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

  • Ana M. Vicedo-Cabrera | vicedo_ana@gva.es Center for Public Health Research (CSISP) - FISABIO, Valencia, Spain.
  • Annibale Biggeri Biostatistics Unit, ISPO Cancer Prevention and Research Institute, Florence; Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, Italy.
  • Laura Grisotto Biostatistics Unit, ISPO Cancer Prevention and Research Institute, Florence; Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, Italy.
  • Fabio Barbone Institutes of Hygiene and Epidemiology, DPMSC, University of Udine, Udine, Italy.
  • Dolores Catelan Biostatistics Unit, ISPO Cancer Prevention and Research Institute, Florence; Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, Italy.

Abstract

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/m3 of NO2 and 4.10 (±2.71) μg/m3 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.

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Published
2013-11-01
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Original Articles
Keywords:
Bayesian kriging, geographical information system, exposure assessment, environmental epidemiology, Italy.
Statistics
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How to Cite
Vicedo-Cabrera, A. M., 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), 87-95. https://doi.org/10.4081/gh.2013.57