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)