Source apportionment of PM <sub>2.5</sub> in Cork Harbour, Ireland using a combination of single particle mass spectrometry and quantitative semi-continuous measurements
Bibliographic record
Abstract
Abstract. An aerosol time-of-flight mass spectrometer (ATOFMS) was co-located with a suite of semi-continuous instrumentation for the quantitative measurement of elemental carbon (EC), organic carbon (OC), sulfate, particle number and PM2.5 mass at a site in Cork Harbour, Ireland for three weeks in August 2008. Off-line analysis of polar organic markers was also performed for the same period. The data collected was used to identify and apportion local and regional sources of PM2.5. Over 550 000 ATOFMS particle mass spectra were generated and classified using the K-means algorithm. The vast majority of particles ionised by the ATOFMS were attributed to local sources, although one class of carbonaceous particles detected is attributed to North American or Canadian anthropogenic sources. The temporality of the ambient ATOFMS particle classes was subsequently used in conjunction with the semi-continuous measurements to apportion PM2.5 mass using positive matrix factorisation. Six factors were obtained, corresponding to vehicular traffic, marine, long-range transport, power generation, domestic solid fuel combustion and shipping traffic. The estimated contribution of each factor to the measured PM2.5 mass was 23%, 14%, 13%, 11%, 5% and 1.5%, respectively. Shipping was found to contribute 18% of the measured particle number (20–600 nm mobility diameter), and thus may have implications for human health considering the size and composition of ship exhaust particles.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".