Abstract by Shelby Taylor
Bayesian Hierarchical Modeling of PM10 Particles
Particles with aerodynamic diameter of 10μm or less, otherwise known as PM10 can carry allergens, pesticides, radioactive material, and other harmful chemicals into the lungs. Studies often use stationary monitors to measure PM10 particles in the air, however they may not accurately measure the true number of particles entering the lungs. The goal of this analysis is to understand the predictive accuracy of stationary monitors and factors such as activity, surface and dust in measuring the true quantity of PM10 particles entering the lungs. We created a hierarchical Bayesian model in order to measure person-specific trends as well as overall trends in the data, taking into account time correlation between measurements.