Modeling pollutant threshold exceedance probabilities in the presence of exogenous variables

Many studies link exposure to various air pollutants to respiratory illness, making it important to identify regions where such exposure risks are high. One way of addressing this problem is by modeling probabilities of exceeding specific pollution thresholds. In this paper, we consider particulate matter with diameter less than 10 microns (PM10) in the North-Italian region Piemonte. The problem of interest is to predict the daily exceedance of 50 micrograms per cubic meter of PM10 based on air pollution data, geographic information, as well as exogenous variables. We use a two-step procedure involving nonparametric modeling in the time domain, followed by spatial interpolation. Resampling schemes are employed to evaluate the uncertainty in these predictions.