Hierarchical space-time modelling of PM10 pollution |
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| Institution: | 1. UBC James Hogg Research Centre, Institute for Heart + Lung Health, St. Paul''s Hospital, The University of British Columbia, Vancouver, BC;2. Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, BC;3. Environmental Health, Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada;1. Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain;2. Exploitation and Prospecting Department, University of Oviedo, 33004 Oviedo, Spain;1. Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy;2. Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden;3. Swiss Tropical and Public Health Institute, Basel, Switzerland;4. University of Basel, Basel, Switzerland;5. INAIL, Department of Occupational & Environmental Medicine, Monteporzio Catone, Italy;6. National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD, USA;7. Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel;8. Institute of Biomedicine and Molecular Immunology “Alberto Monroy”, National Research Council, Palermo, Italy;9. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Cambridge, MA, USA |
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| Abstract: | In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM10 pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM10 process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM10 data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM10 measurements available; the case of PM10 level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM10 levels. |
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