Dynamic assessment of exposure to air pollution using mobile phone data

Abstract

BackgroundExposure to air pollution can have major health impacts, such as respiratory and cardiovascular diseases. Traditionally, only the air pollution concentration at the home location is taken into account in health impact assessments and epidemiological studies. Neglecting individual travel patterns can lead to a bias in air pollution exposure assessments.MethodsIn this work, we present a novel approach to calculate the daily exposure to air pollution using mobile phone data of approximately 5 million mobile phone users living in Belgium. At present, this data is collected and stored by telecom operators mainly for management of the mobile network. Yet it represents a major source of information in the study of human mobility. We calculate the exposure to NO2 using two approaches: assuming people stay at home the entire day (traditional static approach), and incorporating individual travel patterns using their location inferred from their use of the mobile phone network (dynamic approach).ResultsThe mean exposure to NO2 increases with 1.27 μg/m3 (4.3 %) during the week and with 0.12 μg/m3 (0.4 %) during the weekend when incorporating individual travel patterns. During the week, mostly people living in municipalities surrounding larger cities experience the highest increase in NO2 exposure when incorporating their travel patterns, probably because most of them work in these larger cities with higher NO2 concentrations.ConclusionsIt is relevant for health impact assessments and epidemiological studies to incorporate individual travel patterns in estimating air pollution exposure. Mobile phone data is a promising data source to determine individual travel patterns, because of the advantages (e.g. low costs, large sample size, passive data collection) compared to travel surveys, GPS, and smartphone data (i.e. data captured by applications on smartphones).

Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

“…These profound research studies can be used for mobility prediction [3,4], urban planning [5][6][7], transportation research [8,9,28], and other fields [10,29]. Among the datasets, mobile phone location data is very special data because mobile phones have an extremely high penetration rate and people usually take their cell phones with them, especially in Asian countries such as China.…”

“…Understanding human mobility is of crucial importance [1,2], with potential benefits for various fields such as mobility prediction [3,4], urban planning [5][6][7], transportation research [8,9], and human health research [10]. With the rapid development of information and communication technology [11] in the past two decades, various types of massive digital footprints generated by humans such as smart card data, call detail records (CDRs), geo-tagged social media data, GPS tracking data, WiFi data, credit-card records data, and their concomitant analytics are used for human mobility research [2,[12][13][14][15][16][17][18].…”

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

“…12 ,19,20,24-35 Quantifying how people move throughout their daily activities within the context of spatial risks enables a better understanding of environmental drivers of infectious disease, as well as chronic disease and other issues that involve long-term differences in exposure and mobility during travel. [36][37][38][39] Recent advances in mobile health (mHealth) technology, together with the increasing penetration of smartphones and the internet, have facilitated the monitoring of traveller health behaviour and assessment of environmental risks, e.g. air pollution, and offer more reliable and more frequently updated 'apps' that consolidate travel health information from multiple sources in travel medicine research and practice.…”

Big Data y nuevas geografías: la huella digital de las actividades humanas

“…Además, estos datos pueden ser cruzados espacialmente con otras fuentes de datos Big Data y con los datos de las estadísticas oficiales. Podemos relacionar, por ejemplo, los niveles de contaminación de la ciudad (obtenidos por medio de sensores) con la presencia de población (estimada a partir de datos de telefonía móvil) para conocer la cantidad de población expuesta a la contaminación en cada momento del día y cada área de la ciudad (Dewulf et al, 2016;Castell et al, 2017). O relacionar los datos de movilidad de la población obtenidos a partir de Twitter con las características del lugar de residencia según los datos censales, para conocer el uso del espacio de la ciudad según grupos sociales o raciales (Netto et al, 2015;Shelton et al, 2015).…”