Estimating artificial level shifts in the presence of smooth trends

Regular measurements of the state of the environment constitute a cornerstone of environmental management. Without the support of long time series of reliable data, we would know much less about changes that occur in the environment and their causes. The present research aimed to explore how improved techniques for data analysis can help reveal flawed data and extract more information from environmental monitoring programmes. Based on our results, we propose that the organization of such monitoring should be transformed from a system for measuring and collecting data to an information system where resources have been reallocated to data analysis. More specifically, this thesis reports improved methods for joint analysis of trends in multiple time series and detection of artificial level shifts in the presence of smooth trends. Furthermore, special consideration is given to methods that automatically detect and adapt to the interdependence of the collected data. The current work resulted in a roadmap describing the process of proceeding from a set of observed concentrations to arrive at conclusions about the quality of the data and existence of trends therein. Improvements in existing software accompanied the development of new statistical procedures.