[Analysis of pollution characteristics of solid waste incinerator fly ash in Zhejiang province].
Bibliographic record
Abstract
Pollutants characteristics, especially in components and contents of heavy metals and PCDD/Fs, were chemical analyzed and toxicological evaluated in fly ash from 6 typical kinds of solid waste incinerators in Zhejiang province. The results indicated that the main elements in fly ash were Si, Ca, Al, Fe, K, Na and Cl. Among all kinds of heavy metals, the Zn content was the highest one, whose average was up to 9 458 mg/kg. Meanwhile, the Cd, Zn, Cu,Cr,Ni, Pb and As contents in the fly ash samples were 642, 127, 22, 18,15, 10 and 2-fold of those in polluted soil, respectively. The leaching ratios of heavy metals in fly ash concluded according to the HJ/T 299-2007 Procedure were lower than its limitation. However, the leaching ratios from NB and LS sample concluded according to the Toxicity Characteristic Leaching Procedure (TCLP) exceeded its limitation. The TEQ of PCDD/Fs from all samples were lower than the limitation in GB 5085.6-2007, while higher than the soil-limitation in Canadian, New Zealand and Sweden, with 105, 59, 401, 369 and 5-fold in the sample HZ, WZ, NB, TZ and HUZ, respectively. It could be concluded that the components and contents of the pollutants were various from different fly ashes. The components of heavy metals were mainly affected by the type of solid wastes. And the technology of the incinerator played important role in the pollution characteristics in fly ash. Thus, it is of significant meaning to study the particulate pollution characteristics of a certain fly ash before disposal or reuse for the purpose of adequate risk assessment and management.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".