Cluster Analysis for Daily Patterns of SO2 and NO2 Measured by the DOAS System in Xiamen
Bibliographic record
Abstract
Daily patterns of air pollutants are important to improve measurement retrievals and to model the regimes of local air quality. In this study, the daily patterns of SO2 and NO2 as well as their association with visibility and meteorological conditions in a suburban area of Xiamen are investigated. To achieve this goal, continuous field measurements were collected with a Differential Optical Absorption Spectroscopy (DOAS) system in 2011. The K-means clustering is used to classify the daily variation cycles of these measurements associated with different visibility and meteorological conditions such as temperature, relative humidity, wind speed and direction. The Davies-Bouldin index strategy is used to determine the optimal number of clusters. The regime of each cluster associated with visibility and meteorological conditions was then explored and compared. The comparative analyses show that both the maximum hourly average concentrations and the maximum daily average concentrations of SO2 and NO2 occurred in spring. Only 0.04 percent and 3.19 percent of the days with SO2 and NO2, respectively, did not comply with the latest national ambient air quality standards of China (GB 3095-2012). Moreover, the clustering results highlighted three representative patterns of daily SO2 concentrations and four representative patterns of daily NO2 concentrations. Both similarities and differences were presented among these clusters. The consistent changes in aerosol concentration with the changes in the measurements of NO2 and SO2 in each cluster provided supplemental evidence for the presence of the daily patterns of SO2 and NO2.
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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 | 0.000 | 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".