Impact of Occupational Exposure to Chemicals in Life Cycle Assessment: A Novel Characterization Model Based on Measured Concentrations and Labor Hours
Bibliographic record
Abstract
According to Lim et al., based on World Health Organization (WHO) data, hazardous chemicals in the workplace are responsible for over 370,000 premature deaths annually. Despite these high figures, life cycle impact assessment (LCIA) does not yet include a fully operational method to consider occupational impacts in its scope over the entire supply chain. This paper describes a novel approach to account for occupational exposure to chemicals by inhalation in LCA. It combines labor statistics and measured occupational concentrations of chemicals from the OSHA database to calculate operational LCIA characterization factors (i.e., intakes per hour worked and impact intensities for 19,069 organic chemical/sector combinations with confidence intervals across the entire U.S. manufacturing industry). For the seven chemicals that most contribute to the global impact, measured workplace concentrations range between 5 × 10(-4) and 3 × 10(3) mg/m(3). Carcinogenic impacts range over 4 orders of magnitude, from 1.3 × 10(-8) and up to 3.4 × 10(-4) DALY per blue-collar worker labor hour. The innovative approach set out in this paper assesses health impacts from occupational exposure to chemicals with population exposure to outdoor emissions, making it possible to integrate occupational exposure within LCIA. It broadens the LCIA scope to analyze hotspots and avoid impact shifting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".