Lognormal distributions capture site-specific variability in enteric virus concentrations in wastewater

As more data on virus concentrations in influent water from wastewater treatment plants (WWTPs) becomes available, establishing best practices for virus measurements, monitoring, and statistical modelling can improve the understanding of virus concentration distributions in wastewater. To support this, we assessed the temporal variability of norovirus, adenovirus, enterovirus, and rotavirus concentrations in influent water across multiple WWTPs in Switzerland, the USA, and Japan. Our findings demonstrate that the lognormal distribution accurately describes temporal variations in concentrations for all viruses at all sites, outperforming the gamma and Weibull distributions, which fail to capture high variability. However, notable differences in variability and uncertainty were observed across systems, underscoring the need for site-specific assessments. Using lognormal parameters, we identified optimal monitoring frequencies that balance cost-effectiveness and precision. For most sites, weekly monitoring was sufficient to estimate the annual average concentration of enteric viruses within a 95% confidence interval of 0.5 log<sub>10</sub>. We further examined the mechanistic basis of the lognormal distribution, highlighting processes that drive its prevalence and shape the behavior of its upper tail. By integrating these insights, this study provides a statistical foundation for optimizing virus monitoring frameworks and informing public health interventions targeting wastewater systems.

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Publication Details

Journal
Environmental Science Water Research & Technology
Published
2025-01-01
DOI
https://doi.org/10.1039/d5ew00286a
Citations
1
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
1.40

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article

Lognormal distributions capture site-specific variability in enteric virus concentrations in wastewater

Alexander J. Devaux, Htet Kyi Wynn, Tamar Kohn, Shotaro Torii et al.
1 citations
Environmental Science Water Research & Technology
SARS-CoV-2 detection and testing
1.40
article

Lognormal distributions capture site-specific variability in enteric virus concentrations in wastewater

Alexander J. Devaux, Htet Kyi Wynn, Tamar Kohn, Shotaro Torii, Chaojie Li
article en
1 citations

Abstract

As more data on virus concentrations in influent water from wastewater treatment plants (WWTPs) becomes available, establishing best practices for virus measurements, monitoring, and statistical modelling can improve the understanding of virus concentration distributions in wastewater. To support this, we assessed the temporal variability of norovirus, adenovirus, enterovirus, and rotavirus concentrations in influent water across multiple WWTPs in Switzerland, the USA, and Japan. Our findings demonstrate that the lognormal distribution accurately describes temporal variations in concentrations for all viruses at all sites, outperforming the gamma and Weibull distributions, which fail to capture high variability. However, notable differences in variability and uncertainty were observed across systems, underscoring the need for site-specific assessments. Using lognormal parameters, we identified optimal monitoring frequencies that balance cost-effectiveness and precision. For most sites, weekly monitoring was sufficient to estimate the annual average concentration of enteric viruses within a 95% confidence interval of 0.5 log<sub>10</sub>. We further examined the mechanistic basis of the lognormal distribution, highlighting processes that drive its prevalence and shape the behavior of its upper tail. By integrating these insights, this study provides a statistical foundation for optimizing virus monitoring frameworks and informing public health interventions targeting wastewater systems.

Environmental Science Water Research & TechnologyVol. 11(12)
Swiss Tropical and Public Health Institute (CH), University of Basel (CH), KWR Water Research Institute (NL), École Polytechnique Fédérale de Lausanne (CH), Swiss Federal Institute of Aquatic Science and Technology (CH), Delft University of Technology (NL)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Bundesamt für Gesundheit, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 15%
SARS-CoV-2 detection and testing
1.40
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