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In wastewater treatment plants (WWTPs), aeration is vital for microbial oxygen needs. To achieve carbon neutrality, optimizing aeration for energy and emissions reduction is imperative. Machine learning (ML) is used in wastewater treatment to reveal complex rules in large data sets has become a trend. In this vein, the present paper proposes an aeration optimization approach based on the extreme gradient boosting–bidirectional long short-term memory (XGB-Bi-LSTM) model via the online monitoring of oxygen transfer efficiency (OTE) and oxygen uptake rate (OUR), thus allowing WWTPs to conserve energy and reduce indirect carbon emissions. The approach uses gain algorithm of XGB to calculate the importance of features and identify important parameters, and then uses Bi-LSTM to predict the target with important parameters as features. Operational data from a WWTP in Suzhou, China, is employed to train and test the approach, the performance of which is compared with ML models suitable for regression prediction tasks (XGB, random forest, light gradient boosting machine, gradient boosting and LSTM). Experimental results show the approach requires only a small number of input parameters to achieve good performance and outperforms other machine-learning models. When OTE and dissolved oxygen (DO) are used as features to predict the alpha factor (αF; since diffusers were used, multiply by the pollution factor F), the R-squared (R2) is 0.9977, the root mean square error (RMSE) is 0.0043, the mean absolute percentage error (MAPE) is 0.0069 and the median absolute error (MedAE) is 0.0032. When the predicted αF and the OUR are used as features to predict the air flow rate of an aeration unit, the R2 is 0.9901, the RMSE is 3.6150, the MAPE is 0.0209 and the MedAE is 1.5472. Using our optimized aeration approach, the energy consumption can be reduced by 23%. [Display omitted] • Online monitoring of the OTE and OUR in a WWTP aeration tank was conducted. • Accurate theoretical air flow rate (q) was derived from OTE and OUR measurements. • Develop XGB-Bi-LSTM models for predicting αF and q. • The models exhibit high predictive accuracy for αF and q , while utilizing a limited number of input parameters. • The approach enables aeration reduction and energy savings. [ABSTRACT FROM AUTHOR]

Gestagens are common pollutants accumulated in the aquatic ecosystem. Gestagens are comprised of natural gestagens (i.e. progesterone) and synthetic gestagens (i.e. progestins). The major contributors of gestagens in the environment are paper plant mill effluent, wastewater treatment plants, discharge from pharmaceutical manufacturing, and livestock farming. Gestagens present in the aquatic environment interact with progesterone receptors and other steroid hormone receptors, negatively influencing fish reproduction, development, and behavior. In fish, the gonadotropin induces 17α, 20β-dihydroxy-4-pregnen-3-one (DHP) production, an important steroid hormone involved in gametogenesis. DHP interacts with the membrane progestin receptor (mPR), which regulates sperm motility and oocyte maturation. Gestagens also interfere with the hypothalamic-pituitary-gonadal (HPG) axis, which results in altered hormone levels in fish. Moreover, recent studies showed that even at low concentrations exposure to gestagens can have detrimental effects on fish reproduction, including reduced egg production, masculinization, feminization in males, and altered sex ratio, raising concerns about their impact on the fish population. This review highlights the hormonal regulation of sperm motility, oocyte maturation, the concentration of environmental gestagens in the aquatic environment, and their detrimental effects on fish reproduction. However, the long-term and combined impacts of multiple gestagens, including their interactions with other pollutants on fish populations and ecosystems are not well understood. The lack of standardized regulations and monitoring protocols for gestagens pollution in wastewater effluent hampers effective control and management. Nonetheless, advancements in analytical techniques and biomonitoring methods provide potential solutions by enabling better detection and quantification of gestagens in aquatic ecosystems. • DHP is an important steroid for sperm motility and oocyte maturation. • DHP interacts with the membrane progestin receptor, which controls sperm motility and oocyte maturation. • Gestagens are common pollutants in the aquatic ecosystem. • Gestagens negatively affect fish reproduction, behavior, and development. [ABSTRACT FROM AUTHOR]

Coastal ecosystems are particularly vulnerable to terrestrial inputs from human-impacted areas. The prevalence of wastewater treatment plants, unable to remove contaminants such as pharmaceuticals (PhACs), leads to their continuous input into the marine environment. In this paper, the seasonal occurrence of PhACs in a semi-confined coastal lagoon (the Mar Menor, south-eastern Spain) was studied during 2018 and 2019 by evaluating their presence in seawater and sediments, and their bioaccumulation in aquatic organisms. Temporal variation in the contamination levels was evaluated by comparison to a previous study carried out between 2010 and 2011 before the cessation of permanent discharges of treated wastewater into the lagoon. The impact of a flash flood event (September 2019) on PhACs pollution was also assessed. A total of seven compounds (out of 69 PhACs analysed) were found in seawater during 2018–2019, with a limited detection frequency (<33%) and concentrations (up to 11 ng/L of clarithromycin). Only carbamazepine was found in sediments (ND–1.2 ng/g dw), suggesting an improved environmental quality in comparison to 2010–2011 (when 24 and 13 compounds were detected in seawater and sediments, respectively). However, the biomonitoring of fish and molluscs showed a still remarkable accumulation of analgesic/anti-inflammatory drugs, lipid regulators, psychiatric drugs and β-blocking agents, albeit not higher than in 2010. The flash flood event from 2019 increased the prevalence of PhACs in the lagoon, compared to the 2018–2019 sampling campaigns, especially in the upper water layer. After the flash flood the antibiotics clarithromycin and sulfapyridine yielded the highest concentrations ever reported in the lagoon (297 and 145 ng/L, respectively), alongside azithromycin in 2011 (155 ng/L). Flash flood events associated with sewer overflows and soil mobilisation, which are expected to increase under climate change scenarios, should be considered when assessing the risks posed by pharmaceuticals to vulnerable aquatic ecosystems in the coastal areas. [Display omitted] • Lower pharmaceutical concentrations were found in Mar Menor lagoon (SE Spain) in 2018–2019 than in 2010–2011. • Reuse of WWTP effluents (since 2015) has likely contributed to the observed decrease in pharmaceutical levels. • A flash flood episode in 2019 increased the presence of pharmaceuticals in the upper seawater layer. • Biomonitoring more informative than grab water regarding pharmaceutical presence in the lagoon. [ABSTRACT FROM AUTHOR]

Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the optimal feature subset to predict best, and the GJO-CLT achieves the best performance for different backtracking windows and prediction steps. These results demonstrate that the prediction system excels in the task of multivariate water quality time series prediction of WWTPs. [Display omitted] • Establish a prediction system based on feature selection, deep learning, and optimization algorithms. • A novel two-stage feature selection method is applied for dimensionality reduction. • The proposed hybrid deep learning model CLT outperforms other commonly used models. • The CLT optimized by GJO improves the prediction performance. [ABSTRACT FROM AUTHOR]

Microplastics are a silent threat that represent a high degree of danger to the environment in its different ecosystems and of course will also have an important impact on the health of living organisms. It is evident the need to have effective treatments for their treatment, however this is not a simple task, this as a result of the behavior of microplastics in wastewater treatment plants due to their different types and nature, their long molecular chain, reactivity against water, size, shape and the functional groups they carry. Wastewater treatment plants are at the circumference of the release of these wastes into the environment. They often act as a source of many contaminations, which makes this problem more complex. Challenges such as detection in the current scenario using the latest analytical techniques impede the correct understanding of the problem. Due to microplastics, treatment plants have operational and process stability problems. This review paper will present the in-depth situation of occurrence of microplastics, their detection, conventional and advanced treatment methods as well as implementation of legislations worldwide in a comprehensive manner. It has been observed that no innovative or new technologies have emerged to treat microplastics. Therefore, in this article, technologies targeting wastewater treatment plants are critically analyzed. This will help to understand their fate, but also to develop state-of-the-art technologies or combinations of them for the selective treatment of microplastics. The pros and cons of the treatment methods adopted and the knowledge gaps in legislation regarding their implementation are also comprehensively analyzed. This critical work will offer the development of new strategies to restrict microplastics. • Microplastics in wastewater treatment plants are challenging due to its types and nature. • The occurrences, detections, treatment methods, and legislations implementation of microplastics were reviewed. • Various aspects of microplastic collection, processing, and characterization are examined. • This will help in understanding cutting-edge treatment technologies for the microplastics. • This work will help in development of new strategies to restrict the microplastics in wastewater. [ABSTRACT FROM AUTHOR]

Using artificial intelligence method to describe general working process is a more meaningful and widely used idea in various practical projects. At the same time, it is also an important way to realize intelligent management. Water pollution is serious all over the world, also the intelligent management of sewage treatment has always been one of the urgent problems to be solved. For this, an intelligent management system is designed in this study to realize automatic monitoring and intelligent decision-making of sewage treatment. However, the existing technology usually trains artificial intelligence models based on historical data, and such models have some limitations in describing nonlinear and complex wastewater treatment processes. Offline machine learning lacks dynamic adaptive characteristics to scene changes. Considering this, this paper designs an online learning-empowered smart management for A2/O process in sewage treatment processes (OL-AP). Online learning is based on the new data generated by the scene transformation, so that the model can learn again and give better results. In this study, relevant simulation experiments are carried out on the sewage treatment data of a sewage treatment plant in Chongqing. Firstly, automatic data collection is realized based on the sensor network of the IoT. Then, according to the preprocessed data, the designed prediction model is trained and a set of parameters with better evaluation indexes is obtained. Finally, online learning uses the latest data samples based on the online feedback of real scenes to optimize the model by retraining and adjusting parameters. [ABSTRACT FROM AUTHOR]

Information on the relative contributions of microplastics coming from different sources is important to help prioritise measures to reduce river contamination levels and limit human and ecological health risks. This paper reports on work which aimed to quantitatively assess the relative concentrations and types of microplastic delivered from differed sources to a second order river. The study was undertaken in a mixed landuse area within a rapidly urbanising catchment in Malaysia. Over a six-week period, water samples were collected from road culverts and drains in residential and industrial areas across the area to assess microplastic concentrations, while inputs from atmospheric deposition and wastewater treatment plants (WWTPs) were also quantified. Microplastic fibres and fragments were the dominant material in all sources, with the majority consisting of styrene-butadiene rubber and nylon. Culverts draining main roads were the main contributor to riverborne microplastic, delivering 42.20 ± 35.29 particles/L directly to the river channel. Road inputs were up to seven times greater than those from residential (8.53 ± 9.91 particles/L) and industrial (5.67 ± 4.88 particles/L) areas. The five WWTPs had removal efficiencies of between 30.95 ± 5.51% and 69.94 ± 22.17%, with their outflows delivering microplastics to the river in concentrations similar to those in uncontrolled residential and industrial drains. Atmospheric deposition across the study area was estimated to be 76.07 ± 32.85 particles/m2/day (=8.35 ± 5.11 particles/L). Mitigation strategies in the study area should focus on improving management of water draining roads, and re-routing discharges from domestic and industrial areas to WWTPs rather than allowing them to flow directly to the river. The low efficiencies of some of the WWTPs are not unusual, and indicate the need for additional water treatment to deal with microplastic present in wastewater. [Display omitted] • The sources of microplastics contributing to contamination of a tropical river were assessed. • Road runoff was the main contributor. • Housing and industrial areas contributed less microplastics. • Microplastic removal efficiency of wastewater treatment plants ranged from 31 to 70%. • Fibres and fragments were the dominant type of microplastic in all sources. [ABSTRACT FROM AUTHOR]

The experience gained over the last hundred years clearly indicates that two groups of viruses represent the main risk for the development of highly transmissible epidemics and pandemics in the human species: influenza viruses and coronaviruses (CoV). Although the search for viruses with pandemic potential in the environment may have an important predictive and monitoring role, it is still based on empirical methodologies, mostly resulting from the clinic and not fully validated for environmental matrices. As far as the SARS-CoV-2 pandemic, currently underway, is concerned, environmental monitoring activities aiming at checking the presence of SARS-CoV-2 in wastewater can be extremely useful to predict and check the diffusion of the disease. For this reason, the present study aims at evaluating the SARS-CoV-2 diffusion by means of a wastewater-based environmental monitoring developed in Piedmont, N–W Italy, during the second and third pandemic waves. Wastewater sampling strategies, sampling points sample pre-treatments and analytical methods, data processing and standardization, have been developed and discussed to give representative and reliable results. The following outcomes has been highlighted by the present study: i) a strong correlation between SARS-CoV-2 concentration in untreated wastewater and epidemic evolution in the considered areas can be observed as well as a predictive potential that could provide decision-makers with indications to implement effective policies, to mitigate the effects of the ongoing pandemic and to prepare response plans for future pandemics that could certainly arise in the decades to come; ii) moreover, the data at disposal from our monitoring campaign (almost 500 samples analysed in 11 months) confirm that SARS-CoV-2 concentrations in wastewater are strongly variable and site-specific across the region: the highest SARS-CoV-2 concentration values have been found in sewer networks serving the most populated areas of the region; iii) normalization of viral concentrations in wastewater through Pepper Mild Mottle Virus (a specific faecal marker) has been carried out and commented; iv) the study highlights the potential of wastewater treatment plants to degrade the genetic material referable to SARS-CoV-2 as well. In conclusion, the preliminary data reported in the present paper, although they need to be complemented by further studies considering also other geographical regions, are very promising. [ABSTRACT FROM AUTHOR]

IoT is a secure communication technology used to transfer data from a physical entity to a device with intelligent analysis tools through a wireless channel. The wastewater treatment method extracts pollutants and transforms them into effluents added to the water supply with minimal environmental effects or recovered directly. The major issue is monitoring the disposal of sewage in the treatment plants. Hence, this paper, Surveillance-based Sewage Wastewater Monitoring System (SSWMS) with IoT, has been proposed for monitoring wastewater treatment and improving water quality. A smart water sensor enabled by IoT monitors water quality, water pressure, and water temperature and quantifies water dynamics to map water flow through the entire treatment facility. The proposed method calculates the wastewater treatment facility's effectiveness and ensures that chemical releases are maintained below allowable levels. Thus, the experimental results show the improved recycling water quality level is raised to 97.98%, enhancing secure communication and less moisture content when compared to other methods. • SSWMS is proposed to control wastewater treatment. • SSWMS improve the consistency of water quality with IoT. • Experimental results indicate improved safe contact & less humidity. • Improved recycling water quality level is raised up to 97.98%. [ABSTRACT FROM AUTHOR]

It has been estimated that over 28 million tonnes of plastics end up in water bodies annually. These plastics degrade into microplastics (MPs), which along with microbeads and MPs from other sources such as wastewater treatment plants continue to threaten the aquatic system. At such small sizes, and corresponding larger surface areas per unit mass/volume, MPs exhibit enhanced capacity for absorbing and desorbing toxic chemicals/additives. Therefore, MPs can serve as vectors through which additives as well as other persistent, bio-accumulative, and toxic chemicals can enter the food chain. Additives are a significant component of most plastic products with some identified as hazardous to health and the environment. One group of additives that has continued to attract interest is organophosphate esters (OPEs), which are used both as flame retardants and plasticizers. Some of these OPEs are suspected carcinogens and endocrine disruptors and have been reported to exert serious toxic effects on freshwater biota. Separate studies on the presence and fate in the freshwater environment of these additives and MPs have emerged recently. However, no studies exist that examine the extent to which plastics additives such as OPEs in sediments are sorbed to MPs as opposed to the sediment itself. This has potentially important implications for the bioavailability of such additives and studies to examine this are recommended. This paper reviews critically the current state-of-knowledge on MPs in freshwater sediments, methods for their analysis, as well as their occurrence, temporal trends, and risks to the freshwater aquatic environment. Moreover, to facilitate the study of additives associated with MPs that have been extracted from sediments, we consider the possible effect of MP isolation methods on the determination of concentrations of associated additives like OPEs. • Techniques for measurement of microplastics (MPs) in freshwater sediments reviewed. • Spatial and temporal trends in MPs in freshwater sediments identified. • Little known about MPs in freshwater sediments from Africa. • MPs role as conduits of contaminants in freshwater environments poorly understood. • Comparing bioavailability of OPEs bound to sediment particles and MPs is a priority. [ABSTRACT FROM AUTHOR]

Drugs of abuse are a group of emerging contaminants. As the prevalence of manufacture and consumption, there is a growing global environmental burden and ecological risk from the continuous release of these contaminants into environment. The widespread occurrence of drugs of abuse in waste wasters and surface waters is due to the incomplete removal through traditional wastewater treatment plants in different regions around the world. Although their environmental concentrations are not very high, they can potentially influence the aquatic organisms and ecosystem function. This paper reviews the occurrence of drugs of abuse and their metabolites in waste waters and surface waters, their bioaccumulation in aquatic plants, fishes and benthic organisms and even top predators, and the toxicological effects such as genotoxic effect, cytotoxic effect and even behavioral effect on aquatic organisms. In summary, drugs of abuse occur widely in aquatic environment, and may exert adverse impact on aquatic organisms at molecular, cellular or individual level, and even on aquatic ecosystem. It necessitates the monitoring and risk assessment of these compounds on diverse aquatic organisms in the further study. • Drugs of abuse are widespread in aquatic environment due to low removal efficiencies. • Drugs of abuse may cause toxic effects at molecular, cellular or individual level. • Drugs of abuse may bioaccumulate in aquatic organisms via aquatic food chain. • Research focusing on fate and combined effects of drugs of abuse is warranted [ABSTRACT FROM AUTHOR]