Elucidating the size distribution of p‑Phenylenediamine-Derived quinones in atmospheric particles
Bibliographic record
Abstract
Transformed from p-phenylenediamines (PPDs) antioxidant, PPD-derived quinones (PPD-Qs) have recently been recognized as emerging contaminants due to their potential negative impacts on the environment and human health. While there have been measurements of airborne PPD-Qs, the size distribution of PPD-Qs and the impact of particle size on PPD transformation chemistry remain largely unknown. Here, through the measurements of atmospheric particles in three megacities in China (Beijing, Xi'an, and Hefei), we find that PPD-Qs are widely distributed in these cities. Further analysis of the size-fractioned particles in Hefei indicates that 48 % of PPD-Qs reside in coarse particles. Given that previous studies mainly focus on the measurement of PPD-Qs in fine particles, the previously reported PPD-Q concentrations and the corresponding human exposure dosages are likely to be significantly underestimated. Furthermore, the ratio of PPD-Q to PPD concentration (PPD-Q/PPD) for particles with size range of 0.056 - 0.1 μm is up to 3 times higher than that with size range of 10 - 18 μm, highlighting the key role of particle size in determining the atmospheric oxidation reactivity of PPDs. Model simulations reveal a size-dependent pattern for the estimated concentration of particulate PPD-Qs in human body. In addition, we also demonstrate that PPD-Qs can induce the formation of cellular reactive oxygen species, suggesting that they may pose risks to human health. Overall, our results emphasize the importance of considering the particle size effect when evaluating the reaction potential and exposure risk of airborne PPD-Qs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".