Analysis of nutrient loads, heavy metals and physicochemical properties of wastewater, wetland grass, and papaya samples: Gondar Malt factory, Ethiopia with global implication

Robust attention was brought to researchers due to deterioration of wastewater quality of lakes and reservoirs as major global concerns by industrial release. The uncontrolled releases of effluents impose serious impacts for both aquatic and terrestrial environments. In the current study, many parameters like nutrient loads, heavy metals and physicochemical properties of wastewater, wetland grass, and papaya samples were analysed. The investigated nutrients, alkalinity, and total hardness in fresh water samples were within the allowable limits except for phosphate in fresh wastewater and alkalinity in wastewater. The detected levels of heavy metals (mg/L) in wastewater samples were:- Cd (0.386-0.905), Cr (ND-0.074), Cu (0.064-0.096), Mn (0.184-1.528), Fe (0.167-4.636), Zn (0.175-0.333), and Pb (0.044-0.892) (mg/L). The studied metals in the wastewater sample, except Cd, Fe, and Pb were lower than the allowable limit. The level of heavy metals in the grass and papaya samples ranged from Cd (37.14-147.62), Cr (ND-8.82), Cu (3.14-8.33), Mn (2.89-85.46), Fe(5.0-65.15), Zn (3.44-36.84), and Pb (ND-60.36) (mg/kg). The detected metals were below the permissible limits, except Cd, Cr, and Pb. The findings of the physicochemical characteristics in wastewater samples were computed: pH (6.61-8.54), temperatures (21.63-26.57 °C), TDS (205.9-1896 mg/L), EC (359.9-3226.67 μs/cm), BOD (12.0-732.67 mg/L), COD (3.67-1691.33 mg/L). Except for temperature and pH, all levels in the wastewater were above the recommended limit for wastewater discharge by USEPA.

Photocatalytic degradation of drugs and dyes using a maching learning approach

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.

Highly Efficient Biosorption of Cationic Dyes via Biopolymeric Adsorbent-Material-Based Pectin Extract Polysaccharide and Carrageenan Grafted to Cellulosic Nonwoven Textile

Water scarcity and contamination have emerged as critical global challenges, requiring the development of effective and sustainable solutions for the treatment of contaminated water. Recently, functionalized polymer biomaterials have garnered significant interest because of their potential for a wide range of water treatment applications. Accordingly, this paper highlights the design of a new adsorbent material based on a cellulosic nonwoven textile grafted with two extracted biopolymers. The layer-by-layer grafting technique was used for the polyelectrolyte multi-layer (PEM) biosorbent production. Firstly, we extracted a Suaeda fruticosa polysaccharide (SFP) and confirmed its pectin-like polysaccharide structure via SEC, NMR spectroscopy, and chemical composition analyses. Afterward, the grafting was designed via an alternating multi-deposition of layers of SFP polymer and carrageenan crosslinked with 1,2,3,4-butanetetracarboxylic acid (BTCA). FT-IR and SEM were used to characterize the chemical and morphological characteristics of the designed material. Chemical grafting via polyesterification reactions of the PEM biosorbent was confirmed through FT-IR analysis. SEM revealed the total filling of material microspaces with layers of grafted biopolymers and a rougher surface morphology. The assessment of the swelling behavior revealed a significant increase in the hydrophilicity of the produced adsorbent system, a required property for efficient sorption potential. The evaluation of the adsorption capabilities using the methylene blue (MB) as cationic dye was conducted in various experimental settings, changing factors such as the pH, time, temperature, and initial concentration of dye. For the untreated and grafted materials, the greatest adsorbed amounts of MB were 130.6 mg/g and 802.6 mg/g, respectively (pH = 4, T = 22 C, duration = 120 min, and dye concentration = 600 mg/L). The high adsorption performance, compared to other reported materials, was due to the presence of a large number of hydroxyl, sulfonate, and carboxylic functional groups in the biosorbent polymeric system. The adsorption process fitted well with the pseudo-first-order kinetic model and Langmuir/Temkin adsorption isotherms. This newly developed multi-layered biosorbent shows promise as an excellent adsorption resultant and cheap-cost/easy preparation alternative for treating industrial wastewater.

Photocatalytic Degradation of Antibiotics via Exploitation of a Magnetic Nanocomposite: A Green Nanotechnology Approach toward Drug-Contaminated Wastewater Reclamation

In the quest for eco-conscious innovations, this research was designed for the sustainable synthesis of magnetite (Fe3O4) nanoparticles, using ferric chloride hexahydrate salt as a precursor and extract of Eucalyptus globulus leaves as both a reducing and capping agent, which are innovatively applied as a photocatalyst for the photocatalytic degradation of antibiotics "ciprofloxacin and amoxicillin". Sugar cane bagasse biomass, sugar cane bagasse pyrolyzed biochar, and magnetite/sugar cane bagasse biochar nanocomposite were also synthesized via environmentally friendly organized approaches. The optimum conditions for the degradation of ciprofloxacin and amoxicillin were found to be pH 6 for ciprofloxacin and 5 for amoxicillin, dosage of the photocatalyst (0.12 g), concentration (100 mg/L), and irradiation time (240 min). The maximum efficiencies of percentage degradation for ciprofloxacin and amoxicillin were found to be (73.51%) > (63.73%) > (54.57%) and (74.07%) > (61.55%) > (50.66%) for magnetic nanocomposites, biochar, and magnetic nanoparticles, respectively. All catalysts demonstrated favorable performance; however, the "magnetite/SCB biochar" nanocomposite exhibited the most promising results among the various catalysts employed in the photocatalytic degradation of antibiotics. Kinetic studies for the degradation of antibiotics were also performed, and notably, the pseudo-first-order chemical reaction showed the best results for the degradation of antibiotics. Through a comprehensive and comparative analysis of three unique photocatalysts, this research identified optimal conditions for efficient treatment of drug-contaminated wastewater, thus amplifying the practical significance of the findings. The recycling of magnetic nanoparticles through magnetic separation, coupled with their functional modification for integration into composite materials, holds significant application potential in the degradation of antibiotics.

Micro/nanorobots for remediation of water resources and aquatic life

Nowadays, global water scarcity is becoming a pressing issue, and the discharge of various pollutants leads to the biological pollution of water bodies, which further leads to the poisoning of living organisms. Consequently, traditional water treatment methods are proving inadequate in addressing the growing demands of various industries. As an effective and eco-friendly water treatment method, micro/nanorobots is making significant advancements. Based on researches conducted between 2019 and 2023 in the field of water pollution using micro/nanorobots, this paper comprehensively reviews the development of micro/nanorobots in water pollution control from multiple perspectives, including propulsion methods, decontamination mechanisms, experimental techniques, and water monitoring. Furthermore, this paper highlights current challenges and provides insights into the future development of the industry, providing guidance on biological water pollution control.

Bacterial nanocellulose and its application in heavy metals and dyes removal: a review

This review discusses the application of bacterial nanocellulose (BNC) and modified BNC in treating wastewater containing heavy metals and dye contaminants. It also highlights the challenges and future perspectives of BNC and its composites. Untreated industrial effluents containing toxic heavy metals are systematically discharged into public waters. In particular, lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), zinc (Zn), and arsenic (As) are very harmful to human health and, in some cases, may lead to death. Several methods such as chemical precipitation, ion exchange, membrane filtration, coagulation, and Fenton oxidation are used to remove these heavy metals from the environment. However, these methods involve the use of numerous chemicals whilst producing high amount of toxic sludge. Meanwhile, the development of the adsorption-based technique has provided an alternative way of treating wastewater using BNC. Bacterial nanocellulose requires less energy for purification and has higher purity than plant cellulose. In general, the optimum growth parameters are crucial in BNC production. Even though native BNC can be used for the removal of heavy metals and dyes, the incorporation of other materials, such as polyethyleneimine, graphene oxide, calcium carbonate and polydopamine can improve sorption efficiencies.

Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis

This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.

The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

The use of chitosan-based composites for environmental remediation: A review

The presence of hazardous pollutants in water sources as a result of industrial activities is a major environmental challenge that impedes the availability of safe drinking water. Adsorptive and photocatalytic degradative removal of various pollutants in wastewater have been recognized as cost-effective and energy-efficient strategies. In addition to its biological activity, chitosan and its derivatives are considered as promising materials for the removal of various pollutants. The abundance of hydroxyl and amino groups in the chitosan macromolecular structure results in a variety of concurrent pollutant's adsorption mechanisms. Furthermore, adding chitosan to photocatalysts increases the mass transfer while decreasing both the band gap energy and the amount of intermediates produced during photocatalytic processes, improving the overall photocatalytic efficiency. Herein, we have reviewed the current design and preparation of chitosan and its composites, as well as their applications for the removal of various pollutants by adsorption and photocatalysis processes. Effects of operating variables such as the pH, catalyst mass, contact time, light wavelength, initial pollutant's concentration, and catalyst recyclability, are discussed. Various kinetic and isotherm models are presented to elucidate the rates, and mechanisms of pollutant's removal, onto chitosan-based composites, and several case studies are presented. Additionally, the antibacterial activity of chitosan-based composites has been discussed. This review aims to provide a comprehensive and up-to-date overview of the applications of chitosan-based composites in wastewater treatment and put forward new insights for the development of highly effective chitosan-based adsorbents and photocatalysts. Finally, the main challenges and future directions in the field are discussed.

Construction of SnO2/CuO/rGO nanocomposites for photocatalytic degradation of organic pollutants and antibacterial applications

Water contamination by reactive dyes is a serious concern for human health and the environment. In this study, we prepared high efficient SnO₂/CuO/rGO nanocomposites for reactive dye degradation. For structural analysis of SnO₂/CuO/rGO nanocomposites, XRD, UV-Vis DRS, SEM, TEM-EDAX, and XPS analysis were used to characterize the physicochemical properties of the material. The characterization results confirmed great crystallinity, purity, and optical characteristics features. For both Rhodamine B (RhB) and Reactive Red 120 (RR120) degradation processes, SnO₂/CuO/rGO nanocomposites were tested for their photocatalytic degradation performance. The SnO₂/CuO/rGO nanocomposites have expressed the degradation rate exposed to 99.6% of both RhB and RR120 dyes. The main reason behind the photocatalytic degradation was due to the formation of OH radical's generation by the composite materials. Moreover, the antibacterial properties of synthesized SnO₂/CuO/rGO nanocomposites were studied against E. coli, S. aureus, B. subtilis and P. aeroginosa and exhibited good antibacterial activity against the tested bacterial strains. Thus, the synthesized SnO₂/CuO/rGO nanocomposites are a promising photocatalyst and antibacterial agent. Furthermore, mechanisms behind the antibacterial effects will be ruled out in near future.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Nanofiltration Treatment of Industrial Wastewater Doped with Organic Dye: A Study of Hydrodynamics and Specific Energy

This study was conducted to eliminate the ions and molecules present in the industrial wastewater received by the municipal wastewater treatment plant (WWTP) of Reghaia, which is located east of Algiers, Algeria. The process was developed for two different study matrices: (a) the wastewater from WWTP and (b) wastewater mixed with Brilliant Blue FCF (BBF) dye to show the influence of the strength of the ionic solution on the treatment. The most effective operating parameters were determined by assessing the residence time distribution applied to the reactor flow regime. Energy analysis showed the viability of a nanofiltration membrane, improving the permeate flux. The nanofiltration process consumed 1.94 kWh/m3 to reduce the chemical oxygen demand (COD) of 63.58% and 48.35% for raw wastewater and doped BBF wastewater, respectively. The results demonstrated that nanofiltration performance with a volume dilution ratio of 1/2 showed the reduction of the COD of 87.2% after 15 min for undoped wastewater, whereas the retention rate decreases to 64% with an increase of dilution ratio to 4/5 for the same water matrix. The influence of a pH of 5 has a significant influence on the composition of the wastewater matrix by the reduction of COD of 49.8% and 59.68% for doped wastewater and raw wastewater, respectively. This could be explained by the isolated points of the membrane in the order of 4.5.