Source Apportionment of Fine-Particle, Water-Soluble Organic Nitrogen and Its Association with the Inflammatory Potential of Lung Epithelial Cells
Bibliographic record
Abstract
Identifying the key chemical compounds and source contributions in ambient particles associated with the burden of cardiopulmonary disease is important to develop cost-effective air pollution mitigating strategies that maximize the protection of public health. To help address this need, we examined 109 daily ambient fine particulate matter samples (PM 2.5 ) that were collected in Beijing in one year. The samples were analyzed for chemical composition including organic and elemental carbons, metals, ions, as well as organic molecular markers. In addition, the secretion of pro-inflammatory cytokines, tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) were measured by exposing lung epithelial cells (A549) to water extracts of PM 2.5 samples. Single pollutant and constituent-PM 2.5 joint linear models were used to estimate the associations. Higher PM 2.5 mass and measured chemical components were found in cold seasons than in warm seasons due to the greater contributions of secondary inorganic sources, biomass burning, and coal combustion. Water-soluble organic nitrogen (WSON) had the strongest associations with levels of pro-inflammatory cytokines compared to PM 2.5 mass and other chemical species in both the one and constituent-PM 2.5 joint linear models. Our study is the first to highlight that ambient WSON from diverse sources dominates the inflammatory potential of lung epithelial cells.
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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 | 0.001 |
| 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) | 0.001 | 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".