Global Sources of Fine Particulate Matter: Interpretation of PM<sub>2.5</sub> Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model
Bibliographic record
Abstract
Exposure to ambient fine particulate matter (PM 2.5 ) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM 2.5 sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM 2.5 to interpret globally dispersed PM 2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM 2.5 composition varies substantially for secondary inorganic aerosols (2.4–19.7 μg/m 3 ), mineral dust (1.9–14.7 μg/m 3 ), residual/organic matter (2.1–40.2 μg/m 3 ), and black carbon (1.0–7.3 μg/m 3 ). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 μg/m 3 ), industry (6.5 μg/m 3 ), and power generation (5.6 μg/m 3 ) are leading sources of outdoor global population-weighted PM 2.5 concentrations. Global population-weighted organic mass is driven by the residential energy sector (64%) whereas population-weighted secondary inorganic concentrations arise primarily from industry (33%) and power generation (32%). Simulation-measurement biases for ammonium nitrate and dust identify uncertainty in agricultural and crustal sources. Interpretation of initial PM 2.5 mass and composition measurements from SPARTAN with the GEOS-Chem model constrained by satellite-based PM 2.5 provides insight into sources and processes that influence the global spatial variation in PM 2.5 composition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".