Using ground-based infrared spectroscopy, satellite measurements, and models to understand short-lived climate forcers and wildfire emissions over Canada
Bibliographic record
Abstract
This thesis uses atmospheric measurements from ground-based Fourier transform infrared (FTIR) spectrometers to study air composition and quality, leveraged with in situ measurements, satellite data, and atmospheric models. The primary instrument, located at the University of Toronto Atmospheric Observatory (TAO), has been measuring since 2002 and is a member of the Network for the Detection of Atmospheric Composition Change (NDACC). This work added four years of data to TAO’s long-term time series of 16 trace gases (C2H2, C2H6, CH3OH, CH4, CHF2Cl, CO, H2CO, HCl, HCN, HCOOH, HF, HNO3, N2O, NH3, O3 and OCS).Simulations by models used in the 2021 Arctic Monitoring and Assessment Programme report on short-lived climate forcers are compared with column-integrated FTIR measurements from five high-latitude NDACC sites. The models exhibit an overall negative bias in tropospheric column values, with CH4 underestimated by 9.7%, CO by 21%, and O3 by 18%, highlighting challenges in simulating Arctic atmospheric composition. The impacts of wildfire emissions on air quality are a concern for populations through both immediate exposure and long-range transport. Three major smoke events in Southern Ontario resulting from the record-breaking 2023 wildfire season are examined using measurements and models. Tropospheric enhancement ratios relative to CO are reported for C2H6, CH3OH, HCN, HCOOH, NH3, and O3. Plume transport is investigated with a back-trajectory model and space-based MOPITT CO, while plume heights are determined with FTIR CO profiles and Micro-Pulse Lidar radiative backscatter. Comparisons with the GEM-MACH-FireWork model show that large-scale plume dispersion is effectively captured, tropospheric columns are overall underestimated, and enhancements are overestimated during smoke events. Using CO measurements from MOPITT and TAO from 2004-2019, changes to the seasonal cycle are assessed in relation to wildfire activity. A new CO peak emerges in August post-2012 in Alberta and Ontario, consistent with previous literature. Public health risks of this change are examined using a difference-in-difference analysis of monthly hospital emissions for nine cardiovascular and respiratory diseases. Using satellite XCO as an exposure metric, findings are suggestive of a link between enhanced wildfire-related CO concentrations after 2012 and worsening health outcomes, with statistically significant results for six disease-province pairings.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".