EFFECTS OF WOOD SPECIES AND RETENTION LEVELS ON REMOVAL OF COPPER, CHROMIUM AND ARSENIC FROM OUT-OF SERVICE CCA TREATED WOOD USING CALCIUM HYPOCHLORITE
Bibliographic record
Abstract
Chromated copper arsenate (CCA) has been one of the most widely used since the 1933’s as a wood preservative to protect wood against decay and insects for exterior applications such as decks, housing, utility poles, play equipment and fences. Although its major use for consumer lumber products was replaced with chromium- and arsenic-free preservatives in the USA, Canada and European Countries by the end of 2003, large volumes of CCA-treated wood has been still coming out-of-service each year. Therefore, at present, the potential loss of heavy metals from CCA-treated wood waste after disposal has become an important problem. The disposal of out-of service CCA treated wood materials at the end of its useful life remains a concern, despite the reduction in CCA-treated wood production. Waste management options for out-of service CCA-treat wood are limited and include minor amounts of recycling and incineration with the remainder going to landfill disposal. Because of new regulations and the concerns about environmental contamination, there is a need in developing methods for recycling out-of-service CCA-treated wood products. Acid extraction, with or without bioremediation, has been extensively studied for removal of copper, chromium and arsenic from out-of service CCA-treated wood. However, none of the researchers addressed the problem about the effects of wood species and retention levels on remediation efficiency. The objectives of this study were to investigate the effects of wood species and retention levels on removal of copper, chromium and arsenic from CCA-treated wood samples using calcium hypochlorite. The results showed that Ca(ClO)2 was very effective to remove copper, chromium and arsenic from CCA-C treated milled wood samples for all three species used in this study. Ca(ClO)2 may be very good option to remediate out-ofservice CCA treated wood not only because of its high and relatively fast extraction efficiency but also because the chemical is relatively inexpensive. However, the results showed that wood species and initial retention levels of CCA-treated wood products played very important role in terms of removal of Cu, Cr and As. Therefore, whichever the remediation methods and chemicals are used for the removal of Cu, Cr and As, initial retention levels of the CCA treated wood products should be taken into account and CCA-treated wood products should be sorted according to their initial retention levels and wood species (if possible) before remediation process.
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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 | 0.000 | 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 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".