Understanding the Environmental Distribution and Potential Health Risks of Pollutants from Deodorant Products: A Review
Bibliographic record
Abstract
Deodorants are frequently used personal care products; however, questions have been raised concerning their possible toxicity to cause air and water pollution, and their potential impact on human health. The degree to which deodorant ingredients, such as fragrance chemicals, antibacterial compounds, aluminium compounds, and preservatives, are toxic depends on their chemical composition. Many of these chemicals have been connected to adverse health effects, such as skin rashes, allergic reactions, endocrine disruption, and respiratory problems. Understanding these chemicals’ toxicity is crucial for determining any potential risks to human health. Spray formulations have the potential to release volatile organic compounds into the air, such as propellants and fragrance chemicals, which can be harmful to human respiratory health and lead to indoor and outdoor air pollution. Improper disposal and wastewater treatment can lead to the contamination of water bodies, potentially impacting aquatic ecosystems and human water supplies. This review provides an overview of the toxicity of deodorant ingredients in various formulations, including sprays, roll-ons, and sticks. The partition coefficients Log Kaw (air-water partition coefficient), Log Koa (airorganic carbon partition coefficient), and Log Kow (octanol-water partition coefficient), values of deodorant ingredients were summarized for assessing their potential for long-range transport, persistence in the environment, and bioaccumulation in organisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".