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Bayesian spatio-temporal modelling for forecasting ground level ozone concentration levels

Bayesian spatio-temporal modelling for forecasting ground level ozone concentration levels
Bayesian spatio-temporal modelling for forecasting ground level ozone concentration levels
Accurate, instantaneous and high resolution spatial air-quality information can better inform the public and regulatory agencies of the air pollution levels that could cause adverse health effects. The most direct way to obtain accurate air quality information is from measurements made at surface monitoring stations across a study region of interest. Typically, however, air monitoring sites are sparsely and irregularly spaced over large areas. That is why, it is now very important to develop space-time models for air pollution which can produce accurate spatial predictions and temporal forecasts.

This thesis focuses on developing spatio-temporal models for interpolating and forecasting ground level ozone concentration levels over a vast study region in the eastern United States. These models incorporate output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model that can forecast up to 24 hours in advance. However, these forecasts are known to be biased. The models proposed here
are shown to improve upon these forecasts for a two-week study period during August 2005.

The forecasting problems in both hourly and daily time units are investigated in detail. A fast method, based on Gaussian models is constructed for instantaneous interpolation and forecasts of hourly data. A more complex
dynamic model, requiring the use of Markov chain Monte Carlo (MCMC) techniques, is developed for forecasting daily ozone concentration levels. A set of model validation analyses shows that the prediction maps that are generated by the aforementioned models are more accurate than the maps based solely on the Eta-CMAQ forecast data. A non-Gaussian measurement error model is also considered when forecasting the extreme levels of ozone concentration. All of the methods presented are based on Bayesian methods and MCMC sampling techniques are used in exploring posterior and predictive distributions.
Yip, Chun Yin
f7d080ba-869c-46c8-b471-dc01e75135de
Yip, Chun Yin
f7d080ba-869c-46c8-b471-dc01e75135de
Sahu, Sujit
33f1386d-6d73-4b60-a796-d626721f72bf
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879

Yip, Chun Yin (2010) Bayesian spatio-temporal modelling for forecasting ground level ozone concentration levels. University of Southampton, School of Mathematics, Doctoral Thesis, 176pp.

Record type: Thesis (Doctoral)

Abstract

Accurate, instantaneous and high resolution spatial air-quality information can better inform the public and regulatory agencies of the air pollution levels that could cause adverse health effects. The most direct way to obtain accurate air quality information is from measurements made at surface monitoring stations across a study region of interest. Typically, however, air monitoring sites are sparsely and irregularly spaced over large areas. That is why, it is now very important to develop space-time models for air pollution which can produce accurate spatial predictions and temporal forecasts.

This thesis focuses on developing spatio-temporal models for interpolating and forecasting ground level ozone concentration levels over a vast study region in the eastern United States. These models incorporate output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model that can forecast up to 24 hours in advance. However, these forecasts are known to be biased. The models proposed here
are shown to improve upon these forecasts for a two-week study period during August 2005.

The forecasting problems in both hourly and daily time units are investigated in detail. A fast method, based on Gaussian models is constructed for instantaneous interpolation and forecasts of hourly data. A more complex
dynamic model, requiring the use of Markov chain Monte Carlo (MCMC) techniques, is developed for forecasting daily ozone concentration levels. A set of model validation analyses shows that the prediction maps that are generated by the aforementioned models are more accurate than the maps based solely on the Eta-CMAQ forecast data. A non-Gaussian measurement error model is also considered when forecasting the extreme levels of ozone concentration. All of the methods presented are based on Bayesian methods and MCMC sampling techniques are used in exploring posterior and predictive distributions.

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Published date: May 2010
Organisations: University of Southampton

Identifiers

Local EPrints ID: 167441
URI: https://eprints.soton.ac.uk/id/eprint/167441
PURE UUID: e3be3738-d12b-454e-ae3b-500dbe8dfeb5
ORCID for Sujit Sahu: ORCID iD orcid.org/0000-0003-2315-3598
ORCID for Jonathan Forster: ORCID iD orcid.org/0000-0002-7867-3411

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Date deposited: 03 Dec 2010 10:21
Last modified: 24 May 2019 00:38

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