Mercury emission and speciation of coal-fired power plants in China
Bibliographic record
Abstract
Abstract. Comprehensive field measurements are needed to understand the mercury emissions from Chinese power plants and to improve the accuracy of emission inventories. Characterization of mercury emissions and their behavior were measured in six typical coal-fired power plants in China. During the tests, the flue gas was sampled simultaneously at inlet and outlet of Selective Catalytic Reduction (SCR), electrostatic precipitators (ESP), and flue gas desulfurization (FGD) using the Ontario Hydro Method (OHM). The pulverized coal, bottom ash, fly ash and gypsum were also sampled in the field. Mercury concentrations in coal burned in the measured power plants ranged from 17 to 385 μg/kg. The mercury mass balances for the six power plants varied from 87 to 116% of the input coal mercury for the whole system. The total mercury concentrations in the flue gas from boilers were at the range of 1.92–27.15 μg/m3, which were significantly related to the mercury contents in burned coal. The mercury speciation in flue gas right after the boiler is influenced by the contents of halogen, mercury, and ash in the burned coal. The average mercury removal efficiencies of ESP, ESP plus wet FGD, and ESP plus dry FGD-FF systems were 24%, 73% and 66%, respectively, which were similar to the average removal efficiencies of pollution control device systems in other countries such as US, Japan and South Korea. The SCR system oxidized 16% elemental mercury and reduced about 32% of total mercury. Elemental mercury, accounting for 66–94% of total mercury, was the dominant species emitted to the atmosphere. The mercury emission factor was also calculated for each power plant.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".