The influence of CMAQ model resolution on predicted air quality and associated health impacts
Bibliographic record
Abstract
Air quality models (AQM) paired with epidemiological data are often used to estimate health related burdens from pollution exposure.The accuracy of these predictions depends, among other factors, on the horizontal resolution of the model.This thesis aims to quantify the impact of horizontal grid resolution by simulating health impacts due to O 3, NO 2 , and PM 2.5 exposure using various model and input resolutions.Adjoint sensitivity analysis was used to predict health impacts, producing results with respect to emissions sources.The results indicate that coarse modeled health impacts underestimate maxima in urban areas and overestimate in rural areas with proximity to urban cores.Additionally, coarse modelling does not sufficiently display spatial variance.Concentration exposure relationships applied to AQM predictions were also used to estimate health impacts.The resolution of individual inputs (meteorology, emissions, and population) were altered to examine which processes were most responsible for the differences due to resolution.The resolution of population had the largest impact on health impact results for all species.The resolution of meteorology and emissions impacted the species to different extents; O 3 was more impacted by meteorology, while NO 2 and PM 2.5 were more influenced by emissions.The influence of specific emission sources can be more adequately determined at fine resolution, benefiting air quality control.Aggregated estimates across the domain did I want to acknowledge the numerous amount of people who have helped and contributed to my completion to this thesis.First off, thank you to my supervisor Dr. Amir Hakami.Your advice, comments, guidance and specifically your patience,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".