Application of Trajectory Clustering and Source Apportionment Methods for Investigating Trans-Boundary Atmospheric PM10 Pollution
Bibliographic record
Abstract
A modeling framework was proposed to investigate the impact of trans-boundary air pollutant transport on regional air quality. This was based on a combination of the HYSPLIT trajectory model, the CAMx air quality model, and the MM5 meteorological model. The examination of atmospheric PM10 pollution in Guangzhou within the Pearl River Delta (PRD) region of southern China was used as a case study. The HYSPLIT and MM5 models were used to qualitatively identify the dominant PM10 pollutant transport pathways that led to PM10 pollution events in Guangzhou, with five clusters of air mass trajectories being examined. The emission source contribution through each transport pathway to Guangzhou’s PM10 concentration was then quantified using a MM5-CAMx modeling system. The results illustrated that the trans-boundary PM10 transport played a critical role in the formation of PM10 pollution events in Guangzhou, with a mean contribution ratio of nearly 49%. In particular, two air mass trajectory clusters that originated from Guangzhou’s surrounding regions were found to be the main pollutant transport pathways, and three surrounding cities (Foshan, Dongguan and Huizhou) had a total emission contribution of nearly 30% to Guangzhou’s PM10 concentration through these two pathways. The emissions from these three cities also accounted for 70 to 94% of the total trans-boundary contributions from Guangzhou’s nine surrounding cities through the five transport pathways. As a result, in order to improve Guangzhou’s air quality, coordinated effort is required to reduce emissions in both Guangzhou itself and its three surrounding cities. It is expected that the presented modeling approach can be applied to air quality studies in many other regions.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".