Trends of trace gases and aerosol over 2003-2023 at Mount Tai, northern China
Bibliographic record
Abstract
China has been experiencing fast-paced urbanization and industrialization as well as stringent air pollution control in the past decades, which are expected to cause drastic changes in the anthropogenic emissions of primary air pollutants (e.g., SO2, NOx and VOCs). Long-term observations are fundamental to the assessment of response of atmospheric composition to the changing anthropogenic emissions, which are, however, very limited in China. Here we integrated the observational data of trace gases and aerosol obtained during 2003-2023 at Mount Tai – the peak of the North China Plain (1534 m above sea level), a highly polluted region of China. The data were analyzed to understand the long-term changes of a variety of trace gases and aerosol properties such as ozone (O3), PM2.5 composition, particle number and size distribution, new particle formation and growth parameters, and O3 depleting substances (ODS). Surface O3 concentrations showed a significant increasing trend in summertime with a rate of ~2 ppbv yr-1, despite the persistent decrease in NOx emissions since 2012, and can be attributed to the increasing VOCs and O3 production efficiency. Sharp reduction in SO2 emissions have resulted in significant decrease of sulfate in PM2.5, whilst nitrate showed a strong increasing trend. A multi-phase chemical box model illustrated that the reduced SO2 and sulfate enhanced nitrate formation by lessening the aerosol acidity and facilitating the partitioning of HNO3 to the particle phase. The apparent formation rate of new particles in spring has increased at Mt. Tai, while the particle growth rate significantly decreased. The contributions of new particles to the cloud condensation nuclei (CCN) were also decreasing. The ODS regulated by the Montreal Protocol (MP) showed a significant downward trend, but the MP-controlled and unregulated halocarbon species showed overall upward trends. We will also present the results about the impacts of COVID lockdown on the regional air quality as observed at Mt. Tai.
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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 | 0.002 |
| 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".