Imperial College London


Faculty of MedicineSchool of Public Health

Chair in Biostatistics



m.blangiardo Website




528Norfolk PlaceSt Mary's Campus






BibTex format

author = {Pirani, M and Gulliver, J and Fuller, GW and Blangiardo, M},
doi = {10.1038/jes.2013.85},
journal = {Journal of Exposure Science and Environmental Epidemiology},
pages = {319--327},
title = {Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas},
url = {},
volume = {24},
year = {2013}

RIS format (EndNote, RefMan)

AB - This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urbanenvironment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared severalspatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. Weconsidered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction ofparticles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primarycomponent of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which sitespecificeffect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site,daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are notconsidered in local-scale dispersion models and day of the week to account for time-varying emission rates not available inemissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed thatconcentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transportcomponent with covariates that account for the residual spatiotemporal variation in the pollution process
AU - Pirani,M
AU - Gulliver,J
AU - Fuller,GW
AU - Blangiardo,M
DO - 10.1038/jes.2013.85
EP - 327
PY - 2013///
SN - 1559-064X
SP - 319
TI - Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas
T2 - Journal of Exposure Science and Environmental Epidemiology
UR -
UR -
VL - 24
ER -