Development of a Manganese Speciation Method for Atmospheric Aerosols in Biologically and Environmentally Relevant Fluids
Bibliographic record
Abstract
Because the health effects of manganese are dependent its oxidation-state, we have improved upon oxidation-state resolved methods to quantify soluble manganese in atmospheric aerosols. Two spectrophotometric methods were adapted for measurements in atmospheric aerosols in order to measure total soluble manganese (Mn sol ) and soluble oxidized manganese [Mn(III) and Mn(IV), Mn ox ]. Using the formaldoxime method, we noted a detection limit two orders of magnitude better than past studies using trace-metal clean techniques and a 1 meter path-length spectrophotometric cell. Extractions of co-located aerosol samples were performed in four environmentally or biologically relevant extract solutions and processed for soluble manganese analysis. The quantity of manganese extracted was a strong function of the fluid, and the greatest amount of manganese was extracted in the rain-water surrogate (acetate buffered solution). Mn sol in East St. Louis, IL, USA (6–20% of the total manganese) was less than the Mn sol in aerosols collected in Toronto, ON, Canada (40% of the total). Mn ox was not detected in the PM10 samples collected in East St. Louis, however Mn ox accounted for around 30% of the PM2.5 soluble manganese in Toronto. Mn ox was not detected in the coarse fraction in Toronto, which may imply that soils are not a source of Mn ox at this site. Oxidized manganese was not recoverable from extracts of samples from East St. Louis spiked with 1 μg Mn ox L−1. This implies that a soluble component of the aerosol is responsible for reduction of oxidized manganese and that the chemical form of manganese in aerosols can quickly change when it comes into contact with a fluid.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".