Optimal design of nature-based solutions in highway runoff management based on resilience to climate and pollution load changes
Bibliographic record
Abstract
Sedimentation ponds (SPs) are nature-based solutions (NBSs) for sustainable stormwater management. SPs control the quantity and quality of runoff and promote biodiversity. Hence, the optimal design of SPs is crucial for ecosystems resilience in urban and natural environments. This study aims to optimize the design of roadside SPs in terms of location and surface area, considering the resilience to stressors such as climate changes and pollution load variations. Accordingly, the highway runoff in a new 22 km highway (E18 Arendal-Tvedestrand) in southern Norway was simulated by the storm water management model (SWMM). The quantity and quality (BOD and TSS values) of highway runoff in all probable scenarios of existing uncertainties were estimated for potential outfall points using the repeated execution model of SWMM coded in MATLAB®. The scenarios were defined based on applying best management practices (BMPs), including grass swale and infiltration trench in different sections of the road that work before SPs, climatic (rainfall quantity estimated by the LARS-WG model), and modeling uncertainties (buildup and washoff coefficients). The generated dataset was then applied to assess the resilience of sedimentation ponds in potential outfalls to climate change and pollution load shocks. The resiliency was quantified for three metrics, including the quantity and quality of receiving runoff to sedimentation ponds and biodiversity in ponds over 25 years (2020-2045). The biodiversity index was defined based on Shannon's Entropy computed from field observation in 12 highway sedimentation ponds across Norway. Using this procedure, it was determined that the proper arrangement of BMPs along the road and the optimal design of ponds enhance the resilience of SPs by 40% over time. This study makes important contributions to stormwater management, the resilient design of NBS, and achieving UN SDG6 (Clean water and sanitation).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".