Inter-comparison of the Elemental and Organic Carbon Mass Measurements from Three North American National Long-term Monitoring Networks
Bibliographic record
Abstract
Abstract. Carbonaceous aerosol is a major contributor to the total aerosol load and being monitored by diverse measurement approaches. Here, ten years of continuous carbonaceous aerosol measurements collected at the Centre of Atmospheric Research Experiments (CARE) in Egbert, Ontario, Canada on quartz filters by three independent networks (Interagency Monitoring of PROtected Visual Environments (IMPROVE), Canadian Air and Precipitation Monitoring Network (CAPMoN), and Canadian Aerosol Baseline Measurement (CABM)) were compared. Specifically, the study evaluated how differences in sample collection and analysis affected the yield of total carbon (TC), organic carbon (OC), and elemental carbon (EC). When all measurements were normalized with respect to concentration measured in a common reference year, OC measurements agreed to within 29–48 % and EC measurement to within 20 % amongst the different networks. Fitted with a sigmoid function, elevated OC and EC concentrations were found when ambient temperature exceeded 10 °C. These increased ambient concentrations of OC during summer were attributed to the secondary organic aerosol (SOA) formation and forest fire emissions, while elevated EC concentrations were attributed to forest fire emissions and increased vehicle emissions. The observations from this study suggest that carbonaceous aerosol measurements, especially EC, can be synchronized across networks if sample collection and analytical method in each network remain internally consistent. This study allows the generation of regional to continental-scale-harmonized concentration data sets for benchmarking of atmospheric chemical transport models that determine emission sources and sinks, and assess the effectiveness of government mitigation policies in improving air quality and reducing reliance on fossil fuel consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".