Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Abstract

Small-scale spatial variability in NO₂ concentrations is analysed with the help of pollution maps. Maps of NO₂ estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO₂ concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO₂ concentrations. The results showed that the data fusion approach was capable of estimating realistic NO₂ concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO₂-LCS with NO₂-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO₂ were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO₂ levels were higher than 40 µg/m³. Road traffic was considered to be the dominant emission source of NO₂ in Sheffield.