Use of duckweed, bentonite and acid to improve water quality of effluent discharge from abattoirs

Intensive animal industries create large volumes of nutrient rich effluent, which, if untreated, has the potential for substantial environmental degradation. Aquatic plants in aerobic lagoon systems have the potential to achieve inexpensive and efficient remediation of effluent, and to recover valuable nutrients that would otherwise be lost. Members of the family Lemnaceae (duckweeds) are widely used in lagoon systems, but despite their widespread use in the cleansing of sewage, only limited research has been conducted into their growth in highly eutrophic media, and little has been done to systematically distinguish between different types of media. This study examined the growth characteristics of duckweed in abattoir effluent, and explored possible ways of ameliorating the inhibitory factors to growth on this medium. A series of pot trials was conducted to test the tolerance of duckweed to abattoir effluent partially remediated by a sojourn in anaerobic fermentation ponds, both in its unmodified form, and after the addition of acid to manipulate pH, and the addition of bentonite. Unmodified abattoir effluent was highly toxic to duckweed, although duckweed remained viable and grew sub optimally in media with total ammonia nitrogen (TAN) concentrations of up to 100 mg/l. Duckweed also grew vigorously in effluent diluted 1:4 v/v, containing 56 mg TAN/L and also modified by addition of acid to decrease pH to 7 and by adding bentonite (0.5%).

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

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

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 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.