Association rule mining of air quality through an improved Apriori algorithm: A case study in 244 Chinese cities

Predicting air pollution is complex due to intertwined factors among local climate, built environment, and development stages. This study leverages K-means clustering and an improved Apriori algorithm to investigate the combined effects of local meteorological, morphological, and socioeconomic factors on air quality in 244 prefectural-level Chinese cities. Results reveal that the secondary industry in GDP and saturation vapor pressure strongly relate to air quality. Severe air pollution occurs when urban development is coupled with reduced green areas and high temperatures, confirming that a single factor cannot predict air quality well. For example, we find that combining low population, low regional GDP, high maximum temperatures, and longer roads worsens air quality in small urban built-up areas. Additionally, temperature and altitude differences associate with highway passenger volume, regional GDP, and population differently. Given our rules mining methods have broader applications in diversified urban environments, this study provides new insights for improving air quality and local Sustainable Development Goals.