Spatio-temporal regression kriging for modelling urban NO2 concentrations |
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Authors: | Vera van Zoest Frank B. Osei Gerard Hoek Alfred Stein |
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Affiliation: | 1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente , Enschede, The Netherlands v.m.vanzoest@utwente.nlhttps://orcid.org/0000-0002-3017-0874;3. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente , Enschede, The Netherlands;4. Institute for Risk Assessment Sciences (IRAS), Utrecht University , Utrecht, The Netherlands |
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Abstract: | ABSTRACT 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. |
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Keywords: | Spatio-temporal kriging variogram air quality sensor network nitrogen dioxide |
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