In Situ Performance of Granular Activated Carbon for Sampling Viruses and Bacteria from Wastewater: Toward Quantitative Passive Sampling for Wastewater-based Epidemiology
Bibliographic record
Abstract
Expanding the WBE footprint to include low-resource settings where small, informal, and ad-hoc wastewater systems and high burdens of disease are co-located requires resource-efficient and adaptable methods. To that end, we deployed passive samplers made of granular activated carbon (GAC) into raw influent at a small wastewater treatment plant continuously over 90 days. Detections of SARS-CoV-2 RNA, respiratory syncytial virus RNA, and human adenovirus DNA on GAC passive samplers were coincident with regional clinical trends during a low-incidence period. GAC also recovered bacterial DNA, including mapA, a gene associated with Campylobacter jejuni. A set of antibiotic resistance genes – tetW, blaTEM, blaCTX – were also quantified from GAC passive samplers and did not show increased relative abundance over exposure durations up to 168 hours. Sequencing of 16S rRNA amplicons indicated the GAC samplers recovered bacterial families abundant in both wastewater and feces. Over 38 deployments up to 168 hours long, the average uptake rate decayed exponentially with exposure duration for 16S rRNA (R2 = 0.972), pepper mild mottle virus (R2 = 0.882), and human adenovirus (R2 = 0.585). GAC passive samplers may afford a resource-efficient approach to producing quantitative data for a wide variety of infectious agents relevant to WBE.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 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".