A digestion method for trace metals recovery from oil and grease contaminated soils
Bibliographic record
Abstract
Oil and grease contaminated soils are difficult to digest using the common HNO 3 –H 2 O 2 digestion method. Even after an overnight pre‐digestion with HNO 3 and subsequent heating, the addition of H 2 O 2 caused violent reactions in soils with oil and grease, resulting in the loss of the samples. At low metal concentrations at least a gram of soil needs to be digested to obtain concentrations measureable by flame atomic absorption spectrometry (FAAS) or inductively coupled plasma–atomic emission spectrometry (ICP‐AES). We developed a modified procedure using HNO 3 –HClO 4 for the analysis of total trace metals that can be used on all types of soils including those with oil and grease. Recovery rates of 99, 94, 114, 92, and 83% for Cd, Cu, Ni, Pb and Zn, respectively, were obtained for standard reference material (SRM) NIST 2710 (Montana Soil). Soils with ≈1000 mg kg −1 oil and grease were successfully digested and gave extract concentrations suitable for analysis on FAAS or ICP‐AES. Compared to the common HNO 3 –H 2 O 2 soil digestion method, the proposed method was as effective (no significant difference) in extracting Cu, Pb, and Zn and was significantly better in liberating Ni from the soil. With the HNO 3 –H 2 O 2 soil digestion method the Cd concentrations were often below the limit of detection by FAAS but were measureable in the HNO 3 –HClO 4 digests. Variability of results using the proposed method was reduced in some cases.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".