Does normalization of SARS-CoV-2 concentrations by Pepper Mild Mottle Virus improve correlations and lead time between wastewater surveillance and clinical data in Alberta (Canada) : comparing twelve SARS-CoV-2 normalization approaches
Copyright © 2022. Published by Elsevier B.V..
Wastewater-based surveillance (WBS) data normalization is an analyte measurement correction that addresses variations resulting from dilution of fecal discharge by non-sanitary sewage, stormwater or groundwater infiltration. No consensus exists on what WBS normalization parameters result in the strongest correlations and lead time between SARS-CoV-2 WBS data and COVID-19 cases. This study compared flow, population size and biomarker normalization impacts on the correlations and lead times for ten communities in twelve sewersheds in Alberta (Canada) between September 2020 and October 2021 (n = 1024) to determine if normalization by Pepper Mild Mottle Virus (PMMoV) provides any advantages compared to other normalization parameters (e.g., flow, reported and dynamic population sizes, BOD, TSS, NH3, TP). PMMoV concentrations (GC/mL) corresponded with plant influent flows and were highest in the urban centres. SARS-CoV-2 target genes E, N1 and N2 were all negatively associated with wastewater influent pH, while PMMoV was positively associated with temperature. Pooled data analysis showed that normalization increased ρ-values by almost 0.1 and was highest for ammonia, TKN and TP followed by PMMoV. Normalization by other parameters weakened associations. None of the differences were statistically significant. Site-specific correlations showed that normalization of SARS-CoV-2 data by PMMoV only improved correlations significantly in two of the twelve systems; neither were large sewersheds or combined sewer systems. In five systems, normalization by traditional wastewater strength parameters and dynamic population estimates improved correlations. Lead time ranged between 1 and 4 days in both pooled and site-specific comparisons. We recommend that WBS researchers and health departments: a) Investigate WWTP influent properties (e.g., pH) in the WBS planning phase and use at least two parallel approaches for normalization only if shown to provide value; b) Explore normalization by wastewater strength parameters and dynamic population size estimates further; and c) Evaluate purchasing an influent flow meter in small communities to support long-term WBS efforts and WWTP management.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:856 |
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| Enthalten in: |
The Science of the total environment - 856(2023), Pt 1 vom: 15. Jan., Seite 158964 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Maal-Bared, Rasha [VerfasserIn] |
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| Links: |
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| Themen: |
2P299V784P |
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| Anmerkungen: |
Date Completed 23.11.2022 Date Revised 16.01.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.scitotenv.2022.158964 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM346827442 |
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| 245 | 1 | 0 | |a Does normalization of SARS-CoV-2 concentrations by Pepper Mild Mottle Virus improve correlations and lead time between wastewater surveillance and clinical data in Alberta (Canada) |b comparing twelve SARS-CoV-2 normalization approaches |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022. Published by Elsevier B.V. | ||
| 520 | |a Wastewater-based surveillance (WBS) data normalization is an analyte measurement correction that addresses variations resulting from dilution of fecal discharge by non-sanitary sewage, stormwater or groundwater infiltration. No consensus exists on what WBS normalization parameters result in the strongest correlations and lead time between SARS-CoV-2 WBS data and COVID-19 cases. This study compared flow, population size and biomarker normalization impacts on the correlations and lead times for ten communities in twelve sewersheds in Alberta (Canada) between September 2020 and October 2021 (n = 1024) to determine if normalization by Pepper Mild Mottle Virus (PMMoV) provides any advantages compared to other normalization parameters (e.g., flow, reported and dynamic population sizes, BOD, TSS, NH3, TP). PMMoV concentrations (GC/mL) corresponded with plant influent flows and were highest in the urban centres. SARS-CoV-2 target genes E, N1 and N2 were all negatively associated with wastewater influent pH, while PMMoV was positively associated with temperature. Pooled data analysis showed that normalization increased ρ-values by almost 0.1 and was highest for ammonia, TKN and TP followed by PMMoV. Normalization by other parameters weakened associations. None of the differences were statistically significant. Site-specific correlations showed that normalization of SARS-CoV-2 data by PMMoV only improved correlations significantly in two of the twelve systems; neither were large sewersheds or combined sewer systems. In five systems, normalization by traditional wastewater strength parameters and dynamic population estimates improved correlations. Lead time ranged between 1 and 4 days in both pooled and site-specific comparisons. We recommend that WBS researchers and health departments: a) Investigate WWTP influent properties (e.g., pH) in the WBS planning phase and use at least two parallel approaches for normalization only if shown to provide value; b) Explore normalization by wastewater strength parameters and dynamic population size estimates further; and c) Evaluate purchasing an influent flow meter in small communities to support long-term WBS efforts and WWTP management | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Combined sewers | |
| 650 | 4 | |a Industrial wastewater | |
| 650 | 4 | |a Mobile populations | |
| 650 | 4 | |a Normalization | |
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| 700 | 1 | |a Qiu, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Qiaozhi |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Tiejun |e verfasserin |4 aut | |
| 700 | 1 | |a Hrudey, Steve E |e verfasserin |4 aut | |
| 700 | 1 | |a Bhavanam, Sudha |e verfasserin |4 aut | |
| 700 | 1 | |a Ruecker, Norma J |e verfasserin |4 aut | |
| 700 | 1 | |a Ellehoj, Erik |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Bonita E |e verfasserin |4 aut | |
| 700 | 1 | |a Pang, Xiaoli |e verfasserin |4 aut | |
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