Spatiotemporally Detailed Quantification of Air Quality Benefits of Emissions–Part II: Sensitivity to Study Parameters and Assumptions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Adjoint modeling, using U.S. EPA’s Community Multiscale Air Quality (CMAQ), has been performed to provide location-specific monetized health benefits from the controls of primary PM 2.5 and PM 2.5 precursors (NO x, SO 2, and NH 3 ) across North America. Source-to-health benefit relationships are quantified using a benefit-per-ton (BPT) metric, accounting for the impacts on premature mortality due to long-term exposure to fine particulate matter. In the base analysis, the approach used a 12 km resolution, four 2-week episodes chosen to capture annual responses, emissions for 2016, and the Global Exposure Mortality Model (GEMM) to link exposures to premature mortality. Here, we investigate the impacts those choices have on results using a range of sensitivity analyses. The choice of four representative episodes led to relatively little bias and error. Finer model resolution, investigated by comparing 36, 12, 4, and 1 km simulations over two urban areas, tended to increase BPT estimates, though the impact was inconsistent between different regions. While BPTs and burden estimates were consistent across resolutions over New York City, they sharply increased for Los Angeles, particularly for NOx and ammonia, leading to 90% increase in burden estimates at 1 km resolution. We find that, for primary PM 2.5 emissions, better resolved population distribution is the main contributing factor to higher BPTs, but for secondary precursor emissions (ammonia and NOx), higher model resolution that avoids dilution in coarser grids is more important. Changing emissions from 2016 to 2001 and 2028 resulted in fairly consistent primary PM 2.5 BPTs but impacted the BPTs for NOx and ammonia more significantly due to changes in SO 2 emissions. We found that BPTs tend to stabilize, as emission changes in 2028 lead to a lower deviation from 2016 BPTs compared to changes from the 2001 episode. The role of the epidemiological model also led to relatively modest uncertainties, 15–30% depending on the species, even when different shapes of concentration–response functions were employed. We found the impact of the choice of CRF to be larger or comparable in size to the reported epidemiological model uncertainties for log–linear CRFs. The adjoining approach proved robust to modeling choices in providing BPT estimates that are highly granular across locations and emitted species.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".