Phytoremediation: Climate change resilience and sustainability assessment at a coastal brownfield redevelopment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.004 | 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 it