Spatio-temporal regression kriging for modelling urban NO2 concentrations. Issue 5 (3rd May 2020)

• Record Type: Journal Article
• Authors: van Zoest, Vera; Osei, Frank B.; Hoek, Gerard; Stein, Alfred
• Is Part Of: International journal of geographical information science. Volume 34:Issue 5(2020)
• Page Start: 851
• Page End: 865
• Publication Date: 2020-05-03
• Subjects: Spatio-temporal kriging -- variogram -- air quality -- sensor network -- nitrogen dioxide; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1667501
• Languages: English
• ISSNs: 1365-8816

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.