This project aims to characterize and encapsulate human activity patterns through statistical analysis of empirical data and deterministic simulation. A main goal is to achieve an understanding and mathematical description of human activities that can be used in human exposure simulation.

This project aims to characterize indoor concentrations of pollutants through measurement and modeling. Indoor environments include detached homes, apartments, offices, bars, restaurants, etc. Specific efforts focus on monitoring, source characterization, ventilation, particle deposition, and indoor-outdoor pollutant transport.

This project seeks to test, calibrate, and develop methods for the real-time monitoring of airborne particle concentrations. Specific efforts focus on different aerosol sources, real-time comparisons among different methods, the impact of fresh versus aged aerosol, effects of humidity, etc.

This project seeks to measure and model indoor and outdoor air pollution dispersion in close proximity to local point sources, including cigars, cigarettes, incense, candles, cooking, etc. Specific efforts make use of multi-point arrays of gas or particle sensors, as well as advanced statistical and deterministic modeling techniques.

This project seeks to develop, apply, and evaluate models of human exposure to air pollution in homes, cars, outdoors, public locations, offices, etc. These models make use of human activity pattern data, material balance models, monitoring data, and environmental parameters to predict exposures and explore their determinants for both individuals and populations. A main goal is to identify ways to reduce or eliminate hazardous exposures from our everyday lives.

This project aims to measure and model dynamic pollutant concentrations and physical parameters of the automobile microenvironment. Specific efforts include determining the air exchange rates of vehicles under wide-ranging circumstances, and measuring in-vehicle air pollutant concentrations and deposition onto in-vehicle surfaces.