Impacts of combustion-generated water on in-plume aqueous-phase chemistry
Bibliographic record
Abstract
In this work within-plume aqueous-phase chemistry is explored utilizing air quality modelling and a plume rise algorithm which includes the effects of combustion-generated water, latent heat release, and in-plume cloud droplet formation. Effluents emitted from high temperature industrial stacks usually contain large amounts of combustion-generated water in gaseous phase (vapour), resulting in high relative humidity within the emitted parcels that make up the plume. As the plume rises in the atmosphere due to buoyancy and cools, the water vapour can condense into droplets, and result in a significant amount of in-plume liquid water. The combined effects of high relative humidity and the presence of liquid-phase water can potentially impact the rate of oxidation of emitted pollutants due to aqueous-phase chemistry within cloud droplets contained within the plume parcel.  Examples include the conversion rates of sulfur dioxide to particulate sulfate and nitrogen dioxide to particulate nitrate. Accounting for in-plume aqueous-phase chemistry can be instrumental in addressing the past discrepancies between predicted and observed levels of secondary aerosols and other gaseous tracers. This work utilizes the Moist-Plume-Rise algorithm (Fathi et al., 2024, under review), which incorporates the thermodynamic effects of combustion-generated water.  The algorithm determines the final height reached by buoyant plumes while keeping track of within-plume water content (vapour, condensed, ice) as it rises. Here, the effect of aqueous phase chemistry taking place within the rising parcel’s condensed water is examined.  The newly developed model feature makes use of information on in-plume water content such as mixing ratio and physical phase over time to perform aqueous-phase chemistry calculations based on the already existing model cloud chemistry modules.  These aqueous-phase chemistry processes and other processes traditionally associated with cloud processing of gases and aerosols can potentially alter the makeup of combustion-source effluents emitted from industrial stacks before they reach neutral buoyancy and are dispersed in the atmosphere. 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".