A comparison of strategies for estimation of ultrafine particle number concentrations in urban air pollution monitoring networks

Abstract

We propose three estimation strategies (local, remote and mixed) for ultrafine particles (UFP) at three sites in an urban air pollution monitoring network. Estimates are obtained through Gaussian process regression based on concentrations of gaseous pollutants (NOx, O3, CO) and UFP. As local strategy, we use local measurements of gaseous pollutants (local covariates) to estimate UFP at the same site. As remote strategy, we use measurements of gaseous pollutants and UFP from two independent sites (remote covariates) to estimate UFP at a third site. As mixed strategy, we use local and remote covariates to estimate UFP. The results suggest: UFP can be estimated with good accuracy based on NOx measurements at the same location; it is possible to estimate UFP at one location based on measurements of NOx or UFP at two remote locations; the addition of remote UFP to local NOx, O3 or CO measurements improves models' performance.

Keywords: Gaussian process regression; Pollution monitoring network; Statistical modelling; Ultrafine particles estimation; Urban air pollution.