This work assessed the performance of a pilot-scale cascade anaerobic digestion (AD) system when treating mixed municipal wastewater treatment sludges. The cascade system was compared with a conventional continuous stirred tank reactor (CSTR) digester (control) in terms of process performance, stability, and digestate quality. The results showed that the cascade system achieved higher volatile solids removal (VSR) efficiencies (28-48%) than that of the reference (25-41%) when operated at the same solids residence time (SRT) in the range of 11-15 days. When the SRT of the cascade system was reduced to 8 days the VSR (32-36%) was only slightly less than that of the reference digester that was operated at a 15-day SRT (39-43%). Specific hydrolysis rates in the first stage of the cascade system were 66-152% higher than those of the reference. Additionally, the cascade system exhibited relatively stable effluent concentrations of volatile fatty acids (VFAs: 100-120 mg/l), while the corresponding concentrations in the control effluent demonstrated greater fluctuations (100-160 mg/l). The cascade system\'s effluent pH and VFA/alkalinity ratios were consistently maintained within the optimal range. During a dynamic test when the feed total solids concentration was doubled, total VFA concentrations (85-120 mg/l) in the cascade system were noticeably less than those (100-170 mg/l) of the control, while the pH and VFA/alkalinity levels remained in a stable range. The cascade system achieved higher total solids (TS) content in the dewatered digestate (19.4-26.8%) than the control (17.4-22.1%), and E. coli log reductions (2.0-4.1 log MPN/g TS) were considerably higher (p < 0.05) than those in the control (1.3-2.9 log MPN/g TS). Overall, operating multiple CSTRs in cascade mode at typical SRTs and mixed sludge ratios enhanced the performance, stability digesters, and digestate quality of AD. PRACTITIONER POINTS: Enhanced digestion of mixed sludge digestion with cascade system. Increased hydrolysis rates in the cascade system compared to a reference CSTR. More stable conditions for methanogen growth at both steady and dynamic states. Improved dewaterability and E. coli reduction of digestate from the cascade system.

The Global Specialized Polio Laboratory at CDC supports the Global Poliovirus Laboratory Network with environmental surveillance (ES) to detect the presence of vaccine strain polioviruses, vaccine-derived polioviruses, and wild polioviruses in high-risk countries. Environmental sampling provides valuable supplementary information, particularly in areas with gaps in surveillance of acute flaccid paralysis (AFP) mainly in children less than 15 years. In collaboration with Guatemala\'s National Health Laboratory (Laboratorio Nacional de Salud Guatemala), monthly sewage collections allowed screening enterovirus (EV) presence without incurring additional costs for sample collection, transport, or concentration. Murine recombinant fibroblast L-cells (L20B) and human rhabdomyosarcoma (RD) cells are used for the isolation of polioviruses following a standard detection algorithm. Though non-polio-Enteroviruses (NPEV) can be isolated, the algorithm is optimized for the detection of polioviruses. To explore if other EV\'s are present in sewage not found through standard methods, five additional cell lines were piloted in a small-scale experiment, and next-generation sequencing (NGS) was used for the identification of any EV types. Human lung fibroblast cells (HLF) were selected based on their ability to isolate EV-A genus. Sewage concentrates collected between 2020-2021 were isolated in HLF cells and any cytopathic effect positive isolates used for NGS. A large variety of EVs, including echoviruses 1, 3, 6, 7, 11, 13, 18, 19, 25, 29; coxsackievirus A13, B2, and B5, EV-C99, EVB, and polioviruses (Sabin 1 and 3) were identified through genomic typing in NGS. When the EV genotypes were compared by phylogenetic analysis, it showed many EV\'s were genomically like viruses previously isolated from ES collected in Haiti. Enterovirus occurrence did not follow a seasonality, but more diverse EV types were found in ES collection sites with lower populations. Using the additional cell line in the existing poliovirus ES algorithm may add value by providing data about EV circulation, without additional sample collection or processing. Next-generation sequencing closed gaps in knowledge providing molecular epidemiological information on multiple EV types and full genome sequences of EVs present in wastewater in Guatemala.

Recently, the hazardous effects of antibiotic micropollutants on the environment and human health have become a major concern. To address this challenge, semiconductor-based photocatalysis has emerged as a promising solution for environmental remediation. Our study has developed Bi2WO6/g-C3N4 (BWCN) photocatalyst with unique characteristics such as reactive surface sites, enhanced charge transfer efficiency, and accelerated separation of photogenerated electron-hole pairs. BWCN was utilized for the oxidation of tetracycline antibiotic (TCA) in different water sources. It displayed remarkable TCA removal efficiencies in the following order: surface water (99.8%) > sewage water (88.2%) > hospital water (80.7%). Further, reusability tests demonstrated sustained performance of BWCN after three cycles with removal efficiencies of 87.3, 71.2 and 65.9% in surface water, sewage, and hospital water, respectively. A proposed photocatalytic mechanism was delineated, focusing on the interaction between reactive radicals and TCA molecules. Besides, the transformation products generated during the photodegradation of TCA were determined, along with the discussion on the potential risk assessment of antibiotic pollutants. This study introduces an approach for utilizing BWCN photocatalyst, with promising applications in the treatment of TCA from various wastewater sources.

The measurement of chemical oxygen demand (COD) is very important in the process of sewage treatment. The value of COD reflects the effectiveness and trend of sewage treatment to a certain extent, but obtaining accurate data requires high cost and labor intensity. To1 solve this problem, this paper proposes an online soft measurement method for COD based on Convolutional Neural Network-Bidirectional Long Short-Term Memory Network-Attention Mechanism (CNN-BiLSTM-Attention) algorithm. Firstly, by analyzing the mechanism of the aerobic tank stage in the Anaerobic-Anoxic-Oxic (A2O) wastewater treatment process, the selection range of input variables was preliminarily determined, and the collected sample dataset was subjected to correlation analysis. Finally, pH, dissolved oxygen (DO), electrical conductivity (EC), and water temperature (T) were determined as input variables for soft measurement prediction of COD.Then, based on the feature extraction ability of CNN and the advantage that BiLSTM is able to capture the backward and forward dependencies in time series data, combined with the attention mechanism that can assign higher weights to the key data, a CNN-BiLSTM-Attention algorithm model was established to soft measure COD in the effluent from the aerobic zone of the A2O wastewater treatment process. At the same time, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) were utilized Three indicators were used to evaluate the model, and the results showed that the model can accurately predict the value of COD and has a high accuracy. At the same time, compared with models such as CNN-LSTM-Attention, CNN-BiLSTM, CNN-LSTM, LSTM, RNN, BP, SVM, XGBoost, and RF etc., the results showed that the CNN-BiLSTM Attention model performed the best, proving the superiority of the algorithm model.The Wilcoxon signed-rank test indicates significant differences between the CNN-BiLSTM-Attention model and other models.

Wastewater treatment plants (WWTPs) are the main barrier to cope with the increased pressure of municipal and industrial wastewater on natural water resources in terms of both polluting load and produced volumes. For this reason, WWTP\'s efficiency should be the highest; thus, their monitoring becomes critical. In conventional WWTPs, biodegradation of pollutants mainly occurs in the biological reactors, and an increasing interest in a deeper characterization of the biomasses involved in these processes (made of biofilms, granules, and suspended activated sludge) rose up in recent years. In this sense, the meta-omics approaches were recently developed to investigate the entire set of biomolecules of a given class in a microbial community with the same general objective: the identification of the biomolecules through the sequence similarity of high degree in the already available databases. Particularly, metaproteomics concerns the identification of all proteins in a microbial community in a given moment or condition. In this chapter, a protocol for the extraction and separation of proteins from activate sludge sampled at WWTPs is proposed.

BACKGROUND: Wastewater surveillance (WWS) acts as a vigilant sentinel system for communities, analysing sewage to protect public health by detecting outbreaks and monitoring trends in pathogens and contaminants. To achieve a thorough comprehension of present and upcoming practices and to identify challenges and opportunities for standardisation and improvement in WWS methodologies, two EU surveys were conducted targeting over 750 WWS laboratories across Europe and other regions. The first survey explored a diverse range of activities currently undertaken or planned by laboratories. The second survey specifically targeted methods and quality controls utilised for SARS-CoV-2 surveillance.
RESULTS: The findings of the two surveys provide a comprehensive insight into the procedures and methodologies applied in WWS. In Europe, WWS primarily focuses on SARS-CoV-2 with 99% of the survey participants dedicated to this virus. However, the responses highlighted a lack of standardisation in the methodologies employed for monitoring SARS-CoV-2. The surveillance of other pathogens, including antimicrobial resistance, is currently fragmented and conducted by only a limited number of laboratories. Notably, these activities are anticipated to expand in the future. Survey replies emphasise the collective recognition of the need to enhance the accuracy of results in WWS practices, reflecting a shared commitment to advancing precision and effectiveness in WWS methodologies.
CONCLUSIONS: These surveys identified a lack of standardised common procedures in WWS practices and the need for quality standards and reference materials to enhance the accuracy and reliability of WWS methods in the future. In addition, it is important to broaden surveillance efforts beyond SARS-CoV-2 to include other emerging pathogens and antimicrobial resistance to ensure a comprehensive approach to protecting public health.

Microbial bioaugmentation of coal is considered as a viable and ecologically sustainable approach for the utilization of low-rank coals (LRC). The search for novel techniques to derive high-value products from LRC is currently of great importance. In response to this demand, endeavors have been undertaken to develop microbially based coal solubilization and degradation techniques. The impact of supplementing activated sludge (AS) as a microbial augmentation to enhance LRC biodegradation was investigated in this study. The LRC and their biodegradation products were characterized using the following methods: excitation-emission Matrices detected fluorophores at specific wavelength positions (O, E, and K peaks), revealing the presence of organic complexes with humic properties. FTIR indicated the increased amount of carboxyl groups in the bioaugmented coals, likely due to aerobic oxidation of peripheral non-aromatic structural components of coal. The bacterial communities of LRC samples are primarily composed of Actinobacteria (up to 36.2%) and Proteobacteria (up to 25.8%), whereas the Firmicutes (63.04%) was the most abundant phylum for AS. The community-level physiological profile analysis showed that the microbial community AS had high metabolic activity of compared to those of coal. Overall, the results demonstrated successful stimulation of LRC transformation through supplementation of exogenous microflora in the form of AS.

Wastewater treatment plants (WWTPs) provide vital services to the public by removing contaminants from wastewater prior to environmental discharge or reuse for beneficial purposes. WWTP workers occupationally exposed to wastewater can be at risk of respiratory or gastrointestinal diseases. The study objectives were to: (1) quantify pathogens and pathogen indicators in wastewater aerosols near different WWTP processes/unit operations, (2) develop a QMRA model for multi-pathogen and multi-exposure pathway risks, and (3) create a web-based application to perform and communicate risk calculations for wastewater workers. Case studies for seven different WWTP job tasks were performed investigating infection risk across nine different enteric and respiratory pathogens. It was observed that the ingestion risk among job tasks was highest for \"walking the WWTP,\" which involved exposure from splashing, bioaerosols, and hand-to-mouth contact from touching contaminated surfaces. There was also a notable difference in exposure risk during peak (5:00am-9:00am) and non-peak hours (9:00am- 5:00am), with risks during the peak flow hours of the early morning assumed to be 5 times greater than non-peak hours. N95 respirator usage reduced median respiratory risks by 77 %. The developed tool performs multiple QMRA calculations to estimate WWTP workers\' infection risks from accidental ingestion or inhalation of wastewater from multiple pathogens and exposure scenarios, which can inform risk management strategies to protect occupational health. However, more data are needed to reduce uncertainty in model estimates, including comparative data for pathogen concentrations in wastewater during peak and non-peak hours. QMRA tools will increase accessibility of risk models for utilization in decision-making.

Wastewater-based epidemiology has expanded as a tool for collecting COVID-19 surveillance data, but there is limited information on the feasibility of this form of surveillance within decentralized wastewater systems (e.g., septic systems). This study assessed SARS-CoV-2 RNA concentrations in wastewater samples from a septic system servicing a mobile home park (66 households) and from two pumping stations serving a similarly sized (71 households) and a larger (1,000 households) neighborhood within a nearby sewershed over 35 weeks in 2020. Also, raw wastewater from a hospital in the same sewershed was sampled. The mobile home park samples had the highest detection frequency (39/39 days) and mean concentration of SARS-CoV-2 RNA (2.7 × 107 gene copies/person/day for the N1) among the four sampling sites. N1 gene and N2 gene copies were highly correlated across mobile home park samples (Pearson\'s r = 0.93, p < 0.0001). In the larger neighborhood, new COVID-19 cases were reported every week during the sampling period; however, we detected SARS-CoV-2 RNA in 12% of the corresponding wastewater samples. The results of this study suggest that sampling from decentralized wastewater infrastructure can be used for continuous monitoring of SARS-CoV-2 infections.

A novel thermotolerant caproic acid-producing bacterial strain, Clostridium M1NH, was successfully isolated from sewage sludge. Ethanol and acetic acid at a molar ratio of 4:1 proved to be the optimal substrates, yielding a maximum caproic acid production of 3.5 g/L. Clostridium M1NH exhibited remarkable tolerance to high concentrations of ethanol (up to 5% v/v), acetic acid (up to 5% w/v), and caproic acid (up to 2% w/v). The strain also demonstrated a wide pH tolerance range (pH 5.5-7.5) and an elevated temperature optimum between 35 and 40 °C. Phylogenetic analysis based on 16S rRNA gene sequences revealed that Clostridium M1NH shares a 98% similarity with Clostridium luticellarii DSM 29923 T. The robustness of strain M1NH and its efficient caproic acid production from low-cost substrates highlight its potential for sustainable bio-based chemical production. The maximum caproic acid yield achieved by Clostridium M1NH was 1.6-fold higher than that reported for C. kluyveri under similar fermentation conditions. This study opens new avenues for valorizing waste streams and advancing a circular economy model in the chemical industry.