Patterns Discovery Dataset for Particulate Matter (PM2.5) Pollution Trends in Japan

Abstract

Air pollution is a leading risk factor for mortality in Japan. Despite improvements in air quality, Particulate Matter 2.5 (PM2.5) remains a significant concern due to its severe health impacts. Although previous studies have analyzed PM2.5 trends in Japan, they often overlook regions consistently exposed to high PM2.5 levels, primarily due to the lack of comprehensive, high-quality datasets. To address this gap, we developed a large-scale dataset containing five years of hourly PM2.5 measurements from approximately 1,900 sensors across Japan. The dataset employs an ER-Schema that isolates PM2.5 data, ensuring a focused analysis by excluding other pollutants. This extensive temporal and spatial coverage enables a more precise examination of long-term PM2.5 exposure patterns. Finally, we validated the dataset by applying established pattern mining models, demonstrating its effectiveness in identifying regions with consistently high PM2.5 levels. The proposed dataset provides a valuable resource for air quality research, supporting the development of targeted mitigation strategies and enhancing understanding of pollution dynamics in Japan.