Regression Analyses of Air Pollution and Transport Based on Multiple Data Sources—A Decision Support Example for Socially Integrative City Planning

In this chapter, we present a study on the inter-relationships between air pollution, transportation, industries, and social activities in a city based on multiple data sources for Tianjin. Tianjin, as one of the locations with Chinese urban living labs (living laboratories (or living labs) are spaces for co-innovation through participatory, transdisciplinary and systemic research), was selected by the TRANS URBAN EU-CHINA project as a representative city because of its size, its industries, and its importance as a main traffic node in order to verify the project results in practice. This chapter describes a top-down approach for the analysis of air pollution where multiple impact factors are taken into account. The insights gained provide evidence for decision-making to facilitate sustainable development with respect to air pollution, which is a valuable goal in order to create more socially integrative cities, as it impacts greatly on the health and well-being of the people in affected parts of the city. The models and analyses identify some important factors impacting the air quality in Tianjin. Furthermore, a cost model for air pollution reduction provides insight into causal factors that should be taken into account while making decisions to lower air pollutants. The models may be beneficial for cities in China and elsewhere and are a contribution to evidence-based urban planning for socially integrative cities.