The importance of moist thermodynamics on neutral buoyancy height for plumes from anthropogenic sources
Bibliographic record
Abstract
Abstract. Plume rise plays a critical role in dispersing pollutants emitted from tall stacks, dictating the height reached by buoyant plumes and their subsequent downwind dispersion. Commonly, plume rise is assumed to be governed by atmospheric stability, the exit momentum and temperature of the effluent released from large stacks. However, an under-recognized influence on plume rise is the effects of entrained and/or co-emitted water, which can change the plume height due to exchange of latent heat associated with phase changes of within-plume water. While many of the stack sources achieve high temperatures of the emitted effluent via combustion, the impact of combustion-generated water on plume rise is often overlooked in large-scale air-quality models. As the rising water condenses or evaporates, it releases or absorbs latent heat, influencing the height reached by the plumes. Our study investigates the effects of latent heat exchange by combustion-generated and entrained water on plume rise. We introduce a novel approach that integrates moist thermodynamics into an empirical parameterization for plume rise, resulting in the development of PRISM (Plume-Rise-Iterative-Stratified-Moist). Long-term (6-month duration) simulations using PRISM exhibit a difference of up to ±100 % in surface concentrations of emitted pollutants near industrial sources compared to previous predictions, emphasizing the substantial influence of moist thermodynamics on plume rise. Our results show up to 50 % improvement in model simulated plume height, through evaluation against aircraft observations over the Canadian Oil Sands. This study pioneers a plume rise sub-grid parameterization integrating moist thermodynamics in iterative calculation of neutral buoyancy height for plumes emitted from industrial stacks, thereby advancing our understanding of plume behaviour and enhancing the accuracy of air-quality modelling. These advancements can potentially contribute to more effective pollution control strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".