European population exposure to airborne pollutants based on a multivariate spatio-temporal model

In this paper, we estimate the distribution of population by exposure to multiple airborne pollutants, taking into account the spatio-temporal variability of daily air quality and the high-resolution spatial spread of human population around Europe. In particular, we consider monitoring network data for five pollutants, namely carbon monoxide, nitrogen dioxide, ozone, coarse and fine particulate matters. The spatial information contained in the large dataset of daily continental air quality is exploited using a multivariate spatio-temporal model capable to cover cross correlation among pollutants, covariates, and missing data as well as spatial and temporal variability and correlation. At the same time, the model is simple enough to be feasible for the large dataset of daily continental air quality over three years. Maximum likelihood estimation is performed using the EM algorithm, and kriging-like spatial estimates are used to compute high-resolution exposure distribution. Moreover, a novel semi-parametric bootstrap technique is used to assess the exposure distribution uncertainty. In this way, we compare the daily population exposure of 33 European countries and three important metropolitan areas in years 2009–2011 using a single flexible model. Extensive tabulations and graphs are reported in the supplementary material.