Characterization of black carbon‐containing particles from soot particle aerosol mass spectrometer measurements on the R/V <i>Atlantis</i> during CalNex 2010
Bibliographic record
Abstract
Abstract We present mass spectrometry measurements of black carbon‐containing particles made on board the R/V Atlantis during the CalNex (California Research at the Nexus of Air Quality and Climate Change) 2010 study using an Aerodyne Research Inc. soot particle aerosol mass spectrometer (SP‐AMS). The R/V Atlantis was deployed to characterize air masses moving offshore the California coast and to assess emissions from sources in urban ports. This work presents a first detailed analysis of the size‐resolved chemical composition of refractory black carbon (rBC) and of the associated coating species (NR‐PM BC ). A colocated standard high‐resolution aerosol mass spectrometer (HR‐AMS) measured the total nonrefractory submicron aerosol (NR‐PM 1 ). Our results indicate that, on average, 35% of the measured NR‐PM 1 mass (87% of the primary and 28% of the secondary NR‐PM 1 , as obtained from the mass‐weighted average of the NR‐PM BC species) was associated with rBC. The peak in the average size distribution of the rBC‐containing particles measured by the SP‐AMS in vacuum aerodynamic diameter ( d va ) varied from ~100 nm to ~450 nm d va , with most of the rBC mass below 200 d va . The NR‐PM BC below 200 nm d va was primarily organic, whereas inorganics were generally found on larger rBC‐containing particles. Positive matrix factorization analyses of both SP‐AMS and HR‐AMS data identified organic aerosol factors that were correlated in time but had different fragmentation patterns due to the different instruments vaporization techniques. Finally, we provide an overview of the volatility properties of NR‐PM BC and report the presence of refractory oxygen species in some of the air masses encountered.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".