Seminar Friday June 14, 2pm
The University of Reading
School of Applied Statistics, room AS-G03
Gavin Shaddick http://www.bath.ac.uk/~masgs
Department of Mathematical Statistics, University of Bath
Modelling Daily Multivariate Pollutant Data at Multiple Sites
(for use in time series studies examining the relationship between
air pollution and health)
Abstract
In conducting time series studies to investigate the relationship between
air pollution and a health outcome, for example respiratory mortality, it is
important to have a good measure of the level of pollution on any particular
day. Often daily measurements are available from a number of monitoring
sites across the study. Each of these monitors may measure different sets of
pollutants, there may be periods of missing data, and all of the recorded
measurements will be subject to error. This paper describes the problems of
combining such data to produce estimates of the levels of pollution that can
be used in modelling the health outcome.
A hierarchical model is used for the analysis, addressing the issues
described, and specifically, allows information from multiple sites on
different pollutants to be combined. This allows an estimate of the
underlying pollution level for each pollutant at each site to be obtained,
incorporating any possible lag structure, along with a measure of
uncertainty. This is particularly useful for accounting for the variation in
the pollution level, whether formally via error-in-variables modelling, or
informally when interpreting the regression coefficients describing the
relationship between risk and pollution.
These methods are used in assessing the relationship between respiratory
mortality and pollution in London for the period 1993-96. A number of
pollutants, including PM10, CO, NO and SO2, were measured at eight sites in
London and the available data used to calculate a daily estimates of the
underlying levels of pollution which can then analysed with respect to the
health data.
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