Monitoring, modelling and understanding the complex spatiotemporal dynamics of air pollution exposure, transport policies, and health burdens.

Authors: Thomas Redfern*, University of Leeds, Nicolas Malleson, University of Leeds, Gillian Harrison, University of Leeds, Frances Hodgson, University of Leeds, Alexis Comber, University of Leeds, Susan Grant-Muller, University of Leeds
Topics: Urban Geography, Geography and Urban Health, Transportation Geography
Keywords: Air pollution, Exposure, Spatio-temporal, No2, Nitrogen Dioxide, Health burden, Transport mode
Session Type: Paper
Day: 4/12/2018
Start / End Time: 10:00 AM / 11:40 AM
Room: Bayside A, Sheraton, 4th Floor
Presentation File: No File Uploaded


Exposure to air pollution contributes to approximately seven million annual deaths globally. Nitrogen Dioxide (NO2) is a common air pollutant with a growing evidence base for health impacts. Estimating the exposure of individuals and populations to NO2 is required for the prediction, monitoring and mitigation of health impacts at national and local scales. Atmospheric concentrations of NO2 are sensitive to a number of landscape features and atmospheric processes, creating a complex spatiotemporal dynamic between the habitation and movement of people, and NO2 exposure. To estimate exposure for individuals during a typical 24 hour period we combine a number of spatiotemporal big-data sources within a machine learning model. NO2 concentrations are modelled at hourly resolution across a 50m grid within the study area (Newcastle, a city in north east England, population 550,000); utilizing data collected by an air quality monitoring network (Newcastle Urban Observatory). GPS tracking data for journeys conducted by ~500 members of the public are analysed to examine how transport mode (e.g. walking, cycling, driving) and route choice produce a range of exposure profiles. We quantify health burdens arising from NO2 exposure using a modified version of the Integrated Transport and Health Impact Modelling Tool (ITHIM) and demonstrate that the relationship between travel mode, route choice, NO2 exposure and health impact is spatially and temporally complex. To facilitate improved estimates of population level NO2 exposure health burdens, new methods utilizing detailed data on the location of individuals over time are required.

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