Spatio-temporal modeling and forecasting of air quality data

Abstract

Respirable Suspended Particulate (RSP) time series data sampled in an air quality monitoring network are found strongly correlated and they are varying in highly similar patterns. This study provides a methodology for spatio-temporal modeling and forecasting of multiple RSP time series, in which the dynamic spatial correlations amongst the series can be effectively utilized. The efficacy of the Spatio-Temporal Dynamic Harmonic Regression (STDHR) model is demonstrated. Based on the decomposition of the observed time series into the trend and periodic components, the model is capable of making forecast of RSP data series that exhibit variation patterns during air pollution episodes and typhoons with dynamic weather conditions. It is also capable to produce spatial predictions of RSP time series up to three unobserved sites. The Noise-variance-ratio (NVR) form of the multivariate recursive algorithm ((M2) algorithm) that derived by the author can greatly facilitate its practical application in both multivariate and univariate time series analysis. The (M2) algorithm allows the spatial correlations to be specified at parametric levels. The state-space (SS) model formulation can flexibly accommodate the existing inter or intra (auto) correlations amongst the parameters of the data series. Applications of the variance intervention (VI) are exploited and illustrated with a real life case study which involves forecasting of RSP data series during an air pollution episode. This illustrates that time series with abrupt changes can be predicted by automatic implementation of the VI approach. The present study also extended the anisotropic Matern model to estimate the dynamic spatial correlation structure of the air quality data by using mean wind speed and prevailing wind direction in defining the spatial anisotropy. The Anisotropic Matern model by Mean Wind Speed and Prevailing Wind Direction (AMMP) model that devised by the author can avoid huge computational burden in estimating variogram at every variation of the underlying spatial structure. Finally, the findings of this dissertation have laid the foundation for further research on multiple time series analysis and estimation of dynamic spatial structure.