The application of methods of uncertain reasoning to the biological classification of river water quality

This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.

Assessment of the impact of discharges from surface water sewers on receiving water quality

A broad based approach has been used to assess the impact of discharges to rivers from surface water sewers, with the primary objective of determining whether such discharges have a measurable impact on water quality. Three parameters, each reflecting the effects of intermittent pollution, were included in a field work programme of biological and chemical sampling and analysis which covered 47 sewer outfall sites. These parameters were the numbers and types of benthic macroinvertebrates upstream and downstream of the outfalls, the concentrations of metals in sediments, and the concentrations of metals in algae upstream and downstream of the outfalls. Information on the sewered catchments was collected from Local Authorities and by observation of the time of sampling, and includes catchment areas, land uses, evidence of connection to the foul system, and receiving water quality classification. The methods used for site selection, sampling, laboratory analysis and data analysis are fully described, and the survey results presented. Statistical and graphical analysis of the biological data, with the aid of BMWP scores, showed that there was a small but persistent fall in water quality downstream of the studied outfalls. Further analysis including the catchment information indicated that initial water quality, sewered catchment size, receiving stream size, and catchment land use were important factors in determining the impact. Finally, the survey results were used to produce guidelines for the estimation of surface water sewer discharge impacts from knowledge of the catchment characteristics, so that planning authorities can consider water quality when new drainage systems are designed.

Ecological studies on:(a) the use of colonisation samplers in relation to biological surveillance of river water quality (b) the requirements of freshwater gastropoda

Some of the factors affecting colonisation of a colonisation sampler, the Standard Aufwuchs Unit (S. Auf. U.) were investigated, namely immersion period, whether anchored on the bottom or suspended, and the influence of riffles. It was concluded that a four-week immersion period was best. S. Auf. U. anchored on the bottom collected both more taxa and individuals than suspended ones. Fewer taxa but more individuals colonised S. Auf. U. in the potamon zone compared to the rhithron zone with a consequent reduction in the values of pollution indexes and diversity. It was concluded that a completely different scoring system was necessary for lowland rivers. Macroinvertebrates colonising S. Auf. U. in simulated streams, lowland rivers and the R. Churnet reflected water quality. A variety of pollution and diversity indexes were applied to results from lowland river sites. Instead of these, it was recommended that an abbreviated species - relative abundance list be used to summarise biological data for use in lowland river surveillance. An intensive study of gastropod populations was made in simulated streams. Lynnaea peregra increased in abundance whereas Potamopyrgas jenkinsi decreased with increasing sewage effluent concentration. No clear-cut differences in reproduction were observed. The presence/absence of eight gastropod taxa was compared with concentrations of various pollutants in lowland rivers. On the basis of all field work it appeared that ammonia, nitrite, copper and zinc were the toxicants most likely to be detrimental to gastropods and that P. jenkinsi and Theodoxus fluviatilis were the least tolerant taxa. 96h acute toxicity tests of P. jenkinsi using ammonia and copper were carried out in a flow-through system after a variety of static range finding tests. P. jenkinsi was intolerant to both toxicants compared to reports on other taxa and the results suggested that these toxicants would affect distribution of this species in the field.

The optimisation of legacy water treatment works to produce a more consistent final water quality

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The ecology of algae in relation to river water equality surveillance

The broad objectives of the work were to develop standard methods for the routine biological surveillance of river water quality, using the non-planktonic algae. Studies on sampling methodology indicated that natural substrata should be sampled directly wherever possible, but for routine purposes, only a semi-quantitative approach was found to be feasible. Artificial substrata were considered to be useful for sample collection in deeper waters, and of three different types tested, Polythene strips were selected for further investigation essentially on grounds of practicality. These were tested in the deeper reaches of a wide range of river types and water qualities: 26 pool sites in 14 different rivers were studied over a period of 9 months. At each site, the assemblages developing on 3 strips following a 4, or less commonly, an 3 week immersion period were analysed quantitatively. Where possible, the natural substrata were also sampled semi-quantitatively at each site, and at a nearby riffle. The results of this survey were very fragmentary: many strips failed to yield useful data, and the results were often difficult to interpret, and of limited value for water quality surveillance purposes. In one river, the Churnet, the natural substrata at 14 riffle sites were sampled semi-quantitatively on 14 occasions at intervals of 4 weeks. In this survey, the results were more readily interpreted in relation to water quality, and no special data processing was found to be necessary or helpful. Further studies carried out on the filamentous green alga Cladophora showed that this alga may have some value as a bioaccumulation indicator for metals, and as a bioassay organism for the assessment of the algal growth promoting potential of natural river waters.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.