Nature-based solutions to capture atmospheric pollutants in urban ecosystems
Bibliographic record
Abstract
Atmospheric pollution is a social problem reflected in cities due to pollutants contributing to various adverse effects on society.Over the last few years, experts have been arguing that cities may play a positive role in the resilience and adaptation strategies against atmospheric pollutants and climate change effects. Recently, Nature-based Solutions (NBS) started to be implemented, focusing on solving environmental problems in place of sole human intervention. The main purpose of this contribution is to apply NBS solutions in Mexico City and the City of Calgary, as well as to compare their effectiveness in both countries.Our contribution begins with previous research conducted about the most feasible NBS to be applied to those cities. The selected solution was the Ecosystem-based adaptation through pocket parks. Consequently, six pocket parks were visited in both cities and a database was created with descriptions of each park. In addition, a historical air quality database of those cities was created too, with the purpose of studying if NBS positively contributes to the decrease of atmospheric pollutant concentrations especially in the areas where pocket parks are placed.These databases were processed through data visualization software, which concluded that the area of the pocket parks, the quantity, and the species of trees in each park may have an important influence on pollutant reduction through the studied NBS.On the other hand, pocket parks have additional features that maintain their importance on the NBS since they have social benefits and contribute against the effects of climate change on cities. This study concludes by recognizing the importance of creating as many recreational spaces as possible that include features that address the needs of cities and citizens in building a better urban environment.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".