Comparative study of DRASTIC-LU and radioactive isotope approaches for assessing groundwater vulnerability to pollution: the case study of Abuja, North Central Nigeria
Bibliographic record
Abstract
Groundwater resources in Abuja, North Central Nigeria, are facing increasing vulnerability to pollution due to urbanization and anthropogenic activity. There are several methods of assessing groundwater vulnerability to contamination. Choosing the appropriate method for the study site, which is paramount for accurate vulnerability assessment, is sometimes very tasking. The DRASTIC-LU and 3H radioactive tracer methods were assessed in this study and applied to the Abuja aquifers. Water scientists have widely adopted these methods in vulnerability assessments. From the final DRASTIC-LU vulnerability map, it was observed that high to very high vulnerability areas were located in southwestern and northeastern parts of the area. 33% of the wells in the entire area exceed the nitrates statutory limits for drinking water, while 87% of the wells exceeding the nitrates statutory limits were located in high to very highly vulnerable areas. The radioactive tracers provided information on an Abuja aquifer-wide basis, showing areas of most recent recharge (post-nuclear) and older recharge (pre-nuclear), respectively. With the tracer approach, areas of preferential flow and diffuse flow indicating high vulnerability and low vulnerability, respectively, were mapped. This systematic review discusses in detail the advantages and limitations of these methods employing the origin-pathway-target model of vulnerability assessments as the basics of the study. Finally, the application of a dual approach involving the combination of the two is best for vulnerability assessments. This study therefore proposes the need for policymakers to adopt combined methodological approaches for sustainable groundwater management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".