A Factor and Trends Analysis of Multidecadal Lower Tropospheric Observations of Arctic Aerosol Composition, Black Carbon, Ozone, and Mercury at Alert, Canada
Bibliographic record
Abstract
Abstract Observations from 1980 to 2013 of 20 aerosol constituents, ozone and mercury at Alert, Canada (82.50°N, 62.35°W), were analyzed for trends and dominant factors of the Arctic haze during winter and spring. Trends reflect changing emissions in Eurasia, the main source region for surface pollution in the high Arctic. SO42−, H+, NH4+, K+, Cu, Ni, Pb, Zn, nonsoil V, nonsoil Mn, and equivalent black carbon decreased between 23% and 80% as emissions declined rapidly in northern Eurasia during the early 1990s. NO3− increased by 20% as aerosol acidity declined. Metals were linked to emissions from smelting and fossil fuel combustion. In winter, ozone increased by 5% over 23 years, consistent with other observations and global modeling. Twelve PMF factors emerged for the dark period (November to February) and 13 for the light period (March to May). Eleven PMF factors are common to both dark and light, a twelfth factor was associated with sulfate in the dark and nitrate in the light, and the thirteenth (light period) was related to ozone and gaseous mercury depletion near Alert. IODINE and NITRATE factors, important for Arctic chemistry, changed with sunlight. In the light, 50% of all NO3− was on the NITRATE factor, while in the dark, most was associated with MODIFIED SEA SALT and equivalent black carbon. In the dark (light), 90% (28%) of iodine were found on the factor IODINE and 58% associated with SEA‐SALT and MODIFIED SEA‐SALT. These results help in understanding the role of atmospheric chemistry in weather and climate processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".