Characterization of Occupational Exposures to Engineered Nanoparticles During the Finishing Process of a Hardwood Floor Manufacturing Plant
Bibliographic record
Abstract
Engineered nanomaterials (ENMs) have an enormous economic impact. In the surface coating industry, titanium dioxide (TiO2) and silicon dioxide (SiO2) nanoparticles are commonly incorporated into varnishes, paints, and finishing products. These ENMs are used for UV-active properties and self-cleaning activities, as well as for the durability and resistance they provide. However, several health concerns are associated with the inhalation of some ENMs. In this case study, occupational exposures to engineered nanoparticles were investigated in an industrial hardwood floor manufacturing plant during the finishing task of an automated spraying equipment. A combination of air and surface sampling was carried out during two workdays. Airborne and deposited particles were measured using a combination of real-time and filter-based sampling methods and analyzed by microscopy and spectrometry. Results indicate that the wood floor coating process generates airborne TiO2 and SiO2 nanoparticles which include individual particles in the nanoscale range (lower than 100 nm) and agglomerated particles of several hundred nanometers containing individual nanoparticles. Finishing activities significantly increased total particle number concentrations (45 620 and 117 880 particles cm-3) and surface-specific mass concentrations (154 µm2 cm-3). Concentrations of TiO2 ranged from 13 to 97 µg m-3 for personal measurements and from 36 to 55 µg m-3 for ambient measurements in the finishing location. Characterization of the deposited particles indicated the dispersion of the engineered airborne nanoparticles from the finishing location to the packaging area. Using a multimetric approach, this study shows high evidence that the worker was exposed to engineered TiO2 and SiO2 nanoparticles during the finishing process of the wood floor production facility. In addition, this study indicates that workers outside the finish spray area could be exposed to airborne engineered TiO2 and SiO2 nanoparticles coming from the finishing process.
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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 | 0.000 | 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) | 0.001 | 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".