An overview of selected emerging outdoor airborne pollutants and air quality issues: The need to reduce uncertainty about environmental and human impacts
Bibliographic record
Abstract
According to the literature, it is estimated that outdoor air pollution is responsible for the premature death in a range from 3.7 to 8.9 million persons on an annual basis across the world. Although there is uncertainty on this figure, outdoor air pollution represents one of the greatest global risks to human health. In North America, the rapid evolution of technologies (e.g., nanotechnology, unconventional oil and gas rapid development, higher demand for fertilizers in agriculture) and growing demand for ground, marine and air transportation may result in significant increases of emissions of pollutants that have not been carefully studied so far. As a result, these atmospheric pollutants insufficiently addressed by science in Canada and elsewhere are becoming a growing issue with likely human and environmental impacts in the near future. Here, an emerging pollutant is defined as one that meets the following criteria: 1) potential or demonstrated risk for humans or the environment, 2) absence of Canada-wide national standard, 3) insufficient routine monitoring, 4) yearly emissions greater than one ton in Canada, 5) insufficient data concerning significant sources, fate, and detection limit, and 6) insufficiently addressed by epidemiological studies. A new methodology to rank emerging pollutants is proposed here based on weighting multiple criteria. Some selected emerging issues are also discussed here and include the growing concern of ultrafine or nanoparticles, growing ammonia emissions (due to rapid expansion of the agriculture), increased methane/ethane/propane emissions (due to the expanding hydraulic fracturing in the oil and gas sector) and the growing transportation sector. Finally, the interaction between biological and anthropogenic pollution has been found to be a double threat for public health. Here, a multidisciplinary and critical overview of selected emerging pollutants and related critical issues is presented with a focus in Canada.Implications: This overview paper provides a selection methodology for emerging pollutants in the atmospheric environment. It also provides a critical discussion of some related issues. The ultimate objective is to inform about the need to 1) address emerging issues through adequate surface monitoring and modeling in order to inform the development of regulations, 2) reduce uncertainties by geographically mapping emerging pollutants (e.g., through data fusion, data assimilation of observations into air quality models) which can improve the scientific support of epidemiological studies and policies. This review also highlights some of the difficulties with the management of these emerging pollutants, and the need for an integrated approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".