Source Attribution of Societal Impacts of PM2.5 Pollution from Regional to Hemispheric Scales
Bibliographic record
Abstract
Chronic exposure to ambient PM2.5 concentrations is one of the leading worldwide mortality risk factor, which has seen an increase over the last decade.Sensitivity analysis is an essential approach in attributing societal impacts to emissions sources in any geographic scale.Source attribution of the societal impacts of PM2.5 pollution offers great potential for informing policy development and implementations.Assessing the location-specific societal benefits of reducing emissions from a regional to hemispheric scale is the major motivation of this work because exposure to ambient PM2.5 concentration is not only a regional concern but also global.This thesis employs adjoint sensitivity analysis, integrating demographic, epidemiological, and economic data, in a full-complexity modeling approach to link the sources of emissions to societal impacts.This study uses the adjoint of U.S. EPA's Community Multiscale Air Quality (CMAQ) model (CMAQ-ADJ) to provide location-specific source attribution of the societal burden of PM2.5 pollution.CMAQ-ADJ model is extended from the regional to the hemispheric platform, including boundary transport of the societal impacts and updates to the chemical representation.The monetized health impacts of coal phase-out regulation in Ontario were retrospectively assessed, as well as those for planned phase-out across Canada.Backward boundary conditions in adjoint model were implemented for the first time for the estimation of the health burden over Canada.Our findings suggest that the coal phase-out had substantial, albeit lower than previously predicted, health benefits within the province, and that the choice of the epidemiological model has an impact on the estimated health benefits.We also evaluate the transboundary impact of US coal-fired electricity generation and its emission control measures over the same period, and find the benefits from U.S. emission reductions to be larger than those from Ontario coal phase-out.continuous learning process.His wise advice, inspiration, support, comments, guidance, and encouragement lead me throughout my Ph.D. journey.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".