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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".