A Bayesian kriging model for estimating residential exposure to air pollution of children living in a high-risk area in Italy.

Vicedo-Cabrera, AM; Biggeri, A; Grisotto, L; Barbone, F; Catelan, D; (2013) A Bayesian kriging model for estimating residential exposure to air pollution of children living in a high-risk area in Italy. Geospatial health, 8 (1). pp. 87-95. ISSN 1827-1987 DOI: https://doi.org/10.4081/gh.2013.57

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A core challenge in epidemiological analysis of the impact of exposure to air pollution on health is assessment of the individual exposure for subjects at risk. Geographical information systems (GIS)-based pollution mapping, such as kriging, has become one of the main tools for evaluating individual exposure to ambient pollutants. We applied universal Bayesian kriging to estimate the residential exposure to gaseous air pollutants for children living in a high-risk area (Milazzo- Valle del Mela in Sicily, Italy). Ad hoc air quality monitoring campaigns were carried out: 12 weekly measurements for sulphur dioxide (SO2) and nitrogen dioxide (NO2) were obtained from 21 passive dosimeters located at each school yard of the study area from November 2007 to April 2008. Universal Bayesian kriging was performed to predict individual exposure levels at each residential address for all 6- to 12-years-old children attending primary school at various locations in the study area. Land use, altitude, distance to main roads and population density were included as covariates in the models. A large geographical heterogeneity in air quality was recorded suggesting complex exposure patterns. We obtained a predicted mean level of 25.78 (± 10.61) µg/m(3) of NO2 and 4.10 (± 2.71) µg/m(3) of SO2 at 1,682 children's residential addresses, with a normalised root mean squared error of 28% and 25%, respectively. We conclude that universal Bayesian kriging approach is a useful tool for the assessment of realistic exposure estimates with regard to ambient pollutants at home addresses. Its prediction uncertainty is highly informative and can be used for both designing subsequent campaigns and for improved modelling of epidemiological associations.

Item Type: Article
Faculty and Department: Faculty of Public Health and Policy > Dept of Social and Environmental Health Research
Research Centre: Centre for Statistical Methodology
PubMed ID: 24258886
Web of Science ID: 330210500009
URI: http://researchonline.lshtm.ac.uk/id/eprint/4646242


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