Increased use of sanitizers and disinfectants during the COVID-19 pandemic: identification of antimicrobial chemicals and considerations for aquatic environmental contamination
Bibliographic record
Abstract
In response to the coronavirus (COVID-19) pandemic, there has been an increased need for personal and environmental decontamination to aid in curbing transmission of the SARS-CoV-2 virus. Products used for this purpose include sanitizers for hands and disinfectants for surfaces. The active chemical ingredients used in these products, termed antimicrobials, can enter waste streams after application and may be emerging as more prominent environmental contaminants. Even prior to COVID-19, there was recognized need to examine their implications for aquatic biota, which is now made more pressing due to their exaggerated use in response to the pandemic. Our objectives were to identify current antimicrobial active ingredients, quantify their increased use, and determine which may be candidates for further consideration as possible aquatic contaminants. By consulting multiple sources of publicly available information in Canada, we identified current-use antimicrobials from the lists of sanitizers and surface disinfectants approved for use against SARS-CoV-2 by Health Canada and the drug registration database. To estimate the use of sanitizers and disinfectants, we evaluated import quantities and grocery store retail sales of related compounds and products (Statistics Canada) and both lines of evidence supported increased use trends. The list of identified antimicrobials was refined to include only candidates with potential to reach aquatic ecosystems, and information on their environmental concentrations and toxicity to aquatic biota was reviewed. Candidate antimicrobials ( n = 32) fell into four main categories: quaternary ammonium compounds (QACs), phenols, acids, and salts. Benzalkonium chloride, a QAC, was the most prominent active ingredient used in both nonalcohol-based hand sanitizers and surface disinfectants. Four QACs followed in prevalence and the next most used antimicrobial was triclosan (hand sanitizers only), an established and regulated environmental contaminant. Little information was found on environmental concentrations of other candidates, suggesting that the majority would fall into the category of emerging contaminants if they enter aquatic systems. Several were classified as acutely or chronically toxic to aquatic biota (Globally Harmonized System), and thus we recommend empirical research begin focusing on environmental monitoring of all candidate antimicrobials as a critical next step, with detection method development first where needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".