Impact of wastewater characteristics and weather events on the N2 and N1 gene target ratios during wastewater surveillance of SARS-CoV-2 at five treatment plants and an upper sewershed location
Bibliographic record
Abstract
Recent fluctuations in the N2/N1 gene target ratios of SARS-CoV-2 were observed in wastewater, even when the dominant variant remained unchanged. This suggests that N2/N1 variations are influenced not only by viral mutations but also by external factors. While previous studies examined the effects of wastewater parameters on SARS-CoV-2 concentrations, there remains a knowledge gap regarding their specific impact on different gene targets. The main objective of this study was to identify external factors that contribute to the observed changes in the N2/N1 ratios in wastewater apart from reduced PCR sensitivity caused by mutations in the variants of concern. Examined factors included wastewater characteristics (pH, wastewater temperature, total and volatile solids, and turbidity) and weather events (precipitation, snow cover, and ambient temperature). Composite samples were collected over a 17-month period from five wastewater treatment plants and an upper sewershed location, spanning several seasons and weather events. SARS-CoV-2 RNA concentrations were measured using RT-qPCR using the N1 and N2 gene targets, and advanced statistical analyses were applied to assess trends and correlations. The results indicate that wastewater characteristics and weather events significantly impact the N2/N1 ratios, with additional effects from sewer size, type, and location. No single parameter consistently impacted the N2/N1 ratio across all sites, rather, impacts were site-specific. However, volatile solids and turbidity showed consistent correlations with N2/N1 ratios at all wastewater treatment plants. Understanding these external impacts is essential for accurately interpreting the changes in N2/N1 ratios and improving wastewater-based epidemiology efforts.
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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 | 0.000 | 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 | 0.000 |
| 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".