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Community-Scale Surveillance of SARS-CoV-2: Optimizing Sampling Strategies for Centralized Wastewater Treatment Plants.
- Source :
-
Environmental Engineering Science . Jan2024, Vol. 41 Issue 1, p7-17. 11p. - Publication Year :
- 2024
-
Abstract
- Wastewater-based surveillance (WBS) for SARS-CoV-2, the virus that causes COVID-19, is a complement to clinical testing that can provide an early warning of community infection. Applying WBS to centralized wastewater treatment plants (WWTPs), otherwise known as water resource recovery facilities, provides an opportunity to collect unbiased viral load information at the community scale. This study focuses on optimizing the sampling strategies from centralized WWTPs to best capture the signal of SARS-CoV-2 in wastewater. From November 2020 to February 2021, a total of 166 grab and 24-h composite wastewater and sludge samples were collected from 3 WWTPs in the Los Angeles (LA) County, with significant differences in size, service area, wastewater composition, and treatment technologies. Results showed that 24-h composite primary influent samples can best represent the trends of SARS-CoV-2 genome concentrations in centralized WWTPs, whereas grab samples showed significant variations in SARS-CoV-2 genome concentrations over a 24-h period. Primary sludge samples showed one log10 higher concentrations of SARS-CoV-2 genomes when compared with primary influent samples. The SARS-CoV-2 genome concentrations in primary influent samples ranged from 2.69 × 102 genome copies (GC)/mL to a high of 4.50 × 103 GC/mL. The virus concentrations in primary sludge ranged from 6.00 × 103 to 9.67 × 104 GC/mL. Ultimately, the SARS-CoV-2 genome concentrations in wastewater and sludge collected from large centralized WWTPs were correlated, and both reflected the general trend of COVID-19 cases in the broader LA metropolitan community. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10928758
- Volume :
- 41
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Environmental Engineering Science
- Publication Type :
- Academic Journal
- Accession number :
- 174909658
- Full Text :
- https://doi.org/10.1089/ees.2023.0142