Estimating urban ultrafine particle distributions with Gaussian process models

Abstract

Urban air pollution have a direct impact on public health. Ultrafine particles (UFPs) are ubiquitous in urban environments, but their distribution are highly variable. In this paper, we take data from mobile deployments in Zürich collected over one year with over 25 million measurements to build a high-resolution map estimating the UFP distribution. More specifically, we propose a new approach using a Gaussian Process (GP) to estimate the distribution of UFPs in the city of Zürich. We evaluate the prediction estimations against results derived from standard General Additive Models in Land Use Regression, and show that our method produces a good estimation for mapping the spatial distribution of UFPs in many timescales.