@article{1d7c8a4c56d24c4085a82b3e3a1ffeb8,
title = "Detection and quantification of SARS-CoV-2 RNA in wastewater and treated effluents: Surveillance of COVID-19 epidemic in the United Arab Emirates",
abstract = "Testing SARS-CoV-2 viral loads in wastewater has recently emerged as a method of tracking the prevalence of the virus and an early-warning tool for predicting outbreaks in the future. This study reports SARS-CoV-2 viral load in wastewater influents and treated effluents of 11 wastewater treatment plants (WWTPs), as well as untreated wastewater from 38 various locations, in the United Arab Emirates (UAE) in May and June 2020. Composite samples collected over twenty-four hours were thermally deactivated for safety, followed by viral concentration using ultrafiltration, RNA extraction using commercially available kits, and viral quantification using RT-qPCR. Furthermore, estimates of the prevalence of SARS-CoV-2 infection in different regions were simulated using Monte Carlo. Results showed that the viral load in wastewater influents from these WWTPs ranged from 7.50E+02 to over 3.40E+04 viral gene copies/L with some plants having no detectable viral RNA by RT-qPCR. The virus was also detected in 85% of untreated wastewater samples taken from different locations across the country, with viral loads in positive samples ranging between 2.86E+02 and over 2.90E+04 gene copies/L. It was also observed that the precautionary measures implemented by the UAE government correlated with a drop in the measured viral load in wastewater samples, which were in line with the reduction of COVID-19 cases reported in the population. Importantly, none of the 11 WWTPs' effluents tested positive during the entire sampling period, indicating that the treatment technologies used in the UAE are efficient in degrading SARS-CoV-2, and confirming the safety of treated re-used water in the country. SARS-CoV-2 wastewater testing has the potential to aid in monitoring or predicting an outbreak location and can shed light on the extent viral spread at the community level.",
keywords = "Detection, Quantification, SARS-CoV-2, Surveillance, UAE, Wastewater",
author = "Hasan, {Shadi W.} and Yazan Ibrahim and Marianne Daou and Hussein Kannout and Nila Jan and Alvaro Lopes and Habiba Alsafar and Yousef, {Ahmed F.}",
note = "Funding Information: The authors are thankful to the Ministry of Interior (MOI) in Abu Dhabi (UAE) for their financial support (Award No. CPRA-2020-027 ), their immense efforts in coordination and bringing the teams at Khalifa University of Science and Technology (KU), Department of Energy (DOE) and Abu Dhabi Police (ADP) (Abu Dhabi – UAE) together in this successful collaboration. The collaborative efforts of the MOI, DOE and ADP under the umbrella of the National Emergency Crisis and Disaster Management Authority (NCEMA) in sample collection, handling and transportation are highly appreciated. We would also like to thank the Khalifa University Center for Membranes and Advanced Water Technology (CMAT), and the Center for Biotechnology (BTC). Finally, the contributions of Osama Alhamoudi, Aamer Alshehhi, Ismail Alhammadi, Ahmed Alafifi from ADP as well as Halawa Alshehhi, Malika Saif, Amna Alzaabi, Noora Taher from MOI are acknowledged. Funding Information: The authors are thankful to the Ministry of Interior (MOI) in Abu Dhabi (UAE) for their financial support (Award No. CPRA-2020-027), their immense efforts in coordination and bringing the teams at Khalifa University of Science and Technology (KU), Department of Energy (DOE) and Abu Dhabi Police (ADP) (Abu Dhabi – UAE) together in this successful collaboration. The collaborative efforts of the MOI, DOE and ADP under the umbrella of the National Emergency Crisis and Disaster Management Authority (NCEMA) in sample collection, handling and transportation are highly appreciated. We would also like to thank the Khalifa University Center for Membranes and Advanced Water Technology (CMAT), and the Center for Biotechnology (BTC). Finally, the contributions of Osama Alhamoudi, Aamer Alshehhi, Ismail Alhammadi, Ahmed Alafifi from ADP as well as Halawa Alshehhi, Malika Saif, Amna Alzaabi, Noora Taher from MOI are acknowledged. Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",
year = "2021",
month = apr,
day = "10",
doi = "10.1016/j.scitotenv.2020.142929",
language = "British English",
volume = "764",
journal = "Science of the Total Environment",
issn = "0048-9697",
publisher = "Elsevier",
}