Source regions of some persistent organic pollutants measured in the atmosphere at Birkenes, Norway
Bibliographic record
Abstract
Abstract. A key feature of POPs (Persistent Organic Pollutants) is their potential for long-range atmospheric transport. In order to better understand and predict atmospheric source-receptor relationships of POPs, we have modified an existing Lagrangian transport model (FLEXPART) to include some of the key processes that control the atmospheric fate of POPs. We also present four years (2004–2007) of new atmospheric measurement data for polychlorinated biphenyls (PCBs) and hexachlorocyclohexanes (HCHs) obtained at Birkenes, an EMEP (European Monitoring and Evaluation Programme) site in southern Norway. The model overestimates measured PCB-28 and γ-HCH concentrations by factors of 2 and 8, respectively, which is most likely because the emissions used as input to the model are overestimated. FLEXPART captures the temporal variability in the measurements very well and, depending on season, explains 31–67% (14–62%) of the variance of measured PCB-28 (γ-HCH) concentrations. FLEXPART, run in a time-reversed (adjoint) mode, was used to identify the source regions responsible for the POP loading at the Birkenes station. Emissions in Central Europe and Eastern Europe contributed 32% and 24%, respectively, to PCB-28 at Birkenes, while Western Europe was found to be the dominant source (50%) for γ-HCH. Intercontinental transport from North America contributed 13% γ-HCH. While FLEXPART has no treatment of the partitioning of POPs between different surface media, it was found a very useful tool for studying atmospheric source-receptor relationships for POPs and POP-like chemicals that do not sorb strongly to atmospheric particles and whose atmospheric levels are believed to be mainly controlled by primary sources.
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 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) | 0.001 | 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 | 0.000 |
| 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".