A new method for measuring airborne elemental carbon using PUF disk passive samplers
Bibliographic record
Abstract
Carbonaceous aerosol species, such as elemental carbon (EC), are important Short-Lived Climate Forcers (SLCFs), contributing to climate and health effects of air pollution. The quantification of carbonaceous aerosols has been conventionally carried out using active air sampling followed by various analytical techniques, such as thermal/thermal-optical analysis. Active sampling requires specific equipment and infrastructure with electricity and therefore may not be the best choice for studying carbonaceous aerosols at remote locations. Passive sampling on the other hand provides a simple and cost-effective alternative to study time-weighted temporal and spatial trends. For the first time in this study, we have developed a method to examine the viability of measuring EC using polyurethane foam passive air samplers (PUF-PAS) coupled with a thermal analysis, i.e., EnCan-Total-900 (ECT9). The method was found reproducible with coefficients of variation as low as 3% for EC measured in ambient passive samples. The method had relatively low background with EC levels in blanks being as low as 0.1% of those in deployed samples, allowing quantification within a wide range of concentrations. The results indicate a homogenous distribution of particles within the PUF-PAS substrate. EC concentrations measured with the passive method were not significantly different from those obtained from active samples at the study sites (p > 0.01). This proof of concept of the PUF-PAS method provides an opportunity to cost-effectively expand measurements of elemental carbon at the global scale, and could be further extended to include other carbonaceous aerosol species in the future. This helps address regional data gaps for improving uncertainties of SLCF impacts on global climate forcing and to inform policy decisions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".