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

Zahmatkesh S 1 , Rezakhani Y 2 , Chofreh AG 3 , Karimian M 4 , Wang C 5 , Ghodrati I 6 Show all authors , Hasan M 7 , Sillanpaa M 8 , Panchal H 9 , Khan R 10

Affiliations 

  • 1 Department of Chemical Engineering, University of Science and Technology of Mazandaran, P.O. Box 48518-78195, Behshahr, Iran; Tecnologico de Monterrey, Escuela de Ingenieríay Ciencias, Puebla, Mexico. Electronic address: sasan-zahmatkesh@mazust.ac.ir
  • 2 Department of Civil Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran
  • 3 Sustainable Process Integration Laboratory, SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology, VUT Brno, Technická 2896/2, 616 00, Brno, Czech Republic
  • 4 Faculty of Civil Engineering, Architecture and Urban Planning, University of Eyvanekey, Iran
  • 5 School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China
  • 6 Department of Computer Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran
  • 7 Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia
  • 8 Faculty of Science and Technology, School of Applied Physics, University Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia; International Research Centre of Nanotechnology for Himalayan Sustainability (IRCNHS), Shoolini University, Solan, 173212, Himachal Pradesh, India; Department of Chemical Engineering, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, P. O. Box 17011, Doornfontein, 2028, South Africa
  • 9 Mechanical Engineering Department, Government Engineering College, Patan, Gujarat, India
  • 10 Faculty of Civil Engineering, Institute of Technology, Shri Ramswaroop Memorial University, Barabanki, 225003, UP, India
Chemosphere, 2023 Jan;310:136837.
PMID: 36252897 DOI: 10.1016/j.chemosphere.2022.136837

Abstract

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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