Contrasting biological potency of particulate matter collected at sites impacted by distinct industrial sources
Bibliographic record
Abstract
Industrial sources contribute a significant proportion of anthropogenic particulate matter (PM) emissions, producing particles of varying composition that may differentially impact health. This study investigated the in vitro toxicity of ambient PM collected near industrial sites in relation to particle size and composition. Size-fractionated particles (ultrafine, PM 0.1–2.5 , PM 2.5–10 , PM >10 ) were collected in the vicinity of steel, copper, aluminium, and petrochemical industrial sites. Human lung epithelial-like A549 and murine macrophage-like J774A.1 cells were exposed for 24 h to particle suspensions (0, 30, 100, 300 μg/cm 2 ). Particle potency was assessed using cytotoxic (resazurin reduction, lactate dehydrogenase (LDH) release) and inflammatory (cytokine release) assays, and regressed against composition (metals, polycyclic aromatic hydrocarbons (PAHs), endotoxin). Coarse (PM 2.5–10 , PM >10 ) particle fractions were composed primarily of iron and aluminium; in contrast, ultrafine and fine (PM 0.1–2.5 ) fractions displayed considerable variability in metal composition (especially water-soluble metals) across collection sites consistent with source contributions. Semi-volatile and PM-associated PAHs were enriched in the fine and coarse fractions collected near metal industry. Cell responses to exposure at equivalent mass concentrations displayed striking differences among sites ( SITE x SIZE and SITE x DOSE interactions, p < 0.05), suggesting that particle composition, in addition to size, impacted particle toxicity. While both J774A.1 and A549 cells exhibited clear particle size-dependent effects, site-dependent differences were more pronounced in J774A.1 cells, suggesting greater sensitivity to particle composition. Plotting particle potency according to cytotoxic and inflammatory response grouped particles by size and site, and showed that particles of similar composition tended to cluster together. Cytotoxic effects in J774A.1 cells correlated with metal and PAH content, while inflammatory responses were associated primarily with endotoxin content in coarse particles. Industrial sources produce particulate emissions with varying chemical composition that differ in their in vitro potency in relation to particle size and the levels of specific constituents.
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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 | 0.001 | 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".