Downloadable estimates of air pollution for England and Wales and estimation of their health effects

 
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RSS Webinar Series

Wednesday 21 February 2018, 03:00pm

 
Location online

Paper:
A Bayesian spatiotemporal model to estimate long-term exposure to outdoor air pollution at coarser administrative geographies in England and Wales by Sabyasachi Mukhopadhyay & Sujit K Sahu (pictured).

The paper was published online in Series A of the journal in June 2017. The publishers, Wiley, will make the paper free to access a couple of weeks before and after the event on 21 February.

Abstract:
Estimation of long term exposure to air pollution levels over a large spatial domain, such as England and Wales, entails a challenging modelling task since exposure data are often only observed by a network of sparse monitoring sites with large amounts of missing data.

In this webinar we discuss estimation of the four most harmful air pollutants: NO2, O3, PM10 and PM2.5, in England and Wales during the five-year period 2007--2011. For each of the four pollutants, using daily data, we develop a point (latitude-longitude) level spatiotemporal Bayesian model which allows us to produce air quality estimates that are the most accurate among the competition.  Monte Carlo methods for spatial and temporal aggregation are developed to obtain aggregated predictions, and their uncertainties, at any given level of an administrative geography, such as local authority. These estimates, now available for download, can readily be used for many purposes such as evaluating compliance with respect to air pollution regulations and modelling of aggregated health outcome data.  Indeed, we illustrate estimation of health effects of air pollution using the developed air pollution estimates.

Presenter: Sujit K Sahu, Professor of Statistics at the University of Southampton
Sujit Sahu is a professor of statistics working at the University of Southampton since 1999. He is an applied statistician interested in reducing uncertainty in data analysis and inference by using hierarchical Bayesian modelling. One of his main research interests lies in air pollution modelling and establishing its linkage with public health. His full career details can be found on his profile page.

Chair: Richard Chandler, Professor of Statistics, University College London
Discussant: Jonathan Rougier, Professor of Statistical Science, Bristol

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Organising Group(s) Royal Statistical Society

 

 

 

 

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