Multi-criteria decision analysis in assessing watershed scale pollution risk: a review of combined approaches and applications
Bibliographic record
Abstract
Decision-making tools have become a prominent methodology in watershed management for many years due to the complexity of environmental systems and requirement for multi-disciplinary expertise. Multi-Criteria Decision Analysis (MCDA) is a systematic methodology, which combines hierarchical structures of a problem and priorities for the alternatives in many fields. This study reviews MCDA applications in pollution risk assessment in the abiotic environments of watersheds for multi-pollutants. Over 80 papers published between 2000 and 2021 are identified in three categories of the Web of Science Core Collection database: “Environmental Sciences”, “Environmental Studies”, and “Water Resources”. The publications are further classified according to different environmental compartments: surface water, groundwater, and soil to investigate MCDA applications in these matrices. Finally, the distribution of the publications according to contaminants and MCDA methods used are also examined. The results reveal that the number of the studies focusing on pollution risk assessment within watersheds has been significantly increasing, especially over the last decade. However, there are still limited MCDA applications linking environmental compartments. Despite several MCDA studies focusing on the vulnerability of watersheds to multiple pollutants, studies related with emerging pollutants are scarce. Moreover, compared to non-point source pollution, studies adopting MCDA to investigate pollutant concentrations coming from point sources are relatively few. According to the overall distributions of MCDA methods, Analytic Hierarchy Process, a commonly found method in the literature that adopts a technique of pairwise comparison to prioritize criteria of prominence, dominates 34% of the publications.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 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".