Wastewater treatment with algal based membrane bioreactor for the future: Removing emerging containments.

The difficulty of developing pollutants in aquatic ecosystems and their potential effects on animals and plants have been raised. Sewage effluent can seriously harm a river's plant and animal life by reducing the water's oxygen content. Due to their increasing use and poor elimination in traditional municipal wastewater treatment plants (WWTPs), pharmaceuticals are one of the developing pollutants that have the potential to penetrate aquatic ecosystems. Due to undigested pharmaceuticals and their metabolites, which constitute a significant class of potentially hazardous aquatic pollutants. Using an algae-based membrane bioreactor (AMBR), the primary objective of this research was to eliminate emerging contaminants (ECs) identified in municipal wastewater. The first part of this research covers the basics of growing algae, an explanation of how they work, and how they remove ECs. Second, it develops the membrane in the wastewater, explains its workings, and uses the membrane to remove ECs. Finally, an algae-based membrane bioreactor for removing ECs is examined. As a result, daily algal production using AMBR technology might range from 50 to 100 mg/Liter. These kinds of machines are capable of nitrogen and phosphorus removal efficiencies of 30-97% and 46-93%, respectively.

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.

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.

MRI plaque imaging detects carotid plaques with a high risk for future cerebrovascular events in asymptomatic patients.

PURPOSE: The aim of this study was to investigate prospectively whether MRI plaque imaging can identify patients with asymptomatic carotid artery stenosis who have an increased risk for future cerebral events. MRI plaque imaging allows categorization of carotid stenosis into different lesion types (I-VIII). Within these lesion types, lesion types IV-V and VI are regarded as rupture-prone plaques, whereas the other lesion types represent stable ones. METHODS: Eighty-three consecutive patients (45 male (54.2%); age 54-88 years (mean 73.2 years)) presenting with an asymptomatic carotid stenosis of 50-99% according to ECST-criteria were recruited. Patients were imaged with a 1.5-T scanner. T1-, T2-, time-of-flight-, and proton-density weighted studies were performed. The carotid plaques were classified as lesion type I-VIII. Clinical endpoints were ischemic stroke, TIA or amaurosis fugax. Survival analysis and log rank test were used to ascertain statistical significance. RESULTS: Six out of 83 patients (7.2%) were excluded: 4 patients had insufficient MR image quality; 1 patient was lost-to-follow-up; 1 patient died shortly after the baseline MRI plaque imaging. The following results were obtained by analyzing the remaining 77 patients. The mean time of follow-up was 41.1 months. During follow-up, n = 9 (11.7%) ipsilateral ischemic cerebrovascular events occurred. Only patients presenting with the high-risk lesion types IV-V and VI developed an ipsilateral cerebrovascular event versus none of the patients presenting with the stable lesion types III, VII, and VIII (n = 9 (11.7%) vs. n = 0 (0%) during follow-up). Event-free survival was higher among patients with the MRI-defined stable lesion types (III, VII, and VIII) than in patients with the high-risk lesion types (IV-V and VI) (log rank test P<0.0001). CONCLUSIONS: MRI plaque imaging has the potential to identify patients with asymptomatic carotid stenosis who are particularly at risk of developing future cerebral ischemia. MRI could improve selection criteria for invasive therapy in the future.