Photocatalytic Chlorine Production from Iron Chlorides in Atmospheric Aerosols: Strategies for Quantifying Methane and Tropospheric Ozone Control
Bibliographic record
Abstract
It was recently discovered that chlorine is produced photocatalytically from mineral dust sea spray aerosols, impacting methane and tropospheric ozone, and an evaluation was made of the climate and environmental impact of a chlorine-based intervention to draw down methane. The generation of chlorine by the iron chlorides Fe(III)Cl(3−n)n will also occur due to iron present in shipping plumes. To study efficiency and environmental implications, there is a need for additional information about the behavior of the process under a range of atmospheric conditions. Here we use box modeling to evaluate whether it is possible to experimentally observe this mechanism in a ship’s plume, or in a plume of pure iron dust, emitted for example from a tower. Detection limits for Cl, Cl2, HOCl, ClO, ClNO3, ClNO2, CO, C2H6, δ13C(CO) and CH2O are determined based on values from the literature. We find that the most promising and low-cost experimental indicators of Cl0 production are the concentration of photoactive iron and the CO:ethane ratio, and Cl2 is a useful indicator if cost is not a limitation. For ships with high NOx emissions, ClNO2 and ClNO3 could also potentially be used, and for towers emitting Fe without NOx the concentration of HOCl and ClO could be used. δ13C(CO) is a very direct method to detect methane removal, but only gives a clear signal for high iron emissions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".