A spatio-temporal geostatistical approach to predicting pollution levels: The case of mono-nitrogen oxides in Madrid

Abstract In spite of the effort made in the last years, NO x is still one of the main pollution problems in large cities. This is why the literature related to predicting NO x levels is certainly extensive. However, most of this literature does not take into account the spatio-temporal dependencies of such NO x levels. As spatio-temporal dependencies are a core aspect of pollution, we propose both a spatio-temporal kriging and a functional kriging strategy to incorporate such dependencies into the prediction procedure. We also use an innovative method for estimating the parameters of the non separable space–time covariance function involved in the spatio-temporal kriging strategy, which significantly reduces the computational burden of traditional likelihood-based methods. The empirical study focuses on Madrid City and is backed by a massive hourly database. Results indicate that the functional strategy outperforms the spatio-temporal procedure at non peripheral sites, which is a remarkable finding due to the high computational requirements of spatio-temporal kriging.