Black Carbon Particles Physicochemical Real-Time Data Set in a Cold City: Trends of Fall-Winter BC Accumulation and COVID-19
Bibliographic record
Abstract
Black carbon (BC) plays an important role in climate and health sciences.Using the combination of a year real-time BC observation (photoacoustic extinctiometer) and data for PM 2.5 and selected co-pollutants, we herein show that annual BC Mass concentration has a bi-modal distribution, in a cold-climate city of Montreal.In addition to the summer peak, a winter BC peak was observed (up to 0.433 μg/m 3 ), lasting over 3 months.A comparative study between two air pollution hotspots, downtown and Montreal international airport indicated that airborne average BC Mass concentration in downtown was 0.344 μg/m 3 , whereas in the residential areas around Montreal airport BC Mass values were over 400% higher (1.487 μg/m 3 ).During the numerous snowfall events, airborne BC Mass concentration decreased.High-resolution scanning/transmission electron microscopy with energy dispersive X-ray spectroscopy analysis of the snow samples provided evidence that airborne BC particles or carbon nanomaterials were indeed transferred from polluted air to snow.During the COVID-19 lockdown, the BC concentration and selected co-pollutants, decreased up to 72%, confirming the predominance of anthropogenic activities in BC emission.This first cold-climate BC data set can be essential for more accurate air quality and climate modeling.About one-third of the Earth's land surface receive snow annually, the impact of this study on air quality, health and climate change is discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".