Soluble Iron in Source‐Based Anthropogenic PM <sub>2.5</sub> Predominantly From Steel Industry and Residential Combustion in China
Bibliographic record
Abstract
Abstract Iron (Fe), especially soluble Fe, is critical in atmospheric fine particulate matter (PM) (PM 2.5 ), while its source contribution remains unclear. Here, we systematically study soluble Fe from various sources through real‐world measurement, laboratory analyses, emission inventory, and model simulation. The water solubility of Fe in PM 2.5 from incomplete combustion sources (i.e., residential solid fuel burning and on‐road vehicle exhaust) was 19.6–1,063.7 times higher than that from industrial sectors, brake wear, and natural dust. In contrast, PM 2.5 from industrial plumes after wet flue gas desulfurization devices exhibited a pH lower than 3, elevating Fe solubility by 11.0–18.6 times via proton‐promoted dissolution. Among all sources, residential combustion and the steel industry were the predominant anthropogenic sources of soluble Fe emissions, with high emission intensity of 70.1 ± 26.5% and 78.8 ± 13.2% in Eastern and Southwestern China, respectively. Our findings highlight the critical roles of incomplete combustion and plume acidity in contributing Fe solubility, providing fundamental data for atmospheric Fe chemistry and ocean primary productivity.
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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 | 0.001 | 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".