We develop computer algorithms and build intelligent applications that discover, collect, fuse, and analyze data from heterogeneous sources to solve real world problems.
Air Quality Modeling
Air quality models are important for studying the impact of air pollutant on health conditions. 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 project, we are building a data mining approach, PRISMS-DA, that utilizes publicly available OpenStreetMap (OSM) data and the IMSC transportation data to automatically generate air quality model for the concentrations for any type of pollutants at various temporal scales. Currently, PRISMS-DA automatically generates (domain-) expert-free model for accurate PM2.5 concentration predictions in the Los Angeles Metropolitan Area. The automatically generated model can be used to improve air quality models that traditionally rely on expert-selected input.
The image below shows the PM2.5 AQI predictions from PRISMS-DA (left) and the traditional spatial interpolation method, IDW (right), for Dec 2016 (top) and Jan 2017 (bottom). As expected, IDW could not generate fine-scale predictions while PRISMS-DA successfully identified intra-city areas where the air quality is typically poor (e.g., the south part of the city near the port of San Pedro and downtown Los Angeles).