Dynamic assessment of exposure to air pollution using mobile phone data

Abstract

Background: Exposure 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.

Methods: In 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).

Results: The mean exposure to NO2 increases with 1.27 μg/m(3) (4.3%) during the week and with 0.12 μg/m(3) (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.

Conclusions: It 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).

Keywords: Air pollution; Dynamic assessment; Exposure; Mobile phone data; Travel patterns.

Fig. 1

Map showing the macro cells of the region of Ghent, overlaid on the road network

Fig. 2

Histogram showing the area (km²) of the macro cells

Fig. 3

Map showing the mean NO₂ concentration for the entire country of Belgium, for both Thursday October 8 and Saturday October 11 2015

Fig. 4

User density per cell on October 8 2015 at 12 am UTC

Fig. 5

Exposure to NO₂ during the weekday for a random user, using the four different approaches (static_hour, dynamic_hour, static_day, dynamic_day)

Fig. 6

Histogram of the average NO₂ concentration that the users are exposed to using the static and dynamic approach, for the week (n = 3,465,917) and weekend day (n = 3,495,453), including the mean reference line for both approaches

Fig. 7

Scatterplot of the exposure to NO₂ calculated with the static and the dynamic approach, for the week (n = 3,465,917) and weekend day (n = 3,495,453)

Fig. 8

Maps of Belgium, showing the statically and dynamically calculated exposure to NO₂, for the week (n = 3,465,917) and weekend day (n = 3,495,453)

Fig. 9

Maps of Belgium showing the difference between the statically and dynamically (dynamic minus static) calculated exposure to NO₂, for both the week (n = 3,465,917) and weekend day (n = 3,495,453)