Application of positive matrix factorization in estimating aerosol secondary organic carbon in Hong Kong and insights into the formation mechanisms
Bibliographic record
Abstract
Abstract. Secondary organic carbon (SOC) is often a significant portion of organic carbon (OC) in ambient particulate matter (PM). The levels and seasonal patterns of SOC in Hong Kong were examined using more than 2000 PM10 measurements made over a 4.5-year period (1998–2002) in a network of ten air quality monitoring stations. The positive matrix factorization (PMF) model was used to analyze this large data set for source identification and apportioning. SOC was subsequently estimated to be the sum of OC present in the secondary sources, i.e., secondary sulfate, secondary nitrate, and secondary organic aerosol. The annual average SOC as estimated by the PMF method was 4.25 µg C/m3 while the summer average was 1.66 µg C/m3 and the winter average was 7.05 µg C/m3. In comparison, the method that uses EC as a tracer for primary carbonaceous aerosol sources to derive SOC overestimated SOC by 70–212% for the summer samples and by 4–43% for the winter samples. The overestimation by the EC tracer method resulted from the inability of obtaining a single OC/EC ratio that represented a mixture of primary sources varying in time and space. We found that SOC and secondary sulfate had synchronous seasonal variation and were correlated in individual seasons, suggesting common factors that control their formation. Considering the well-established fact that both gas phase oxidation and in-cloud processing are important formation pathways for sulfate, the synchronicity of SOC and sulfate suggests that in-cloud pathways are also important for SOC formation. Additionally, the presence of SOC was found to be enhanced more than that of secondary sulfate in the winter. We postulate this to be a combined result of favorable partitioning of semivolatile SOC species in the particle phase and more abundant SOC precursors in the winter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".