Search Results (157)

Wastewater-based surveillance can track community SARS-CoV-2, but RNA signals at wastewater treatment plants are affected by sampling, analytical recovery and transfer to solids. We quantified SARS-CoV-2 RNA in influent and biologically treated effluent from eight Slovak municipal wastewater treatment plants during the period October–December 2020. Twenty-seven 24 h influent composites and seven same-day effluent samples were analysed by reverse-transcription quantitative PCR targeting ORF1b, S, E and RdRp. RNA was detected in 20/27 influents (74%; 95% CI, 54–89%) and 3/7 effluents (43%; 95% CI, 10–82%). Influent loads ranged from 4.66 × 10⁹ to 5.60 × 10¹³ copies day⁻¹; the maximum population-normalised load was 5.84 × 10⁵ copies per population equivalent per day. All seven effluent concentrations were lower than paired influents (exact sign test, p = 0.0156). Apparent log₁₀ reductions were 0.18 and 0.76 for quantifiable effluents, approximately 1.75 for one detection at the operational limit of detection, and ≥1.9–≥2.5 for four non-detects. A one-day process profile at the selected plant detected RNA in activated-sludge supernatant, digested-sludge supernatant and reject water. Because solids, infectivity and whole-process recovery were not measured, the results describe RNA occurrence rather than viral inactivation or a mass balance. These pilot data provide a historical baseline for Slovakia and define requirements for repeated sampling, process controls and paired liquid-solid measurements. Full article

This study evaluated the chemically enhanced high-rate activated sludge (CE-HRAS) process utilizing polyferric sulfate (PFS), a coagulant gaining popularity in wastewater treatment plants owing to its low corrosivity toward metal equipment, to maximize energy recovery from municipal wastewater. Comparative bench-scale tests using actual wastewater were conducted under practical conditions at 17–26 °C. Although the soluble chemical oxygen demand (COD) removal rate remained comparable to that of the high-rate activated sludge (HRAS) process, PFS addition significantly improved suspended and colloidal COD removal. The CE-HRAS process achieved an organic matter recovery rate of 43–58% relative to the influent organic load, 2.0 to 3.2 times higher than that of the HRAS process. However, the methane conversion ratio of CE-HRAS sludge in the medium-temperature period was only 0.37 times that of HRAS sludge due to anaerobic digestion suppression. Consequently, the methane recovery rate relative to the influent organic load was virtually equivalent between the HRAS and CE-HRAS processes (0.11 ± 0.01 and 0.12 ± 0.00 g-COD-CH₄/g-COD, respectively), demonstrating that enhanced carbon capture does not inherently translate into increased biomethane yield. Therefore, future strategies must focus on optimizing coagulant dosages or developing integrated downstream processes to mitigate digestion inhibition and maximize energy recovery. Full article

Machine learning is widely proposed as a basis for smart wastewater treatment plant (WWTP) management, yet reported performance is rarely benchmarked against trivial alternatives. This study uses six years of monitoring from a municipal WWTP in Serbia serving 18,000 population equivalents, comprising 1017 fully measured days. Total nitrogen removal falls from 79.4% outside summer to 67.4% in June to August (p = 4 × 10⁻¹⁸), in five of six years, while influent load, hydraulic flow and phosphorus removal are unchanged, which points to oxygen limitation. Four model families are benchmarked against persistence and a ten-measurement rolling mean on one chronological hold-out period. Predictive skill depends decisively on forecast horizon: at one step ahead nothing beats both baselines, whereas at five steps nitrogen reaches R² of 0.516 against 0.388 and 0.320. Chemical oxygen demand (COD) never beats a rolling mean. A random forest identifies nitrogen exceedance of 15 mg/L with an area under the curve of 0.90. Explanation of that model shows it recognises a breach already under way rather than one beginning, which sets the operating conditions for its use. Evaluating on interpolated rather than measured days raises apparent accuracy from R² of 0.34 to 0.82. Full article

Constructed wetlands (CWs) are increasingly used for decentralized wastewater treatment and reuse where conventional infrastructure is difficult to sustain. This review integrates evidence on hydraulic reliability, biogeochemical performance, reuse safety, sustainability trade-offs, and process-based modeling, and derives design implications for Neotropical settings. Organic-matter and suspended-solids removal is generally more robust than nitrogen, phosphorus, and pathogen control. More reliable performance is associated with controlled hydraulic loading, solids-limiting pretreatment, stable flow distribution and water levels, and protection against stormwater-driven short-circuiting and clogging. Reuse should therefore be evaluated against fit-for-purpose microbial, nutrient, salinity, and chemical endpoints rather than removal efficiency alone. We propose seasonal monitoring, tracer or residence-time-distribution assessment where decisions depend on hydraulic efficiency, locally calibrated design envelopes, and a minimum reporting set covering climate/season, flow and loading, HRT/HLR, configuration, pretreatment, media, monitoring duration, influent/effluent concentrations, and hydraulic indicators. This study is a structured narrative review with thematic synthesis; literature identification and corpus reporting were informed by applicable PRISMA 2020 principles. Three Scopus searches yielded 593 records; after removal of 37 duplicates, 556 unique records remained. The final thematic corpus comprises 80 reports, of which 30 are present in the Scopus exports and 50 were identified through complementary routes. Full article

Anaerobic digestion (AD) is a common waste management method for producing renewable energy from biogas. However, animal manures typically have low C/N ratios, which can limit biogas recovery. This study aims to optimize biogas production through the co-digestion of goat manure (GM) with high-C/N lignocellulosic rice husk (RH) and sludge as the inoculum. Characterization of the substrate and inoculum revealed a low GM C/N ratio (GM = 21.3), whereas RH has a high-C/N (RH = 107.3). The volatile solids-to-total solids (VS/TS) ratios were around 82–85% for GM and RH. Batch experiments were conducted at different organic loading rates, with an inoculum-to-substrate ratio of 2:1 (mL:g), at 36 ± 1 °C for 65 days. This study investigates optimizing biomethane recovery by using serum-bottle biomethane potential (BMP) and compares kinetic performance and yields across different GM-to-RH ratios with the characteristics of the influent and effluent. The highest BMP values (mL CH₄/g_VS) occurred at 100% GM (245.1), followed by 90% GM (233.6) and 30% GM (232.5), indicating a strong synergy between GM and RH at specific mixing ratios. Kinetic modeling using both the modified Gompertz and first-order models effectively described digestion dynamics, allowing for estimation of potential lag phases and maximum production rates. These models aligned well with experimental data across substrates, aiding process design. Overall, the results show that strategic co-digestion of GM with RH can maximize methane recovery, with defined substrate ratios and a clear understanding of the kinetics essential for scale-up and sustainable biogas production. Full article

Short-term wastewater flow forecasting is essential for pump-station scheduling, influent-load regulation, and overflow-risk warning in wastewater treatment systems. However, wastewater flow series often exhibit strong nonlinearity, large-amplitude fluctuations, and sensitivity to model hyperparameters, which makes accurate real-time prediction challenging. To address these issues, this study proposes an attention-enhanced hybrid deep learning framework optimized by an Improved Harris Hawks Optimisation algorithm. The prediction target is the wastewater flow value at the next target time step, and the model uses only historical wastewater flow observations as input. The framework integrates a Temporal Convolutional Network to extract local fluctuation and multi-scale temporal features, a Bidirectional Long Short-Term Memory network to capture contextual dependencies within the historical input window, and a Simple Attention Module to recalibrate high-dimensional features and emphasize key information. The improved optimisation algorithm further enhances hyperparameter search through hybrid initialization, early-stage differential evolution mutation, stagnation detection with diversity injection, and late-stage Lévy flight perturbation. Experimental results show that the unoptimized Temporal Convolutional Network (TCN)–Bidirectional Long Short-Term Memory (BiLSTM)–Simple Attention Module (SimAM) model achieved an R² of 0.9697, an RMSE of 231.1821, and an MAE of 177.5324 on the validation set. After Improved Harris Hawks Optimization (IHHO) optimization, these values improved to 0.9728, 218.8228, and 161.8777, respectively. Compared with the unoptimized model, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced by approximately 5.35% and 8.82%, respectively, while R² increased by 0.0031. These results indicate that the proposed framework improves prediction accuracy, stability, and robustness under the tested conditions. It provides a feasible technical solution for short-term wastewater flow forecasting and supports intelligent operation management of wastewater treatment systems. Full article

In the context of water scarcity and tightening environmental requirements, improving the energy efficiency of biological wastewater treatment processes has become particularly important. The aeration tank is one of the most energy-intensive and dynamically complex units, strongly affected by the variability in influent flow and composition. Conventional PID controllers do not provide predictive disturbance compensation and often result in excessive aeration and increased energy consumption. The study proposes an intelligent control approach based on a digital twin, neural network-based influent flow forecasting, and model predictive control (MPC). The digital twin represents a dynamic model of the biological process incorporating key state variables, including substrate, activated sludge, and dissolved oxygen concentrations. The LSTM neural network model is employed to predict the hydraulic load based on historical plant operation data, as well as to compensate for residual nonlinear dynamics that are not represented by the linearized MPC model. The predicted influent flow values are incorporated into the MPC framework as measured disturbances, enabling the generation of anticipatory control actions for the aeration system. The adequacy of the digital twin was validated using operational data from the wastewater treatment facilities of Semey city and was characterized by RMSE = 0.14 mg/L, MAE = 0.09 mg/L, R² = 0.94, and MAPE = 6.3%. The simulation results demonstrated that, compared with the fuzzy PID controller, the application of MPC reduced the RMSE by 57.1%, decreased the overshoot from 24% to 8%, reduced the integral absolute error (IAE) by 60.9%, and lowered the energy consumption of the aeration system by 21.1%, while maintaining the dissolved oxygen concentration within the permissible operating range. The proposed Advisory MPC architecture is compatible with existing PLC–SCADA systems and can serve as a basis for the gradual digital modernization of wastewater treatment facilities without modifying the existing automation loops. Full article

This study established long-term wastewater surveillance of Aichi virus (AiV) in Maryland. AiV, a member of the Kobuvirus genus associated with acute gastroenteritis, has established itself as an integral marker for wastewater-based monitoring; however, two key research questions remain unaddressed for the Baltimore metropolitan area: (1) whether AiV is consistently detectable in municipal wastewater throughout the year, and (2) whether its concentrations exhibit a measurable seasonal pattern. To address these hypotheses, influent samples were collected on a weekly basis from WWTP-A and WWTP-B from January to December 2023 (with grab sampling conducted at WWTP-A and automated collection deployed for the influent sampling of the water treatment plant B). All samples (n = 51) were subjected to PEG 8000 concentration, RNA extraction, cDNA synthesis, and RT-qPCR quantification. We observed AiV RNA in 94.12% of the samples from both facilities (25/51 at WWTP-A and 23/51 at WWTP-B) with concentrations that ranged from 2.5 to 3.63 log₁₀ gc/L and a seasonal pattern showing consistent declines: loads for WWTP-A declining from winter (3.58 log₁₀ gc/L) to fall (2.56) and for WWTP-B from winter (3.28 log₁₀ gc/L) to fall (2.31). The year-round constant AiV presence provides a strong basis for its use as a stable viral marker within wastewater-based epidemiology efforts. Full article

The growing urbanisation of India is a major contributor to the production of 72,368 million litres of wastewater daily. Unfortunately, not even 28 to 31% of the generated wastewater receives proper treatment before disposal, putting public health, water quality, and ecological conditions at risk. Traditional wastewater treatment technologies have been proven effective, but they cannot be applied in decentralised settings due to excessive initial investment costs, continuous power needs, and the need for expert supervision. Constructed wetlands (CWs) provide an efficient and environmentally friendly option for decentralised treatment, but these systems suffer from a gradual loss of effectiveness associated with the problem of media-clogging in traditional setups. This research investigates the functioning and efficiency of the Modified Towery Bio-rack Constructed Wetland (MTBRCW) technology designed specifically to mitigate media-clogging issues. The MTBRCW is tested on the basis of its performance under continuous operating conditions for thirteen months (January 2025 to January 2026), as well as on the effectiveness of the treatment at eight different hydraulic retention times (days 1 to 8). A pilot-scale MTBRCW system was monitored through two periodic sampling events (S1 and S2) conducted during each month of operation. The pilot-scale MTBRCW unit is made up of an inlet storage tank (volume 0.099 m³) followed by two wetland containers (volume 0.034 m³ each) planted with Typha angustifolia and Chrysopogon zizanioides (vetiver grass). In continuous testing mode, influent–effluent paired samples are collected for eight days at each HRT (totalling eighty samples), and samples are analysed according to APHA Standard Methods for pH, BOD, COD, TN, and TP. In continuous testing mode, the MTBRCW exhibits high removal efficiencies at the levels of 89.8% for BOD, 87.5% for COD, 78.2% for TN, and 74.4% for TP. The BOD/COD of the effluent was within the prescribed CPCB discharge limits for all thirteen months of the study, and the TN levels were adhered to in 12 out of 13 months, with one non-compliance event recorded only in July 2025 (effluent TN = 10.8 mg/L), coinciding with the peak monsoon hydraulic loading rate of 0.28 m³/m²·d. TP remained within CPCB limits in all thirteen months. In batch testing mode, removal efficiencies are 94.9% for BOD and 89.9% for COD by day 8. In addition, there were no indications of clogging or any reduction in hydraulic performance during the entire period of the tests through the use of visual inspections and measurement of the outlet flows, but this can only be seen as an observation in a field operation, and not as proof of the hydraulic performance of the system, since no tracer test or measurement of hydraulic conductivity was conducted. Full article

Floating wetlands have emerged as a sustainable alternative for improving water quality, and although some studies have investigated their performance, there is still much to be understood regarding their integration with energy-generating technologies. This study evaluated a combined system of floating wetlands and microbial fuel cells (MFCs) for treating real wastewater and generating bioelectricity. Experiments were conducted in batch mode to simulate application in natural water bodies, using real wastewater collected on different dates. As a result of the natural variability of the influent, initial chemical oxygen demand (COD) concentrations of 405 and 289 mg/L were observed. Performance was assessed in terms of organic matter and nitrogen removal, as well as voltage generation. COD removal efficiencies reached 50% and 69% for the higher and lower organic loads, respectively, indicating improved treatment at reduced concentrations. Maximum removals of 56% for ammoniacal nitrogen (NH₃-N) and 40% for total nitrogen (TN) were achieved, reflecting moderate nutrient removal capacity. Voltage generation was sustained for approximately 21 days, confirming stable bioelectrochemical activity, and power output was found to depend on the organic load serving as substrate for electrogenic microorganisms. Overall, the system represents a viable approach for wastewater treatment with the added benefit of energy recovery, although its performance is influenced by influent characteristics and operation conditions. Full article

The biological treatment risk associated with wastewater containing the novel pigment intermediate N,N′-(1,4-phenylene)bis(acetoacetamide) has not been previously characterized. This study systematically evaluated the tolerance and performance of a laboratory-scale anaerobic–anoxic–oxic (AAO) system subjected to progressively increasing loadings of high-concentration (COD > 10,000 mg·L⁻¹) wastewater. During a 39-day trial, the influent proportion was incrementally increased from 0.57% to 52.14% without system collapse. Complete microbial adaptation required approximately seven days. The anaerobic unit exhibited the highest sensitivity to shock loads, followed by the oxic unit, while the anoxic unit remained stable. GC-MS analysis confirmed the degradation of complex organic intermediates throughout the treatment stages, and TEST-based predictions indicated that the effluent exhibited lower predicted toxicity than the influent. Notably, cessation of mother liquor addition resulted in system self-recovery, further demonstrating robust shock resistance. This study provides the first experimental evidence of (i) unit-specific shock sensitivity (anaerobic > oxic > anoxic), (ii) a quantified adaptation period of approximately seven days, (iii) an operational threshold of 52.14% mother liquor without causing system collapse, and (iv) self-recovery following load cessation in an AAO system treating wastewater containing N,N′-(1,4-phenylene)bis(acetoacetamide). These findings extend previous AAO toxicity studies on industrial wastewater and present a practical, cost-effective mitigation strategy for full-scale applications. Full article

A pilot-scale moving-bed biofilm reactor (MBBR), operated under alternating intermittent aeration (IA) and non-aeration phases, was used for single-stage carbon (C) and nitrogen (N) removal from high-fluctuating municipal wastewater via simultaneous nitrification–denitrification. The reactor was operated under highly variable chemical oxygen demand to total nitrogen (COD/TN) ratios, low dissolved oxygen (DO) conditions, and progressively extended non-aerated periods to evaluate process robustness under real operational conditions. An active denitrifying biofilm developed on the carriers after 23 days of the anoxic start-up, as confirmed by batch activity tests. Under the most carbon-limited condition tested (COD/TN = 5.5), the application of 16 h·d⁻¹ of non-aerated phases at DO levels of 0–1.0 mg·L⁻¹ enabled simultaneous COD, N–NH₄⁺ and TN removal efficiencies of 70, 95 and 84%, respectively. These results confirm that transient IA is an effective strategy for simultaneous C and N removal at very low COD/TN ratios and real fluctuating influent concentrations. Energy assessment showed that extended non-aeration phases reduced blower energy demand by 67% and total plant energy consumption by 34%, improving the environmental sustainability of the single-stage process. The main novelty of this study lies in the pilot-scale validation of an IA-MBBR for SND using real municipal wastewater under naturally fluctuating C and N loadings, thereby bridging previous laboratory-scale evidence with realistic operating conditions. Full article

The presence of xenobiotics in wastewater, particularly emerging contaminants such as pharmaceuticals, poses an ecotoxicological risk to the environment and human health. One of the main pharmaceutical products detected in water is diclofenac, which can be sold without a prescription. The lack of health regulations indicates the necessity of finding environmentally friendly treatment alternatives to remove this type of contaminant. Among these alternatives, biotechnology, specifically biological processes, offers a sustainable option compared to conventional treatments. Current treatment methods used in wastewater treatment plants are ineffective at removing diclofenac, a chlorinated aromatic compound highly resistant to degradation processes. In recent years, new treatment methods have gained prominence due to the favorable results they have yielded, including physicochemical, biological, and advanced processes. Biological treatments are notable for their low cost and the high level of effectiveness and efficiency with which they can remove toxic compounds. For this reason, the aim of this research project was to evaluate the degradation efficiency of a biological treatment in a bioreactor using a microbial community consisting of five bacterial strains, which was isolated from a pharmaceutical effluent and cultivated in a continuous culture system. Removal efficiencies ranging from 99.38 to 99.98% were achieved at various volumetric loading rates (from 0.087 to 1.043 g L⁻¹d⁻¹). Influents and effluents from the biological reactor were analyzed using bioassays to determine any potential toxic effects. The results showed that the effluents did not elicit a negative response in the bioindicators, indicating high toxicity in the influents. Full article

The performance of a sequencing batch biofilter granular reactor (SBBGR), followed by a dual media granular activated carbon (GAC) column, was evaluated in terms of its ability to remove selected per- and polyfluoroalkyl substances (PFAS) from landfill leachate. The results show that the SBBGR achieved an overall reduction of 51%, with the preferential removal of long-chain PFAS, while short-chain PFAS were only partially removed. Subsequent GAC treatment exhibited compound-specific breakthrough behavior, which was governed by chain length. Short-chain PFAS (e.g., perfluorobutanoic acid) exhibited rapid bed volumes at 50% breakthrough (BV₅₀ ≈ 88), whereas long-chain PFAS (e.g., perfluorooctanoic acid and perfluorooctanesulfonic acid) were substantially more retained (BV₅₀ ≈ 446 and 361, respectively), with perfluorohexanesulfonic acid and perfluorodecanoic acid failing to reach BV₅₀ within the monitored period. Mass balance analysis showed that the hybrid GAC column captured ~73% of the influent PFAS mass. This resulted in >80–95% retention of long-chain PFAS and <40% retention of short-chain PFAS. Although long-chain PFAS were preferentially adsorbed, mobile short-chain species dominated residual effluent loads. These findings highlight the need for optimized contact times or dual-media strategies to control the breakthrough of short-chain PFAS. Full article

Anaerobic biodegradation is the most affordable method for the degradation of N,N-dimethylformamide. However, the degradation efficiency depends on the concentration. To elucidate the responses of microbial community to N,N-dimethylformamide load, microbial diversity, composition and functional changes at different concentrations of 100, 2000, and 3500 mg/L were analyzed. Results showed that as the N,N-dimethylformamide influent concentration increased from 100 to 2000 mg/L, the removal rate stabilized at 90%, whereas it decreased to ~75% at concentrations over 2000 mg/L. Microbial community diversity increased, and specialists were enriched at 3500 mg/L. Patescibacteria (42.88% and 42.90%), Bacillota (18.52% and 18.54%), and Pseudomonadota (7.13% and 7.09%) were the dominant phyla at 100 mg/L and 2000 mg/L, respectively, and Patescibacteria (16.88%) and Pseudomonadota (15.34%) were the dominant phyla at 3500 mg/L. Methylotrophic methanogeneic (Methanolobus and Methanomassiliicoccus) and syntrophic electron-donating bacteria (Clostridium and Trichococcus) were significantly enriched. DMF-degrading genes (fdh, rfA/nrfH, and ATPase) and methylotrophic methanogenesis genes (mcr, mta, and mtm) were significantly upregulated. Therefore, the degradation of N,N-dimethylformamide was characterized by a parallel carbon flux distribution, “methylamine-driven methanogenesis + further oxidation/integration of single-carbon intermediates”, and the nitrogen flux tended to enter a reductive nitrogen cycle characterized by retention and reuse. Full article