From Legacy to Emerging Groundwater Contaminants: Combining Advanced Monitoring Tools to Assess Sources and In Situ (Bio)Transformation
Bibliographic record
Abstract
Aquifers worldwide are under increasing pressure from the widespread use of synthetic chemicals, many of which, alone or as a mixture, are persistent, mobile, and toxic. Legacy and emerging organic contaminants pose major challenges for groundwater remediation, often involving substantial cleanup costs and requiring efficient monitoring strategies to trace their sources and transformation mechanisms. Groundwater bacteria have demonstrated remarkable capabilities to degrade diverse organic contaminants, particularly petroleum hydrocarbons and chlorinated solvents. However, the biotransformation of emerging contaminants, for example, pesticides and per- and polyfluoroalkyl substances (PFAS), remains only partially understood. This review explores the potential of integrating conventional hydrogeochemical monitoring with multielement compound-specific isotope analysis (ME-CSIA), biomolecular tools, and reactive transport modeling to evaluate sources and in situ contaminant transport and transformation. This integrative approach can provide decisive information on contaminant removal processes and enhance the selection of remedial actions and the evaluation of remediation efficiency. Furthermore, the synergy observed in multidisciplinary case studies has significantly advanced our ability to elucidate the sources and transformation of legacy contaminants in groundwater. This foundation is now essential for transferring knowledge to address the complex challenges posed by the pervasiveness of emerging contaminants in groundwater and develop suitable remediation and monitoring strategies.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".