Applying a Kinetic Multi-Layer Model of Surface and Bulk Chemistry in Epithelial Lung Lining Fluid to Estimate Spatial Variations in the Production of Reactive Oxygen Species in Response to PM2.5 Iron and Copper
Bibliographic record
Abstract
Background: Certain metals may play an important role in the adverse health effects of fine particulate air pollution (PM2.5) but few models are available to predict spatial variations in these pollutants.Methods: We conducted large-scale air monitoring campaigns during summer 2016 and winter 2017 in Toronto, Canada to characterize spatial variations in iron (Fe) and copper (Cu) concentrations in PM2.5. Information on Fe and Cu concentrations at each site were paired with a kinetic multi-layer model of surface and bulk chemistry in the lung epithelial lining fluid to estimate the possible impact of these metals on the production of reactive oxygen species (ROS) in exposed populations. Land use data around each monitoring site were used to develop predictive models for Fe, Cu, and their estimated combined impact on ROS generation.Results: Spatial variations in Fe, Cu, and ROS greatly exceeded that of PM2.5 mass concentrations. In addition, Fe, Cu, and estimated ROS concentrations were 15, 18, and 9 times higher during summer compared to winter with little difference observed for PM2.5. In leave-one out cross validation procedures final multivariable models explained the majority of spatial variations in annual mean Fe (R2= 0.68), Cu (R2=0.79), and ROS (R2= 0.65).Conclusions: The combined use of PM2.5 metals data with a kinetic multi-layer model of surface and bulk chemistry in the human lung epithelial lining fluid may offer a novel means of estimating PM2.5 health impacts beyond simple mass concentrations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".