Source apportionment of particles at Station Nord, North East Greenland during 2008–2010 using COPREM and PMF analysis
Bibliographic record
Abstract
Abstract. In order to develop strategies for controlling and reducing Arctic air pollution, there is a need to understand the basic mechanisms for determining the fate of air pollution in the Arctic. Sources of atmospheric particles at Station Nord (81° 36' N, 16° 40' W) in North East Greenland were evaluated for a two-year period from March 2008 to February 2010. Source apportionment using Positive Matrix Factorization (PMF) and COnstrained Physical REceptor Model (COPREM) was based on measurements of black carbon, elements (Al, Si, S, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Zr, Pb) and inorganic ions (SO2, SO42−, Na+, NH4+, NO3−, Cl2−. In general, source apportionment results by PMF and COPREM showed good agreement. Five sources adequately explained the measurements, which included a Marine and a Soil source of natural origin and three additional anthropogenic sources, which were all influenced by metal industries. One anthropogenic source was dominated by Zn of which air mass back trajectories using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model suggested a Canadian Arctic origin, despite certain influences from Southern and Eastern origins. Another anthropogenic source was characterised by high concentrations of Pb and As, which has been historically referred to as a Combustion source at Station Nord. The impacts of large-scale industry in Siberia, Russia were evident through high Cu concentrations in both the Combustion source and an additional Cu/Ni source. Br correlated well with the anthropogenic species S and Pb though the elements are unlikely to have a common origin. More likely, sulphuric acid aerosols serve as transport containers for Br species of marine origin. Of particular relevance to climate, sources of black carbon were identified to be mainly anthropogenic and most probably of Siberian origin (80–98%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".