Surface NO2 Derived from Pandora Column Measurements in Toronto and Detroit-Windsor
Bibliographic record
Abstract
Atmospheric trace gases near the Earth’s surface can have important human and environmental health impacts. In particular, the trace gas nitrogen dioxide (NO2), which is commonly emitted by traffic, biomass burning, and industrial sources, can be a major threat to human respiratory health, leading to increased rates of asthma, lung cancer, and overall mortality. In the Greater Toronto Area (GTA) and in the Detroit-Windsor Area (DWA), NO2 and other trace gases are being measured by ground-based Pandora UV-visible spectrometers that are part of the Pandonia Global Network. We present NO2 surface volume mixing ratios derived from Pandora direct sun total column measurements to monitor air quality in these two urban areas. The conversion method uses three inputs in addition to the Pandora total columns: (1) the stratospheric NO2 column from the Ozone Monitoring Instrument (OMI), (2) the free troposphere NO2 column from the GEOS-Chem chemical transport model, and (3) the ratio of NO2 surface volume mixing ratio to planetary boundary layer column from Environment and Climate Change Canada’s regional air quality forecast model, Global Environmental Multi-scale-Modelling Air quality and Chemistry (GEM-MACH). The derived estimates of surface NO2 are compared with in situ measurements, and their level of agreement is assessed for dependence on meteorological conditions, including wind speed and direction, temperature, and boundary layer height. The mean bias between the derived estimates and in situ measurements ranges from -1.0 ppbv to -2.6 ppbv. This bias has been found to vary with boundary layer height, so a method to account for this dependence has been developed to improve the results. This presentation will provide an overview of this column-to-surface conversion method, a summary of results for each site in the GTA and DWA, and an outline of plans toward using this approach to improve and validate satellite estimates of surface NO2.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".