Unraveling the Fate and Transport of DNAPLs in Heterogeneous Aquifer Systems—A Critical Review and Bibliometric Analysis
Bibliographic record
Abstract
Dense nonaqueous phase liquid (DNAPL) in the subsurface environment beyond the permissible limit poses a threat to human health and a suite of ecological services. An accurate prediction of the concentration and mass fluxes of DNAPL at environmentally sensitive locations and their temporal variations can be obtained using robust and efficient fate and transport mathematical models. Thus, this study evaluated 412 articles published from 1990 to 2022 utilizing the Scopus® database to provide a quantitative overview of the present trends and future perspectives of the DNAPL transport research field, especially fate and transport models via bibliometric analysis. The major findings of the published literature based on the scale of the study and type of modeling framework, relationships of governing parameters with a scale of study, and recent developments in the mathematical models were discussed. The country-citation analysis revealed the USA and Canada as leading countries in DNAPL fate and transport research field. The findings of this study uncovered a need for studies considering low-permeability and stagnant regions, as well as the variable nature of the flow, transport, and reaction parameters to understand the complex plume evolution dynamics of DNAPLs under field-scale conditions. The nonlinear interactions between various flow and transport phenomena should be quantified during a model’s development by applying spatial- and time-varying global sensitivity analyses. The outcomes of the bibliometric survey, visual analysis, and concise review presented in this study can provide a wide range of references, emerging topics, and prospects to emphasize less focused on topics of DNAPL transport research.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.099 | 0.098 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".