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This thesis documents research carried out to investigate the possibility of reliably and affordably predicting NOx emission levels from diesel engines. NOx emissions are of increasing concern, principally due to the various detrimental health and environmental impacts that they can have. These emissions are also on the increase with diesel engines contributing significantly to their formation. This contribution is set to continue and is likely to become magnified, particularly with diesel engines enjoying strong market share in a range of industries and increasing influence in other market areas in recent years. Despite the above mentioned concerns and the existence of some understanding of NOx formation in diesel engines, it is very difficult to quantify the amount of NOx that a diesel engine is producing. A range of potential benefits from being able to achieve this are identified in the research documented in this thesis. Subsequently, possible solutions, in terms of measurement and prediction, are investigated and prediction is determined to potentially be the more successful approach. Detail is provided of how test facilities are designed and empirical data is collected to help with the development of the NOx prediction software. A range of data analysis techniques were considered with the chosen ones being selected on the basis that they best meet the defined criteria. The way in which the chosen techniques are applied is described both in terms of the research itself and in terms of how the method will be employed by the sponsoring company and/or the company's customers. Throughout the research consideration was maintained of how the technique would ultimately be used and consequently this had a significant impact on how the research was carried out. The results obtained from the developed method have been compared with the equivalent empirical results. The comparison shows that the developed method can effectively predict the NOx emission levels from a diesel engine over a range of operating conditions. In addition to the conclusions drawn in relation to the research findings, a number of potential future developments are proposed; both in terms of the commercial implications of the findings and also how the developed method might be advanced further.

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The spatial data quality analysis is essential in environmental modelling for efficiently addressing the environmental change. As the complexity of data sets and the modelling capability of computer systems increase, the need to address the quality of both data and models is increasingly important. Integration with environmental modelling, the spatial data quality analysis and the geocomputation paradigm have been three important areas of GIS research. In this research they are brought together in the context of coastal oil spill modelling. The research covers the issues of measurement, modelling and management of spatial data quality. Coupling GIS and environmental modelling, the systematic solution is developed for coastal oil spill modelling which is representative of complex environmental models. The procedures of geospatial data quality analysis were implemented not only with existing GIS funLionality but also with various Geocomputation techniques. Spatial data quality analyses of inputs and model performances, which include sensitivity analyses, error propagation analyses and fitness-for-use analyses, were carried out for the coastal oil spill modelling. The results show that in coastal oil spill modelling, a better understanding and improvement of spatial data quality can be achieved through such analyses. The examples illustrate both the diversity of techniques and tools required when investigating spatial data quality issues in environmental modelling. The evidence of feasibility and practicality are also provided for these flexible analysis approaches. An overall methodology is developed at each stage of a project; with particular emphasis at inception to ensure adequate data quality on which to construct the models. Furthermore, the coupling strategy of GIS and environmental modelling is revised to include a geo-data quality analysis (GQA) engine. With growing availability of proprietary and public domain software suitable for spatial data quality analysis, GQA engines will be formed with the evolution of such software into tightly-coupled collection of tools external to GIS. The GQA engine would itself be tightly-coupled with GIS and environmental models to form a modelling framework.

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