Estimated Long-Term (1981–2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements
Bibliographic record
Abstract
Abstract Accurate data concerning historical fine particulate matter (PM2.5) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM2.5 concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM2.5 measurements when available and otherwise with historical estimates of PM2.5 from PM10 measurements or total suspended particle (TSP) measurements. The estimated PM2.5 concentrations were generally consistent with direct ground-based PM2.5 measurements over their duration from 1988 onward (R2 = 0.6 to 0.85) and to a lesser extent with PM2.5 inferred from PM10 measurements from 1985 to 1998 (R2 = 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM2.5 from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m–3). The population-weighted annual average PM2.5 over North America decreased from 22 ± 6.4 μg m–3 in 1981, to 12 ± 3.2 μg m–3 in 1998, and to 7.9 ± 2.1 μg m–3 in 2016, with an overall trend of −0.33 μg m–3 yr–1 (95% CI: −0.35, −0.31).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".