Chemical and dynamical identification of emission outflows during the <i>HALO</i> campaign EMeRGe in Europe and Asia
Bibliographic record
Abstract
The number of large urban agglomerations is steadily increasing worldwide. At a local scale, their emissions lead to air pollution, directly affecting people's health. On a global scale, their emissions lead to an increase of greenhouse gases, affecting climate. In this context, in 2017 and 2018, the airborne campaign EMeRGe (Effect of Megacities on the transport and transformation of pollutants on the Regional to Global scales) investigated emissions of European and Asian major population centres (MPCs) to improve the understanding and predictability of pollution outflows. Here, we present two methods to identify and characterise pollution outflows probed during EMeRGe. First, we use a set of volatile organic compounds (VOCs) as chemical tracers to characterise air masses by specific source signals, i.e. benzene from anthropogenic pollution of targeted regions, acetonitrile from biomass burning (BB, primarily during EMeRGe-Asia), and isoprene from fresh biogenic signals (primarily during EMeRGe-Europe. Second, we attribute probed air masses to source regions and estimate their individual contribution by constructing and applying a simple emission uptake scheme for the boundary layer which combines FLEXTRA back trajectories and EDGAR carbon monoxide (CO) emission rates (acronyms are provided in the Appendix). During EMeRGe-Europe, we identified anthropogenic pollution outflows from northern Italy, southern Great Britain, the Belgium–Netherlands–Ruhr (BNR) area and the Iberian Peninsula. Additionally, our uptake scheme indicates significant long-range transport of pollution from the USA and Canada. During EMeRGe-Asia, the pollution outflow is dominated by sources in China and Taiwan, but BB signals from Southeast Asia and India contribute as well. Outflows of pre-selected MPC targets are identified in less than 20 % of the sampling time, due to restrictions in flight planning and constraints of the measurement platform itself. Still, EMeRGe combines in a unique way near- and far-field measurements, which show signatures of local and distant sources, transport and conversion fingerprints, and complex air mass compositions. Our approach provides a valuable classification and characterisation of the EMeRGe dataset, e.g. for BB and anthropogenic influence of potential source regions and paves the way for a more comprehensive analysis and various model studies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".