Fate of disposable face masks in high-solids anaerobic digestion: Experimental observations and review of potential environmental implications
Bibliographic record
Abstract
Face masks became a part of our daily life amid the global COVID-19 (SARS-CoV-2) pandemic. Most of the face masks are made for single-use and primarily disposed of in garbage bins with other non-recyclable wastes. To date, little is known about how disposable face masks in municipal solid waste (MSW) would interfere with high-solids anaerobic digestion (HSAD) in waste management facilities. Here, we first report preliminary results from a lab experiment conducted with the organic fraction of municipal solid waste (OFMSW) amended with used disposable face masks. The lab-scale HSAD systems were operated with percolate recirculation comparable to commercial HSAD systems typically used for full-scale processing of OFMSW. The results suggested that the presence of face masks in OFMSW could negatively affect methane productivity and kinetics. In the digesters amended with face masks, total cumulative methane production decreased by up to 18%, along with a 12-29% decrease in maximum methane production rates than the control digester (without face masks). Moreover, lag phases increased by 7-14%. The results also suggested that the type of polymeric materials used in face masks would be more critical than their total number/loading in the digester, which warrants further investigation. The visual inspection of digestate showed that the face masks were mostly undegraded after 40 days of operation. Much remains unknown about how the undegraded face masks will affect the digestate management practices, such as composting, land application, and landfilling. However, the review of existing literature suggested that they can be a potential source of plastic and microplastic pollution and amplify transmission of antibiotic resistance genes to the ecosystem. In summary, this study underscores the importance of developing safe and reliable disposal guidelines and management plans for single-use face masks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".