Northern midlatitude baseline ozone: Long-Term changes and the COVID-19 impact
Bibliographic record
Abstract
Abstract. A non-linear change in baseline ozone concentrations at northern midlatitudes has been quantified over preceding decades. During the past few years several studies, using linear trend analyses, report relatively small trends over selected time periods – results inconsistent with the earlier developed picture. We show that reported COVID-19 related ozone changes in the background troposphere based on the linear analysis are significantly larger than those derived considering recent long-term decreases in background ozone, which the linear trend analyses do not quantify. We further point out that the extensive loss of lower stratospheric ozone in the unprecedented 2020 springtime Arctic stratospheric ozone depletion event likely reduced the natural source to the troposphere rendering the background anomalously low that year. Consideration of these two issues indicates that the COVID-19 restrictions had a much smaller impact on background tropospheric ozone in 2020 than previously reported. A consensus understanding of baseline ozone changes and their causes is important for formulating policies to improve ozone air quality; cooperative, international emission control efforts aimed at continuing or even accelerating the ongoing decrease in in hemisphere-wide background ozone concentrations may be the most effective approach to further reducing urban and rural ozone in the more developed northern midlatitude countries, as well as improving ozone air quality in all countries within these latitudes. Analysis of baseline ozone measurements over several years following the COVID-19 impact is expected to provide a firm basis for resolving the inconsistencies between the two views of long-term northern midlatitude ozone changes and better quantifying the COVID-19 impact.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".