Distribution Characteristics of Total Mercury in Imported Coals at Shanghai Port
Bibliographic record
Abstract
As China swings from being a net coal exporter into the world's largest coal importer, mercury in imported coals has become an increasingly significant source of atmospheric mercury pollution. The research of distribution characteristics of total mercury in imported coals could be a significant initiative for scientific assessment of the immigration risk of mercury in coal imports and protecting environment security. Based on the American Environmental Protection Agency method 7473, which is suitable for soil samples, sediments, sludge, wastewater and groundwater, total mercury concentrations in 123 imported coal samples at Shanghai Port were determined using a direct mercury analyzer. The robust statistical description of total mercury content in imported coals shows that the median of mercury concentrations in 123 imported coals is 0.043 mg/kg and the Norm IQR is 0.025 mg/kg. On the basis of Chinese coal industry standard MT/T 963—2005, the imported coals at Shanghai Port are mainly special low mercury coal and low mercury coal. It is worthy of attention that medium mercury coal and high mercury coal were found in Indonesian coals. The occurrence modes of mercury in coal affect its final emissions, which has a guiding significance on mercury removal technology. The correlation analysis of ash, sulfur and mercury content shows that the occurrence modes of mercury are mainly sulfide form in Indonesian coals and Russian coals based on the relationship between total mercury and sulfur content instead of ash content. According to the positive relationship between total mercury and sulfur content and the negative relationship between total mercury and ash content, the mercury was contained in organic matter in Canadian coals. According to the negative relationship between total mercury and sulfur content and the positive relationship between total mercury and ash content, the mercury was contained in aluminum silicate form in Australian coals. Compared to cold atomic absorption spectrometry, hydride generation atomic fluorescence spectrometry and other traditional analysis methods, the direct mercury analyzer method for determination of mercury in coal established in this paper shorten the inspection process, improve work efficiency, and is worthy of popularization and application. The research of distribution characteristics of total mercury in imported coals could provide reference for scientific assessment of the immigration risk of mercury in imported coals and comprehensive utilization of imported coals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".