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. / published_or_final_version / Geography / Doctoral / Doctor of Philosophy

This thesis presents precipitation chemistry data from a dense network of 18 bulk and one wet-only precipitation collectors across the Metropolitan County of Greater Manchester, in the North-West of England, between January 1987 and December 1988. The samples were analysed for the major ions in precipitation. The data were used to investigate the spatial and temporal variability of non marine sulphate, nitrate, ammonium, calcium and hydrogen, their sources and potential atmospheric removal processes. This study has demonstrated the significant spatial variability of non marine sulphate, nitrate, ammonium, calcium and hydrogen ion concentrations in bulk precipitation from a dense urban network. The spatial pattern of hydrogen showed a significant inverse relationship with that of calcium, showing the importance of calcium species as the principal buffering agent of urban precipitation chemistry. A significant relationship between calcium and non marine sulphate was found using advanced statistical techniques. Calcium is proposed to principally originate from urban 'dust' particles which react with either sulphur dioxide or sulphate particles to ·produce enhanced concentrations of non marine sulphate in precipitation. A significant relationship was also found between nitrate and ammonium using the same statistical techniques and trajectory analysis. It is proposed that ammonia from sources within the U.K. and the conurbation itself, undergoes gas phase reactions with nitric acid to form ammonium nitrate, and that the spatial variability of nitrate in precipitation is likely to be the result of the strong spatial variability of ammonium. It may also be possible that calcium carbonate, from local sources, reacts with ammonium sulphate particles, liberating ammonia. The temporal variability of non marine sulphate, nitrate, ammonium and hydrogen ion concentrations in precipitation was consistent with patterns observed by other workers. Local and meso scale emissions do not seem to greatly affect the temporal variabi1ity of these ion concentrations, the major controlling factor being meteorology. The temporal variability of ammonium and nitrate deposition is more susceptible to the effects of local and meso scale emissions. Mean network concentrations of non marine sulphate, nitrate, ammonium and hydrogen did not differ great1y to interpolated values from the secondary national network for the year 1988. However, sophisticated statistical analysis comparing selected data from the G.M. network to those from two nearby national network sites revealed important differences regarding the sources of some ions in precipitation and their relative strengths.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

The scope for this project will be to explore, define and satisfy the current need for protection devices against air pollution. The outline for the project will be to explore the matter from an entrepreneurial perspective where the lev­el of feasibility will be of great importance. Cost efficiency, branding and market are all important factors that should align with the product design process and outcome.

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That air pollutants of different kind has impact on our climate can hardly have avoided anyone during the past years environment explosion in the media. But how is it actually with the reverse, i. e. the climate's impact on air pollutants? The aim with our degree thesis is to try to find out if different weather factors, statistically seen, promotes atmospheric pollutants and particles in the air and vice versa. We got access to data on air pollutants and weather factors that was registered during year 2007 at Femmanhusets air pollutant and weather station in Gothenburg. In order to do a statistical investigation of these data, and to be able to analyze if it occurs relations of various kind, we used the statistics program SPSS. In SPSS we worked with correlation, regression and multiple linear regressions in order to get our statistical relations between atmospheric pollutants and weather conditions. Our results showed that ozone is the atmospheric pollutant that is most weather sensitive and ozone has also proven to have the strongest connections to the weather factors. Also nitrogen monoxide, nitrogen dioxide and carbon monoxide are considerably influenced by the weather, however not in equally big extent as ozone. Sulphur dioxide and PM10 is on the other hand influenced in a low extent and it is in these cases difficult to say if the weather actually has any influence at all. Wind speed was, apart from sulphur dioxide and PM10, the weather factor that were strongest correlated to the remaining atmospheric pollutants. The relative humidity and the air pressure were the two factors that overall gave the weakest connections to the pollutants. As a conclusion, we can say that our results can be seen as an indication to the public about how sensitive people should minimize their time outdoors in the inner city on cold and calm days. It is when these weather conditions occur that the highest concentrations of atmospheric pollutants, statistically seen, can be found. However, this does not apply for ozone. For ozone, they should instead be aware on sunny and windy days as it, statistically seen, is days where high concentrations of ozone can be found in the air.

The AQHI, currently used by the Canadian government, is a multi-pollutant public health information tool that is based upon extensive Canadian epidemiological evidence. As the AQHI is a relatively new metric, there is little published information about the accuracy, and behaviour of this metric both spatially and temporally. The goal of this work was to provide more information to the scientific community on the spatial and temporal behaviour of the AQHI in the Halifax, Nova Scotia region. Sampling was conducted in both the winter and summer of 2009, at 50 sites distributed around the city and at the central NAPS site in downtown Halifax. Statistical analysis was conducted using daily calculated AQHI values. AQHI values in the region were predominantly in the 1 to 3 range on the AQHI scale which corresponds to very good air quality. The Government reported AQHI was found to be significantly different from the 50 sample site s AQHI values for both summer and winter (P=<0.001 for both seasons). The Government reported AQHI was significantly higher (P=0.05) than the AQHI calculated for the 50 sampling sites. Analysis identified that more than 50% of the daily AQHI index values were reported differently than the local sites, most commonly over predicted by one AQHI index point. Analysis also indicated a temporal trend of disagreement between the reported and sampled AQHI values. It was observed that during periods when the AQHI was higher, there was greater disagreement between that reported and the sample site AQHI value. This finding raises some concern regarding the behaviour of the AQHI in both larger cities and over the next decade as Halifax increases in size. The miss-reporting of AQHI values also raises some concern for epidemiological work, if the AQHI is used as an exposure metric it could over estimate exposure to air pollution. However, the AQHI is a useful scientific measure having a number of advantages, first it is a multi pollutant measure based on sound epidemiological evidence linking a mixture of three major air pollutant metrics to health effects and second that it has been distilled into a form that is readily understood by the public. This project has been successful in providing more information to the scientific community on the spatial and temporal variation of the AQHI in the Halifax region. It has been able to identify both seasonal and temporal variation, reinforced the understanding of pollutant behaviour and has begun to provide information on the behaviour of the AQHI on small urban scales and provide valuable information for both researchers and policy makers on the AQHI from a public health context.

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