Unraveling the Spatiotemporal Heterogeneity in the Oxidation Potential of Fine Particulate Matter in China
Bibliographic record
Abstract
The oxidative potential (OP) of fine particulate matter (PM 2.5 ) is a more robust health impact indicator than mass concentration alone. While China’s stringent air pollution controls have significantly reduced PM 2.5 levels, the temporal evolution of OP and its relationship with PM 2.5 remain unclear. This study establishes a national PM 2.5 OP database (2000–2020) using a source-oriented CMAQ model. Key findings reveal that OP decreases alongside PM 2.5 concentrations over the study period, but the decline in OP (16%) is less pronounced than that of PM 2.5 (37%) after 2012, leading to a 35% increase in mass-normalized OP (OP m ). Source apportionment analysis identifies distinct phase-specific drivers: anthropogenic secondary organic aerosols (ASOA) were the dominant contributor to OP increases (∼68%) prior to 2012, while reductions in transportation, power generation, and biomass burning emissions drove OP declines during 2012–2017. Post-2017, stringent controls on anthropogenic non-methane volatile organic compounds (NMVOCs) made ASOA the dominant declining source. Notably, urban-rural disparities in OP exposure exceed those of PM 2.5 mass concentrations, primarily reflecting differences in dominant emission sources. As the first nationwide study to systematically evaluate the decoupling of PM 2.5 mass and OP trends in China, these findings underscore the need for targeted strategies to mitigate PM-related health risks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".