Toxic Beauty: Evaluating the Toxicological Risks of Heavy Metals in Facial Cosmetics and Implications for Public Health in Calabar, Nigeria
Bibliographic record
Abstract
Facial cosmetics beautify, but combined usage may expose users to harmful heavy metals. This study evaluates that toxic risk. A survey involving 300 female participants was conducted to identify the most commonly used cosmetic brands—Brands A, B, C, and D. A total of 576 cosmetic samples, including foundations, face powders, lipsticks, and eye pencils, were purchased bimonthly over six months. Samples were prepared through acid digestion and analyzed for metal content using atomic absorption spectrophotometry. Health risk assessment was conducted using systemic exposure dosage (SED), margin of safety (MoS), hazard quotient (HQ), hazard index (HI), and lifetime cancer risk (LCR) models. Metals concentrations (mg/kg) across all brands were in the following ranges: lead (0.066-0.789), cadmium (0.093-0.787), chromium (0.049-0.543), cobalt (0.048-0.902), nickel (0.033-0.704), and iron (0.021-0.641). Metal concentrations varied significantly across brands and product types (ANOVA, p≤0.05). Nickel and cadmium levels in most products exceeded WHO and Health Canada limits. The SED values revealed that Co, Cd, and Pb posed the highest risks, particularly in Brand A and Brand C products. MoS calculations indicated that Cd and Co posed significant safety concerns. HQ values confirmed that Cd and Co exceeded the non-carcinogenic risk threshold. The HI values were above unity, suggesting cumulative health risks. LCR values for Cd, Ni, and Cr were above the acceptable risk range, indicating potential cancer risks. The study concludes that prolonged use of studied cosmetics poses both carcinogenic and non-carcinogenic risks. Routine screening of cosmetic products to ensure compliance with safety standards is strongly recommended.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".