Given the critical importance of addressing effluent quality concerns, the present study was dedicated to developing a dynamic simulation model based on the Activated Sludge Model 1 (ASM1) of a wastewater treatment plant located in Maghnia City, Algeria. The model calibration process involved collecting and analyzing 56 samples from the plant over a period of 18 months (from July 2021 to January 2023). Thirteen physicochemical parameters were analyzed to identify the variations in their water quality over time. Stoichiometric and kinetic parameters were adjusted during the plant calibration process. These modifications resulted in a reasonable alignment with the investigated variables, enabling the accurate prediction of the wastewater treatment plants (WWTPs)’ steady-state behavior regarding the removal measurements of chemical oxygen demand (COD), total suspended solids (TSS), and ammonium (NH4-N). The model was validated using 14-day measurements spanning a 4-month duration, and the results indicated good agreement between the observed and simulated effluent variable of chemical oxygen demand (COD) with a root mean square error (RMSE) of 23%. These findings highlight the utility of the ASM1 Model in comprehending and managing the intricate dynamics of the activated sludge process in wastewater treatment plants.

This study aimed to develop a comprehensive Ain El Houtz Wastewater Treatment Plant (WWTP) model that represents its biological nutrient removal process to simulate its performance and assess the model's predictability. Operational data was collected and analyzed over three years (2020 to 2023), to characterize the water quality of influent and effluent discharged from the plant. Physicochemical parameters such as Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD₅), Ammonium-Nitrogen (NH₄), Nitrite-Nitrogen (N-NO₂ ⁻), Nitrate-Nitrogen (N-NO₃ ⁻), and Phosphate ions (PO₄-³) were considered. Using the GPS-X software modeling platform, a process flow diagram was developed to integrate the ASM2d model for biological nutrient removal. Through the sensitivity analysis of kinetic and stoichiometric parameters, the research identified the key parameters that impacted the nutrient removal efficiency, which in turn further guided the calibration process. The calibration adjustments focused primarily on parameters associated with denitrification, autotrophic growth, and oxygen saturation coefficients. Statistical measures such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the model’s performance in both steady-state and dynamic-state validation scenarios. Results indicated that for the steady state the MAE and RMSE were the same, NH₄ (6.06) N-NO₂ ⁻& N-NO₃ ⁻ (1.36), and PO₄-³ (3.167), while for dynamic-state we noticed a difference between the MAE and RMSE for the concentration, indicating the complexity of modeling nutrient removal processes. It was observed that PO₄ ⁻³ concentration was not affected by the sensitivity analysis, possibly due to the lack of availability of specific process for the phosphorus removal in the treatment plant, further studies are needed to be carried out to address this issue in detail.

Water infrastructure management relies on information, communication strategies, and affordable technologies. This paper used demand analysis and modeling to guide strategic decision-making in sustainable water management for the urban cluster in Tlemcen, Algeria. To achieve this, the water supply and demand of the study area were assessed over the past three decades. The Water Evaluation and Planning (WEAP) system was employed considering different future scenarios to help decision-makers consider the best choices for sustainable urban water resources management. The results showed that the average water production and distribution efficiency was only 46% due to the high network loss. Therefore, urgent action should be considered to increase the efficiency of the distribution network. Moreover, the outcome showed severe unmet demand in 2050, which can be managed by improving the water networks, increasing conventional water production, and reducing personal water consumption. In cooperation with key stakeholders, new scenarios can be analyzed to develop efficient water management policies and to implement sustainable water allocation approaches.

This study investigated groundwater suitability for irrigation in the Hennaya Irrigated region of Northwest Algeria. The research pursued two primary objectives: first, to establish the hydrochemical origin of the groundwater through comprehensive analyses including hydrochemical parameters, diagrams, and hierarchical clustering; and second, to assess its suitability for irrigation based on key criteria such as the Water Quality Index (WQI), Wilcox, and US Salinity diagrams. The analysis revealed a high level of groundwater suitability for irrigation, as indicated by various indices: Sodium Adsorption Ratio (SAR) values ranged from 1.69 to 2.55 (Excellent), Sodium Percentage (Na%) ranged from 24.22% to 36.98% (Good), and the Residual Sodium Carbonate (RSC) was negative, falling between −8.91 to −1.70 meq/L (Safe). Kelly’s Ratio (KR) ranged from 0.32 to 0.59 (Good), and the Permeability Index (PI) was between 62% and 99% (Moderate). Supported by the Analytic Hierarchy Process (AHP) and spatial analysis, the Water Quality Index (WQI) values ranged from 69.25 to 88.71, categorizing the groundwater in the study area as ‘Good’ quality. While suitable for irrigation, the groundwater showed slight salinity (EC 1247–2010 μS/cm) and alkalinity (pH 7.09–8.02), with elevated total dissolved solids (TDSs) ranging from 990 to 1930 mg/L, approaching the permissible limits for optimal agricultural use. The dominant ion concentrations (Ca²⁺ > Na⁺ > Mg²⁺ > K⁺; HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻) indicate a mixed hydrochemical facies influenced by both water–rock interactions and evaporative processes. Although these findings are promising, they highlight the necessity for preventive measures. Ongoing proactive management and continuous monitoring are essential to ensure the long-term sustainability and protection of groundwater resources in the region.

Three-step graphic showing the ASM1 calibration workflow for the Ain Elhoutz wastewater plant: routine effluent COD data are fractionated and four parameters are optimised at 18 °C, then the same parameters are validated at 22 °C without re-tuning. Mechanistic simulation of municipal activated sludge plants relies predominantly on Activated Sludge Model No. 1 (ASM1), yet routine application is hindered by over-parameterisation: calibrating the full kinetic set to dynamic plant data invites equifinality and loss of predictive power. This study develops a sensitivity-Driven ASM1 calibration workflow for a low-load plant in north-western Algeria, extending our earlier steady-state and dynamic simulation work in the region. ASM1 was implemented in GPS-X, and influent chemical oxygen demand (COD) was fractionated using the CEMAGREF protocol supplemented by a post-calibration mass-balance adjustment. Four parameters (XBH, μmax, H, YH, and KS), the subset repeatedly identified as dominant for effluent COD variance and practically identifiable in recent sensitivity and collinearity studies, were optimised against a targeted dynamic calibration window at 18 °C using the Nelder–Mead simplex algorithm and then tested on an independent seasonal dataset at 22 °C. Calibration achieved strong agreement (ℛ2 = 0.79), validation confirmed acceptable predictive performance (ℛ2 = 0.68) with no systematic bias, while the uncalibrated nitrogen variables showed a consistent over-prediction, quantified and attributed in Section 3. Requiring only routine COD data, the workflow provides a transferable, operator-grade template for dynamic ASM1 calibration at municipal plants with comparable monitoring programmes.