Investigating the Impact of Aviation Activity on Fine Particulate Matter, Black Carbon, and Ultrafine Particles Using Flight Track Data at the Ottawa International Airport
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Ambient particulate matter pollution has been found to increase in concentration downwind from airports. This relationship is mostly evident for particles in the ultrafine size range. Furthermore, epidemiological research has found that those who live near airports have increased risks of premature mortality, decreased lung function, and adverse birth outcomes. Previous exposure studies of airport emissions have been based in urban centers, making it difficult to selectively measure airport emissions without the contribution of other related sources. Our aim was to characterize the relationships between air pollutant particle measures (ultrafine particles [UFP], fine particulate matter [PM 2.5 ], black carbon [BC]) and air traffic (landings and take-offs [LTO]) at the Ottawa International Airport [YOW]. A monitoring site was established in greenspace approximately 600 m east of YOW and away from roadways and urban development. Air pollutant particles were measured continuously from June 2022 to January 2023. Flight track data was used to derive hourly LTO counts. Analyses of source directionality showed that UFP concentrations were higher when downwind from the airport. Further, when wind speeds were less than 20 km/h, UFP and LTO showed similar diurnal trends. No evidence of these associations was evident for PM 2.5 and BC. After selecting for airport wind directions and wind speeds less than 20 km/h, linear regression models showed each additional takeoff led to a 10–13% increase in the 50th to 99th UFP concentration percentiles. Our findings support policies designed to reduce potential health impacts of airport emissions on the exposed community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".