Advances in science and applications of air pollution monitoring: A case study on oil sands monitoring targeting ecosystem protection
Bibliographic record
Abstract
The potential environmental impact of air pollutants emitted from the oil sands industry in Alberta, Canada, has received considerable attention. The mining and processing of bitumen to produce synthetic crude oil, and the waste products associated with this activity, lead to significant emissions of gaseous and particle air pollutants. Deposition of pollutants occurs locally (i.e., near the sources) and also potentially at distances downwind, depending upon each pollutant’s chemical and physical properties and meteorological conditions. The Joint Oil Sands Monitoring Program (JOSM) was initiated in 2012 by the Government of Canada and the Province of Alberta to enhance or improve monitoring of pollutants and their potential impacts. In support of JOSM, Environment and Climate Change Canada (ECCC) undertook a significant research effort via three components: the Air, Water, and Wildlife components, which were implemented to better estimate baseline conditions related to levels of pollutants in the air and water, amounts of deposition, and exposures experienced by the biota. The criteria air contaminants (e.g., nitrogen oxides [NOx], sulfur dioxide [SO2], volatile organic compounds [VOCs], particulate matter with an aerodynamic diameter <2.5 μm [PM2.5]) and their secondary atmospheric products were of interest, as well as toxic compounds, particularly polycyclic aromatic compounds (PACs), trace metals, and mercury (Hg). This critical review discusses the challenges of assessing ecosystem impacts and summarizes the major results of these efforts through approximately 2018. Focus is on the emissions to the air and the findings from the Air Component of the ECCC research and linkages to observations of contaminant levels in the surface waters in the region, in aquatic species, as well as in terrestrial and avian species. The existing evidence of impact on these species is briefly discussed, as is the potential for some of them to serve as sentinel species for the ongoing monitoring needed to better understand potential effects, their potential causes, and to detect future changes. Quantification of the atmospheric emissions of multiple pollutants needs to be improved, as does an understanding of the processes influencing fugitive emissions and local and regional deposition patterns. The influence of multiple stressors on biota exposure and response, from natural bitumen and forest fires to climate change, complicates the current ability to attribute effects to air emissions from the industry. However, there is growing evidence of the impact of current levels of PACs on some species, pointing to the need to improve the ability to predict PAC exposures and the key emission source involved. Although this critical review attempts to integrate some of the findings across the components, in terms of ECCC activities, increased coordination or integration of air, water, and wildlife research would enhance deeper scientific understanding. Improved understanding is needed in order to guide the development of long-term monitoring strategies that could most efficiently inform a future adaptive management approach to oil sands environmental monitoring and prevention of impacts. Implications: Quantification of atmospheric emissions for multiple pollutants needs to be improved, and reporting mechanisms and standards could be adapted to facilitate such improvements, including periodic validation, particularly where uncertainties are the largest. Understanding of baseline conditions in the air, water and biota has improved significantly; ongoing enhanced monitoring, building on this progress, will help improve ecosystem protection measures in the oil sands region. Sentinel species have been identified that could be used to identify and characterize potential impacts of wildlife exposure, both locally and regionally. Polycyclic aromatic compounds are identified as having an impact on aquatic and terrestrial wildlife at current concentration levels although the significance of these impacts and attribution to emissions from oil sands development requires further assessment. Given the improvement in high resolution air quality prediction models, these should be a valuable tool to future environmental assessments and cumulative environment impact assessments.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".