Background, urban, and industrial NO2 estimated from TEMPO satellite observations
Bibliographic record
Abstract
The hourly tropospheric NO2 vertical column density (VCD) values measured by TEMPO were used to study the NO2 diurnal andseasonal variability in 34 urban areas over North America and the Caribbean during the period from August 2023 to October 2024. A recently developed algorithm (Fioletov et al., 2024) isolated three components in tropospheric NO2 data: background NO2, NO2 from urban emissions, and from industrial point sources, and then each of these components was analyzed separately. The method is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by a meteorological reanalysis. Population density and surface elevation data as well as coordinates of major industrial sources were used in the analysis. The background component demonstrated a clear diurnal cycle with a maximum in the early morning and the minimum in the late afternoon. The urban and industrial components, expressed as total NO2 mass in urban and industrial plumes, did not show any obvious diurnal cycle in most areas. Only the Los Angeles and Mexico City urban components demonstrated a clear cycle with a maximum in the late morning and a minimum in the late afternoon. Differences between workday and weekend NO2 levels were also studied. Urban plume NO2 values on Sundays were typically 30%–60% less than workday plume values throughout the day. The exception was Havana, where the difference between working day and Sunday values was only 15%. Fioletov, V., McLinden, C. A., Griffin, D., Zhao, X., and Eskes, H.: Global seasonal urban, industrial, and background NO2 estimated from TROPOMI satellite observations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1991, 2024.
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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) | 0.001 | 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".