Iron in Soils and Road Dust is Modulated by Coal-Fired Power Plant Sulfur Making Toxic PM2.5
Bibliographic record
Abstract
Transition metal ions, such as water-soluble iron (WS-Fe), are toxic components of fine particulate matter (PM2.5). In Atlanta, GA, from 1998 to 2013, WS-Fe was the PM2.5 species most associated with adverse cardiovascular outcomes in a previous study. We examined this data set to investigate the sources of WS-Fe and effects of air quality regulations on ambient levels of WS-Fe. Insoluble forms of iron in mineral and traffic dust combined with sulfate from coal-fired electrical generating units (EGU) were converted to soluble forms by sulfate-driven acid-dissolution. Sulfate produced both the highly acidic aerosol (summer pH 1.5-2) and liquid water required for the aqueous phase acid-dissolution, but variability in WS-Fe was mainly driven by particle liquid water. These processes were more pronounced in summer when particles were most acidic, whereas in winter the relative importance of WS-Fe from combustion emissions increased. Although WS-Fe represents a minute mass fraction (0.15%) of PM2.5, the observed high correlation between WS-Fe and PM2.5 mass (r=0.67) may result from these formation routes and account for some association between mass and adverse health seen in past studies. Similar processes are expected in many regions, implying these unexpected benefits from coal-burning reduction may be widespread.
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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) | 0.000 | 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) | 0.001 | 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".