Search Results (2,071)

The time-scale mismatch between rapid influent fluctuations and slow biochemical responses hinders the stability of wastewater treatment plants (WWTPs). Existing models often fail to capture shock signals due to noise interference (“signal pollution”). To address this, we propose the HD-MAED-LSTM model, which employs a “decompose-and-conquer” strategy. Targeting the dynamic characteristics of different components, this study innovatively designs heterogeneous attention mechanisms: utilizing Long-term Dependency Attention to capture the global evolution of the trend component, employing Multi-scale Periodic Attention to reinforce the cyclic patterns of the seasonal component, and using Gated Anomaly Attention to keenly capture sudden shocks in the residual component. In a case study, the effectiveness of the proposed model was validated based on one year of operational data from a large-scale industrial WWTP. HD-MAED-LSTM outperformed baseline models such as Transformer and LSTM in the medium-to-long-term (10-h) prediction of COD, TN, and TP, clearly demonstrating the positive role of differentiated modeling. Notably, in the core task of shock load early warning, the model achieved an F1-Score of 0.83 (superior to Transformer’s 0.77 and LSTM’s 0.67), and a Mean Directional Accuracy (MDA) as high as 0.93. Ablation studies confirm that the specialized attention mechanism is the key performance driver, reducing the Mean Absolute Error (MAE) by 56.7%. This framework provides precise support for shifting WWTPs from passive response to proactive control. Full article

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analyzing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analyzing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge-computing-based smart framework that captures data from sensors, including ultraviolet, electrochemical, and microfluidic, along with other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyzes the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments, and other treatment plans. A logic layer is designed at the server level to process the real-time data from the sensor clusters and identify the discharge of nutrients, metals, and emerging contaminants in the water that affect the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis, and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonize sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad hoc analyses even during critical conditions. A user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI-enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability. Full article

Wastewater treatment systems retain a significant proportion of microplastics (MPs) derived from domestic and industrial discharges; however, these emerging pollutants are not completely removed and tend to accumulate in the biological sludge generated during the treatment process. In this study, three biological-type wastewater treatment plants (WWTPs) located in Acapulco, Mexico, were analyzed. The concentrations of MPs in the biological sludge ranged from 830 to 9300 particles/L. Using differential scanning calorimetry (DSC), the predominant polymers identified were high-density polyethylene (HDPE), polyethylene terephthalate (PET), and polypropylene (PP). It was estimated that the monthly concentrations of MPs in the sludge could reach up to 5.36 × 10⁹ particles/L, while the annual concentrations could rise to 3.55 × 10¹⁰ particles/L. These findings highlight the urgent need to review and update the regulatory framework related to the use of residual sludge for agricultural purposes, since high loads of MPs and their transfer pose a potential risk to soil quality, ecosystem health, and long-term environmental sustainability. Full article

Water quality monitoring is essential for assessing a freshwater ecosystem’s status. This knowledge is indispensable for selecting restoration measures to ensure the provision of ecosystem services and sustainable growth of human communities. Remote sensing (RS) has proven to be effective for this purpose, offering broad coverage and high temporal and spatial resolution, which is particularly important for small water bodies. In this study, UAV-based multispectral imagery is employed to estimate key water quality parameters, namely, Chlorophyll-a (Chl-a) and turbidity, which are relevant to global and national legislation and policies. Machine learning models were developed using the support vector regression (SVR) algorithm. The Chl-a model resulted in an R² value of 0.49 and an RMSE of 0.24 μg/L, while the turbidity model resulted in an R² value of 0.70 and an RMSE of 0.38 Formazin Nephelometric Unit (FNU). These models enabled the generation of detailed spatial distribution maps of water quality indicators for the studied river. The proposed approach provides valuable information that supports monitoring for both pressure and restoration impacts, promoting the sustainability of freshwater ecosystems. Full article

With accelerating urbanization, rivers have been severely polluted, resulting in widespread black and odorous waterways. The coagulation–sedimentation and contact oxidation bypass treatment process is characterized by low operational cost and simple operation and management. In this study, a coagulation–sedimentation–contact oxidation biofilter process was developed to treat heavily polluted malodorous blackwater. Among the tested biofilm carriers, rigid aramid fiber exhibited the fastest biofilm formation and the best pollutant removal performance. Based on a comprehensive evaluation of effluent quality and treatment capacity, the optimal operating conditions of the proposed process were identified as a PAC dosage of 50 mg/L, an air-to-water ratio of 7:1, and a hydraulic retention time (HRT) of 2 h. Under these conditions, the effluent concentrations of chemical oxygen demand (COD), ammonia nitrogen (NH₄⁺-N), and suspended solids (SSs) were consistently maintained below 30, 5, and 5 mg/L, respectively. Moreover, the optimized system demonstrated strong resistance to shock loading, maintaining stable operation at influent COD and SS concentrations of approximately 150 mg/L and 40 mg/L, respectively, while complying with the Class A Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants. This study provides an efficient treatment strategy for malodorous blackwater remediation. Full article

An integrated field–analytical framework was applied to quantify the impact of two small-settlement treatment facilities (TF1 and TF2) on the Irtysh River (East Kazakhstan). The main objective of this study is to quantify effluent-driven dilution and non-conservative changes in key water-quality indicators downstream of TF1 and TF2 and to evaluate parsimonious models for predicting effluent-outlet BOD and COD from upstream measurements. Paired upstream–downstream control sections are sampled in 2024–2025 for 22 indicators, and plant influent–effluent records are compiled for key wastewater variables. Chloride-based conservative mixing indicated very strong dilution (approximately $D\approx 2.0\times {10}^3$ for TF1 and $D\approx 4.2\times {10}^2$ for TF2). Deviations from the mixing line were summarized using a transformation diagnostic $\theta$. At TF1, several constituents exceeded mixing expectations ($\theta \approx 13$ for COD, $\theta \approx 42$ for ammonium, and $\theta \approx 6$ for phosphates), while nitrate shows net attenuation $\theta <0$. At TF2, $\theta$ values cluster near unity, indicating modest deviations. Under a small-sample regime $\left(\mathrm{N}=10\right)$ and leave-one-out validation, regularized regression provided accurate forecasts of effluent-outlet BOD and COD. Lasso under LOOCV performed best (BOD_after: RMSE = 0.626, MAE = 0.459, and $R^2=0.976$; COD_after: RMSE = 0.795, MAE = 0.634, and $R^2=0.997$). The results reconcile strong reach-scale dilution with constituent-specific local departures and support targeted modernization and operational forecasting for water-quality management in small facilities. Full article

Quality control of drinking water is essential for safeguarding public health, particularly in densely populated urban environments. Environmental microbiological monitoring can complement conventional surveillance by providing deeper insights into the dissemination of pathogens and antimicrobial resistance genes within aquatic systems. In this study, we assessed the quality of wastewater and treated water from two urban water supply systems, representing the southern and northern regions of Porto Alegre, Rio Grande do Sul, Brazil, across four climatic seasons between 2024 and 2025. Fifteen water samples were analyzed, including raw water from Guaíba Lake and treated water collected from public distribution points. The Water Quality Index was calculated, microbiological indicators were quantified, and carbapenem resistance genes were detected using molecular assays. Most treated water samples complied with established bacteriological standards; however, the bla_OXA-48-like gene was recurrently detected in both wastewater and treated water. No resistance genes were identified during the summer, whereas the bla_VIM gene was detected exclusively in spring samples. The presence of carbapenem resistance genes in the absence of cultivable coliforms suggests the persistence of extracellular DNA or viable but non-culturable bacteria, highlighting limitations inherent to conventional microbiological monitoring. Integrating classical microbiological methods with molecular assays enables a more comprehensive assessment of water quality and strengthens evidence-based decision-making within a One Health framework. Full article

The valorization of nutrient-rich institutional effluents represents a promising route for sustainable algal biotechnology. This study investigates the potential of canteen wastewater (CW) as an alternative culture medium for Spirulina platensis, integrating wastewater treatment with high-value carotenoid and lipid production. Growth performance, biochemical composition, and nutrient removal efficiencies were systematically evaluated in 2 L photobioreactors under optimized conditions. Spirulina cultured in 75% CW under 180 μmol photons m⁻² s⁻¹ achieved a biomass productivity of 0.071 g L⁻¹ day⁻¹, nearly three-fold higher than the synthetic BG-11 control (0.023 g L⁻¹ day⁻¹). Nutrient remediation was highly efficient, with 92.12% nitrate and 90.05% phosphate removal, effectively reducing effluent concentrations below discharge limits. Biochemical profiling revealed that wastewater-grown biomass contained 54.3% protein and 7.85% lipids, with a remarkable carotenoid yield of 21.81 mg g⁻¹ DW—significantly higher than the control (6.85 mg g⁻¹ DW). Mechanistic analysis suggests that the balanced nutrient stoichiometry (C:N:P ≈ 30:4:1) and mixotrophic conditions enhanced biomass quality while mitigating ammonia toxicity. This study demonstrates the first integrated application of canteen wastewater for dual-purpose bioremediation and pigment-rich biomass production, establishing a scalable circular bioeconomy framework for institutional waste management. Full article

This research employed “Response Surface Methodology (RSM)” to assess the effectiveness of electrocoagulation (EC) in treating olive mill wastewater (OMW) before applying adsorption with olive stone biochar (OS) as a sustainable adsorbent. Several parameters, including reaction time, current density (CD), inter-electrode distance, and the number of electrodes, were optimized. Analysis using Minitab 22.2 resulted in robust regression models with high coefficients of determination (R²). The optimal parameters were CD of 12.41 mA/cm², a time of 45.61 min, an inter-electrode spacing of 1 cm, and a maximum of 6 electrodes, resulting in an energy consumption (ENC) of 9.85 kWh/m³. Significant pollutant percentage removals were achieved: 72.32% for total Kjeldahl nitrogen (TKN), 80.74% for turbidity, 57.44% for total phenol (TPh), 56.9% for soluble chemical oxygen demand (COD_soluble), and 56.6% for total chemical oxygen demand (COD_total). After the EC, the adsorption of pollutants was conducted using OS biochar that was generated through the pyrolysis of OS at a temperature of 500 °C. FTIR analysis of the biochar revealed key absorption bands that indicated the presence of inorganic compounds, aromatic C=C, and phenolic groups O-H. The integrated EC and adsorption (ECA) process demonstrated markedly higher efficiencies, with TPh removal reaching 61.41%, turbidity reduction at 81.92%, TKN reduction at 77.78%, COD_soluble reduction at 70.31%, COD_total reduction at 65.1%, and project cost of $2.88/m³. The ECA process presents a promising treatment approach for OMW. Full article

This study proposes HGBRCond, a machine learning model for conductivity prediction in controlled biodegradation processes. Eight regression algorithms were evaluated using experimental data (n = 424) from a micro-pilot treatment system. HGBRCond, based on Histogram-Gradient Boosting Regression (best performing ML model), achieved optimal performance (R² = 0.877 ± 0.011, RMSE = 10.235 ± 0.54 µS/cm) through 10-fold cross-validation. Unlike standard HGBR and previous conductivity models that lack comprehensive validation frameworks, HGBRCond integrates rigorous statistical validation (cross-validation, sensitivity analysis, confidence intervals) with multi-level interpretability (Morris screening, SHAP analysis, feature importance), achieving a 6.8% performance improvement over standard gradient boosting approaches while addressing mechanistic interpretability gaps present in prior work. However, limitations constrain direct potential industrial applicability: limited dataset (n = 424), narrow conductivity range (285–360 µS/cm), strong dissolved oxygen dependence, sensitivity across two critical parameters, constant flowrate, and validation restricted to controlled conditions. These constraints require model recalibration for potential industrial application. Future work will focus on model validation across extended operational ranges using industrial samples and full-scale testing to establish applicability beyond controlled experimental settings. Full article

Introduction: Open-circuit cooling systems (OCCSs), integral to many industrial processes, often release blowdown water containing elevated concentrations of treatment chemicals. These discharges, if uncontrolled, pose substantial risks to aquatic ecosystems and human health. This study addresses the environmental implications of chemical emissions from OCCS blowdown through the development of a predictive model designed to estimate contaminant concentrations in receiving water bodies. Methods: The research employs a computational model based on mass-balance equations to simulate the dynamics of chemical emissions from blowdown water. It incorporates key operational variables, including flow rates, degradation rates, and evaporation characteristics. The model evaluates two chemical dosing strategies, continuous and fractional, and their resultant pollutant dispersal patterns in river systems. Validation was performed using empirical data from sulfuric acid (H₂SO₄) applications at a nuclear power plant between 2015 and 2022. Results: The model demonstrated strong agreement with observed sulfate ion concentrations in the receiving water body, confirming its predictive reliability. Continuous dosing resulted in stable levels of pollutants, while fractional dosing caused temporary peaks that did not exceed regulatory limits. Conclusion: The modeling of blowdown water reveals important implications for river water quality and suggests that current wastewater management practices may be insufficient, benefiting from the integration of predictive modeling for blowdown discharges in industrial settings. Full article

Water scarcity continues to challenge arid regions such as Egypt, where growing population demands, climate change impacts, and increasing agricultural pressures intensify the need for sustainable water management. Treated wastewater has emerged as a viable alternative resource, provided that the effluent meets stringent quality standards for safe reuse. The purpose of this study was to develop a comprehensive model of the Khorshed Wastewater Treatment Plant (KWWTP) to depict the processes used for biological nutrient removal. Operational data was gathered and examined over a period of 18 months to describe the quality of wastewater discharged by the Advanced Sequencing Batch Reactor (ASBR) of the plant, using specific physicochemical parameters like TSS, COD, BOD₅, and N-NO₃⁻. A process flow diagram integrating the Activated Sludge Model No. 1 (ASM1) for biological nutrient removal was created using the GPS-X. The study determined the parameters influencing the nutrient removal efficiency by analyzing the responsiveness of kinetic and stoichiometric parameters. Variables related to denitrification, autotrophic growth, and yield for heterotrophic biomass were the main focus of the calibration modifications. The results showed that the Root Mean Square Error (RMSE) for the dynamic-state was COD (0.02), BOD₅ (0.07), N-NO₃⁻ (0.75), and TSS (0.82), and for the steady state was COD (0.04), BOD₅ (0.11), N-NO₃⁻ (0.67), and TSS (0.10). Since the model’s accuracy was deemed acceptable, it provides a validated foundation for future scenario analysis and operational decision support that produces a trustworthy model for predicting effluent data for the concentrations of TSS, COD, BOD₅, and N-NO₃⁻ in steady state conditions. Dynamic validation further confirmed model reliability, despite modest discrepancies in TSS and nitrate predictions; addressing this issue necessitates further research. Full article

The worldwide population is anticipated to reach 10.12 billion by the year 2100, thereby amplifying the necessity for sustainable agricultural methodologies to secure food availability while reducing ecological consequences. Conventional synthetic pesticides, while capable of increasing crop yields by as much as 50%, present considerable hazards such as toxicity, the emergence of resistance, and environmental pollution. This review examines biopesticides, originating from microbial (e.g., Bacillus thuringiensis, Trichoderma spp.), plant, or animal sources, as environmentally sustainable alternatives which address pest control through mechanisms including antibiosis, hyperparasitism, and competition. Biopesticides provide advantages such as biodegradability, minimal toxicity to non-target organisms, and a lower likelihood of resistance development. The global market for biopesticides is projected to be valued between USD 8 and 10 billion by 2025, accounting for 3–4% of the overall pesticide sector, and is expected to grow at a compound annual growth rate (CAGR) of 12–16%. To mitigate production costs, agro-industrial byproducts such as rice husk and starch wastewater can be utilized as economical substrates in both solid-state and submerged fermentation processes, which may lead to a reduction in expenses ranging from 35% to 59%. Strategies for process intensification, such as the implementation of intensified bioreactors, continuous cultivation methods, and artificial intelligence (AI)-driven monitoring systems, significantly improve the upstream stages (including strain development and fermentation), downstream processes (such as purification and drying), and formulation phases. These advancements result in enhanced productivity, reduced energy consumption, and greater product stability. Patent activity, exemplified by 2371 documents from 1982 to 2021, highlights advancements in formulations and microbial strains. The integration of circular economy principles in biopesticide production through process intensification enhances the safety, quality, and sustainability of food systems. Projections suggest that by the 2040s to 2050s, biopesticides may achieve market parity with synthetic alternatives. Obstacles encompass the alignment of regulations and the ability to scale in order to completely achieve these benefits. Full article

Wastewater surveillance gained popularity as a tool supporting public health decision-making during the COVID-19 pandemic. In this study, we monitored four distinct socially vulnerable communities in Cincinnati, Ohio, by monitoring four subsewersheds using 15 upstream locations over two time periods: spring/summer (2023) and fall/winter (2023–2024). The goal of our study was to evaluate the feasibility and effectiveness of monitoring wastewater in socially and economically diverse subsewersheds. A number of 24 h composite samples were collected twice a week and analyzed for SARS-CoV-2 viral loads in the four subsewersheds and two wastewater treatment plants (WWTPs). Wastewater quality parameters (electric conductivity, pH, temperature, ORP) were also measured continuously. During the fall/winter period, increased clinical cases were correlated with high SARS-CoV-2 viral concentrations indicated by both subsewershed and WWTP monitoring. In our study, subsewershed monitoring did not provide early warning of SARS-CoV-2 levels in wastewater and cases compared to WWTP wastewater monitoring during the fall/winter period when outbreaks with higher pathogen levels often occur. This was possibly due to the proximity of the selected subsewersheds to the WWTPs. Although two socially vulnerable subsewersheds had higher SARS-CoV-2 viral concentrations in wastewater, the most vulnerable subsewershed had the lowest wastewater concentrations and the lowest number of reported cases during our study. Therefore, social vulnerability is not always the best predictor of the community COVID-19 burden since other factors may play a role in community infection, including transiency and population age distribution. This study presents some challenges and important findings from subsewershed SARS-CoV-2 wastewater monitoring during two seasons in Ohio. Full article