Trends in Chemical Composition of Global and Regional Population-Weighted Fine Particulate Matter Estimated for 25 Years
Bibliographic record
Abstract
We interpret in situ and satellite observations with a chemical transport model (GEOS-Chem, downscaled to 0.1° × 0.1°) to understand global trends in population-weighted mean chemical composition of fine particulate matter (PM 2.5 ). Trends in observed and simulated population-weighted mean PM 2.5 composition over 1989–2013 are highly consistent for PM 2.5 (−2.4 vs −2.4%/yr), secondary inorganic aerosols (−4.3 vs −4.1%/yr), organic aerosols (OA, −3.6 vs −3.0%/yr) and black carbon (−4.3 vs −3.9%/yr) over North America, as well as for sulfate (−4.7 vs −5.8%/yr) over Europe. Simulated trends over 1998–2013 also have overlapping 95% confidence intervals with satellite-derived trends in population-weighted mean PM 2.5 for 20 of 21 global regions. Over 1989–2013, most (79%) of the simulated increase in global population-weighted mean PM 2.5 of 0.28 μg m –3 yr –1 is explained by significantly ( p < 0.05) increasing OA (0.10 μg m –3 yr –1 ), nitrate (0.05 μg m –3 yr –1 ), sulfate (0.04 μg m –3 yr –1 ), and ammonium (0.03 μg m –3 yr –1 ). These four components predominantly drive trends in population-weighted mean PM 2.5 over populous regions of South Asia (0.94 μg m –3 yr –1 ), East Asia (0.66 μg m –3 yr –1 ), Western Europe (−0.47 μg m –3 yr –1 ), and North America (−0.32 μg m –3 yr –1 ). Trends in area-weighted mean and population-weighted mean PM 2.5 composition differ significantly.
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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 | 0.002 |
| 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".