The impact of a warming climate on ozone induced mortality
Bibliographic record
Abstract
BACKGROUND: Ozone concentrations are expected to increase in a warming climate. This rate of change in ozone concentrations with temperature is referred to as the climate penalty factor (CPF). We use a mathematical approach called the adjoint method to estimate the sensitivity of total mortality due to short-term ozone exposure in North America to changes in temperature at any location. AIMS: We aim to identify areas where change in temperatures has the largest impact on population mortality due to short-term exposure to ozone. We also try to quantify by what magnitude these impacts differ from one location to another, and why high impact locations exhibit such behavior. METHODS: We use the adjoint sensitivity analysis modules developed for the USEPA’s community multiscale air quality (CMAQ) model. This method enables us to differentiate between the North American mortality influences induced by changes in temperatures at various locations. We account for the impact of temperature rise on ozone through atmospheric chemistry, moisture content, and vegetative (biogenic) emissions. RESULTS: Our model-based estimations for CPF are in good agreement with regional trends observed in the eastern US (1.4 - 2.4 ppb/C). We estimate a total of 370 additional summertime deaths for each degree increase in atmospheric temperatures. Most importantly, the unique high-NOx environments in urban areas make their warming by far most influential in contributing to increased North American mortality. We attribute this significantly larger contribution in urban areas to the different role played by water vapor in such environments. CONCLUSIONS: We conclude that our results point to a) viability of urban NOx emission controls (i.e., mobile emission reductions) as a climate change adaptation measure, and b) sizeable contribution of the urban heat island (UHI) effect to North American air pollution mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".