Exposure to air pollution in the form of fine particulate matter () is known to cause diseases and cancers. Consequently, the public are increasingly seeking health warnings associated with levels of using mobile phone applications and websites. Often, these existing platforms provide one-size-fits-all guidance, not incorporating user specific personal preferences.
This study demonstrates an innovative approach using Bayesian methods to support personalised decision making for air quality. We present a novel hierarchical spatio-temporal model for city air quality that includes buildings as barriers and captures covariate information. Detailed high resolution data from a single mobile air quality sensor is used to train the model, which is fit using R-INLA to facilitate computation at operational timescales. A method for eliciting multi-attribute utility for individual journeys within a city is then given, providing the user with Bayes-optimal journey decision support. As a proof-of-concept, the methodology is demonstrated using a set of journeys and air quality data collected in Brisbane city centre, Australia.
This article was first posted without an Acknowledgements section. The Acknowledgements section was added on 8 January 2020.
Gavin Shaddick’s time on this project was funded by the Health Inequalities and Air Quality in Scotland (HEALTH-IEAIR) SECURE project. The data and R code used in this paper are available in the associated Supplementary Material.
"Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA." Bayesian Anal. 16 (1) 61 - 91, March 2021. https://doi.org/10.1214/19-BA1193