Bibliographic record
Abstract
Black Carbon (BC) is a component of particulate matter, emitted from the incomplete combustion of carbonaceous fuels. The presence of BC in the atmosphere can disrupt the atmospheric radiation budget, and exposure to BC can adversely affect human health. Multi-wavelength light absorption-based dual-spot aethalometers can be used to quantify the source and characteristics of BC from traffic or biomass burning-based sources. However, aethalometer measurements are affected by artifacts such as aerosol loading and light scattering; hence, they often need correction to reduce measurement uncertainty. This work assesses the performance of the recently developed portable aethalometer (MA300, AethLabs). Due to their portability and ease of usage, MA300s can be suitable for mobile and personal exposure monitoring. Here, we evaluate BC concentration and source apportionment accuracy of three MA300 units relative to a widely used aethalometer, the AE33 (Magee Scientific). Synchronous field measurements were performed at a major traffic intersection during regular and wildfire smoke-affected days in Vancouver, Canada. We find that MA300 reported BC mass concentrations were strongly correlated (Slope range between 0.73 and 1.01, with R2 = 0.9) compared to the reference instrument, yet there is visible instrumental variability (15 %) across three units. The mean absolute error of MA300 reported BC concentrations ranged between 0.44–0.98 ug m-3 with the highest deviations observed in wildfire smoke-affected polluted days. From the aerosol light absorption measurement perspective, MA300s tend to underestimate the absorption coefficients (babs) across the five wavelengths. UV channel light absorption results were subjected to the highest amount of noise, leading to systematic bias in source apportionment analysis. We investigated the application of the latest non-linear aethalometer correction protocols in the MA300 and found that flow fluctuations enhanced noise across all channels, compared to onboard instrument correction. We also identify that the UV (λ = 370 nm) channel absorption measurements are most sensitive to instrumental artifacts during the wildfire smoke-affected period. Hence, as an alternative to traditional UV and IR (λ = 880 nm)-based BC source apportionment methods, in this work, we tested the Blue (λ = 470 nm) and IR wavelengths for BC source apportionment calculation. By adopting Blue-IR based source apportionment technique in MA300, the apportioned BC components improves on average in the order of 10 % when compared against the reference monitor's results.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.463 | 0.016 |
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; both teacher heads agree on what is shown here.
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".