Retrieval of NO <sub>2</sub> profiles from 3 years of Pandora MAX-DOAS measurements in Toronto, Canada
Bibliographic record
Abstract
Abstract. The purpose of this work is to derive new nitrogen dioxide (NO2) vertical profiling data products from Pandora spectrometers and investigate the factors contributing to the bias of this dataset relative to established ground-based and spaceborne datasets. Possible applications of the NO2 vertical profile dataset include air quality monitoring and satellite validation studies. We explore the application of the optimal estimation method to Pandora multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements to retrieve vertical profile information for NO2. We use the Heidelberg Profile (HeiPro) retrieval algorithm to derive, for the first time, NO2 profiles and partial columns (0–4 km) from Pandora MAX-DOAS measurements from 2018–2020 from Downsview, a suburban neighbourhood in the northern end of Toronto, Canada, that is subject to local traffic emissions and urban influences. Validation of the new dataset was done via comparison with official Pandora direct-Sun measurements, in situ observations, satellite data, and an air quality forecasting model. We find that, for tropospheric partial column comparisons, the HeiPro dataset has a positive mean relative bias to Pandora direct-Sun (61 ± 9.7 %) and TROPOspheric Monitoring Instrument (TROPOMI; 37 ± 51 %) observations, as well as the Global Environmental Multiscale–Modelling Air quality and Chemistry (GEM-MACH) model output (67 ± 7.1 %), with similar seasonal and diurnal cycles in the bias with Pandora direct-Sun and GEM-MACH data. Contributing factors to the large bias of HeiPro-to-Pandora direct-Sun measurements were investigated, and NO2 heterogeneity, combined with differences between direct-Sun and multi-axis viewing geometries, was found to contribute a maximum of 52 % of the total relative bias during morning measurement times. For surface NO2 comparisons, we find that HeiPro measurements capture the magnitude and diurnal variability in surface NO2 reasonably well (mean relative bias to in situ surface NO2 of −9.7 ± 7.5 %) but are biased low compared to GEM-MACH (mean relative bias of −37 ± 2.4 %). Compared to HeiPro, the GEM-MACH model profiles are biased high in the lower boundary layer and biased low in the free troposphere.
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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 | 0.001 | 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 | 0.000 |
| 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 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".