The core aims of this PhD project are to understand spatiotemporal variability of air quality and determine the main drivers of air pollution in urban areas by: (a) deploying a dense network of air quality sensors based on multiple criteria, and (b) employing various air quality modelling and mapping techniques including geostatistical interpolations, statistical and dispersion modelling and data fusion approaches.
The project also aims to analyse the suitability of low-cost sensors for urban air quality monitoring and present an advance methodology for their validation. A multi-criteria Air Quality Monitoring Network (AQMN) was structured based on economic, social and environmental indicators. The network was made of several layers of sensors including reference sensors (most accurate sensors recommended by EU and DEFRA), low-cost sensors (e.g., AQMesh and Envirowatch E-MOTEs) and IoT (internet of things) sensors.
The data from the AQ network was used in AQ mapping and modelling validations. Land-Use Regression (LUR) and Airviro dispersion modelling were carried out for predicting air pollutant levels, analysing spatiotemporal variability of air pollution levels, quantifying emission sources and identifying the main AQ controlling factors.
In Airviro several emission scenarios were tested, which showed that NOx concentrations were mainly controlled by road traffic, whereas PM10 concentrations were controlled by point sources. LUR model demonstrated that among predictor variables altitude had negative significant effect, whereas major roads, minor roads and commercial areas had positive significant effect on NO2 concentrations.
To further improve the maps, modelled and measured concentrations were fused (integrated) to produce high-resolution maps in Sheffield. Furthermore, time series analysis was performed to analyses temporal variability of air pollution concentrations in Sheffield.