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Drug resistance has become a matter of great concern, with many bacteria now resist multiple antibiotics. This study depicts the occurrence of antibiotic-resistant bacteria (ARB) and resistance patterns in five full-scale hospital wastewater treatment plants (WWTPs). Samples of raw influent wastewater, as well as pre- and post-disinfected effluents, were monitored for targeted ARB and resistance genes in September 2022 and February 2023. Shifts in resistance profiles of Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii antimicrobial-resistant indicators in the treated effluent compared to that in the raw wastewater were also worked out. Ceftazidime (6.78 × 105 CFU/mL) and cefotaxime (6.14 × 105 CFU/mL) resistant species showed the highest concentrations followed by ciprofloxacin (6.29 × 104 CFU/mL), and gentamicin (4.88 × 104 CFU/mL), in raw influent respectively. WWTP-D employing a combination of biological treatment and coagulation/clarification for wastewater decontamination showed promising results for reducing ARB emissions from wastewater. Relationships between treated effluent quality parameters and ARB loadings showed that high BOD5 and nitrate levels were possibly contributing to the persistence and/or selection of ARBs in WWTPs. Furthermore, antimicrobial susceptibility tests of targeted species revealed dynamic shifts in resistance profiles through treatment processes, highlighting the potential for ARB and ARGs in hospital wastewater to persist or amplify during treatment.

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Potential toxicity of treated effluents of selected natural rubber processing industries was evaluated by integrating physicochemical analysis with Daphnia magna and Poecilia reticulata bioassays as ecotoxicity tools. Further, the efficacy of the constructed wetland treatments practiced by the industries for reducing the ecotoxicity of the final effluents reaching the receiving water course was assessed. Even after passing through the constructed wetlands, some of the measured physicochemical parameters of the final effluents did not comply with the stipulated rubber processing effluent regulatory limits. Acute toxicity data of treated effluents demonstrated greater susceptibility of D. magna compared to P. reticulata. Erythrocytic abnormality tests with P. reticulata revealed that rubber industry effluents contained cytogenotoxic contaminations which had not been completely eliminated by the treatment processes. Wetland treatment technique was not effective in reducing the cytogenotoxic effects of final effluents reaching the receiving water course. The use of ecotoxicity tools for optimization of rubber industry effluent treatment processes would help to reduce potential toxic/cytogenotoxic effects of effluent receiving waterbodies considering sustainable development goals focusing on ecosystem safety.

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The treatment of wastewater is highly challenging due to large fluctuations in flowrates, pollutants, and variable influent water compositions. A sequencing batch reactor (SBR) and modified SBR cycle-step-feed process (SSBR) configuration are studied in this work to effectively treat municipal wastewater while simultaneously removing nitrogen and phosphorus. To control the amount of dissolved oxygen in an SBR, three axiomatic control strategies (proportional integral (PI), fractional proportional integral (FPI), and fuzzy logic controllers) are presented. Relevant control algorithms have been designed using plant data with the models of SBR and SSBR based on ASM2d framework. On comparison, FPI showed a significant reduction in nutrient levels and added an improvement in effluent quality. The overall effluent quality is improved by 0.86% in FPI in comparison with PI controller. The SSBR, which was improved by precisely optimizing nutrient supply and aeration, establishes a delicate equilibrium. This refined method reduces oxygen requirements while reliably sustaining important biological functions. Focusing solely on the FPI controller's performance in terms of total air volume consumption, the step-feed SBR mechanism achieves an excellent 11.04% reduction in consumption.

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The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.

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Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants' operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

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In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

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In wastewater treatment plants (WWTPs), the stochastic nature of influent wastewater and operational and weather conditions cause fluctuations in effluent quality. Data-driven models can forecast effluent quality a few hours ahead as a response to the influent characteristics, providing enough time to adjust system operations and avoid undesired consequences. However, existing data for training models are often incomplete and contain missing values. On the other hand, collecting additional data by installing new sensors is costly. The trade-off between using existing incomplete data and collecting costly new data results in three data challenges faced when developing data-driven WWTP effluent forecasters. These challenges are to determine important variables to be measured, the minimum number of required data instances, and the maximum percentage of tolerable missing values that do not impede the development of an accurate model. As these issues are not discussed in previous studies, in this research, for the first time, a comprehensive analysis is done to provide answers to these challenges. Another issue that arises in all data-driven modeling is how to select an appropriate forecasting model. This paper addresses these issues by first testing nine machine learning models on data collected from three wastewater treatment plants located in Iran, Australia, and Spain. The most accurate forecaster, Bayesian network, was then used to address the articulated challenges. Key variables in forecasting effluent characteristics were flow rate, total suspended solids, electrical conductivity, phosphorus compounds, wastewater temperature, and air temperature. A minimum of 250 samples was needed during the model training to achieve a great reduction in the forecasting error. Moreover, a steep increase in the error was observed should the portion of missing values exceed 10%. The results assist plant managers in estimating the necessary data collection effort to obtain an accurate forecaster, contributing to the quality of the effluent.

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The accurate prediction and assessment of effluent quality in wastewater treatment plants (WWTPs) are paramount for the efficacy of sewage treatment processes. Neural network models have exhibited promise in enhancing prediction accuracy by simulating and analyzing diverse influent parameters. In this study, a back propagation neural network hybrid model based on a tent chaotic map and sparrow search algorithm (Tent_BP_SSA) was developed to predict the effluent quality of sewage treatment processes. The prediction performance of the propose hybrid model was compared with traditional neural network models using five performance indicators (MAE, RMSE, SSE, MAPE and R2). Specifically, in comparison with the prior Tent_BP_SSA, Tent_BP_SSA2 demonstrated notable enhancements, with the R2 increasing from 0.9512 to 0.9672, while MAE, RMSE, SSE, and MAPE decreased by 9.62%, 18.84%, 24.80%, and 47.10%, respectively. These indicators collectively affirm that the utilization of higher-order input parameters ensures improved accuracy of the Tent_BP_SSA2 hybrid model in predicting effluent quality. Moreover, the Tent_BP_SSA2 model exhibited robust prediction ability (R2 of 0.9246) when applied to assess the effluent quality of an actual sewage treatment plant. The incorporation of integrated models comprising the sparrow search optimizing algorithm, tent chaotic mapping, and higher-order magnitude decomposition of input parameters has demonstrated the capacity to enhance the accuracy of effluent quality prediction. This study illuminates novel perspectives on the prediction of effluent quality and the assessment of effluent warnings in WWTPs.

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Extensive water and chemicals are used in the textile industry processes. Therefore, treatment of textile wastewater is vital to protect the environment, maintain the public health, and recover resources. However, due to poor operation and plant performance the partially treated textile wastewater was directly discharged to a nearby river. Thus, the aim of this study was to characterize the wastewater physicochemical properties and evaluate the performance of the textile factory-activated sludge process wastewater treatment plant (WWTP) in Bahir Dar, Ethiopia. In inlet and outlet of the WWTP, samples were collected for 6 months and analyzed on-site and in a laboratory for parameters including, dissolved oxygen, pH, temperature, total Kjeldhal nitrogen (TKN), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSS), total nitrogen (TN), total phosphorous (TP), nitrite, nitrate, and metallic compounds. The TSS, BOD5, COD, TP, nitrite, ammonia, and total chromium result were above the discharge limit with 73.2 mg/L, 48.45 mg/L, 144.08 mg/L, 7.9 mg/L, 1.36 mg/L, 1.96 mg/L, and 0.16 mg/L, respectively. Multiple regression models were developed for each overall, net moving average, and instantaneous effluent quality index (EQI). The predictor parameters BOD5, TN, COD, TSS, and TP (R2 = 0.995 to 1.000) estimated the net pollution loads of all predictors as 492.55 kg/day and 655.44 kg/day. Except TN, TKN, and NO3, the remaining six performance parameters were violating the permissible limit daily. Furthermore, the overall plant efficiency was predicted as 38 % and 42 % for the moving average and instantaneous EQI, respectively. Our study concluded that the integrated regression models and EQI can easily estimate the plant efficiency and daily possible pollution load.

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The design of constructed wetlands (CWs) is critical to ensure effective wastewater treatment. However, limited availability of reliable data can hamper the accuracy of CW effluent predictions, thus increasing design costs and time. In this study, a novel effluent prediction framework for CWs is proposed, utilizing data dimensionality reduction and virtual sample generation. By using four the machine learning algorithms (Cubist, random forest, support vector regression, and extreme learning machine), important features of CW design are identified and used to build prediction models. The extreme learning machine algorithm achieved the highest determination coefficient and lowest error, identifying it as the most suitable algorithm for effluent prediction. A multi-distribution mega-trend-diffusion algorithm with particle swarm optimization was employed to generate virtual samples. These virtual samples were then combined with real samples to retrain the prediction model and verify the optimization effect. Comparative analysis demonstrated that the integration of virtual samples significantly improved the prediction accuracy for ammonium and chemical oxygen demand. The root mean square error decreased by averages of 60.5% and 42.1%, respectively, and the mean absolute percentage error by averages of 21.5% and 23.8%, respectively. Finally, a CW design process is proposed based on prediction models and virtual samples. This integrated forward prediction and reverse design tool can efficiently support CW design when sample sizes are limited, ultimately leading to more accurate and cost-effective design solutions.

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Industrial reverse osmosis concentrate (ROC) was electrochemically oxidized using a continuous-flow system (CFS) with a front buffer tank. Multivariate optimization including Plackett-Burman (PBD) and central composite design based on response surface method (CCD-RSM) was implemented to investigate the effects of characteristic (e.g., recirculation ratio (R value), ratio of buffer tank and electrolytic zone (RV value)) and routine (e.g., current density (i), inflow linear velocity (v) and electrode spacing (d)) parameters. R, v values and current density significantly influenced chemical oxygen demand (COD) and NH4+-N removal and effluent active chlorine species (ACS) level, while electrode spacing and RV value had negligible effects. High chloride content of industrial ROC facilitated the generation of ACS and subsequent mass transfer, low hydraulic retention time (HRT) of electrolytic cell improved the mass transfer efficiency, and high HRT of buffer tank prolonged the reaction between the pollutants and oxidants. The significance levels of COD removal, energy efficiency, effluent ACS level and toxic byproduct level CCD-RSM models were validated by statistical test results, including higher F value than critical effect value, lower P value than 0.05, low deviation between predicted and observed values, and normal distribution of calculated residuals. The highest pollutant removal was achieved at a high R value, a high current density and a low v value; the highest energy efficiency was achieved at a high R, a low current density and a high v value; the lowest effluent ACS and toxic byproduct levels were achieved at a low R value, a low current density and a high v value. Following the multivariate optimization, the optimum parameters were decided to be v = 1.2 cm h-1, i ≥ 8 mA cm-2, d ≥ 4, RV = 10-20 and R = 1 to achieve better effluent quality (i.e., lower effluent pollutant, ACS and toxic byproduct levels).

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Partial nitritation-anammox (PN/A) process is known as an energy-efficient technology for wastewater nitrogen removal, which possesses a great potential to bring wastewater treatment plants close to energy neutrality with reduced carbon footprint. To achieve this goal, various PN/A processes implemented in a single reactor configuration (one-stage system) or two separately dedicated reactors configurations (two-stage system) were explored over the past decades. Nevertheless, large-scale implementation of these PN/A processes for low-strength municipal wastewater treatment has a long way to go owing to the low efficiency and effectiveness in nitrogen removal. In this work, we provided a comprehensive analysis of one-stage and two-stage PN/A processes with a focus on evaluating their engineering application potential towards mainstream implementation. The difficulty for nitrite-oxidizing bacteria (NOB) out-selection was revealed as the critical operational challenge to achieve the desired effluent quality. Additionally, the operational strategies of low oxygen commonly adopted in one-stage systems for NOB suppression and facilitating anammox bacteria growth results in a low nitrogen removal rate (NRR). Introducing denitrification into anammox system was found to be necessary to improve the nitrogen removal efficiency (NRE) by reducing the produced nitrate with in-situ utilizing the organics from wastewater itself. However, this may lead to part of organics oxidized with additional oxygen consumed in one-stage system, further compromising the NRR. By applying a relatively high dissolved oxygen in PN reactor with residual ammonium control, and followed by a granules-based anammox reactor feeding with a small portion of raw municipal wastewater, it appeared that two-stage system could achieve a good effluent quality as well as a high NRR. In contrast to the widely studied one-stage system, this work provided a unique perspective that more effort should be devoted to developing a two-stage PN/A process to evaluate its application potential of high efficiency and economic benefits towards mainstream implementation.

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Mainstream nitrogen removal via anammox is widely recognized as a promising wastewater treatment process. However, its application is challenging at large scale due to unstable suppression of nitrite-oxidizing bacteria (NOB). In this study, a pilot-scale mainstream anammox process was implemented in an Integrated Fixed-film Activated Sludge (IFAS) configuration. Stable operation with robust NOB suppression was maintained for over one year. This was achieved through integration of three key control strategies: i) low dissolved oxygen (DO = 0.4 ± 0.2 mg O2/L), ii) regular free nitrous acid (FNA)-based sludge treatment, and iii) residual ammonium concentration control (NH4 + with a setpoint of ∼8 mg N/L). Activity tests and FISH demonstrated that NOB barely survived in sludge flocs and were inhibited in biofilms. Despite receiving organic-deficient wastewater from a pilot-scale High-Rate Activated Sludge (HRAS) system as the feed, the system maintained a stable effluent total nitrogen concentration mostly below 10 mg N/L, which was attributed to the successful retention of anammox bacteria. This study successfully demonstrated large-scale long-term mainstream anammox application and generated new practical knowledge for NOB control and anammox retention.

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The primary objective of this study was to establish two-level structured control techniques based on the globally known benchmark simulation model no. 1 (BSM1) and the Bürger-Diehl settler model in order to improve effluent quality. The latter was based on the activated sludge model no. 1 (ASM1), while the classic Takacs model was superseded by the more recent Bürger-Diehl settler model with enhanced predictive potential. A two-level hierarchical control structure was considered to maintain the dissolved oxygen concentration basing on the ammonia levels and also a nitrate controller was considered to improve nitrogen removal efficiency. Fractional order PI controllers were considered at the secondary level and advanced control techniques, namely, MPC and fuzzy, were implemented at the primary level. Two advance control schemes, being, FPI-MPC and FPI-fuzzy were designed in the present work. The controllers were designed based on the plant model which was identified using prediction-error minimization method. It was observed that the implemented control strategies in consideration with the plant modifications showed a profound impact in improving the plant performance in terms of the effluent quality. FPI-fuzzy resulted in noticeable 60% and 53% reduction in total nitrogen violations for dry and storm climatic conditions, respectively.

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Treatment of faecal sludge (FS) has been a major challenge in most developing countries of Sub-Saharan Africa due to the difficulties in finding appropriate technology. Previous studies have however highlighted the potentials of the vertical flow constructed wetland for FS treatment, yet efforts in the identification of potential indigenous plant species as macrophyte for the Sudano-Sahelian ecological zone have been unsuccessful due to toxic levels of FS quality. This research studied the macrophyte potentials of indigenous bamboo species and bamboo biochar as a conditioner for FS treatment in a vertical flow constructed wetland (VFCW). Typical yard scale experiment consisting of filter media of sand supported at the base with gravels and planted with Bamboo shoots was used. Treatments were Bamboo Constructed Wetland (CW) and Faecal Sludge (FS) load only (CW-FS), Bamboo CW with a mixture of FS and Bamboo biochar (CW-BCH), unplanted drying bed with a mixture of FS and bamboo biochar (UDB-BCH) and an unplanted drying bed with FS (UDB-FS), and in triplicates. Control setup (CTR) consisted of Bamboo CW irrigated with wastewater. Morphological development (plant height, number of plants, number of leaves and culm diameter) of indigenous Bamboo species and reduction of faecal contaminants were monitored. Loading of FS was carried out in a single batch twice per week with a hydraulic loading rate of 56.47/mm/d with an annual Total Solid loading rate of 155.6 and 233.2 kg TS/m2/year for CW-FS and CW-BCH respectively. The bamboo species adapted to the complex wetland conditions, observed by a progressive increase in morphological development for all the treatments. Removal efficiencies of effluent quality parameters generally ranged from 70 to 99%, except for PO4 3-, TOC and TDS and indicator micro-organisms which were found below 50%. A strong positive linear relationship was determined among species growth parameter with coefficient (r) ranging between 0.83 - 0.99. Except for pH and TSS, all the effluent quality parameters exceeded the national allowable limits for safe discharge. Nonetheless, the study demonstrated positive potentials for adopting indigenous bamboo species as emergent macrophytes for FS treatment using VFCW. Further treatment to reduce contaminant levels in a second to a third series of a connected constructed is recommended wetland prior to reuse for agriculture.

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The use of nitrification inhibition as a concentrating step for ammonium (NH4+), for the purpose of increasing the potential for simultaneous recovery of phosphate (PO43-) and NH4+ from effluent streams of an aerobic sequencing batch reactor (SBR) system, has never been investigated in the literature. Therefore, the present study aimed to determine the effect of the inhibition of nitrification on both the reactor performance and effluent quality in a laboratory scale aerobic SBR system. In order to compare the observed results, a separate reactor, where the inhibition was not applied, was operated as a control reactor (CR) under the identical operational conditions used for the inhibitory reactor (IR). Experimental results for the reactor performance showed that effluents with low total suspended solids (< 50 mg/L) and chemical oxygen demand concentrations (> 90% of removal efficiency based on the influent concentration of 500 mg/L) were achieved for both SBRs by obtaining an activated sludge with a sludge volume index < 60 mL/g after the acclimation period. In the same period, the effluent PO43-, NH4+, and nitrate (NO3-) concentrations were found to be 17.0 ± 4.0, 1.26 ± 0.84, and 21.5 ± 39 mg/L for the CR and 10.0 ± 4.4, 3.9 ± 2.4, and 9.2 ± 1.5 mg/L for the IR, respectively. During this period, 94% of the removed NH4+ (NH4+rem.) was converted to NO3- in the CR, indicating almost complete nitrification occurred in the reactor. However, only 47% of the NH4+rem. was converted to NO3- in the IR as a result of the inhibition of nitrification, meaning a partial inhibition (53%) occurred due to the inhibition treatment. These results clearly demonstrated that the inhibition of nitrification allowed the effluent NH4+ concentrations to increase by suppressing the formation of NO3- ions. Based on the results, it can be concluded that inhibition of nitrification in an aerobic SBR system creates a potential for conserving the effluent NH4+ concentration and increasing consecutive recovery of PO43- together with NH4+ from the effluent discharges.

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To meet the increasingly stringent discharge standards of wastewater treatment plants (WWTPs) in the Taihu Lake Basin, the Chinese government successively established the National Special Water Project Program to develop new technologies to retrofit and upgrade existing wastewater treatment processes during the 11th, 12th, and 13th Five-Year Plans. However, there is a lack of systematic sorting of the existing research outcomes, and thus hinders the application and promotion of the upgrade technologies. Based on the outcomes of the National Special Water Project and a field survey, this research analyzed the current status of wastewater treatment in the Taihu Lake Basin and systematically integrated the retrofitting measures of WWTPs in terms of achieving the Grade IA of the national standard and local stricter discharge standards (DB 32/1072-2018 and DB 33/2169-2018). In particular, the boundary conditions, design parameters, specific recommendations of the technologies, and some typical engineering cases were provided accordingly. Finally, this study discussed the future development directions of WWTPs during the upgrade process from the perspective of carbon neutrality and digitalization. The present work will hopefully assist in retrofitting and constructing WWTPs to achieve the stricter effluent discharge criteria and help optimize the design and construction of WWTPs in the best way.

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Real-time evaluation of the fighting activities during a sudden unknown disaster like the COVID-19 pandemic is a critical challenge for control. This study demonstrates that the temporal variations of effluents from hospital sewage treatment facilities can be used as an effective indicator for such evaluation. Taking a typical infection-suffering city in China as an example, we found that there was an obvious decrease in effluent ammonia and COD concentrations in line with the start of city lockdown, and its temporal variations well indicated the major events happened during the pandemic control. Notably, the lagging period between the change point of effluent residual chlorine and the change points of COD and ammonia concentration coincided with a period in which there was a deficiency in local medical resources. In addition, the diurnal behavior of effluents from designated hospitals has varied significantly at different stages of the pandemic development. The effluent ammonia peaks shifted from daytime to nighttime after the outbreak of the COVID-19 pandemic, suggesting a high workload of the designated hospitals in fighting the rapidly emerging pandemic. This work well demonstrates the necessary for data integration at the wastewater-medical service nexus and highlights an unusual role of the effluents from hospital sewage treatment facilities in revealing the status of fighting the pandemic, which helps to control the pandemic.

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Quantitative image analysis (QIA) is a simple and automated method for process monitoring, complementary to chemical analysis, that when coupled to mathematical modelling allows associating changes in the biomass to several operational parameters. The majority of the research regarding the use of QIA has been carried out using synthetic wastewater and applied to activated sludge systems, while there is still a lack of knowledge regarding the application of QIA in the monitoring of aerobic granular sludge (AGS) systems. In this work, chemical oxygen demand (COD), ammonium (N-NH4+), nitrite (N-NO2-), nitrate (N-NO3-), salinity (Cl-), and total suspended solids (TSS) levels present in the effluent of an AGS system treating fish canning wastewater were successfully associated to QIA data, from both suspended and granular biomass fractions by partial least squares models. The correlation between physical-chemical parameters and QIA data allowed obtaining good assessment results for COD (R2 of 0.94), N-NH4+ (R2 of 0.98), N-NO2- (R2 of 0.96), N-NO3- (R2 of 0.95), Cl- (R2 of 0.98), and TSS (R2 of 0.94). While the COD and N-NO2- assessment models were mostly correlated to the granular fraction QIA data, the suspended fraction was highly relevant for N-NH4+ assessment. The N-NO3-, Cl- and TSS assessment benefited from the use of both biomass fractions (suspended and granular) QIA data, indicating the importance of the balance between the suspended and granular fractions in AGS systems and its analysis. This study provides a complementary approach to assess effluent quality parameters which can improve wastewater treatment plants monitoring and control, with a more cost-effective and environmentally friendly procedure, while avoiding daily physical-chemical analysis.

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This study proposed the selection of cost-effective additives generated from different activity sectors to enhance and stabilize the start-up, as well as the transitional phases, of semi-continuous food waste (FW) anaerobic digestion. The results showed that combining agricultural waste mixtures including wheat straw (WS) and cattle manure (CM) boosted the process performance and generated up to 95% higher methane yield compared to the control reactors (mono-digested FW) under an organic loading rate (OLR) range of 2 to 3 kg VS/m3·d. Whereas R3 amended with unmarketable biochar (UBc), to around 10% of the initial fresh mass inserted, showed a significant process enhancement during the transitional phase, and more particularly at an OLR of 4 kg VS/m3·d, it was revealed that under these experimental conditions, FW reactors including UBc showed an increase of 144% in terms of specific biogas yield (SBY) compared to FW reactors fed with agricultural residue. Hence, both agricultural and industrial waste were efficacious when it came to boosting either FW anaerobic performance or AD effluent quality. Although each co-substrate performed under specific experimental conditions, this feature provides decision makers with diverse alternatives to implement a sustainable organic waste management system, conveying sufficient technical details to draw up appropriate designs for the recovery of various types of organic residue.