Bridging the Divide between Air Quality Monitoring, Management and Policy in the Sea-to-Sky Airshed: A method for analyzing and interpreting large volume air quality data for management and policy guidance
Bibliographic record
Abstract
Air pollution has increasingly been the focus of management and policy efforts since the early 1950s. Networks of monitoring stations for data to inform, create, focus, assess and improve air pollution management and policy. However, monitoring systems can become disconnected from air quality management and policy without analysis and interpretation to bridge the divide. This thesis develops a method of analyzing and interpreting large volume air quality data into key air pollutant trends and characteristics to guide air quality management and policy. The method is applied to air quality data between 2002 and 2013 in the Sea-to-Sky Airshed, located in south-western British Columbia, Canada. At the time of study, this airshed contained a monitoring system that had been growing increasingly disconnected from the airshed’s air quality management and policy. Applying this method uncovered significant instances of inaccurate and missing air quality data, and identified the airshed’s key pollutant trends and characteristics. These findings were then used to create recommendations for improving the resource efficiency and quality of the airshed’s monitoring, management and policy. Also identified were applications of R and R’s OpenAir package which are estimated to significantly reduce analysis time and offer additional analysis options.
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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 | 0.026 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".