Are UV filters better together? A comparison of the toxicity of individual ultraviolet filters and off-the-shelf sunscreens to Daphnia magna
Bibliographic record
Abstract
Organic ultraviolet filters (UVFs) are known to contaminate many aquatic ecosystems, with much environmental contamination attributed to the use of UVF-containing skin care products such as sunscreens during aquatic recreation. Most studies addressing the impact of sunscreen contamination have focused on the effects of UVFs under the assumption that they are the primary contaminants of concern from sunscreen pollution; however, the extent to which the toxicity of UVFs is representative of the environmental impacts of the whole sunscreen mixture is unknown. To address this knowledge gap, this study compared the mixture toxicity of five off-the-shelf sunscreen spray products containing the UVFs avobenzone, homosalate, octisalate, octocrylene and oxybenzone to the toxicity of each UVF in isolation to the freshwater invertebrate Daphnia magna . It was found that sunscreen toxicity was not proportional to their total UVF content, as the sunscreen containing the fewest UVFs was approximately equivalent to the sunscreen with the most UVFs, causing ≥90 % mortality and inhibiting all daphnid reproduction over 21 d exposures. Sunscreen toxicity was typically lower than expected when compared to the toxicity of each individual UVF within the mixture, as some sunscreens causing ≤20 % mortality contained octocrylene and/or oxybenzone at concentrations exceeding those which caused 90 % mortality during exposure to the UVF alone. Despite sunscreens causing large impairments in reproduction, growth and metabolism, poor correlations existed between the severity of most sublethal endpoints with respect to the measured UVF content of each sunscreen. Overall, these results indicate that potential antagonistic relationships between sunscreen ingredients can greatly reduce the toxicity of UVFs, creating more uncertainty regarding the level of threat that UVFs pose to the environment as a result of sunscreen contamination. • UV filter toxicity alone is used to estimate sunscreen contamination effects. • D. magna sensitivity to 5 sunscreen mixtures and isolated UV filters was compared. • Sunscreen toxicity correlated poorly with total UV filter concentrations. • UV filters were more toxic alone than sunscreens of equivalent concentrations. • Antagonistic mixture effects may be reducing sunscreen toxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".