Adsorption of SARS-CoV-2 onto granular activated carbon (GAC) in wastewater: Implications for improvements in passive sampling
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Bibliographic record
Abstract
Based on recent studies, passive sampling is a promising method for detecting SARS-CoV-2 in wastewater surveillance (WWS) applications. Passive sampling has many advantages over conventional sampling approaches. However, the potential benefits of passive sampling are also coupled with apparent limitations. We established a passive sampling technique for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater using electronegative filters. Though, it was evident that the adsorption capacity of the filters constrained their use. This work intends to demonstrate an optimized passive sampling technique for SARS-CoV-2 in wastewater using granular activated carbon (GAC). Through bench-scale batch-adsorption studies and sewershed deployments, we established the adsorption characteristics of SARS-CoV-2 and two human feacal viruses (PMMoV and CrAssphage) onto GAC. A pseudo-second-order model best-described adsorption kinetics for SARS-CoV-2 in either deionized (DI) water and SARS-CoV-2, CrAssphage, and PMMoV in wastewater. In both laboratory batch-adsorption experiments and in-situ sewershed deployments, the maximum amount of SARS-CoV-2 adsorbed by GAC occurred at ~60 h in wastewater. In wastewater, the maximum adsorption of PMMoV and CrAssphage by GAC occurred at ~60 h. In contrast, the adsorption capacity was reached in DI water seeded with SARS-CoV-2 after ~35 h. The equilibrium assay modeled the maximum adsorption quantity (qmax) in wastewater with spiked SARS-CoV-2 concentrations using a Hybrid Langmuir-Freundlich equation, a qmax of 2.5 × 109 GU/g was calculated. In paired sewershed deployments, it was found that GAC adsorbs SARS-CoV-2 in wastewater more effectively than electronegative filters. Based on the anticipated viral loading in wastewater, bi-weekly sampling intervals with deployments up to ~96 h are highly feasible without reaching adsorption capacity with GAC. GAC offers improved sensitivity and reproducibility to capture SARS-CoV-2 RNA in wastewater, promoting a scalable and convenient alternative for capturing viral pathogens in wastewater.
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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 | 0.000 |
| 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 it