Emission Characteristics of Mercury in Zn Smelting Process
Bibliographic record
Abstract
Stationary combustion sources such as coal-fired power plants, waste incinerators, industrial manufacturing, etc. are recognized as major sources of mercury emissions. Due to rapid economic growth, zinc production in Korea has increased significantly during the last 30 years. Total zinc production in Korea exceeded 739,000 tons in 2008, and Korea is currently the third largest zinc producing country in the world. Previous studies have revealed that zinc smelting has become one of the largest single sectors of total mercury emissions in the World. However, studies on this sector are very limited, and a large gap in the knowledge regarding emissions from this sector needs to be bridged. In this paper, Hg emission measurements were performed to develop emission factors from zinc smelting process. Stack sampling and analysis were carried out utilizing the Ontario Hydro method and US EPA method 101A. Preliminary data showed that <TEX>$Hg^0$</TEX> concentrations in the flue gas ranged from 4.56 to <TEX>$9.90\;{\mu}g/m^3$</TEX> with an average of <TEX>$6.40\;{\mu}g/m^3$</TEX>, Hg(p) concentrations ranged from 0.03 to <TEX>$0.09\;{\mu}g/m^3$</TEX> with an average of <TEX>$0.04\;{\mu}g/m^3$</TEX>, and RGM concentrations ranged from 0.23 to <TEX>$1.17\;{\mu}g/m^3$</TEX> with an average of <TEX>$6.40\;{\mu}g/m^3$</TEX>. To date, emission factors of 7.5~8.0 g/ton for Europe, North America and Australia, and of 20 or 25 g/ton for Africa, Asia and South America are widely accepted by researchers. In this study, Hg emission factors were estimated using the data measured at the commercial facilities as emissions per ton of zinc product. Emission factors for mercury from zinc smelting pross ranged from 4.32 to 12.96 mg/ton with an average of 8.31 mg/ton. The emission factors that we obtained in this study are relatively low, considering Hg contents in the zinc ores and control technology in use. However, as these values are estimated by limited data of single measurement of each, the emission factor and total emission amount must be updated in future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.001 | 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 teacher head, 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".