Do meteorological variables impact air quality differently across urbanization gradients? A case study of Kaohsiung, Taiwan, China
Bibliographic record
Abstract
Air pollution has become a major challenge to global urban sustainable development, necessitating urgent solutions. Meteorological variables are key determinants of air quality; however, research on their impact across different urban gradients remains limited, and their mechanisms are largely unexplored. This study investigates the dynamic effects of meteorological variables on air quality under varying levels of urbanization using Kaohsiung City, Taiwan, as a case study. Meteorological and air pollutant data from monitoring stations in Kaohsiung, Taiwan, for the year 2023 were collected and analyzed. The Air Quality Index (AQI) was used to quantify air quality levels, and Granger causality tests and Vector Autoregression (VAR) models were employed to analyze the dynamic relationships between meteorological variables and AQI. The results revealed that: (1) Suburban areas exhibited significantly better air quality than urban and near-urban areas, with annual AQI values of 59.58 in Meinong (outskirts), 67.86 in Renwu (suburbs area), and 76.73 in Qianjin (urban area), showing a progressive improvement in air quality from urban to suburban areas, primarily due to lower levels of urbanization and abundant forest resources; (2) Temperature and relative humidity emerged as key meteorological variables influencing AQI, with Granger causality tests indicating that temperature significantly affects AQI, especially in urban areas. Impulse response analysis revealed that temperature had a notable positive and negative correlation effect on AQI over lagged periods, while wind speed showed a negative correlation with AQI in suburban areas, gradually shifting to a positive correlation over time; (3) Variance decomposition indicated that temperature had the largest impact on AQI in urban areas, particularly with cumulative lag effects, while wind speed was the main variables influencing air quality in suburban areas. This study provides scientific evidence for future urban planning and environmental management, supporting the development of more effective air quality improvement strategies to promote sustainable urban development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".