Modelling Impacts of Mobility on Urban Air Quality and Health: Scenario Analysis for the Barcelona Metropolitan Area (Metropolitan Mobility Plan)

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Background The main objective of the project is to develop an integrated mobility and intra-urban air pollution exposure and health model with high spatial resolution to be used as a tool in the environmental assessment of the Barcelona Metropolitan Mobility Plan (PMMU). Following an integrated approach, we assess the mobility derived pollution exposure and health impacts, with the aim of analysing social and spatial disparities, in accordance to different economic, urban and infrastructure development scenarios and the implementation of different measures of the PMMU. Methods The integrated model, implemented on GIS, includes: i) model of urban mobility and emissions ii) model of air pollution exposure and health (iii) population and public facilities forecasts. The model for urban mobility and emissions integrates, for the first time, both motorized and non-motorized means of transport and provides scenarios of future mobility, according to different origin-destination matrices and transport infrastructure. It estimates the mobility derived emissions of NOx, NO2, PM10, PM2,5 using disaggregated emission factors according to different vehicle typology and speeds. A proximity method is implemented to provide high spatial resolution of pollutants distribution, and estimate population and public facilities exposure. The model of air pollution exposure and health uses a land-use regression model to predict pollution concentrations, and a quantitative health impact assessment approach, including a description of the distribution of health effects on the population. Results The ongoing analysis on the population and public facilities emissions exposure shows some interesting results. In 2014, 30% of the population living in the metropolitan area of Barcelona were exposed to high levels of NO2 emissions (>10 Tn/day), 30% were exposed to medium levels (5-10 Tn/day) and the remaining 40% at low levels ( Conclusions The model helps to identify hotspots of pollutant exposure and the socio-ecological trade-offs of different measures (infrastructure, technological, economic, traffic restrictions), to be able to define the best measures for a more sustainable and healthy mobility system. Future developments of the model will allow to analyse the specific effect of different traffic restriction measures in population exposure and health, and the impact in different socioeconomic groups.