Journal of Exposure Science & Environmental Epidemiology https://doi.org/10.1038/s41370-018-0058-5 ARTICLE Population dynamics based on mobile phone data to improve air pollution exposure assessments Miguel Picornell 1 ● Tomás Ruiz 2 ● Rafael Borge 3 ● Pedro García-Albertos 1 ● David de la Paz 3 ● Julio Lumbreras 3 Received: 20 November 2017 / Revised: 16 May 2018 / Accepted: 10 June 2018 © Springer Nature America, Inc. 2018 Abstract 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 NO 2 . 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. Keywords: population exposure ● population dynamics ● mobile phone data ● air pollution Introduction Air pollution is one of the greatest challenges cities face today. More than 80% of people living in urban areas are exposed to air quality levels that exceed the World Health Organization limits [1]. Pollutants are generated from a wide range of sources, including industry, transport, agri- culture, waste management, and households. Road transport is a major source in cities, being one of the main con- tributors to emission and ambient concentration of nitrogen oxides (NO x ) and particulate matter (PM) [2]. A large number of epidemiological studies have reported statistical associations indicating that exposure to air pollution increases the risk of suffering severe diseases such as lung cancer or chronic and acute respiratory diseases [3, 4]. Seeking to reduce the negative effects of road transport, cities are encouraging a shift towards more sustainable modes of transport by fostering public transport, car-shar- ing, cycling or walking. Likewise, several cities have already implemented policies restricting vehicle access, managing parking or calming traffic. In order to evaluate the impacts of those policies, it is essential to conduct popula- tion exposure assessments to air pollutants [5]. Population exposure estimations rely on both ambient air concentration and population presence. Although the optimal exposure estimation approach depends on the specific aim of epide- miological studies, it is important to incorporate both rea- listic pollution levels and human time-activity patterns [6]. The estimation of pollutant concentration within urban areas is usually based on data collected from air quality mon- itoring stations and modeling techniques that improve the spatio-temporal resolution of the information [7–10]. Recent studies are using new methodologies to assess urban population exposure based on alternative technologies and mobile monitoring approaches [11–13]. However, there is still a need to define methods to consistently assess popu- lation exposure to air pollution at city scale. Information regarding the spatial distribution of popula- tion along the day (from now on population dynamics) is * Miguel Picornell miguel.picornell@nommon.es 1 Nommon Solutions and Technologies S.L., Diego de León, 47, Madrid 28043, Spain 2 Universitat Politècnica de València, Camí de Vera, s/n, València 46022, Spain 3 Universidad Politécnica de Madrid (UPM), José Gutiérrez Abascal, 2, Madrid 28006, Spain 1234567890();,: 1234567890();,: