Spatio-temporal modelling of urban sensor network data: Mapping air quality risks in Eindhoven, the Netherlands

Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. This thesis aims to evaluate the data quality and usability of an air quality sensor network in Eindhoven. The focus is on hourly nitrogen dioxide (NO2) concentrations, as NO2 has a large spatio-temporal variability and strong association with health effects. The first objective addresses outlier detection based on a spatio-temporal classification. Different spatio-temporal classes are defined, reflecting urban background vs. urban traffic stations, weekdays vs. weekends and four periods per day. Truncated normal distributions are used to set thresholds for the definitions of outliers in each spatio-temporal class. The second objective addresses calibration of the sensors, evaluating different methods in terms of temporal stability, spatial transferability and sensor specificity. A poor spatial transferability of the calibration parameters was found for all methods, consistent with different responses of individual sensors to environmental factors such as temperature and relative humidity. Due to their spatial and temporal variability, calibration parameters require regular updates and sensor-specific recalibrations. The third research objective addresses prediction of air pollutant concentrations using a spatio-temporal regression kriging framework. Prediction maps are created at fine spatio-temporal resolution, which can be used in infrastructural decision-making and epidemiological studies. The final research objective addresses health risk mapping. A panel study is set up to collect daily data on symptoms in asthmatic children. In a Bayesian analysis, these are combined with a priori information from literature to obtain accurate health effect estimates and subsequent burden of disease maps. After careful evaluation of the data quality and removal of outliers, this thesis shows that the low-cost air quality sensor network data can be used to map air pollutant concentrations at a fine spatial and temporal resolution. These maps can be used to estimate burden of disease at the within-city level.