Bibliographic record
Abstract
This thesis examines total columns of C2H2, C2H6, CH4, CH3OH, CO, H2CO, HCl, HCN, HCOOH, HF, HNO3, N2O, NH3, and O3 measured using Fourier transform infrared (FTIR) spectroscopy to study Toronto-area atmospheric composition. The thesis has three scientific objectives: to quantify trends in the time series of trace gas concentrations, to determine how emissions from biomass burning events affect air quality over Toronto and whether observations in Toronto can be used to quantify wildfire emissions, and to examine the spatial representativeness and temporal variability of the FTIR NH3 columns over Toronto.Trends and enhancement events were determined by fitting trended Fourier series to the total columns, and bootstrapping was used to identify the statistical significance. Trends from 2002 to 2019 were examined, and the GEOS-Chem chemical transport model was used to identify major sources of CO and CH4 over Toronto, which were CH4 oxidation and wetland emissions, respectively. Transport of wildfire plumes over the site results in enhanced columns of biomass burning species. Several simultaneous enhancements of CO, HCN, and C2H6 were observed, and the measured columns were used to derive emission ratios and emission factors for HCN and C2H6 for fire events in 2012, 2015, and 2017. For the 2015 and 2017 events, simultaneous enhancements of HCOOH and CH3OH were observed, and their emission ratios and emission factors were also examined. Atmospheric NH3 is a pollutant, and a major source of fine particulate matter. In this study, three NH3 datasets were used: TAO FTIR total columns, three years of surface in situ measurements, and ten years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. All three time series showed positive trends in NH3 over Toronto: 3.56 ± 0.85 %/year from 2002 to 2019 in the FTIR columns, 8.88 ± 5.08 %/year from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/year from 2008 to 2018 in the IASI columns. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".