Relationship between chemical composition and oxidative potential of secondary organic aerosol from polycyclic aromatic hydrocarbons
Bibliographic record
Abstract
Abstract. Owing to the complex nature and dynamic behaviors of secondary organic aerosol (SOA), its ability to cause oxidative stress (known as oxidative potential, or OP) and adverse health outcomes remains poorly understood. In this work, we probed the linkages between the chemical composition of SOA and its OP, and investigated impacts from various SOA evolution pathways, including atmospheric oligomerization, heterogeneous oxidation, and mixing with metal. SOA formed from photooxidation of the two most common polycyclic aromatic hydrocarbons (naphthalene and phenanthrene) were studied as model systems. OP was evaluated using the dithiothreitol (DTT) assay. The oligomer-rich fraction separated by liquid chromatography dominates DTT activity in both SOA systems (52 ± 10 % for naphthalene SOA (NSOA), and 56 ± 5 % for phenanthrene SOA (PSOA)). Heterogeneous ozonolysis of NSOA was found to enhance its OP, which is consistent with the trend observed in selected individual oxidation products. DTT activities from redox-active organic compounds and metals were found to be not additive. When mixing with highly redox-active metal (Cu), OP of the mixture decreased significantly for 1,2-naphthoquinone (42 ± 7 %), 2,3-dihydroxynaphthalene (35 ± 1 %), NSOA (50 ± 6 %), and PSOA (43 ± 4 %). Evidence from proton nuclear magnetic resonance (1H NMR) spectroscopy illustrates that such OP reduction upon mixing can be ascribed to metal–organic binding interactions. Our results highlight the role of aerosol chemical composition under atmospheric aging processes in determining the OP of SOA, which is needed for more accurate and explicit prediction of the toxicological impacts from particulate matter.
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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) | 0.000 | 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 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".