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. 2022 Feb 25:809:151169.
doi: 10.1016/j.scitotenv.2021.151169. Epub 2021 Oct 23.

SARS-CoV-2 RNA surveillance in large to small centralized wastewater treatment plants preceding the third COVID-19 resurgence in Bangkok, Thailand

Jatuwat Sangsanont  1 , Surapong Rattanakul  2 , Akechai Kongprajug  3 , Natcha Chyerochana  3 , Montakarn Sresung  3 , Nonnarit Sriporatana  4 , Nasamon Wanlapakorn  5 , Yong Poovorawan  5 , Skorn Mongkolsuk  6 , Kwanrawee Sirikanchana  7
Affiliations

SARS-CoV-2 RNA surveillance in large to small centralized wastewater treatment plants preceding the third COVID-19 resurgence in Bangkok, Thailand

Jatuwat Sangsanont et al. Sci Total Environ. .

Abstract

Wastewater surveillance for SARS-CoV-2 RNA has been a successful indicator of COVID-19 outbreaks in populations prior to clinical testing. However, this has been mostly conducted in high-income countries, which means there is a dearth of performance investigations in low- and middle-income countries with different socio-economic settings. This study evaluated the applicability of SARS-CoV-2 RNA monitoring in wastewater (n = 132) to inform COVID-19 infection in the city of Bangkok, Thailand using CDC N1 and N2 RT-qPCR assays. Wastewater influents (n = 112) and effluents (n = 20) were collected from 19 centralized wastewater treatment plants (WWTPs) comprising four large, four medium, and 11 small WWTPs during seven sampling events from January to April 2021 prior to the third COVID-19 resurgence that was officially declared in April 2021. The CDC N1 assay showed higher detection rates and mostly lower Ct values than the CDC N2. SARS-CoV-2 RNA was first detected at the first event when new reported cases were low. Increased positive detection rates preceded an increase in the number of newly reported cases and increased over time with the reported infection incidence. Wastewater surveillance (both positive rates and viral loads) showed strongest correlation with daily new COVID-19 cases at 22-24 days lag (Spearman's Rho = 0.85-1.00). Large WWTPs (serving 432,000-580,000 of the population) exhibited similar trends of viral loads and new cases to those from all 19 WWTPs, emphasizing that routine monitoring of the four large WWTPs could provide sufficient information for the city-scale dynamics. Higher sampling frequency at fewer sites, i.e., at the four representative WWTPs, is therefore suggested especially during the subsiding period of the outbreak to indicate the prevalence of COVID-19 infection, acting as an early warning of COVID-19 resurgence.

Keywords: COVID-19 pandemic; Environmental surveillance; Human sewage; Sewage treatment plants; Wastewater-based epidemiology.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Sampling locations in (a) Bangkok, Thailand; (b) large (L1 – L4; red), medium (M1 – M4; blue), and small (S1 – S11; green) municipal WWTPs.
Fig. 2
Fig. 2
Heat maps showing the (a) RT-qPCR cycle threshold values (Ct) of SARS-CoV-2 N1 gene (Ct < 38) in the wastewater influent samples from the seven sampling events conducted in the four large (L1 – L4), four medium (M1 – M4), and 11 small (S1 – S11) WWTPs with N2 gene positive detection (Ct < 40) marked by framing; (b) number of new COVID-19 cases reported daily in the districts serviced by the four large (AL1 – AL4), four medium (AM1 – AM4), and 11 small (AS1 – AS11) WWTPs. Dark cells indicate no sample collection.
Fig. 3
Fig. 3
Normalized SARS-CoV-2 RNA loads (×106 GC/PE-day) in influents of the 19 WWTPs plotted in the bar graph and compared with new clinical confirmed cases per 100,000 population from all districts in Bangkok (solid line) and in the districts served by the 19 WWTPs (dotted line). The positive detection rates of N1 and N2 genes in the wastewater samples are shown by closed circles.
Fig. 4
Fig. 4
Maps of Bangkok showing the viral loads from each of the 19 WWTPs (in circles) and the 5-day averaged new COVID-19 cases on the following sampling dates: (a) January 14, 2021, (b) January 21, 2021, (c) February 4, 2021, (d) February 17, 2021, (e) March 10, 2021, (f) March 25, 2021, and (g) April 8, 2021.
Fig. 4
Fig. 4
Maps of Bangkok showing the viral loads from each of the 19 WWTPs (in circles) and the 5-day averaged new COVID-19 cases on the following sampling dates: (a) January 14, 2021, (b) January 21, 2021, (c) February 4, 2021, (d) February 17, 2021, (e) March 10, 2021, (f) March 25, 2021, and (g) April 8, 2021.
Fig. 5
Fig. 5
Spearman's rank correlation coefficients (Spearman's Rho) between virus positive detection rates and 5-day averaged new cases from all corresponding served districts.
Fig. 6
Fig. 6
Spearman's rank correlation coefficients (Spearman's Rho) between viral loads (log10 GC/d) and 5-day averaged new cases at different lag times from 0 to 40 days for (a) all 19 WWTPs and (b) large, medium, and small WWTPs.
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