Abstract
Air quality monitoring is based on pollutants concentration levels, typically recorded in metropolitan areas. These exhibit spatial and temporal dependence as well as seasonality trends, and their analysis demands flexible and robust statistical models. Here we propose to model the measurements of particulate matter, composed by atmospheric carcinogenic agents, by means of a Bayesian nonparametric dynamic model which accommodates the dependence structures present in the data and allows for fast and efficient posterior computation. Lead by the need to infer the probability of threshold crossing at arbitrary time points, crucial in contingency decision making, we apply the model to the time-varying density estimation for a PM2.5 dataset collected in Santiago, Chile, and analyze various other quantities of interest derived from the estimate.
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