Evaluation of sampling and monitoring designs for water quality

Haggarty, Ruth Alison (2012) Evaluation of sampling and monitoring designs for water quality. PhD thesis, University of Glasgow.

Abstract

Assessing water quality is of crucial importance to both society and the environment. Deterioration in water quality through issues such as eutrophication presents substantial risk to human health, plant and animal life, and can have detrimental effects on the local economy. Long-term data records across multiple sites can be used to investigate water quality and risk factors statistically, however, identification of underlying changes can only be successful if there is a sufficient quantity of data available. As vast amounts of resources are required for the implementation and maintenance of a monitoring network, logistically and financially it is not possible to employ continuous monitoring of all water environments. This raises the question as to the optimal design for long-term monitoring networks which are capable of capturing underlying changes. Two of the main design considerations are clearly where to sample, and how frequently to sample.
The principal aim of this thesis is to use statistical analysis to investigate frequently used environmental monitoring networks, developing new methodology where appropriate, so that the design and implementation of future networks can be made as effective and cost efficient as possible. Using data which have been provided by the Scottish Environment Protection Agency, several data from Scottish lakes and rivers and a range of determinands are considered in order to explore water quality monitoring in Scotland. Chapter 1 provides an introduction to environmental monitoring and both existing statistical techniques, and potential challenges which are commonly encountered in the analysis of environmental data are discussed. Following this, Chapter 2 presents a simulation study which has been designed and implemented in order to evaluate the nature and statistical power for commonly used environmental sampling and monitoring designs for surface waters. The aim is to answer questions regarding how many samples to base the chemical classification of standing waters, and how appropriate the currently available data in Scotland are for detecting trends and seasonality. The simulation study was constructed to investigate the ability to detect the different underlying features of the data under several different sampling conditions.
After the assessment of how often sampling is required to detect change, the remainder of the thesis will attempt to address some of the questions associated with where the optimal sampling locations are. The European Union Water Framework Directive (WFD) was introduced in 2003 to set compliance standards for all water bodies across Europe, with an aim to prevent deterioration, and ensure all sites reach `good' status by 2015. One of the features of the WFD is that water bodies can be grouped together and the classification of all members of the group is then based on the classification of a single representative site. The potential misclassification of sites means one of the key areas of interest is how well the existing groups used by SEPA for classification capture differences between the sites in terms of several chemical determinands. This will be explored in Chapter 3 where a functional data analysis approach will be taken in order to investigate some of the features of the existing groupings. An investigation of the effect of temporal autocorrelation on our ability to distinguish groups of sites from one another will also be presented here.
It is also of interest to explore whether fewer, or indeed more groups would be optimal in order to accurately represent the trends and variability in the water quality parameters. Different statistical approaches for grouping standing waters will be presented in Chapter 4, where the question of how many groups is statistically optimal is also addressed. As in Chapter 3, these approaches for grouping sites will be based on functional data in order to include the temporal dynamics of the variable of interest within any analysis of group structure obtained. Both hierarchical and model based functional clustering are considered here. The idea of functional clustering is also extended to the multivariate setting, thus enabling information from several determinands of interest to be used within formation of groups. This is something which is of particular importance in view of the fact that the WFD classification encompasses a range of different determinands.
In addition to the investigation of standing waters, an entirely different type of water quality monitoring network is considered in Chapter 5. While standing waters are assumed to be spatially independent of one another there are several situations where this assumption is not appropriate and where spatial correlation between locations needs to be accounted for. Further developments of the functional clustering methods explored in Chapter 4 are presented here in order to obtain groups of stations that are not only similar in terms of mean levels and temporal patterns of the determinand of interest, but which are also spatially homogenous. The river network data explored in Chapter 5 introduces a set of new challenges when considering functional clustering that go beyond the inclusion of Euclidean distance based spatial correlation. Existing methodology for estimating spatial correlation are combined with functional clustering approaches and developed to be suitable for application on sites which lie along a river network.
The final chapter of this thesis provides a summary of the work presented and discussion of limitations and suggestions for future directions.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: monitoring, water quality, river networks, cluster analysis, functional data analysis, Water Framework Directive
• Subjects: H Social Sciences > HA Statistics
• Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Supervisor's Name: Scott, Prof. E. Marian and Miller, Dr. Claire
• Date of Award: 2012
• Depositing User: Ms Ruth Haggarty
• Unique ID: glathesis:2012-3789
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 21 Dec 2012 11:47
• Last Modified: 21 Dec 2012 11:53
• URI: https://theses.gla.ac.uk/id/eprint/3789