Wastewater solids drive comparability of sampling methods for SARS-CoV-2 wastewater and environmental surveillance

  • Md Pervez Kabir
  • , Elizabeth Renouf
  • , Lakshmi Pisharody
  • , Élisabeth Mercier
  • , Patrick M. D'Aoust
  • , Shen Wan
  • , Nada Hegazy
  • , Tram Nguyen
  • , Chandler Wong
  • , Felix Addo
  • , Emma Tomalty
  • , Tyson E. Graber
  • , Robert Delatolla

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Passive sampling has proven to be a reliable and cost-effective method in wastewater and environmental surveillance (WES) during the COVID-19 pandemic. In passive sampling, wastewater solids were collected from the wastewater networks, homogenized in solutes and analyzed the supernatant to measure the SARS-CoV-2 RNA concentrations in wastewater. However, the direct impact of wastewater solids content collected via passive sampling on SARS-CoV-2 RNA measurements has not been previously evaluated. In this study, we analyzed wastewater solids collected using Auto, Torpedo, COSCa-ball samplers, and primary sludge samples from a wastewater treatment plant to measure SARS-CoV-2 RNA concentrations in wastewater. Results showed significant variation (p < 0.05) in wastewater solids content (i.e., TS and VS) across Auto, Torpedo, COSCa-ball samplers, and primary sludge samples. Despite differences in solids content, SARS-CoV-2 RNA concentrations in wastewater solids from passive samplers can be effectively compared (p > 0.05) to autosampler and primary sludge samples. To evaluate the influences of wastewater solids content on SARS-CoV-2 RNA measurement, we used a linear mixed-effects model. The model demonstrated that wastewater solids content had no direct effect on SARS-CoV-2 RNA measurements across the sampling methods and primary sludge samples. Overall, this study established a standardized experimental approach for implementing passive samplers as a viable alternative to conventional autosampler in WES for emerging pathogens.

Original languageEnglish
Article number115374
JournalJournal of Environmental Chemical Engineering
Volume13
Issue number1
DOIs
StatePublished - 1 Feb 2025
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 6 - Clean Water and Sanitation
    SDG 6 Clean Water and Sanitation

Keywords

  • Autosampler
  • Linear fixed effects model
  • Passive sampling
  • Primary sludge
  • Solids content

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Waste Management and Disposal
  • Pollution
  • Process Chemistry and Technology

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