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Record W4246652504 · doi:10.5194/acp-2018-897

Establishing Long-term Measurements of Halocarbons at Taunus Observatory

2018· preprint· en· W4246652504 on OpenAlexaboutno aff
Tanja Schuck, Fides Lefrancois, Franziska Gallmann, Danrong Wang, Markus Jesswein, Jesica Hoker, Harald Bönisch, Andreas Engel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric Administration
KeywordsObservatoryEnvironmental scienceAtmospheric sciencesGreenhouse gasMeteorologyBaseline (sea)Air mass (solar energy)PhysicsGeologyOceanographyAstrophysics

Abstract

fetched live from OpenAlex

Abstract. In late 2013, a whole air flask collection program started at the Taunus Observatory (TO) in central Germany. Being a rural site in close vicinity to the densely populated Rhein-Main area, Taunus Observatory allows to assess local and regional emissions. Owed to its altitude of 825 m, the site also regularly experiences background conditions, especially when air masses approach from north-westerly directions. With a large footprint area mainly covering central Europe north of the Alps, halocarbon measurements at the site have the potential to improve the data base for estimation of regional and total European halogenated greenhouse gas emissions. Flask samples are collected weekly for offline analysis using a GC-MS system employing a quadrupole as well as a time-of-flight mass spectrometer. As background reference, additional samples are collected approximately bi-weekly at the Mace Head Atmospheric Research Station (MHD) when air masses approach from the site's clean air sector. Thus the TO time series can be linked to the in-situ AGAGE measurements and the NOAA flask sampling program at MHD. An iterative baseline identification procedure separates polluted samples from baseline data. While there is good agreement of baseline mixing ratios between TO and MHD, with a larger variability of mixing ratios at the continental site, measurements at TO are regularly influenced by elevated halocarbon mixing ratios. Here, first time series are presented for CFC-11, CFC-12, HCFC-22, HFC-134a, HFC-227ea, HFC-245fa, and dichloromethane. While atmospheric mixing ratios of the CFCs decrease, they increase for the HCFC and the HFCs. Small unexpected differences between CFC-11 and CFC-12 are found with regard to the occurrence of high mixing ratio events and seasonality, although production and use of both compounds are strictly regulated by the Montreal Protocol, and therefore a similar decrease of atmospheric mixing ratios should occur. Dichloromethane, a solvent about which recently concerns have risen regarding its growing influence on stratospheric ozone depletion, does not show a significant trend with regard to both, baseline mixing ratios and the occurrence of pollution events at Taunus Observatory for the time period covered, indicating stable emissions in the regions that influence the site. An analysis of HYSPLIT trajectories reveals differences in halocarbon mixing ranges depending on air mass origin.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.070
GPT teacher head0.253
Teacher spread0.183 · 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 designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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