Emissions and health risks from the use of 3D printers in an occupational setting
Bibliographic record
Abstract
The aim of this study was to determine concentrations of particulates and volatile organic compounds (VOCs) emitted from 3D printers using polylactic acid (PLA) filaments at a university workroom to assess exposure and health risks in an occupational setting. Under typical-case (one printer) and worst-case (three printers operating simultaneously) scenarios, particulate concentration (total and respirable), VOCs and formaldehyde were measured. Air samples were collected in the printing room and adjacent hallway. Size-resolved levels of nano-diameter particles were also collected in the printing room. Total particulate levels were higher in the worst-case scenario (0.7 mg/m3) vs. typical-case scenario (0.3 mg/m3). Respirable particulate and formaldehyde concentrations were similar between the two scenarios. Size-resolved measurements showed that most particles ranged from approximately 27 to 116 nm. Total VOC levels were approximately 6-fold higher during the worst-case scenario vs. typical situation with isopropyl alcohol being the predominant VOC. Airborne concentrations in the hallway were generally lower than inside the printing room. All measurements were below their respective occupational exposure limits. In summary, emissions of particulates and VOCs increased when multiple 3D printers were operating simultaneously. Airborne levels in the adjacent hallway were similar between the two scenarios. Overall, data suggest a low risk of significant and persistent adverse health effects. Nevertheless, the health effects attributed to 3D printing are not fully known and adherence to good hygiene principles is recommended during use of this technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".