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Record W4407103557 · doi:10.5194/egusphere-2025-349

Current-use and organochlorine pesticides' multi-annual trends in air in Central Europe: primary and unidentified secondary sources

2025· preprint· en· W4407103557 on OpenAlexaff
Ludovic Mayer, Lisa Melymuk, Adéla Holubová Šmejkalová, Jiří Kalina, Petr Kukučka, Jakub Martiník, Petra Příbylová, Petr Šenk, Pourya Shahpoury, Gerhard Lammel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsTrent University
FundersHorizon 2020 Framework ProgrammeGrantová Agentura České Republiky
KeywordsOrganochlorine pesticidePesticideEnvironmental sciencePrimary (astronomy)Current (fluid)Environmental protectionEnvironmental chemistryGeographyEcologyChemistryOceanographyBiologyGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract. This study investigated 48 current-use pesticides (CUPs) and 30 organochlorine pesticides (OCPs) in ambient air at a rural-agricultural site in the Czech Republic, with biweekly sampling over three and 10 years, respectively. Despite being banned decades ago, OCPs persist in the atmosphere, with revolatilisation from soils apparent in summer. Temporal trend analysis revealed decreasing atmospheric concentrations for several OCPs, which indicate diminishing reservoirs in environmental compartments especially soil over the years. For β- and γ-HCH, o,p’- and p,p’-DDE, o,p’-DDD, o,p’- and p,p’-DDT, α-chlordane, and mirex levelling off is observed, which points to recently enhanced secondary sources in the region or beyond i.e., reversal of the direction of air-surface exchange or recent mobilisation from soils, water bodies, or the cryosphere. CUP concentrations peaked during application seasons, with multi-annual trends either insignificant or declining. For compounds like chlorpyrifos and fenpropimorph, declining trends aligned with regulatory bans, though their presence in the atmosphere was evident one-year post-ban, suggesting persistence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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 teacher head, not a consensus.

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
Published2025
Admission routes1
Has abstractyes

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