Case Study: Impact of Diurnal Variations and Stormwater Dilution on SARS-CoV-2 RNA Signal Intensity at Neighborhood Scale Wastewater Pumping Stations.
Bao Nguyen QuocPrakit SaingamRaymond RedCornJohn A CarterTanisha JainPieter CandryMeghan GattusoMeei-Li W HuangAlexander L GreningerJohn Scott MeschkeAndrew BryanMari K H WinklerPublished in: ACS ES&T water (2022)
Wastewater based epidemiology (WBE) has emerged as a tool to track the spread of SARS-CoV-2. However, sampling at wastewater treatment plants (WWTPs) cannot identify transmission hotspots within a city. Here, we sought to understand the diurnal variations (24 h) in SARS-CoV-2 RNA titers at the neighborhood level, using pump stations that serve vulnerable communities (e.g., essential workers, more diverse communities). Hourly composite samples were collected from wastewater pump stations located in (i) a residential area and (ii) a shopping district. In the residential area, SARS-CoV-2 RNA concentration (N1, N2, and E assays) varied by up to 42-fold within a 24 h period. The highest viral load was observed between 5 and 7 am, when viral RNA was not diluted by stormwater. Normalizing peak concentrations during this time window with nutrient concentrations (N and P) enabled correcting for rainfall to connect sewage to clinical cases reported in the sewershed. Data from the shopping district pump station were inconsistent, probably due to the fluctuation of customers shopping at the mall. This work indicates pump stations serving the residential area offer a narrow time period of high signal intensity that could improve the sensitivity of WBE, and tracer compounds (N, P concentration) can be used to normalize SARS-CoV-2 signals during rainfall.
Keyphrases
- sars cov
- wastewater treatment
- respiratory syndrome coronavirus
- antibiotic resistance genes
- air pollution
- physical activity
- south africa
- nucleic acid
- high throughput
- liquid chromatography tandem mass spectrometry
- positron emission tomography
- high resolution
- mass spectrometry
- electronic health record
- big data
- simultaneous determination