Urban form and seasonal PM2.5 dynamics: Enhancing air quality prediction using interpretable machine learning and IoT sensor data

Abstract

This study investigates the critical issue of how urban form characteristics influence PM_2.5 concentrations, a key concern for public health in densely populated cities. Traditional monitoring methods have faced data gaps and methodological limitations. To address this, we employed interpretable machine learning (ML) models with data from 1,069 Internet-of-Things (IoT) sensors across Seoul, South Korea (September 2020–August 2023). Over 80 urban form variables—including density, transportation, road design, building morphology, and land use—were analyzed using Recursive Feature Elimination to identify key factors affecting PM_2.5 concentrations within three buffer zones (300-m, 500-m, 1-km). The random forest model demonstrated the highest accuracy, with an R² of 95 % for autumn and 96 % for spring. Our findings show higher PM_2.5 levels in colder months, driven by road width and building density in autumn and traffic and industrial activity in winter. In summer, green spaces and meteorological conditions were primary factors, while spring air quality was notably impacted by localized traffic emissions around highways and bus stops. This study offers robust predictions and actionable insights for urban planning and air quality management. Future research could integrate additional environmental variables and expand sensor coverage to further refine predictive models.