A prediction system for estimation, analysis and visualization has been developed to model spatial patterns of traffic-related air pollution. In this study, several geostatistical techniques are used for prediction of NO2 and PM10.
The primary data for geostatistical methods originate from sample points that are generated from a network of automatic monitoring stations and, in addition, complemented by other sample points estimated by geographically weighted regression (GWR).