Air pollution is one of the greatest challenges cities are facing today and improving air quality is a pressing need to reduce negative health impacts. In order to efficiently evaluate which are the most appropriate policies to reduce the impact of urban pollution sources (such as road traffic), it is essential to conduct rigorous population exposure assessments. One of the main limitations associated with those studies is the lack of information about population distribution in the city along the day (population dynamics). The pervasive use of mobile devices in our daily lives opens new opportunities to gather large amounts of anonymized and passively collected geolocation data allowing the analysis of population activity and mobility patterns. This study presents a novel methodology to estimate population dynamics from mobile phone data based on a user-centric mobility model approach. The methodology was tested in the city of Madrid (Spain) to evaluate population exposure to NO2. A comparison with traditional census-based methods shows relevant discrepancies at disaggregated levels and highlights the need to incorporate mobility patterns into population exposure assessments.
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Road average speed in the city of Madrid in 2014 was 24 km/h according to city council open database (link). Since distance is measured as a linear trajectory and other modes (bus, metro, walking, etc.) may have lower average speeds, it was considered appropriate to define an average speed of 15 km/h for all kind of trips.
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This study was supported by the Madrid City Council and the TECNAIRE-CM (innovative technologies for the assessment and improvement of urban air quality) scientific program funded by the Directorate General for Universities and Research of the Greater Madrid Region (S2013/MAE-2972)
Conflict of interest
The authors declare that they have no conflict of interest.
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Picornell, M., Ruiz, T., Borge, R. et al. Population dynamics based on mobile phone data to improve air pollution exposure assessments. J Expo Sci Environ Epidemiol 29, 278–291 (2019). https://doi.org/10.1038/s41370-018-0058-5
- population exposure
- population dynamics
- mobile phone data
- air pollution
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