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Record W2156209025 · doi:10.1029/2002jd002862

A backward modeling study of intercontinental pollution transport using aircraft measurements

2003· article· en· W2156209025 on OpenAlexaboutno aff
A. Stohl, C. Forster, Sabine Eckhardt, N. Spichtinger, H. Huntrieser, J. Heland, Hans Schlager, Stefan Wilhelm, Frances H. Arnold, Owen R. Cooper

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsPlumeEnvironmental sciencePollutionTroposphereMeteorologyAtmospheric sciencesEmission inventoryAir pollutionPollutantAir quality indexGeologyGeographyChemistry

Abstract

fetched live from OpenAlex

In this paper we present simulations with a Lagrangian particle dispersion model to study the intercontinental transport of pollution from North America during an aircraft measurement campaign over Europe. The model was used for both the flight planning and a detailed source analysis after the campaign, which is described here with examples from two episodes. Forward calculations of emission tracers from North America, Europe, and Asia were made in order to understand the transport processes. Both episodes were preceded by stagnant conditions over North America, leading to the accumulation of pollutants in the North American boundary layer. Both anthropogenic sources and, to a lesser extent, forest fire emissions contributed to this pollution, which was then exported by warm conveyor belts to the middle and upper troposphere, where it was transported rapidly to Europe. Concentrations of many trace gases (CO, NO y , CO 2 , acetone, and several volatile organic compounds; O 3 in one case) and of ambient atmospheric ions measured aboard the research aircraft were clearly enhanced in the pollution plumes compared to the conditions outside the plumes. Backward simulations with the particle model were introduced as an indispensable tool for a more detailed analysis of the plume's source region. They make trajectory analyses (which, to date, were mainly used to interpret aircraft measurement data) obsolete. Using an emission inventory, we could decompose the tracer mixing ratios at the receptors (i.e., along the flight tracks) into contributions from every grid cell of the inventory. For both plumes we found that emission sources contributing to the tracer concentrations over Europe were distributed over large areas in North America. In one case, sources in California, Texas, and Florida contributed almost equally, and smaller contributions were also made by other sources located between the Yucatan Peninsula and Canada. In the other case, sources in eastern North America, including moderate contributions from forest fires, were most important. The plume's maximum was mainly caused by anthropogenic emissions from the New York area. To our knowledge, this is the first case reported where a pollution plume from a megacity was reliably detected over another continent.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.102
GPT teacher head0.326
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations371
Published2003
Admission routes1
Has abstractyes

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