An evaluation of European air pollution regulations for particulate matter monitored from a heterogeneous network
An evaluation of European air pollution regulations for particulate matter monitored from a heterogeneous network
Statistical methods are needed for evaluating many aspects of air pollution regulations increasingly adopted by many different governments in the European Union. The atmospheric particulate matter (PM) is an important air pollutant for which regulations have been issued recently. A challenging task here is to evaluate the regulations based on data monitored on a heterogeneous network where PM has been observed at a number of sites and a surrogate has been observed at some other sites. This paper develops a hierarchical Bayesian joint space-time model for the PM measurements and its surrogate between which the exact relationship is unknown, and applies the methods to analyse spatio-temporal data obtained from a number of sites in Northern Italy. The model is implemented using MCMC techniques and methods are developed to meet the regulatory demands. These enablefull inference with regard to process unknowns, calibration, validation, predictions in time and space and evaluation of regulatory standards.
bayesian inference, hierarchical model, markov chain monte carlo, separable spatio-temporal process, stationarity
943-961
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Nicolis, Orietta
6b67f441-906f-4e64-ab00-f87839e2d908
20 October 2008
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Nicolis, Orietta
6b67f441-906f-4e64-ab00-f87839e2d908
Sahu, Sujit K. and Nicolis, Orietta
(2008)
An evaluation of European air pollution regulations for particulate matter monitored from a heterogeneous network.
Environmetrics, 20 (8), .
(doi:10.1002/env.965).
Abstract
Statistical methods are needed for evaluating many aspects of air pollution regulations increasingly adopted by many different governments in the European Union. The atmospheric particulate matter (PM) is an important air pollutant for which regulations have been issued recently. A challenging task here is to evaluate the regulations based on data monitored on a heterogeneous network where PM has been observed at a number of sites and a surrogate has been observed at some other sites. This paper develops a hierarchical Bayesian joint space-time model for the PM measurements and its surrogate between which the exact relationship is unknown, and applies the methods to analyse spatio-temporal data obtained from a number of sites in Northern Italy. The model is implemented using MCMC techniques and methods are developed to meet the regulatory demands. These enablefull inference with regard to process unknowns, calibration, validation, predictions in time and space and evaluation of regulatory standards.
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Published date: 20 October 2008
Keywords:
bayesian inference, hierarchical model, markov chain monte carlo, separable spatio-temporal process, stationarity
Organisations:
Statistics
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Local EPrints ID: 147815
URI: http://eprints.soton.ac.uk/id/eprint/147815
ISSN: 1180-4009
PURE UUID: ddd4e4e0-7184-4f95-bad9-136c20b473c2
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Date deposited: 26 Apr 2010 13:25
Last modified: 14 Mar 2024 02:44
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Author:
Orietta Nicolis
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