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  5. A physicochemical model for rationalizing SARS-CoV-2 concentration in sewage. Case study: The city of Thessaloniki in Greece

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Article
en
2020

A physicochemical model for rationalizing SARS-CoV-2 concentration in sewage. Case study: The city of Thessaloniki in Greece

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en
2020
Vol 755
Vol. 755
DOI: 10.1016/j.scitotenv.2020.142855

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Maria Petala
Maria Petala

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Maria Petala
Dimitra Dafou
Margaritis Kostoglou
+13 more

Abstract

Detection of SARS-CoV-2 in sewage has been employed by several researchers as an alternative early warning indicator of virus spreading in communities, covering both symptomatic and asymptomatic cases. A factor that can seriously mislead the quantitative measurement of viral copies in sewage is the adsorption of virus fragments onto the highly porous solids suspended in wastewater, making them inaccessible. This depends not only on the available amount of suspended solids, but also on the amount of other dissolved chemicals which may influence the capacity of adsorption. On this account, the present work develops a mathematical framework, at various degrees of spatial complexity, of a physicochemical model that rationalizes the quantitative measurements of total virus fragments in sewage as regards the adsorption of virus onto suspended solids and the effect of dissolved chemicals on it. The city of Thessaloniki in Greece is employed as a convenient case study to determine the values of model variables. The present data indicate the ratio of the specific absorption (UV

How to cite this publication

Maria Petala, Dimitra Dafou, Margaritis Kostoglou, Thodoris D. Karapantsios, Ειrini Kanata, A. Chatziefstathiou, Foteini Sakaveli, K. Kotoulas, Minas Arsenakis, Emmanuel Roilides, Theodoros Sklaviadis, Symeon Metallidis, Anna Papa, Efstratios Stylianidis, Athanasios N. Papadopoulos, Nikolaos Papaioannou (2020). A physicochemical model for rationalizing SARS-CoV-2 concentration in sewage. Case study: The city of Thessaloniki in Greece. , 755, DOI: https://doi.org/10.1016/j.scitotenv.2020.142855.

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Publication Details

Type

Article

Year

2020

Authors

16

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1016/j.scitotenv.2020.142855

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