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.

This study evaluated the treatment efficiency of two selected fillers and their combination for improving the water quality of aquaculture wastewater using a packed bed biofilm reactor (PBBR) under various process conditions. The fillers used were nanosheet (NS), activated carbon (AC), and a combination of both. The results indicated that the use of combined fillers and the hydraulic retention time (HRT) of 4 h significantly enhanced water quality in the PBBR. The removal rates of chemical oxygen demand, NO2-─N, total suspended solids（TSS）, and chlorophyll a were 63.55%, 74.25%, 62.75%, and 92.85%, respectively. The microbiota analysis revealed that the presence of NS increased the abundance of microbial phyla associated with nitrogen removal, such as Nitrospirae and Proteobacteria. The difference between the M1 and M2 communities was minimal. Additionally, the microbiota in different PBBR samples displayed similar preferences for carbon sources, and carbohydrates and amino acids were the most commonly utilized carbon sources by microbiota. These results indicated that the combination of NS and AC fillers in a PBBR effectively enhanced the treatment efficiency of aquaculture wastewater when operated at an HRT of 4 h. The findings provide valuable insights into optimizing the design of aquaculture wastewater treatment systems.

As waste discharge into numerous river systems escalates, the pollution of water bodies typically rises. Given the limited capacity of rivers to withstand pollution and their constrained self-cleaning capabilities, treated pollutants from waste discharge must be released into the river. Despite numerous models and algorithms proposed for managing river water quality to meet standards, literature, to our awareness, lacks the utilization of a comprehensive multi-criteria group decision-making approach for water quality management, particularly in river systems. Therefore, this research introduces a new, comprehensive multi-criteria group decision-making for the management of water quality in the Haraz River basin, located in Iran. To do so, the water quality of the basin, a one-dimensional water quality model, QUAL2Kw, was employed to simulate and calibrate the water quality along the river. The simulation results revealed that the downstream water quality violates the water quality standards. To mitigate this issue, various scenarios for waste load allocation (WLA) were evaluated, including no wastewater treatment, primary wastewater treatment, advanced secondary wastewater treatment utilizing the activated sludge (AS) method, and advanced wastewater treatment via the membrane bioreactor (MBR) method. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS group decision-making model, it was determined that the optimal solution was the implementation of secondary wastewater treatment utilizing the activated sludge method for the 11 PS of pollution, while still adhering to Iranian water quality standard. In addition, the findings of the present study indicate that the implementation of primary wastewater treatment, advanced secondary wastewater treatment utilizing AS, and advanced wastewater treatment through MBR within the study area led to a significant enhancement in water quality. This enhancement ranged from 35 to 105% across various scenarios when compared to conditions where no actions were taken to the treatment of water.

The activated carbon was produced in the first phase of this investigation by chemically activating hazelnut shell waste with H3PO4. Composite materials were obtained by coating the activated carbon with zinc oxide, whose BET surface area was calculated as 1278 m2 g-1. ZnO-doped ZnO/AC composite was synthesized as an adsorbent for its possible application in the elimination of organic dyestuff MB, and its removal efficiency was investigated. Morphological properties of ZnO/AC were characterized using analytical methods such as XRD, SEM, and BET. The adsorption system and its parameters were investigated and modeled using the response surface method of batch adsorption experiments. The experimental design consisted of three levels of pH (3, 6.5, and 10), initial MB concentration (50, 100, and 150 mg L-1), dosage (0.1, 0.3, and 0.5 g 100 mL-1), and contact time (5, 50, and 95 min). The results from the RSM suggested that the MB removal efficiency was 98.7% under the optimum conditions of the experimental factors. The R2 value, which expresses the significance of the model, was determined as 99.05%. Adsorption studies showed that the equilibrium data fit well with the Langmuir isotherm model compared to Freundlich. The maximum adsorption capacity was calculated as 270.70 mg g-1.

Solar Fenton is an important and extensively used advanced oxidation process (AOP) to degrade pharmaceutical pollutants. The objective of this study was to evaluate the performance of simultaneous degradation of the mixed pollutants (amoxicillin, acetaminophen, and ciprofloxacin) for an aqueous solution using the solar Fenton process. Operating parameters such as pH, iron doses, H2O2 doses, pollutant concentrations, and time were studied. From the experimental results, the ideal conditions were obtained for the removal of mixed pollutants such as pH 3, Fe2+ 0.04 mM, H2O2 4 mM, the concentration of the mixed pollutants 5 mg/L, solar radiation 400 W/m2, and time 10 min, respectively. The pseudo-first-order kinetics were utilized to investigate the degradation efficacy of the mixed pollutants. The result of the study indicates that the degradation efficiency was > 99% for the mixed pollutants. A maximum of 63% mineralization was observed, and hydroxyl radical scavenger effects were studied. The best optimal conditions were applied to assess the spiked wastewater (municipal wastewater (MWW) and hospital wastewater (HWW)). The highest elimination rates for AMX, ACET, and CIP were observed as 65%, 89%, and 85% for MWW and 76%, 92%, and 80% for HWW, respectively. The degraded by-products were detected by LC-ESI-MS in the water matrix (aqueous solution and spiked wastewater), and ECOSAR analysis was performed for the transformed products. The study concluded that the solar Fenton technique is promising and effective for the removal of mixed pollutants from the water matrix.

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.

The anaerobic membrane bioreactor (AnMBR) is a promising technology for not only water reclamation but also virus removal; however, the virus removal efficiency of AnMBR has not been fully investigated. Additionally, the removal efficiency estimation requires datasets of virus concentration in influent and effluent, but its monitoring is not easy to perform for practical operation because the virus quantification process is generally time-consuming and requires specialized equipment and trained personnel. Therefore, in this study, we aimed to identify the key, monitorable variables in AnMBR and establish the data-driven models using the selected variables to predict virus removal efficiency. We monitored operational and environmental conditions of AnMBR in Sendai, Japan and measured virus concentration once a week for six months. Spearman\'s rank correlation analysis revealed that the pH values of influent and mixed liquor suspended solids (MLSS) were strongly correlated with the log reduction value of pepper mild mottle virus, indicating that electrostatic interactions played a dominant role in AnMBR virus removal. Among the candidate models, the random forest model using selected variables including influent and MLSS pH outperformed the others. This study has demonstrated the potential of AnMBR as a viable option for municipal wastewater reclamation with high microbial safety.

Activated sludge is the centerpiece of biological wastewater treatment, as it facilitates removal of sewage-associated pollutants, fecal bacteria, and pathogens from wastewater through semi-controlled microbial ecology. It has been hypothesized that horizontal gene transfer facilitates the spread of antibiotic resistance genes within the wastewater treatment plant, in part because of the presence of residual antibiotics in sewage. However, there has been surprisingly little evidence to suggest that sewage-associated antibiotics select for resistance at wastewater treatment plants via horizontal gene transfer or otherwise. We addressed the role of sewage-associated antibiotics in promoting antibiotic resistance using lab-scale sequencing batch reactors fed field-collected wastewater, metagenomic sequencing, and our recently developed bioinformatic tool Kairos. Here, we found confirmatory evidence that fluctuating levels of antibiotics in sewage are associated with horizontal gene transfer of antibiotic resistance genes, microbial ecology, and microdiversity-level differences in resistance gene fate in activated sludge.

The presence of a large amount of organic and inorganic pollutants in dairy effluent is a substantial environmental issue. This study investigated electrocoagulation (EC) as a potential treatment method for dairy wastewater under different operating conditions, such as applied voltage (5-25 V), electrolysis time (30-90 min), and inter-electrode distance (1-2 cm) by using aluminum electrodes. This study focuses on achieving the maximum removal of BOD, COD, and nitrate in dairy effluents with the aforementioned operating conditions. The process was optimized using the response surface methodology (RSM) and Taguchi method. RSM method optimized the electrocoagulation operating conditions such as the voltage at 23.75 V, time of 90 min, and inter-electrode distance at 1.07 cm. This optimization achieved the maximum removal percentage of BOD, COD, and nitrate at 79.06%, 84.35%, and 79.64%, respectively, in dairy effluent. Taguchi method optimized the electrocoagulation parameters such as the voltage at 25 V, time duration of 90 min, and inter-electrode distance of 1.00 cm, showcasing improved removal percentages of BOD, COD, and nitrate as 90.54%, 89.28%, and 82.74% respectively. The current study attempts to understand the optimization efficiencies between Taguchi method and response surface method for diary wastewater treatment.