Estimating Exposure by Loose-Coupling an Air Dispersion Model and a Geospatial Information System
Bibliographic record
Abstract
The regulation of air quality is important for ensuring the health of a population. Current air quality decision support systems are very useful if the user possesses sufficient data to operate them and the necessary expertise to interpret their results. In general, these systems suffer as a result of their excessive complexity. The present study describes the development of a scalable air quality decision support system using the CALPUFF air dispersion model and a Geospatial Information System (GIS). This system uses receptor level exposure modeling and outputs from CALPUFF to estimate the relative impacts on human populations from multiple air pollution sources by calculating intake, defined as the amount of pollution that is inhaled by a population and intake fraction, defined as the fraction of pollutant emitted by a pollution source that is inhaled by a population. Unlike ground level pollution concentration, intake and intake fraction consider receptors and offer a more valuable estimate of pollution exposure, especially when faced with limited input data. The system also leverages the inherent strength of GIS to improve accessibility of geospatial data by generating maps of ground level pollutant concentration, intake, and intake fraction using graduated color schemes. This enables any user to identify potentially hazardous pollution sources and prioritize decisions such as development, maintenance, and decommission.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".