POLLUTION-CONTROL TECHNOLOGIES IN COAL-FIRED POWER PLANTS AND THEIR IMPACT ON AEROSOL NUCLEATION AND GROWTH IN EMISSIONS PLUMES
Bibliographic record
Abstract
Nucleation and growth of particles in coal-fired power-plant plumes can greatly contribute to particle concentrations near source regions. Pollution-control technologies have been added to coal-fired power plants to reduce emissions of SO2 and NOX; however, their cumulative effects may be increasing in-plume particle production. Therefore, a quantitative understanding of in-plume particle production is needed to determine the implications of emission controls on the climate system. Changes in particle production with changing emissions for coal-fired power plants are simulated using the SAM-TOMAS large-eddy simulation model. For the W.A. Parish power plant, the model predicts increased particle production due to the emissions control technologies. From this, a general understanding of particle production rate changes with NOX and SO2 emissions is plotted, and estimates of US coal-fired power plant production rate changes are created. Additional particle production mechanisms are discussed, in particular an exploration of particle production from ammonia slip of NOX emission controls.
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 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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".