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Record W2463107592

[Analysis of pollution characteristics of solid waste incinerator fly ash in Zhejiang province].

2011· article· en· W2463107592 on OpenAlexaboutno aff
Dongsheng Shen, Yuan-Ge Zheng, Jun Yao, Meizhen Wang, Yu Zhang, Feng-Tao Wang, Xin-Gen Fan

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashLeaching (pedology)Toxicity characteristic leaching procedureIncinerationPollutantEnvironmental chemistryMunicipal solid wastePollutionEnvironmental scienceHeavy metalsParticulatesChemistryWaste managementMetallurgySoil waterMaterials scienceSoil science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.181
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes1
Has abstractyes

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