Wastewater treatment with nanomaterials for the future: A state-of-the-art review.

Aquatic and terrestrial ecosystems are both threatened by toxic wastewater. The unique properties of nanomaterials are currently being studied thoroughly for treating sewage. Nanomaterials also have the advantage of being capable of removing organic matter, fungi, and viruses from wastewater. Advanced oxidation processes are used in nanomaterials to treat wastewater. Additionally, nanomaterials have a large effective area of contact due to their tiny dimensions. The adsorption and reactivity of nanomaterials are strong. Wastewater treatment would benefit from the development of nanomaterial technology. Second, the paper provides a comprehensive analysis of the unique characteristics of nanomaterials in wastewater treatment, their proper use, and their prospects. In addition to focusing on their economic feasibility, since limited forms of nanomaterials have been manufactured, it is also necessary to consider their feasibility in terms of their technical results. According to this study, the significant adsorption area, excellent chemical reaction, and electrical conductivity of nanoparticles (NPs) contribute to the successful treatment of wastewater.

SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks.

The COVID-19 outbreak led to the discovery of SARS-CoV-2 in sewage; thus, wastewater treatment plants (WWTPs) could have the virus in their effluent. However, whether SARS-CoV-2 is eradicated by sewage treatment is virtually unknown. Specifically, the objectives of this study include (i) determining whether a mixed matrixed membrane (MMM) is able to remove SARS-CoV-2 (polycarbonate (PC)-hydrous manganese oxide (HMO) and PC-silver nanoparticles (Ag-NP)), (ii) comparing filtration performance among different secondary treatment processes, and (iii) evaluating whether artificial neural networks (ANNs) can be employed as performance indicators to reduce SARS-CoV-2 in the treatment of sewage. At Shariati Hospital in Mashhad, Iran, secondary treatment effluent during the outbreak of COVID-19 was collected from a WWTP. There were two PC-Ag-NP and PC-HMO processes at the WWTP targeted. RT-qPCR was employed to detect the presence of SARS-CoV-2 in sewage fractions. For the purposes of determining SARS-CoV-2 prevalence rates in the treated effluent, 10 L of effluent specimens were collected in middle-risk and low-risk treatment MMMs. For PC-HMO, the log reduction value (LRV) for SARS-CoV-2 was 1.3-1 log10 for moderate risk and 0.96-1 log10 for low risk, whereas for PC-Ag-NP, the LRV was 0.99-1.3 log10 for moderate risk and 0.94-0.98 log10 for low risk. MMMs demonstrated the most robust absorption performance during the sampling period, with the least significant LRV recorded in PC-Ag-NP and PC-HMO at 0.94 log10 and 0.96 log10, respectively.

A comprehensive review of various approaches for treatment of tertiary wastewater with emerging contaminants: what do we know?

In the last few decades, environmental contaminants (ECs) have been introduced into the environment at an alarming rate. There is a risk to human health and aquatic ecosystems from trace levels of emerging contaminants, including hospital wastewater (HPWW), cosmetics, personal care products, endocrine system disruptors, and their transformation products. Despite the fact that these pollutants have been introduced or detected relatively recently, information about their characteristics, actions, and impacts is limited, as are the technologies to eliminate them efficiently. A wastewater recycling system is capable of providing irrigation water for crops and municipal sewage treatment, so removing ECs before wastewater reuse is essential. Water treatment processes containing advanced ions of biotic origin and ECs of biotic origin are highly recommended for contaminants. This study introduces the fundamentals of the treatment of tertiary wastewater, including membranes, filtration, UV (ultraviolet) irradiation, ozonation, chlorination, advanced oxidation processes, activated carbon (AC), and algae. Next, a detailed description of recent developments and innovations in each component of the emerging contaminant removal process is provided.

An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese oxide and silver nanoparticles: A novel application of artificial neural network based simulation in MATLAB.

This study aimed to determine the efficacy of novel ultrafiltration and mixed matrix membrane (MMM) composed of hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) for the removal of biological oxygen demand (BOD) and chemical oxygen demand (COD). In the polycarbonate (PC) MMM, the weight percent of HMO and Ag-NP has been increased from 5% to 10%. A neural network (ANN) was used in this study to compare PC-HMO and Ag-NP. MMM was evaluated in combination with HMO and Ag-NP loadings in order to assess their effects on pure water flux, mean pore size, porosity, and efficacy in removing BOD and COD. HMO and Ag-NPs can decrease membrane porosity in the casting solution while increasing mean pore size. According to the study's findings, the artificial neural network model appears to be highly appropriate for predicting the removal of BOD and COD. To develop a successful model, a suitable input dataset was selected, which consisted of BOD and COD. An ideal model architecture for MMM was proposed based on an optimal number of hidden layers (2 layers) and neurons (5-8 neurons). Experiments and predicted data show a strong correlation between the developed models. BOD was predicted with an excellent R2 and a low root mean square error (RMSE) of 0.99 and 0.05%, respectively, while COD was predicted with an excellent R2 and a low RMSE of 0.99 and 0.09%, respectively. Based on the results, Ag-NP was found to be an excellent candidate for the preparation of MMMs as well as convenient for the removal of BOD and COD from polluted water sources.

Review of concerned SARS-CoV-2 variants like Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529), as well as novel methods for reducing and inactivating SARS-CoV-2 mutants in wastewater treatment facilities.

The coronavirus known as COVID-19, which causes pandemics, is causing a global epidemic at a critical stage today. Furthermore, novel mutations in the SARS-CoV-2 spike protein have been discovered in an entirely new strain, impacting the clinical and epidemiological features of COVID-19. Variants of these viruses can increase the transmission in wastewater, lead to reinfection, and reduce immunity provided by monoclonal antibodies and vaccinations. According to the research, a large quantity of viral RNA was discovered in wastewater, suggesting that wastewater can be a crucial source of epidemiological data and health hazards. The purpose of this paper is to introduce a few basic concepts regarding wastewater surveillance as a starting point for comprehending COVID-19's epidemiological aspects. Next, the observation of Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529) in wastewater is discussed in detail. Secondly, the essential information for the initial, primary, and final treating sewage in SARS-CoV-2 is introduced. Following that, a thorough examination is provided to highlight the newly developed methods for eradicating SARS-CoV-2 using a combination of solar water disinfection (SODIS) and ultraviolet radiation A (UVA (315-400 nm)), ultraviolet radiation B (UVB (280-315 nm)), and ultraviolet radiation C (UVC (100-280 nm)) processes. SARS-CoV-2 eradication requires high temperatures (above 56°C) and UVC. However, SODIS technologies are based on UVA and operate at cooler temperatures (less than 45°C). Hence, it is not appropriate for sewage treatment (or water consumption) to be conducted using SODIS methods in the current pandemic. Finally, SARS-CoV-2 may be discovered in sewage utilizing the wastewater-based epidemiology (WBE) monitoring method.