Spatial modelling of air pollution for open smart cities

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsHalf of the world’s population already lives in cities, and by 2050 two-thirds of the world’s population are expected to further move into urban areas. This urban growth leads to various environmental, social and economic challenges in cities, hampering the Quality of Life (QoL). Although recent trends in technologies equip us with various tools and techniques that can help in improving quality of life, air pollution remains the ‘biggest environmental health risk’ for decades, impacting individuals’ quality of life and well-being according to World Health Organisation (WHO). Many efforts have been made to measure air quality, but the sparse arrangement of monitoring stations and the lack of data currently make it challenging to develop systems that can capture within-city air pollution variations. To solve this, flexible methods that allow air quality monitoring using easily accessible data sources at the city level are desirable. The present thesis seeks to widen the current knowledge concerning detailed air quality monitoring by developing approaches that can help in tackling existing gaps in the literature. The thesis presents five contributions which address the issues mentioned above. The first contribution is the choice of a statistical method which can help in utilising existing open data and overcoming challenges imposed by the bigness of data for detailed air pollution monitoring. The second contribution concerns the development of optimisation method which helps in identifying optimal locations for robust air pollution modelling in cities. The third contribution of the thesis is also an optimisation method which helps in initiating systematic volunteered geographic information (VGI) campaigns for detailed air pollution monitoring by addressing sparsity and scarcity challenges of air pollution data in cities. The fourth contribution is a study proposing the involvement of housing companies as a stakeholder in the participatory framework for air pollution data collection, which helps in overcoming certain gaps existing in VGI-based approaches. Finally, the fifth contribution is an open-hardware system that aids in collecting vehicular traffic data using WiFi signal strength. The developed hardware can help in overcoming traffic data scarcity in cities, which limits detailed air pollution monitoring. All the contributions are illustrated through case studies in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under the broader framework of the open smart city and for urban health research