Experimental Determination of Mercury Photoreduction Rates in Cloudwater
Bibliographic record
Abstract
Abstract Redox chemistry controls the behavior of mercury during long‐range atmospheric transport. Despite major progress in understanding the oxidation process in the atmosphere, the mechanism of mercury reduction, especially the aqueous‐phase and multi‐phase reactions in cloudwater, remains poorly known. Here we report experimentally determined rates of in‐cloud mercury photoreduction using cloudwater samples collected from a forested area in the Canary Islands, Spain. Mercury concentrations in cloudwater varied greatly from 0.04 to 19.9 nM (7.89 ± 6.11 nM) for total mercury and 0.02 to 4.98 nM (1.16 ± 1.49 nM) for the dissolved fraction, with particulate mercury being the dominant fraction in most of the samples. The mercury concentrations were elevated in comparison with those previously reported for atmospheric waters, reflecting the impact of a large‐scale forest wildfire that occurred shortly before the sampling campaign on a neighboring island. Mercury photoreduction was determined by two independent methods: under natural solar radiation at a nearby mountain site located in the free troposphere and under UV radiation in the laboratory. In addition to aqueous‐phase photoreduction, multi‐phase photoreduction involving particulate mercury also occurred in cloudwater, with the latter becoming dominant once dissolved mercury had been depleted. Overall, the pseudo‐first‐order rate constants for in‐cloud mercury photoreduction varied from 0.07 to 0.21 hr −1 , which are one order of magnitude lower than the values presumed in global mercury transport models. Our results suggest that in‐cloud reactions alone are insufficient to account for mercury reduction in the atmosphere and that other pathways, such as gas‐phase reactions, must exist and need to be properly incorporated into future models.
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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 | 0.001 |
| 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 | 0.001 | 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".