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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

S. Zahmatkesh, Y. Rezakhani, AG. Chofreh, M. Karimian, C. Wang, I. Ghodrati, M. Hasan, M. Sillanpaa, H. Panchal, R. Khan

. 2023 ; 310 (-) : 136837. [pub] 20221014

Language English Country England, Great Britain

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc22032057

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.

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$a The COVID-19 outbreak led to the discovery of SARS-CoV-2 in sewage; thus, wastewater treatment plants (WWTPs) could have the virus in their effluent. However, whether SARS-CoV-2 is eradicated by sewage treatment is virtually unknown. Specifically, the objectives of this study include (i) determining whether a mixed matrixed membrane (MMM) is able to remove SARS-CoV-2 (polycarbonate (PC)-hydrous manganese oxide (HMO) and PC-silver nanoparticles (Ag-NP)), (ii) comparing filtration performance among different secondary treatment processes, and (iii) evaluating whether artificial neural networks (ANNs) can be employed as performance indicators to reduce SARS-CoV-2 in the treatment of sewage. At Shariati Hospital in Mashhad, Iran, secondary treatment effluent during the outbreak of COVID-19 was collected from a WWTP. There were two PC-Ag-NP and PC-HMO processes at the WWTP targeted. RT-qPCR was employed to detect the presence of SARS-CoV-2 in sewage fractions. For the purposes of determining SARS-CoV-2 prevalence rates in the treated effluent, 10 L of effluent specimens were collected in middle-risk and low-risk treatment MMMs. For PC-HMO, the log reduction value (LRV) for SARS-CoV-2 was 1.3-1 log10 for moderate risk and 0.96-1 log10 for low risk, whereas for PC-Ag-NP, the LRV was 0.99-1.3 log10 for moderate risk and 0.94-0.98 log10 for low risk. MMMs demonstrated the most robust absorption performance during the sampling period, with the least significant LRV recorded in PC-Ag-NP and PC-HMO at 0.94 log10 and 0.96 log10, respectively.
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$a Rezakhani, Yousof $u Department of Civil Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran
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$a Chofreh, Abdoulmohammad Gholamzadeh $u Sustainable Process Integration Laboratory, SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology, VUT Brno, Technická 2896/2, 616 00, Brno, Czech Republic
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$a Karimian, Melika $u Faculty of Civil Engineering, Architecture and Urban Planning, University of Eyvanekey, Iran
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$a Wang, Chongqing $u School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China
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$a Ghodrati, Iman $u Department of Computer Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran
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$a Hasan, Mudassir $u Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia
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$a Panchal, Hitesh $u Mechanical Engineering Department, Government Engineering College, Patan, Gujarat, India
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$a Khan, Ramsha $u Faculty of Civil Engineering, Institute of Technology, Shri Ramswaroop Memorial University, Barabanki, 225003, UP, India
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