Health assessment and mitigating solutions to heat-pollution induced by urban traffic
Bibliographic record
Abstract
In the context of global warming , urban heat island (UHI) effect is increasing with the rapid urbanization. As a major driver of urbanization, urban transportation sector emits tremendous heat, which intensifies the UHI and further increases the health risks. Meanwhile, under the impact of UHI, emissions of nitrogen oxides and other gases react to generate tropospheric ozone and other harmful pollutants, further degrading urban air quality and posing immense health threats. Therefore, real-time assessment of urban traffic related heat and pollution coupled health risks are imperative, further providing reasonable suggestions of mitigation measures . This study aims to develop a coupled hazard-vulnerability-exposure framework to obtain the hourly spatiotemporal patterns and real-time zoning of health risks from urban traffic. Four districts in Suzhou city are selected as study area, including Wu Zhong, Hu Qiu, Gu Su, and Xiang Cheng districts. Targeted risk alleviation strategies are proposed by reducing vulnerabilities based on blue and green infrastructures , medical infrastructures, and real time shade. Results showed that two peak risk hours were 11:00 and 15:00, with high risks of 49.1% across the study area. Peak risks in central zones were 1.8 times of those in peripheral regions. Medical infrastructure had the largest influence on vulnerability mitigation, followed by green infrastructure. The impact of real-time shading on reducing health hazards substantially increased during sunrise and sunset periods, thus the shading structures or tall trees within traffic zones can be augmented. This work can provide insights into developing urban health risk management and regulation strategies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".