Trace element speciation under coal fired power station conditions
Bibliographic record
Abstract
Abstract:- Coal combustion from power stations is one of the largest contributors of potentially toxic trace elements to the environment. Some trace elements may be released in range of valencies, often with varying toxicity and bioavailability. Hence, determination of trace element speciation in coals and their combustion products is important for conducting comprehensive risk assessments of the emissions from coal-fired power stations. This study focuses on speciation of selected trace elements, As, Cr, and Se, in coal combustion products and Hg in flue gas, which were sampled at one Australian power station. Results showed that As, Cr and Se are all present in a range of valency states in coal. Concentrations of As and Se in the bottom ash as well as the more toxic hexavalent chromium were less than the detection limits. Fly ash contained the more toxic As3+ form at 10 % of total arsenic, while selenium was mainly found in Se4+ form. Hexavalent chromium (Cr6+) in fly ash was 2.7 % of the total fly ash chromium. Mercury speciation in flue gas was determined using the Ontario Hydro sampling train and analysis technique. Approximately 58 % of the total mercury in flue gas was released in the elemental form (Hg0), which has the highest residence time in the environment among all mercury species.
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".