Tremper, PaulTill RiedelBudde, Matthias2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37685The central question of this paper is whether interpolation techniques applied to a distributed sensor network can indeed provide more information than using the constant background of an urban reference station to measure air pollution. We compare different interpolation techniques based on temporal-spatial machine learning in terms of their applicability for correctly predicting personal exposure. Using a dataset of stationary low-cost sensors, we estimate exposure on a route through the city and compare it to mobile measurements. The results show that while different machine learning-based interpolation methods yield quite different results, validation of machine learning-based approaches is still challenging.enAir QualityGaussian Process RegressionValidation10.18420/informatik2021-0221617-5468