Detection of fecal coliforms and SARS-CoV-2 RNA in sewage and recreational waters in the Ecuadorian Coast: a call for improving water quality regulation

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Abstract

Wastewater surveillance represents an alternative approach for the diagnosis and early detection of infectious agents of public health importance. This study aimed to evaluate SARS-CoV-2 and other quality markers in oxidation lagoons, estuarine areas and seawater at Guayas and Santa Elena in Ecuador. Sample collections were conducted twice at 42 coastal sites and 2 oxidation lagoons during dry and rainy seasons (2020-2021). Physico-chemical and microbiological parameters were evaluated to determine organic pollution. Quantitative reverse transcription PCR was conducted to detect SARS-CoV-2. Results showed high levels of Escherichia coli and low dissolved oxygen concentrations. SARS-CoV-2 was detected in sea-waters and estuaries with salinity levels between 34.2-36.4 PSU and 28.8 °C-31.3 °C. High amounts of fecal coliforms were detected and correlated with the SARS-CoV-2 shedding. We recommend to decentralized autonomous governments in developing countries such as Ecuador to implement corrective actions and establish medium-term mechanisms to minimize a potential contamination route.

HIGHLIGHTS

  • SARS-CoV-2 RNA was detected in estuaries, bays and the wastewater treatment systems in Playas and Santa Elena.

  • High levels of fecal coliforms were detected along shorelines.

  • Water quality parameters revealed a negative impact on the beaches studied associated with human activities.

Article activity feed

  1. SciScore for 10.1101/2022.01.04.22268771: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsField Sample Permit: Water sampling and in situ analyses: A total of 84 surface seawater samples were collected at 15 beaches (A1-O22) and two oxidation lagoons, in Playas (OL-A) and Punta Carnero (OL-B) (Figure 1, Table 1) between July 18th, 2020 and January 30th, 2021.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.