Pollutant emission estimation
We combine field measurements with sophisticated modeling tools to characterize pollutant emissions from various sources, particularly motor-vehicles, from local to regional scale.
30 m resolution isoprene emission factor map for continental US
Roadway-link level NOx emissions in Tampa, FL
Air quality modeling
We uses both empirical and mechanistic air quality models to simulate the spatiotemporal distributions of air pollutants concentrations, and how the concentrations respond to variations in emissions
Funding: Electric Power Research Institute
Annual average PM2.5 concentration field as simulated by 14 different methods, including the simple central monitor, aerosol optical depth, dispersion model (RLINE), chemical transport model (CMAQ), and hybrid data fusion techniques
Air pollution sensor
We design and deploy low-cost air quality monitors to measurement pollutant concentrations in the ambient environment. We are working on designing the Smart and Trustworthy AIR quality network (STAIR) with up to 100 nodes for Orlando, FL.
Funding: National Science Foundation Cyber Physical System - Medium
Sensor attached to UCF campus shuttle and measured PM2.5 concentrations along the route
Rendered image of our own STAIR low-cost air quality monitor
Exposure and health impact estimation
We use big data and smart phone technologies to better understand individual movements in space and time, and use these data to improve air pollution exposure estimation for epidemiological studies.
CMAQ simulated pollutant concentrations (background) VS exposures estimated for over 9,000 individuals using their cell phone location data (CDR) , plotted at their corresponding home locations (inlaid circles)