Spatio-temporal regression kriging for modelling urban NO2 concentrations

V. Van Zoest*, F.B. Osei, Gerard Hoek, A. Stein

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
53 Downloads (Pure)


Recently developed urban air quality sensor networks are used to monitor air pollutant concentrations at a fine spatial and temporal resolution. The measurements are however limited to point support. To obtain areal coverage in space and time, interpolation is required. A spatio-temporal regression kriging approach was applied to predict nitrogen dioxide (NO2) concentrations at unobserved space-time locations in the city of Eindhoven, the Netherlands. Prediction maps were created at 25 m spatial resolution and hourly temporal resolution. In regression kriging, the trend is separately modelled from autocorrelation in the residuals. The trend part of the model, consisting of a set of spatial and temporal covariates, was able to explain 49.2% of the spatio-temporal variability in NO2 concentrations in Eindhoven in November 2016. Spatio-temporal autocorrelation in the residuals was modelled by fitting a sum-metric spatio-temporal variogram model, adding smoothness to the prediction maps. The accuracy of the predictions was assessed using leave-one-out cross-validation, resulting in a Root Mean Square Error of 9.91 μg m−3, a Mean Error of −0.03 μg m−3 and a Mean Absolute Error of 7.29 μg m−3. The method allows for easy prediction and visualization of air pollutant concentrations and can be extended to a near real-time procedure.
Original languageEnglish
Pages (from-to)851-865
Number of pages15
JournalInternational journal of geographical information science
Issue number5
Early online date27 Sep 2019
Publication statusPublished - 3 May 2020


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