Search Results (128)

Coagulation–flocculation, historically reliant on simple inorganic salts, has evolved into a technically sophisticated process that is central to the removal of turbidity, suspended solids, organic matter, and an expanding array of micropollutants from complex wastewaters. This review synthesizes six decades of research, charting the transition from classical aluminum and iron salts to high-performance polymeric, biosourced, and hybrid coagulants, and examines their comparative efficiency across multiple performance indicators—turbidity removal (>95%), COD/BOD reduction (up to 90%), and heavy metal abatement (>90%). Emphasis is placed on recent innovations, including magnetic composites, bio–mineral hybrids, and functionalized nanostructures, which integrate multiple mechanisms—charge neutralization, sweep flocculation, polymer bridging, and targeted adsorption—within a single formulation. Beyond performance, the review highlights persistent scientific gaps: incomplete understanding of molecular-scale interactions between coagulants and emerging contaminants such as microplastics, per- and polyfluoroalkyl substances (PFAS), and engineered nanoparticles; limited real-time analysis of flocculation kinetics and floc structural evolution; and the absence of predictive, mechanistically grounded models linking influent chemistry, coagulant properties, and operational parameters. Addressing these knowledge gaps is essential for transitioning from empirical dosing strategies to fully optimized, data-driven control. The integration of advanced coagulation into modular treatment trains, coupled with IoT-enabled sensors, zeta potential monitoring, and AI-based control algorithms, offers the potential to create “Coagulation 4.0” systems—adaptive, efficient, and embedded within circular economy frameworks. In this paradigm, treatment objectives extend beyond regulatory compliance to include resource recovery from coagulation sludge (nutrients, rare metals, construction materials) and substantial reductions in chemical and energy footprints. By uniting advances in material science, process engineering, and real-time control, coagulation–flocculation can retain its central role in water treatment while redefining its contribution to sustainability. In the systems envisioned here, every floc becomes both a vehicle for contaminant removal and a functional carrier in the broader water–energy–resource nexus. Full article

Stricter requirements on nutrient removal in wastewater treatment are being imposed by rapid urbanization and tightening water-quality standards. Despite their excellent solid–liquid separation and effective biological treatment, MBRs in conventional operation remain hindered by membrane fouling, limited robustness to influent variability, and elevated energy consumption. In recent years, precise process control and resource-oriented operation have been enabled by the integration of artificial intelligence (AI) with MBRs. Advances in four areas are synthesized in this review: optimization of MBR control architectures, intelligent adaptation to multi-source wastewater, regulation of membrane operating parameters, and enhancement of nitrogen and phosphorus removal. According to reported studies, increases in total nitrogen and total phosphorus removal have been achieved by AI-driven strategies while energy use and operating costs have been reduced; under heterogeneous influent and dynamic operating conditions, stronger generalization and more effective real-time regulation have been demonstrated relative to traditional approaches. For large-scale deployment, key challenges are identified as improvements in model interpretability and applicability, the overcoming of data silos, and the realization of multi-objective collaborative optimization. Addressing these challenges is regarded as central to the realization of robust, scalable, and low-carbon intelligent wastewater treatment. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Wastewater treatment plants (WWTPs) face increasing pressure to handle higher volumes of water due to climate change causing storm surges, which current infrastructure cannot handle. Aerobic granular sludge (AGS) is a promising alternative to activated sludge systems due to their improved settleability property, lowering the land footprint and improving efficiency. This research investigates the optimisation of a lab-scale sequencing batch reactor (SBR) into a continuous flow reactor through mathematical modelling, sensitivity analysis, and a computational fluid dynamic model. This is all applied for the future goal of scaling up the model designed to a full-scale continuous flow reactor. The mathematical model developed analyses microbial kinetics, COD degradation, and mixing flows using Reynolds and Froude numbers. To perform a sensitivity analysis, a Python code was developed to investigate the stability when influent concentrations and flow rates vary. Finally, CFD simulations on ANSYS Fluent evaluated the mixing within the reactor. An 82% COD removal efficiency was derived from the model and validated against the SBR data and other configurations. The sensitivity analysis highlighted the reactor’s inefficiency in handling high-concentration influents and fast flow rates. CFD simulations revealed good mixing within the reactor; however, they did show issues where biomass washout would be highly likely if applied in continuous flow operation. All of these results were taken under deep consideration to provide a new reactor configuration to be studied that may resolve all these downfalls. Full article

Substantial boosts in the low-energy nano-oxygenation of incoming process water were achieved at a municipal wastewater treatment plant (WWTP) upstream of activated sludge (AS) aeration lanes on a single-pass basis by means of an electric field nanobubble (NB) generation method (with unit residence times of the order of just 10–15 s). Both ambient air and O₂ cylinders were used as gas sources. In both cases, it was found that the levels of dissolved oxygen (DO) were maintained far higher for much longer than those of conventionally aerated water in the AS lane—and at DO levels in the optimal operational WWTP oxygenation zone of about 2.5–3.5 mg/L. In the AS lanes themselves, there were also excellent conversions to nitrate from nitrite, owing to reactive oxygen species (ROS) and some improvements in BOD and E. coli profiles. Nanobubble-enhanced Dissolved Air Flotation (DAF) was found to be enhanced at shorter times for batch processes: settlement dynamics were slowed slightly initially upon contact with virgin NBs, although the overall time was not particularly affected, owing to faster settlement once the recruitment of micro-particulates took place around the NBs—actually making density-filtering ultimately more facile. The development of machine learning (ML) models predictive of NB populations was carried out in laboratory work with deionised water, in addition to WWTP influent water for a second class of field-oriented ML models based on a more narrow set of more easily and quickly measured data variables in the field, and correlations were found for a more facile prediction of important parameters, such as the NB generation rate and the particular dependent variable that is required to be correlated with the efficient and effective functioning of the nanobubble generator (NBG) for the task at hand—e.g., boosting dissolved oxygen (DO) or shifting Oxidative Reductive Potential (ORP). Full article

Streptococcus pyogenes is a bacterial pathogen known to be the causative agent in many different illnesses, with Group A Streptococcus (GAS) pharyngitis (strep throat), being one of the more prevalent. The spread and severity of GAS pharyngitis can grow exponentially if individuals are not taking the proper precautions. Wastewater surveillance has been used to test for numerous different pathogens that humans spread throughout a community and in this study, we utilized wastewater surveillance to monitor GAS pharyngitis in a small city. Over a year, 57 wastewater influent samples were tested for S. pyogenes and three commonly tested respiratory viruses (Respiratory Syncytial Virus (RSV), SARS-CoV-2, Influenza A). Three microbial indicators and population normalizers (CrAssphage, Pepper mild mottle virus (PMMoV), and Mycobacterium) were tested as well to compare and contrast each indicator’s value and range over time. Wastewater data was then compared to publicly available search term data as clinical data was not readily available. There was a high correlation between the collected molecular data and the publicly available search term data for Streptococcus pyogenes. Additionally, this study provided more information about the seasonal trend of S. pyogenes throughout the year through molecular data and allowed for the ability to track peak infection months in this small city. Overall, these results highlight the substantial benefits of using wastewater surveillance for the monitoring of GAS pharyngitis. This study also provides helpful insights into future studies about the prevalence of respiratory bacteria and their seasonal trends in wastewater, allowing for public health systems to provide mitigation strategies. Full article

Biological wastewater treatment systems exhibit a wide range of operating conditions and influent substrate types. Artificial intelligence methods, particularly artificial neural networks (ANNs), are increasingly being employed to manage this complexity. This study uses ANN modeling to identify the key operational parameters that influence the efficacy of anaerobic sulfide oxidation (ASO) biotechnology, which simultaneously treats nitrite and sulfide. The ANN model was further analyzed through SHAP analysis to determine the key operational parameters. The dataset used in this study was derived from previously published operational data of ASO reactors. The results revealed that the sulfide-to-nitrite (S:N) ratio and hydraulic retention time (HRT) had the greatest impact on sulfide removal. In contrast, influent sulfide and nitrite concentrations had no effect on the prediction of effluent pH. While other parameters had a positive effect, HRT had a slight negative impact on effluent pH, with the S:N ratio having the most effect. Furthermore, while other factors contributed to sulfate generation, sulfide influent and HRT had a significant impact. Predicting nitrite-nitrogen (NO₂⁻-N) removal is mostly dependent on the S:N ratio and influent pH. To enhance ASO reactor performance for sulfide and nitrite removal, it is recommended to prioritize the optimization of the S:N ratio and HRT, as these parameters have the greatest impact on key treatment outcomes, including sulfide and NO₂⁻-N removal and sulfate formation. Full article

Wastewater-based epidemiology (WBE) offers a promising tool for sexually transmitted infection (STI) surveillance, especially in settings where underdiagnosis or social stigma complicates conventional reporting. To assess its utility, we conducted a year-long study examining six STIs, Chlamydia trachomatis, Treponema pallidum, Neisseria gonorrhoeae, human immunodeficiency virus (HIV), hepatitis C virus (HCV), and herpes simplex virus (HSV), in weekly composite samples from the primary influent of a small-sized Midwestern wastewater treatment plant. Pathogen detection and quantification were performed via digital PCR. Among the tested targets, Gonorrhea, HIV, HCV, and HSV were detected at the highest frequencies, often in 40–50% of the samples, while Chlamydia and Syphilis appeared less frequently. Despite the variability in detection patterns, this study demonstrates that even infrequent signals can reveal community-level shedding of poorly reported or asymptomatic infections. Although month-to-month wastewater data were not strongly correlated with corresponding clinical records, which could potentially reflect delayed healthcare seeking and pathogen-specific shedding dynamics, the overall findings underscore WBE’s ability to complement existing surveillance by capturing infections outside traditional healthcare channels. These results not only advance our understanding of STI prevalence and population shedding but also highlight the practical benefits of WBE as an early warning and targeted intervention tool. Full article

This study presents the long-term performance evaluation of a full-scale hybrid membrane bioreactor (MBR)–nanofiltration (NF) system for the treatment of high-strength municipal landfill leachate from the Istanbul–Şile Kömürcüoda facility. Over a 16-month operational period, influent and effluent samples were analyzed for key parameters, including chemical oxygen demand (COD), ammonium nitrogen (NH₄⁺-N), total phosphorus (TP), suspended solids (SS), and temperature. The MBR unit consistently achieved high removal efficiencies for COD and NH₄⁺-N (93.5% and 98.6%, respectively), while the NF stage provided effective polishing, particularly for phosphorus, maintaining a TP removal above 95%. Seasonal analysis revealed that the biological performance peaked during spring, likely due to optimal microbial conditions. To support intelligent control strategies, artificial neural network (ANN) models were developed to predict effluent COD and NH₄⁺-N concentrations using influent and operational parameters. The best-performing ANN models achieved R² values of 0.861 and 0.796, respectively. The model’s robustness was validated through RMSE, MAE, and 95% confidence intervals. Additionally, Principal Component Analysis (PCA) and Random Forest algorithms were employed to determine the parameter importance and nonlinear interactions. The findings demonstrate that the integration of hybrid membrane systems with AI-based modeling can enhance treatment efficiency and forecasting capabilities for landfill leachate management, offering a resilient and data-driven approach to sustainable operation. Full article

Olive mill wastewater (OMW) is deemed a substantial environmental pollutant, particularly in Mediterranean regions. Lower and middle-income countries, including Jordan, suffer from water scarcity and increasing demand for water, especially for drinking and irrigation purposes. Subsequently, the management and treatment of OMW represents a major concern. This study investigates the feasibility of utilizing Jordanian kaolinite as a simple, readily available, green, and sustainable adsorbent to mitigate the environmental impact of untreated or partially treated OMW. In this work, purified kaolinite (PK) was activated with sodium ions at room temperature. The characterization of PK and sodium-activated kaolinite (PK-NaCl) was accomplished using FTIR, XRD, TGA, and BET surface area analyses. The adsorption performance of both PK and PK-NaCl for OMW treatment were evaluated through batch and column experiments. The key physiochemical parameters of OMW were systematically analyzed in all influent and effluent samples to evaluate the treatment efficiency. In all cases, sodium-activated kaolinite significantly enhances treatment efficiency. The adsorption of total phenolic compounds (TPCs) onto both PK and PK-NaCl adsorbents was studied with respect to initial concentration, adsorbent dosage, and temperature. The maximum adsorption capacity was 8.88 mg/g for PK-NaCl, which was higher than that of PK, at an adsorbent dose of 1.0 g and a temperature of 323 K. The Langmuir and Freundlich isotherm models to describe the adsorption equilibrium were implemented, and both displayed good fit with the experimental data. Additionally, the removal efficiencies of heavy metal (i.e., Zn, Fe and Mn) ions were also evaluated. The findings demonstrated that the PK-NaCl completely removed all tested heavy metal ions, regardless of their initial concentrations. Therefore, the cost-effective and easily prepared PK-NaCl significantly improved the adsorption capacity and presents a promising treatment solution for OMW. This approach could be highly beneficial for olive mills across the Mediterranean regions to mitigate the environmental impact of OM waste. Full article

The ubiquity of diclofenac (DCF) in the environment has raised significant concerns. Diclofenac is a non-steroidal anti-inflammatory drug that has been found in various environmental matrices at minimum concentrations that are harmful to aquatic and terrestrial organisms. Traditional wastewater treatment plants (WWTPs) are not fully equipped to remove a range of pharmaceuticals, and that explains the continued ubiquity of DCF in surface waters. In this study, an Fe₃O₄/SiO₂ nanocomposite prepared from acid mine drainage and coal fly ash was applied for the removal of DCF from wastewater. Major functional groups (Si–O–Si and Fe–O) were discovered from FTIR. TEM revealed uniform SiO₂ nanoparticle rod-like structures with embedded dark spherical nanoparticles. SEM-EDS analysis discovered a sponge-like structure fused with Fe₃O₄ nanoparticles that had significant Si, O, and Fe content. XRD demonstrated the crystalline nature of the nanocomposite. The surface properties of the nanocomposite were evaluated using BET and were 67.8 m²/g, 0.39 cm³/g, and 23.2 nm for surface area, pore volume, and pore size, respectively. Parameters that were suspected to be affecting the removal process were evaluated, including pH, nanocomposite dosage, and sample volume. The detection of DCF was conducted on high-performance liquid chromatography with diode-array detection (HPLC-DAD). Under optimum conditions, the adsorption process was monolayer, and physisorption was described using the Langmuir and Dubinin-Radushkevich (D-R) isotherm models. The kinetic data best fitted the pseudo-first order kinetic model, indicating a physisorption adsorption process. The thermodynamic experimental data confirmed that the adsorption process was spontaneous. The results obtained from real water samples showed 95.28% and 97.44% removal efficiencies from influent and effluent: 94.83% and 88.61% from raw sewage and final sewage, respectively. Overall, this work demonstrated that an Fe₃O₄/SiO₂ nanocomposite could be successfully prepared from coal fly ash and acid mine drainage and could be used to remove DCF in wastewater. Full article

Efficient wastewater treatment with high-quality effluent and minimal operational costs and carbon emissions is vital for safeguarding the ecological environment and promoting human health. However, the wastewater treatment process is extremely complicated due to the characteristics of multiple treatment mechanisms, high disturbance variability and nonlinear behaviors; therefore, optimizing the wastewater treatment process through intelligent control is a long-standing challenge for researchers and operators. Machine learning models are regarded as effective tools for wastewater treatment with better simulating and controlling complex nonlinear behaviors. With the aid of bibliometric analysis, this paper aimed to summarize worldwide research progress and trends in the application of machine learning to wastewater treatment among 1226 related publications. The findings indicate that China and the United States are the two leading countries, with publications of 342 and 209, respectively, while the United States is an outstanding global collaboration leader in this field. Research institutions and authors are mainly from developing countries, and China accounts for the largest proportion of these. The analysis of journal and cited journal contributions report that almost all of the top 10 journals in publications belong to the Q1 quartile (9/10). Overall, future research will likely focus on developing systematic, strong and multi-objective models for wastewater treatment. A hybrid model could take advantage of two or more machine learning models or mechanistic models, which have been verified as excellent models for tackling limited data. Thus, predicting the pollutants in the effluent rather than the influent using hybrid models is attracting increasing attention because effective prediction contributes to reducing the loading shock of influent sharp fluctuation to wastewater treatment effluent quality. Also, the development of advanced data acquirement devices and the AI model prediction with partially default data should also be another focus of future research. Full article

The operational complexity of wastewater treatment systems mainly stems from the diversity of influent characteristics and the nonlinear nature of the treatment process. Together, these factors make the control of effluent quality in wastewater treatment plants (WWTPs) difficult to manage effectively. To address this challenge, constructing accurate effluent quality models for WWTPs can not only mitigate these complexities, but also provide critical decision support for operational management. In this research, we introduce a deep learning method that fuses multi-source data. This method utilises various indicators to comprehensively analyse and predict the quality of effluent water: water quantity data, process data, energy consumption data, and water quality data. To assess the efficacy of this method, a case study was carried out at an industrial effluent treatment plant (IETP) in Anhui Province, China. Deep learning algorithms including long short-term memory (LSTM) and gated recurrent unit (GRU) were found to have a favourable prediction performance by comparing with traditional machine learning algorithms (random forest, RF) and multi-layer perceptron (MLP). The results show that the R² of LSTM and GRU is 1.36%~31.82% higher than that of MLP and 9.10%~47.75% higher than that of traditional machine learning algorithms. Finally, the RReliefF approach was used to identify the key parameters affecting the water quality behaviour of IETP effluent, and it was found that, by optimising the multi-source feature structure, not only the monitoring and management strategies can be optimised, but also the modelling efficiency of the model can be further improved. Full article

Flow virometry (FVM) offers a promising approach for monitoring viruses and virus-like particles (VLPs) in environmental samples. This study compares levels of non-specific VLPs across a wastewater treatment plant (WWTP) with levels of somatic coliphage, (F+) specific coliphage, Pepper Mild Mottle Virus (PMMoV), CrAssphage (CrAss), and Tomato Brown Rugose Fruit Virus (ToBRFV). All targets were quantified in influent, secondary-treated effluent, and tertiary-treated effluent at the University of California, Davis Wastewater Treatment Plant (UCDWWTP) over 11 weeks. We established an FVM-gating boundary for VLPs using bacteriophages T4 and ϕ6 as well as four phages isolated from wastewater. We then utilize T4 alongside three submicron beads as quality controls in the FVM assay. Coliphage was measured by standard plaque assays, and genome copies of PMMoV, CrAss, and ToBRFV were measured by digital droplet (dd)PCR. FVM results for wastewater revealed distinct microbial profiles at each treatment stage. However, correlations between VLPs and targeted viruses were poor. Trends for virus inactivation and removal, observed for targeted viruses during wastewater treatment, were consistent with expectations. Conversely, VLP counts were elevated in the WWTP effluent relative to the influent. Additional sampling revealed a decrease in VLP counts during the filtration treatment step following secondary treatment but a substantial increase in VLPs following ultraviolet disinfection. Defining application boundaries remain crucial to ensuring meaningful data interpretation as flow cytometry and virometry take on greater significance in water quality monitoring. Full article