Degradation of RDX, TNT, and HMX during EPA 8330B Sample Processing and Analysis of Soils under Hydrated Lime or Dithionite-Based Chemical Remediation
Bibliographic record
Abstract
Marc-Olivier Turcotte-Savarda & Sylvie Brochu*aa Defence Research and Development Canada, Valcartier Research Center, Québec, QC, CanadaCONTACT Sylvie Brochu Sylvie.Brochu@drdc-rddc.gc.ca Defence Research and Development Canada, Valcartier Research Center, 2459 de la Bravoure Road, Québec, QC G3J 1X5, CanadaColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/bssc.ABSTRACTThe remediation efficiency of soils containing energetic materials (EM) is assessed using SW-846 USEPA Method 8330B. However, the extraction, which is performed by sonicating the soil samples in acetonitrile for several hours, could lead to additional degradation of EM during sample processing, and consequently, to an overestimation of remediation efficiency. To verify this, soil samples that were spiked with controlled amounts of EM were briefly exposed to remediation reagents, such as MuniRem® (a commercial sodium dithionite-based formulation) or hydrated lime, and analyzed using SW-846 USEPA Method 8330B. The most affected EM of this study was 2,4,6-trinitrotoluene (TNT), for which complete degradation was observed after exposure to hydrated lime or pH-buffered MuniRem®. Losses of 1,3,5-trinitro-1,3,5-triazinane (RDX) reached 30 ± 20% upon treatment with full pH-buffered MuniRem® and 90 ± 10% when exposed to lime. The concentrations of 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX) were near the method’s lower limit of quantification, and subjected to large errors, which prevented us from drawing any clear conclusions regarding its degradation under the studied experimental conditions. These results highlight the necessity of performing appropriate soil sample treatments to quench the remaining hydrated lime or sodium dithionite prior to the extraction and analysis steps with SW-846 USEPA Method 8330B. Quenching of remaining remediation reagents may possibly be also required for other remediation reagents and EM.
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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".