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Record W4392759517 · doi:10.5194/egusphere-egu24-12966

Impacts of combustion-generated water on in-plume aqueous-phase chemistry

2024· preprint· en· W4392759517 on OpenAlexaff
Sepehr Fathi, Paul A. Makar, Wanmin Gong, Alexandru Lupu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsPlumeCombustionAqueous solutionEnvironmental sciencePhase (matter)ChemistryEnvironmental chemistryMeteorologyOrganic chemistryGeography

Abstract

fetched live from OpenAlex

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. 

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.020
GPT teacher head0.288
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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