Novel application of satellite and in-situ measurements to map surface-level NO <sub>2</sub> in the Great Lakes region
Bibliographic record
Abstract
Abstract. Ozone Monitoring Instrument (OMI) tropospheric NO2 vertical column density data were used in conjunction with in-situ NO2 concentrations collected by permanently installed monitoring stations to infer 24 h surface-level NO2 concentrations at 0.1° (~11 km) resolution. The region examined included rural and suburban areas, and the highly industrialised area of Windsor, Ontario, which is situated directly across the US-Canada border from Detroit, MI. Photolytic NO2 monitors were collocated with standard NO2 monitors to provide qualitative data regarding NOz interference during the campaign. The accuracy of the OMI-inferred concentrations was tested using two-week integrative NO2 measurements collected with passive monitors at 18 locations, approximating a 15 km grid across the region, for 7 consecutive two-week periods. When compared with these passive results, satellite-inferred concentrations showed an 18% positive bias. The correlation of the passive monitor and OMI-inferred concentrations (R=0.69, n=115) was stronger than that for the passive monitor concentrations and OMI column densities (R=0.52), indicating that using a sparse network of monitoring sites to estimate concentrations improves the direct utility of the OMI observations. OMI-inferred concentrations were then calculated for four years to show an overall declining trend in surface NO2 concentrations in the region. Additionally, by separating OMI-inferred surface concentrations by wind direction, clear patterns in emissions and affected down-wind regions, in particular around the US-Canada border, were revealed.
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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 | 0.001 |
| 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 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".