It has been estimated that air pollution, including both particulate matter and gaseous compounds, causes annually 4.2 million premature deaths worldwide due to respiratory and cardiovascular diseases (WHO 2016). Helsinki aims to answer the challenges on reducing greenhouse gas emissions and population growth by altering some of the access roads to city boulevards, which will impact the health of the people moving in and accessing the new areas. It is of utmost importance to plan future urban areas bearing in mind the health effects and understand who will likely bear them (i.e. the socio-economic distribution of these effects). Ultimately, this understanding allows to understand how planning solutions can contribute to improving the quality of life of future residents.
Exposure to air pollutants is known to have links with socioeconomic and built environment structures. Unequal distribution in exposure can lead to, or enforce, the systemic health disparities between population groups, further pronounced by the often higher vulnerability of the disadvantaged groups. For example, empirical research from US has established that individuals and communities with lower socioeconomic status are in many cases exposed to higher levels of pollutants (e.g. Gray et al. 2013, Hajat et al. 2013). However, contradictory results have been reported as well, e.g. from New York where people with higher socioeconomic status had higher concentrations of pollutants (Maroko 2012). These kinds of results indicate that the type of urban development, including the locations of people and amenities, are location-specific and tied to the overall development of the urban fabric.
Results from Europe, especially those using detailed spatial data are small in number, and their results are perhaps even more mixed (Fairburn et al. 2019). Thus, further understanding on the co-locations is needed, and above all, we don’t currently have any information on how urban planning decisions contribute, or could contribute, to environmental justice.
The CousCOUS project will provide multiple advancements to the current state-of-the-art, by using more advanced statistical and machine learning methods, using the individual-level at as detailed as possible spatial resolution and looking at the relationship between relocation patterns and air quality. Furthermore, the novelty of the project lies in population forecasting, which will be combined with predictions about future air quality and traffic flows, allowing more holistic understanding the various effects urban planning decisions have.