Phytoremediation: Climate change resilience and sustainability assessment at a coastal brownfield redevelopment
Bibliographic record
Abstract
Phytoremediation offers a nature based solution (NBS) for contaminated soil remediation; however, its application under a brownfield redevelopment context has not been well studied. Moreover, climate change could impact large numbers of contaminated sites, yet there remains little research on the potential impacts for remediation. This study examined phytoremediation at a brownfield redevelopment in the San Francisco Bay area, where thousands of cleanup sites are vulnerable to rising sea levels. Life cycle assessment (LCA) was used to determine both primary and secondary impacts and the system's resilience to various sea level scenarios and hydroclimatic conditions was investigated. It was found that the phytoremediation project rendered only a small environmental footprint, and was associated with low cost and substantial socioeconomic benefits. For instance, it fitted well with the site redevelopment setting by offering attractive landscape features. Moreover, under a modeled moderate sea level rise scenario, the groundwater hydraulic gradient at the site decreased, which was coupled with greater natural biodegradation and reduced plume migration, and, therefore, lower life cycle impact. There was also minimal increase in the vapor intrusion risk with increased sea level. Overall, phytoremediation at the site was found to be resilient to a moderate sea level rise and other hydroclimatic effects induced by climate change. However, the system performance responded to increasing sea level rise in a non-linear manner. Under a high sea level rise scenario, the system is predicted to perform abruptly worse.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".