Residues of Explosives in Groundwater Leached from Soils at a Military Site in Eastern Germany
Bibliographic record
Abstract
Abstract Sensitive analytical procedures are needed for detection of a broad range of explosive compounds and transformation products in groundwater. This study was thus conducted to (1) assess the utility of various instrumental techniques for detection of such substances in groundwater and to 2) isolate the principal toxic components leached from contaminated soils to groundwater at a former military site in Eastern Germany. The organonitrogen explosives and possible transformation products investigated, including aniline, 2‐NA, 4‐M‐2‐NA, 2‐M‐5‐NA, 2,4‐DNA, NB, 2‐NT, 3‐NT, 4‐NT, 2,6‐DAT, 2‐M‐3‐NA, 2,3‐DNT, 2,4‐DNT, 3,4‐DNT, 4‐A‐2‐NT, 2‐A‐4‐NT, TNB, 4‐A‐2,6‐DNT, 3,5‐DNA, 2‐A‐4,6‐DNT, 1,3‐DNB, 4‐MA, 2,6‐DNT, TNT, 3,5‐DNT, and 2,4‐DA‐6‐NT. Various instrumental methods were used, including LC/UV and LC/amperometric detection with mass spectrometry confirmation of analytes. GC/MS and LC/MS techniques using a Hewlett Packard MSD, a Finnigan MAT GCQ ion trap, and a Fisons AutospecQ were applied. Instrumental detection limits were about 0.4 µg/L using tandem mass spectrometry. For some sites, the groundwater was contaminated with up to 6000 µg/L per component, whereas the levels of transformation products were detected as low as 0.4 µg/L. The residues of explosives are believed to have leached directly from contaminated soils at the sites. Fractionation of groundwater samples followed by toxicity testing using microtox bioassays, revealed that the nitro compounds (NB, 2,4‐DNT, 2,4,6‐TNT, 4‐A‐2,6‐DNT, and 2‐A‐4,6‐DNT) were in general the principal acute toxic components at most sites investigated, whereas the mutagenic effects were assigned to the amino compounds. Acknowledgments This work was funded in part by the Germany‐Canada Bilateral Program and conducted in support of the PhD thesis of Katrin Spiegel. The support of Professor Welsch, University of Ulm, Germany, and Mr. Helmut Bianchi is gratefully acknowledged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".