Attenuation of Metals and Inorganics- Insights from Laboratory Testing Approaches.pdf
Bibliographic record
Abstract
Natural Attenuation of Metals and Inorganics: Insights from Laboratory Testing Approaches Authors Ms. Larissa Smith - Canada - SiREM Mr. Michael Healey - Canada - SiREM Mr. Jeff Roberts - Canada - SiREM Ms. Allison Kreinberg - United States - Geosyntec Consultants, Inc. Ms. Crystal Wilson - United States - Geosyntec Consultants, Inc. Mr. Lane Dorman - United States - Geosyntec Consultants, Inc. Mr. Andrzej Przepiora - Canada - Geosyntec Consultants, Inc. Abstract Laboratory treatability studies can be used to evaluate and optimize groundwater treatment options for contaminants found at coal combustion residual (CCR) sites. An important consideration in groundwater treatment selection is the suitability of monitored natural attenuation (MNA) as a treatment component or as a stand-alone passive treatment. This presentation will focus on the use of treatability testing to evaluate enhanced and natural attenuation treatment options for redox sensitive metals in CCR-impacted groundwater. In one demonstration, a comprehensive laboratory program was performed based upon the USEPA’s tiered approach to identify the natural attenuation processes, rates, attenuation capacities, and longevity of arsenic (As),lithium (Li), sulfate (SO42-), and boron (B) impacted groundwater from CCR impoundment sites. At the Site, the testing was used to develop site-specific sorption coefficients, demonstrate that after sorption, potential desorption back into groundwater would not be affected by redox conditions, and provide insight into the attenuation mechanisms. These findings helped to support MNA as part of the corrective action plan submitted to the regulator. In a second demonstration, zero valent iron (ZVI) was used to reduce and sorb As from groundwater. The results from the treatability study were used to support the feasibility assessment for the Site.
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 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 | 0.000 | 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) | 0.000 | 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".