A comprehensive PM2.5 vulnerability index for medium-sized cities based on environmental big data
Bibliographic record
Abstract
In 2021, the World Health Organization (WHO) tightened its particulate matter advisory standards for the first time in 16 years. This means that even small amounts of airborne particles can cause adverse health effects. Therefore, comprehensive and multidimensional approaches are required to effective PM management. This study aims to develop a comprehensive PM2.5 vulnerability index utilizing dynamic data that changes in time to provide real-time information on PM2.5 vulnerability in areas with limited social infrastructures. The target area is Chuncheon City, which is located in the downwind area of Seoul and is surrounded by mountains, making it prone to pollutant stagnation. The target period is the winter season of January-March 2022 (i.e., the post COVID-19 period). To utilize data with different individual units, normalization was performed using the Min-Max method, and the vulnerability index was calculated using the Principal Component Analysis (PCA) method to resolve multicollinearity among variables. In Chuncheon, a remote region (e.g., Dongsan-myeon) showed the lowest PM2.5 vulnerability index and a sub-rural region (e.g., Sinbuk-eup) showed the highest one. The difference in the vulnerability index depending on each region is expected to be utilized as basic data for establishing measures to deal with PM2.5 problems.AcknowledgmentThis research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE) and thank you to National Air Emission Inventory and Research Center for providing the data and this work was suported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2024-00356913).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".