Assessing the Air Quality Health Benefits of Location-Specific Emission Controls: A Source Attribution Study using Adjoint Sensitivity Analysis
Bibliographic record
Abstract
Background. Air quality management strategies that seek to maximize the benefits of emission control policies are best informed by source-specific air quality damage estimates. Adjoint sensitivity analysis is a tool that allows modellers to differentiate between emissions in different locations and their influences on air quality-related health effects and can thus guide emission reduction policies to preferentially target sources whose emissions are most influential on health. Aims. In this work, we aim to create a streamlined approach for comparing health benefits to costs of air pollution abatement using the source-specific information offered by adjoint sensitivity analysis. Methods. We use the adjoint of CMAQ to integrate epidemiological data with adjoint chemical transport modeling. We attribute national averted mortality in Canada and the U.S. (expressed in monetary terms as a “health benefit”) to location-specific emission reductions of NOx, VOCs and other species in North America. Results. Our past work on ozone and NO2-related mortality suggests significant spatial variability of health benefits related to emission reductions across North America. The largest Canadian health benefits come from emission reductions upwind of Toronto; reaching upwards of $250K/day and $50K/day (for 10% reductions in NOx and VOCs). For the U.S., we observe consistently higher-magnitude influences, and in some major urban areas we estimate negative influences (disbenefits) of controlling emissions (e.g., -$680K/day from New York for NOx) whose interpretation requires consideration for long-term air quality planning. As a continuation of past work, we conduct a multi-pollutant analysis and investigate the temporal component of health benefit influences related to emission controls in various locations. Conclusions. The source specificity of adjoint heath benefit influences offers valuable information for selective targeting of emissions for strategic air quality management.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".