Abstract

Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically generate an air quality model for the concentrations of fine particulate matter less than 2.5 μm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a …

Date

November 7, 2017

Authors

Yijun Lin, Yao-Yi Chiang, Fan Pan, Dimitrios Stripelis, José Luis Ambite, Sandrah P Eckel, Rima Habre

Book

Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems

Pages

1-10