed. In this study, the problem of reliability was studied on Tehran West Town wastewater treatment plant. This plant is a conventional activated sludge process, and the effluent is reused in landscape irrigation. The fault tree diagram was established with the violation of allowable effluent BOD as the top event in the diagram, and the deficiencies of the system were identified based on the developed model. Some basic events are operator’s mistake, physical damage, and design problems. The analytical method is minimal cut sets (based on numerical probability) and Monte Carlo simulation. Basic event probabilities were calculated according to available data and experts’ opinions. The results showed that human factors, especially human error had a great effect on top event occurrence. The mechanical, climate, and sewer system factors were in subsequent tier. Literature shows applying FTA has been seldom used in the past wastewater treatment plant (WWTP) risk analysis studies. Thus, the developed FTA model in this study considerably improves the insight into causal failure analysis of a WWTP. It provides an efficient tool for WWTP operators and decision makers to achieve the standard limits in wastewater reuse and discharge to the environment.

Wastewater from wheat starch industries is the one with high chemical oxygen demand (COD) level that has adverse effects on the environment and thus special attention to its treatment for the discharge limits satisfaction is crucial. Biological treatment methods have challenges such as requiring extensive space and process time, high sludge production, and efficient management and operation demands. To overcome these challenges, electrochemical methods such as electrocoagulation (EC) and electro-Fenton (EF) can be efficient approaches due to their higher process speeds, minimal facility requirements, and easy operation which make them economically viable. In this study, electrochemical processes, including EC and EF methods were applied for wastewater treatment of a wheat starch industry. After preliminary experiments to identify the effective factors and ranges, the response surface method (RSM) was applied to design the experiments. In RSM seven factors were considered including initial COD, pH, electrode distances, process time, temperature, current intensity, and hydrogen peroxide concentration along with the COD removal efficiency as the response. Statistical analysis showed that hydrogen peroxide concentration and initial COD had the most significant impact, while pH had the least effect on COD removal in the electrochemical process. The optimum results showed that for synthetic wastewater with an initial COD range of 2000–4000 mg/L a COD removal of 75–85% for EC and 89–93% removal for EF were obtained. The results were validated for raw natural wastewater with 88% removal for EC and 92% for EF. In conclusion, while the removal efficiency of the EF process was superior to EC, the former incurs higher costs due to the use of hydrogen peroxide.

The steel industry is known for its high water consumption and wastewater production, making it crucial to evaluate the environmental sustainability of water use in this sector. A product's water footprint (WF) represents the total freshwater volume used to produce consumer goods. In this study, the WF concept was used to evaluate the sustainability of water consumption in a steel plant in southeastern Iran. The WF components, including blue and gray water, were calculated for the steel production process from extraction to pelletizing. The findings revealed that indirect WF, such as water used in energy production, was nearly equal to direct WF, representing water used in the production process. To reduce the WF, seven scenarios based on the 3Rs (Reduction, Reuse, Recycling) strategies were proposed. The M-TOPSIS method, a multi-attribute decision-making technique, was used to prioritize the scenarios based on their effectiveness in gray and blue WF reduction, financial worth (income–expense balance), and the priority of the 3Rs strategy. The results indicated that the scenario of wastewater reuse from nearby cities achieved the highest priority due to its significant impact on reducing blue WF (by 38%) at a reasonable cost. The findings demonstrate that the proposed methodology can be applied to similar production systems.