Building Urban Resilience with Nature-Based Solutions: A Multi-Scale Case Study of the Atmospheric Cleansing Potential of Green Infrastructure in Southern Ontario, Canada
Bibliographic record
Abstract
Green infrastructure is a nature-based solution that supports sustainable development and restores urban, suburban, and peri-urban environments. Using a multi-scale evaluation, this study explores the impact of the application of green infrastructure, as a form of atmospheric cleansing, on tropospheric nitrogen dioxide. The impacts are not limited to specific green infrastructure treatments nor geographic location and land use type. Using both site-specific stationary air monitoring and coarser resolution satellite derived remote sensing, this study demonstrates the nature-based remediation effect of green infrastructure on nitrogen dioxide concentrations in Southern Ontario, Canada. At these scales, remote sensing and stationary air monitoring observations support the hypothesis that green infrastructure can cleanse the atmosphere by reducing nitrogen dioxide through scavenging by trees and dense vegetation at the neighbourhood level, consistent with the findings from microscale field campaigns. The study showed a clear link between compact, built-up, industrialized areas and higher nitrogen dioxide levels at the mesoscale, particularly notable to the west of the city of Toronto. Nature-based solutions provide an opportunity to address the impacts of urbanization, increase climate resilience, and support healthy urban environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".