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 (NO 2 ) variabilities over the United States and Europe, but thorough analysis for the impacts on tropospheric NO 2 in China is still lacking. Here we provide a comparative analysis for the observed and modeled (Goddard Earth Observing System-Chem) tropospheric NO 2 in early 2020 in China. Both ozone monitoring instrument and surface NO 2 measurements show marked decreases in NO 2 abundances due to the 2019 novel coronavirus (COVID-19) controls. However, we find a large discrepancy between observed and modeled NO 2 changes over highly polluted provinces: the observed reductions in tropospheric NO 2 columns are about 40% lower than those in surface NO 2 concentrations. By contrast, the modeled reductions in tropospheric NO 2 columns are about two times higher than those in surface NO 2 concentrations. This discrepancy could be driven by the combined effects from uncertainties in simulations and observations, associated with possible inaccurate simulations of lower tropospheric NO 2 , larger uncertainties in the modeled interannual variabilities of NO 2 columns, as well as insufficient consideration of aerosol effects and a priori NO 2 variability in satellite retrievals. In addition, our analysis suggests a small influence from free tropospheric NO 2 backgrounds in E. China in winter. This work demonstrates the challenge to interpret wintertime tropospheric NO 2 changes in China, highlighting the importance of integrating surface NO 2 observations to provide better analysis for NO 2 variabilities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".