Current Management Status of Mercury Emissions from Coal Combustion Facilities - International Regulations, Sampling Methods, and Control Technologies
Bibliographic record
Abstract
Mercury (Hg), which is mainly emitted from coal-fired power plants, remains one of the most toxic compounds to both humans and ecosystems. Hg pollution is not a local or regional issue, but a global issue. Hg compounds emitted from anthropogenic sources such as coal-fired power plants, incinerators, and boilers, can be transported over long distances. Since the last decade, many European countries, Canada, and especially the United States, have focused on technology to control Hg emissions. Korea has also recently showed an interest in managing Hg pollution from various combustion sources. Previous studies indicate that coal-fired power plants are one of the major sources of Hg in Korea. However, lack of Hg emission data and feasible emission controls have been major obstacles in Hg study. \n In order to achieve effective Hg control, understanding the characteristics of current Hg sampling methods and control technologies is essential. There is no one proven technology that fits all Hg emission sources, because Hg emission and control efficiency depend on fuel type, configuration of air pollution control devices, flue gas composition, among others. Therefore, a broad knowledge of Hg sampling and control technologies is necessary to select the most suitable method for each Hg-emitting source. \n In this paper, various Hg sampling methods, including wet chemistry, dry sorbents trap, field, and laboratory demonstrated control technologies, and international regulations, are introduced, with a focus on coal-fired power plants.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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 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".