Extensive use of face masks during COVID-19 pandemic: (micro-)plastic pollution and potential health concerns in the Arabian Peninsula
Bibliographic record
Abstract
Face masks are primary line of defense to reduce the transmission risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). World Health Organization (WHO) has already updated the guidelines and advised the use of face masks in public areas essentially. This has dramatically increased the production and use of face masks in many parts of the world. Arabian Peninsula is comprised of six countries where the public perception of following WHO guidelines is high. In this study, we highlight the concerns relating to extensive use of face masks in this region, particularly in the context of (micro-)plastic pollution. We computed the number of face masks to be used in each of the countries of Arabian Peninsula for varying levels of acceptance rate and average number of daily usages. Accordingly, the amount of (micro-)plastic that could come into the terrestrial and marine environment is also reported. Saudi Arabia, being the most populated country in the region may contribute up to 32-235 thousand tons of (micro-)plastic which is nearly half of the amount in the whole Peninsula. On the other hand, an extremely high infection rate in Qatar (25.74%) may also lead to a significant increase of (micro-)plastic content due to high public acceptance rate and living standards. The high (micro-)plastic fraction is of significant concern because it ends up in the marine ecosystems. Further, it allows colonization of several pathogenic microorganisms (bacteria, viruses, fungal filaments, and spores) and might serve as carriers of disease transmission finally affecting the living organisms habituating these ecosystems. It is suggested that appropriate regulations on face masks waste should be devised to avoid any unwanted consequences in the near future.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".