Advances in characterization of black carbon particles and their associated coatings using the soot-particle aerosol mass spectrometer in Singapore, a complex city environment
Bibliographic record
Abstract
Atmospheric black carbon can act as a short-lived climate forcer and carrier of toxic compounds. This work aims to utilize aerosol compositions detected by a soot-particle aerosol mass spectrometer to advance our understanding of the emission and atmospheric processing of refractory BC (rBC) in Singapore. Positive matrix factorization (PMF) analysis of rBC and organic aerosols (OAs) (PMF base ) identified two traffic factors with differences in rBC content, coating thickness, and diurnal pattern, which could potentially help differentiate gasoline and diesel vehicular emissions. Additionally, two secondary OA (SOA) factors influenced by local chemistry and/or regional transport (less-oxidized oxygenated OA (LO-OOA) and more-oxidized OA (MO-OOA)) were identified. Including metals in the PMF (PMF metal ) improved the quality of source apportionment significantly. An industrial- and shipping-influenced OA separated from traffic emissions was strongly associated with heavy metals (e.g., V + and Ni + ) that might pose higher potential risks to human health. Two biomass burning OA (BBOA) factors with different degrees of oxygenation were also identified. Although the aged BBOA component was highly oxidized, its strong association with K 3 SO4- distinguished it from other background MO-OOAs, which generally lacked distinctive OA signatures. Integration of both metals and inorganic aerosols (IAs) into the PMF (PMF all ) further identified an additional aged BBOA component that was associated with nighttime IAs and organo-nitrate formation. Furthermore, PMF all revealed concurrent LO-OOA and nitrate formation during daytime, whereas photochemical production of MO-OOAs was linked to acidic sulfate formation, indicating the importance of investigating the interaction between SOA and IA formation and their mixing state in complex city environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".