Mobile phone data to support air pollution exposure assessment
DOI:
https://doi.org/10.71014/sieds.v79i4.319Keywords:
mobile network data, air pollution, exposure assessment, health risksAbstract
Air pollution is one of the greatest environmental risks to health according to the World Health Organization. Depending on the characteristics of the data sources we can organise the personal exposure assessment methods in two classification criteria: (i) spatial-temporal variations of individuals’ activities (point-fixed or trajectory-based) and (ii) characterisation of air quality (variable or uniform).
In line with this approach, the paper presents a study based on Mobile Network Operator (MNO) data to evaluate spatial-temporal variations in human presence along with pollutant measurements to estimate people's exposure to air pollutants. MNO data enable a longitudinal analysis of human presence with high spatial and time resolution. This allows assigning different levels of air pollution exposure to the population at a specific time and in a specific location.
The proposed method can be useful for policymakers to assess the crowdedness of air-polluted areas over time and to find suitable solutions to mitigate the exposure. In addition, our results can be exploited for improved estimation of the risks inherent to the population exposure to air pollution in urban areas and for epidemiological studies.
References
BEELEN R, STAFOGGIA M, RAASCHOU NIELSEN O, ANDERSEN ZJ, XUN WW, KATSOUYANNI K, DIMAKOPOULOU K, BRUNEKREEF B, WEINMAYR G, HOFFMANN B, WOLF K, SAMOLI E, HOUTHUIJS D, NIEUWENHUIJSEN M, OUDIN A, FORSBERG B, OLSSON D, SALOMAA V, LANKI T, YLI TUOMI T, OFTEDAL B, AAMODT G, NAFSTAD P, DE FAIRE U, PEDERSEN NL, ÖSTENSON C G, FRATIGLIONI L, PENELL J, KOREK M, PYKO A, et al. 2014. Long term exposure to air pollution and cardiovascular mortality: an analysis of 22 European cohorts. Epidemiology, Vol. 25, pp. 368-78. DOI: https://doi.org/10.1097/EDE.0000000000000076
BUSILLO C., CALASTRINI F., GUARNIERI F. 2022, Rappresentatività spaziale delle stazioni della rete di monitoraggio di qualità dell’aria Toscana, Report Tecnico Consorzio LaMMA – Laboratorio di Monitoraggio e Modellistica Ambientale per lo sviluppo sostenibile, https://www.regione.toscana.it/-/elenco-pubblicazioni-inerenti-la-rappresentativita-spaziale-delle-stazioni-di-rilevamento-della-qualita-dell-aria-in-toscana
DE FAUSTI F., RADINI R., TUOTO T., VALENTINO L. 2024. Mobile phone data for population estimates and for mobility and commuting pattern analyses, RIEDS - Rivista Italiana di Economia, Demografia e Statistica, Vol. 78, No.1, pp. 235-247. DOI: https://doi.org/10.71014/sieds.v78i1.275
DEWULF, B., NEUTENS, T., LEFEBVRE, W. et al. 2016. Dynamic assessment of exposure to air pollution using mobile phone data. Int J Health Geogr, Vol. 15, No.14. https://doi.org/10.1186/s12942-016-0042-z DOI: https://doi.org/10.1186/s12942-016-0042-z
DIAS D, TCHEPEL O. 2018. Spatial and Temporal Dynamics in Air Pollution Exposure Assessment. Int J Environ Res Public Health. Vol. 15, No.3, pp. 558. https://doi.org/10.3390/ijerph15030558 DOI: https://doi.org/10.3390/ijerph15030558
GRAY C., EDWARDS S., LYNN M. 2013. Race, socioeconomic status, and air pollution exposure in North Carolina, Environmental Research, Vol. 126, pp. 152-158, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2013.06.005 DOI: https://doi.org/10.1016/j.envres.2013.06.005
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Copyright (c) 2025 Erika Cerasti, Cristina Faricelli, Paolo Mattera, Roberta Radini, Tiziana Tuoto
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