Modelling Air Quality in a Rural Area of Unconventional Oil and Gas Development for Health and Justice
Bibliographic record
Abstract
Located in northeastern British Columbia, Canada, is the Montney Formation, one of the largest stores of natural gas in North America. Horizontal drilling and hydraulic fracturing, together referred to as unconventional oil and gas development (UOGD), are a public health concern that has been largely unexplored in Canada. Unconventional oil and gas development contributes to environmental hazards, including air pollution, that have been found to impact human health and contribute to disproportionate exposures amongst systemically and structurally disadvantaged communities. In this dissertation, I present three studies: two that demonstrate novel methods for estimating air pollution in regions with minimal ground monitoring and intensive UOGD and one that explores possible disproportionate exposures to air pollution and oil and gas-related hazards throughout nearby communities. In the first study, I develop land use regression models to estimate recent concentrations of 12 gases and particles for a prospective birth cohort in northeastern British Columbia. I use these estimates to explore the spatial and temporal variation of air pollutants in this region and provide air pollution concentrations at the homes of pregnant individuals. In the second study, I combine machine learning with satellite datasets to estimate and backcast eighteen years of fine particulate matter concentrations, capturing daily variations in pollutant concentrations. This study explores temporal variations of air pollution, assesses long-term trends, and supports a retrospective study of UOGD and asthma exacerbations. The final study uses an environmental justice lens to explore possible disproportionate exposures to air pollution and oil and gas-related hazards amongst socioeconomically vulnerable communities and Indigenous populations. This dissertation makes significant contributions to the fields of air pollution and environmental justice research relating to UOGD and rural communities. I demonstrate frameworks for modelling air pollution and assessing environmental justice in regions of resource extraction and minimal monitoring using publicly available sources of data. This work also supports two unique epidemiological studies in northeastern British Columbia: one exploring UOGD and birth outcomes and another investigating possible associations between UOGD and asthma exacerbations. As natural gas extraction is expected to continue to increase in this area alongside the growing liquified natural gas industry in British Columbia, the three unique studies of this dissertation pave the way for a larger investigation of the impacts of UOGD in remote areas of Canada and environmental justice implications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".