Decadal Variabilities in Tropospheric Nitrogen Oxides Over United States, Europe, and China
Bibliographic record
Abstract
Abstract Global trends in tropospheric nitrogen dioxide (NO2) have changed dramatically in the past decade. Here, we investigate tropospheric NO2 variabilities over United States, Europe, and E. China in 2005–2018 to explore the mechanisms governing the variation of this critical pollutant. We found large uncertainties in the trends of anthropogenic nitrogen oxides (NOx) emissions, for example, the reductions of NOx emissions, derived with different approaches and data sets, are in the range of 35%–50% over the United States and 15%–45% over Europe in 2005–2018. By contrast, the analysis in this work indicates declines of anthropogenic NOx emissions by about 40% and 25% over the United States and Europe, respectively, in 2005–2018, and about 20% over E. China in 2012–2018. However, the shift of major NOx sources from power generation to industrial and transportation sectors has led to noticeable diminishing effects in emission controls. Furthermore, satellite measurements exhibit the influence of NO2 background levels over the United States and Europe, which offset the impacts of anthropogenic emission declines, resulting in flatter trends of tropospheric NO2 over the United States and Europe. Our analysis further reveals underestimation of background NO2 by chemical transport models, which can lead to inaccurate interpretations of satellite measurements. We use surface in‐situ NO2 observations to diagnose the satellite‐observed NO2 trends and find top‐down NOx emissions over urban grids represent the changes in anthropogenic NOx emissions better. This work highlights the importance of comprehensive applications of different analysis approaches to better characterizing atmospheric composition evolution.
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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".