Health risk assessment of heavy metals, BTEX and polycyclic aromatic hydrocarbons (PAHs) in the workplace in a secondary oil re-refining factory
Bibliographic record
Abstract
The present study aimed to evaluate the health risk of heavy metals (iron, zinc, cadmium, arsenic, nickel, lead, and mercury), benzene, toluene, ethylbenzene and xylene (BTEX), and polycyclic aromatic hydrocarbons (PAHs) in the workplace in a secondary oil re-refining factory. In this descriptive, cross-sectional study, samples were collected and analyzed using the NIOSH 1501 method at eight sampling points with determined concentrations of BTEX, 16Ʃ PAHs, and heavy metals. The concentration of each pollutant was evaluated based on the type of workers (packaging/filtration). The risk assessment of the contaminants was carried out using the RAIS software, and the risk of non-carcinogenic compounds was estimated based on the reference respiratory concentration (mg/m3). The results showed that the lifetime cancer risk index (LCR) cumulative risk of the heavy metals was within the definitive risk range at all the sampling points. The highest carcinogenic risk of LCR belonged to arsenic at sampling point H (filtration chamber), and the highest cumulative carcinogenic risk of the total contaminants in the environmental sampling points belonged to sampling point H. At all the sampling points, the LCR carcinogenic risk accumulation for the hydrocarbon compounds was within the definitive range. The maximum cumulative lifetime cancer risk for the hydrocarbon compounds was evaluated in filtration (point B), and the most hazardous carcinogens were arsenic, benzo(a)pyrene, and naphthalene. The proposed risk assessment method was observed to be comprehensive, and its results could be used for corrective and controlling measures and the prioritization of risk reduction resources.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".