Large discrepancy between observed and modeled wintertime tropospheric NO<sub>2</sub> variabilities due to COVID-19 controls in China
Bibliographic record
Abstract
Abstract Recent studies demonstrated the difficulties to explain observed tropospheric nitrogen dioxide (NO2) variabilities over the United States and Europe, but thorough analysis for the impacts on tropospheric NO2 in China is still lacking. Here we provide a comparative analysis for the observed and modeled (Goddard Earth Observing System-Chem) tropospheric NO2 in early 2020 in China. Both ozone monitoring instrument and surface NO2 measurements show marked decreases in NO2 abundances due to the 2019 novel coronavirus (COVID-19) controls. However, we find a large discrepancy between observed and modeled NO2 changes over highly polluted provinces: the observed reductions in tropospheric NO2 columns are about 40% lower than those in surface NO2 concentrations. By contrast, the modeled reductions in tropospheric NO2 columns are about two times higher than those in surface NO2 concentrations. This discrepancy could be driven by the combined effects from uncertainties in simulations and observations, associated with possible inaccurate simulations of lower tropospheric NO2, larger uncertainties in the modeled interannual variabilities of NO2 columns, as well as insufficient consideration of aerosol effects and a priori NO2 variability in satellite retrievals. In addition, our analysis suggests a small influence from free tropospheric NO2 backgrounds in E. China in winter. This work demonstrates the challenge to interpret wintertime tropospheric NO2 changes in China, highlighting the importance of integrating surface NO2 observations to provide better analysis for NO2 variabilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".