Explore the vast range of titles available.

This research mainly focused on the phosphorus removal performance of pilot-scale vertical flow constructed wetlands with steel slag (SS) and modified steel slag (MSS). First, bench-scale experiments were conducted to evaluate the phosphorus adsorption capacity. Results showed that the Langmuir model could better describe the adsorption characteristics of the two materials; the maximum adsorption of MSS reached 12.7 mg/g, increasing by 34% compared to SS (9.5 mg/g). Moreover, pilot-scale constructed wetlands with SS and MSS were set up outdoors. Then, the influence of hydraulic retention time (HRT) and phosphorus concentration in phosphorus removal for two wetlands were investigated. Results revealed that better performance of the two systems could be achieved with an HRT of 2 d and phosphorus concentration in the range of 3–4.5 mg/L; the system with MSS had a better removal efficiency than the one with SS in the same control operation. Finally, the study implied that MSS could be used as a promising substrate for wetlands to treat wastewater with a high phosphorus concentration. However, considering energy consumption, SS could be regarded as a better alternative for substrate when treating sewage with a low phosphorus concentration.

Water is an indispensable resource for human activity and the environment. Industrial activities generate vast quantities of wastewater that may be heavily polluted or contain toxic contaminants, posing environmental and public health challenges. Different industries generate wastewater with widely varying characteristics, such as the quantity generated, concentration, and pollutant type. It is essential to understand these characteristics to select available treatment techniques for implementation in wastewater treatment facilities to promote sustainable water usage. This review article provides an overview of wastewaters generated by various industries and commonly applied treatment techniques. The characteristics, advantages, and disadvantages of physical, chemical, and biological treatment methods are presented.

The aim of this study was to characterize the efficiency of an intensified process of vertical flow constructed wetland having the following particularities: (i) biological pretreatment by trickling filter, (ii) FeCl3 injection for dissolved phosphorus removal and (iii) succession of different levels of redox conditions along the process line. A pilot-scale set-up designed to simulate a real-scale plant was constructed and operated using real wastewater. The influences of FeCl3 injection and water saturation level within the vertical flow constructed wetland stage on treatment performances were studied. Three different water saturation levels were compared by monitoring: suspended solids (SS), total phosphorus (TP), dissolved chemical oxygen demand (COD), ammonium, nitrate, phosphate, iron, and manganese. The results confirmed the good overall efficiency of the process and the contribution of the trickling filter pretreatment to COD removal and nitrification. The effects of water saturation level and FeCl3 injection on phosphorus removal were evaluated by analysis of the correlations between the variables. Under unsaturated conditions, good nitrification and no denitrification were observed. Under partly saturated conditions, both nitrification and denitrification were obtained, along with a good retention of SSs. Finally, under saturated conditions, the performance was decreased for almost all parameters.

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.

Constructed wetlands (CWs) are affordable and reliable green technologies for the treatment of various types of wastewater. Compared to conventional treatment systems, CWs offer an environmentally friendly approach, are low cost, have fewer operational and maintenance requirements, and have a high potential for being applied in developing countries, particularly in small rural communities. However, the sustainable management and successful application of these systems remain a challenge. Therefore, after briefly providing basic information on wetlands and summarizing the classification and use of current CWs, this study aims to provide and inspire sustainable solutions for the performance and application of CWs by giving a comprehensive review of CWs’ application and the recent development of their sustainable design, operation, and optimization for wastewater treatment. To accomplish this objective, thee design and management parameters of CWs, including macrophyte species, media types, water level, hydraulic retention time (HRT), and hydraulic loading rate (HLR), are discussed. Besides these, future research on improving the stability and sustainability of CWs are highlighted. This article provides a tool for researchers and decision-makers for using CWs to treat wastewater in a particular area. This paper presents an aid for informed analysis, decision-making, and communication. The review indicates that major advances in the design, operation, and optimization of CWs have greatly increased contaminant removal efficiencies, and the sustainable application of this treatment system has also been improved.

An alternative material to activated carbon for water remediation is reported: a porous material based on crosslinked cyclodextrins that is better than activated carbons at adsorbing a range of pharmaceuticals, pesticides and other anthropogenic pollutants. Near-instant removal of organic micropollutants from water Water purification and remediation is often carried out using various forms of activated carbon; it is inexpensive, but only partially removes many organic pollutants. However, regenerating activated carbon for reuse is energy intensive, requiring high temperatures, and performance decreases upon recycling. Now William Dichtel, Damian Helbling and colleagues have developed an alternative to activated carbon for water remediation: a high-surface-area, mesoporous polymer of β-cyclodextrin. Not only does the material outperform activated carbons at adsorbing a range of pharmaceuticals, pesticides and other pollutants, but it is easily regenerated by washing at room temperature. The global occurrence in water resources of organic micropollutants, such as pesticides and pharmaceuticals, has raised concerns about potential negative effects on aquatic ecosystems and human health 1 , 2 , 3 , 4 , 5 . Activated carbons are the most widespread adsorbent materials used to remove organic pollutants from water but they have several deficiencies, including slow pollutant uptake (of the order of hours) 6 , 7 and poor removal of many relatively hydrophilic micropollutants 8 . Furthermore, regenerating spent activated carbon is energy intensive (requiring heating to 500–900 degrees Celsius) and does not fully restore performance 9 , 10 . Insoluble polymers of β-cyclodextrin, an inexpensive, sustainably produced macrocycle of glucose, are likewise of interest for removing micropollutants from water by means of adsorption 11 . β-cyclodextrin is known to encapsulate pollutants to form well-defined host–guest complexes, but until now cross-linked β-cyclodextrin polymers have had low surface areas and poor removal performance compared to conventional activated carbons 11 , 12 , 13 . Here we crosslink β-cyclodextrin with rigid aromatic groups, providing a high-surface-area, mesoporous polymer of β-cyclodextrin. It rapidly sequesters a variety of organic micropollutants with adsorption rate constants 15 to 200 times greater than those of activated carbons and non-porous β-cyclodextrin adsorbent materials 7 , 8 , 11 , 12 , 13 . In addition, the polymer can be regenerated several times using a mild washing procedure with no loss in performance. Finally, the polymer outperformed a leading activated carbon for the rapid removal of a complex mixture of organic micropollutants at environmentally relevant concentrations. These findings demonstrate the promise of porous cyclodextrin-based polymers for rapid, flow-through water treatment.

A review of the characterization and functions of extracellular polymeric substances (EPS) of microbial aggregates in biological wastewater treatment systems is presented in this paper. EPS represent the complex high-molecular-weight mixture of polymers excreted by microorganisms generated from cell lysis as well as adsorbed inorganic and organic matter from wastewater. EPS exhibit a three-dimensional, gel-like, highly hydrated matrix that facilitates microbial attachment, embedding, and immobilization. EPS play multiple roles in containments removal, and the main components of EPS crucially influence the properties of microbial aggregates, such as adsorption ability, stability, and formation capacity. Moreover, EPS are important to sludge bioflocculation, settleability, and dewatering properties and could be used as carbon and energy sources in wastewater treatment. However, due to the complex structure of EPS, related knowledge is incomplete, and further research is necessary to understand fully the precise roles in biological treatment processes.

Driven by energy neutral/positive of wastewater treatment plants, significant efforts have been made on the research and development of mainstream partial nitritation and anaerobic ammonium oxidation (anammox) (PN/A) (deammonification) process since the early 2010s. To date, feasibility of mainstream PN/A process has been demonstrated and proven by experimental results at various scales although with the low loading rates and elevated nitrogen concentration in the effluent at low temperatures (15–10 °C). This review paper provides an overview of the current state of research and development of mainstream PN/A process and critically analyzes the bottlenecks for its full-scale application. The paper discusses the following: (i) the current status of research and development of mainstream PN/A process; (ii) the interactions among aerobic ammonium-oxidizing bacteria, aerobic nitrite-oxidizing bacteria, anammox bacteria, and heterotrophic bacteria; (iii) the suppression of aerobic nitrite-oxidizing bacteria; (iv) process and bioreactors; and (v) suggested further studies including efficient and robust carbon concentrating pretreatment, deepening of understanding competition between autotrophic nitrogen-converting organisms, intensification of biofilm anammox activity, reactor design, and final polishing.

Microplastics (MPs) cannot be completely removed from water/wastewater in conventional wastewater treatment plants (WWTPs) and drinking water treatment plants (DWTPs). According to the literature analysis, membrane technologies, one of the advanced treatment technologies, are the most effective and promising technologies for MP removal from water and wastewater. In this paper, firstly, the properties of MPs commonly present in WWTPs/DWTPs and the MP removal efficiency of WWTPs/DWTPs are briefly reviewed. In addition, research studies on MP removal from water/wastewater by microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), and membrane bioreactors (MBRs) are reviewed. In the next section, membrane filtration is compared with other methods used for MP removal from water/wastewater, and the advantages/disadvantages of the removal methods are discussed. Moreover, the problem of membrane fouling with MPs during filtration and the potential for MP release from polymeric membrane structure to water/wastewater are discussed. Finally, based on the studies in the literature, the current status and research deficiencies of MP removal by membrane technologies are identified, and recommendations are made for further studies.

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff), chemical oxygen demand (CODeff) and total nitrogen (TNeff). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BODeff, the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both CODeff and TNeff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.