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No description available.

The contamination of groundwater by arsenic is currently a major concern in Bangladesh. Arsenic in groundwater was first detected in 1993 following reports of many people suffering from arsenical diseases. Further investigations showed the extent of the problem with large areas of the country's water supply being affected and millions of people at serious risk of arsenic poisoning. Technology for arsenic removal from water already exists. However, the socioeconomic conditions which prevail in Bangladesh, do not permit implementation of this type of technology on grounds of cost. The main objective of this study was to develop a low cost technique for the removal of arsenic from contaminated groundwater using the naturally occurring iron, which is another water quality constraint in Bangladesh. The approach was to form arsenic-iron complexes by coprecipitation and adsorption of arsenic on iron. It has been demonstrated that provided the iron levels are sufficiently high (say >_ 1.2 mg/1), simple shaking of a container and allowing the arsenic-iron complex to settle out for 3 days could reduce the concentration of arsenic from 0.10 mg/l to Bangladesh standard (0.05 mg/1). In experimental program, As(III) form of arsenic was used as this form is more likely to be present in groundwater. From laboratory studies, it was shown that the removal rate was largely controlled by the Fe/As ratio, pH and the As concentration. Arsenic removal increases with increasing Fe/As ratio and is favoured by increasing pH in the range of 5 to 8. Separation of the precipitates was achieved by settlement. Following prolonged settlement, it was found that arsenic removal could exceed the removal achieved by filtration through a 0.45 μm filter paper. The experiments demonstrated that about 77% arsenic removal could be achieved from water containing 0.2 mg/l As(III), 4.0 mg/1 Fe at pH 7.5 by manual flocculation (1 min manual mixing) and 3 days settlement. The use of ordinary charcoal, which is cheap and easily available, was investigated for removal of arsenic and was found to be ineffective. From maps of the known distributions of As, Fe and pH, it was evident that 63% of the area in Bangladesh complied with the Bangladesh standard for arsenic. By interpreting the maps and applying the potential removal by coprecipitation-adsorption and settlement technique, it was estimated that a further 8% of area would comply with the Bangladesh standard freeing an additional 7 million people from arsenic contamination.

There is a concern that concentration-based targets for soil clean-up are arbitrary, and do not necessarily relate to the environmental threat posed by the residual contamination. The development of sensitive, reliable, and ecologically relevant biological tests for oil-polluted soils would address these problems, and form a valuable complement to chemical analysis. Three biological test systems were chosen for examining the impact of oil contamination on soil microbes: (1) lux-marked bacterial biosensors, (2) Biological carbon source utilization profiles, and (3) phospholipid fatty acid (PLFA) community profiles. This allowed the testing of effects at three different levels of ecological complexity: respectively, single species; culturable bacteria; and eubacteria and eukaryotes. The use of specific bioluminescent biosensors (i.e. with lux genes fused to promoters from hydrocarbon degradation pathways) allowed the rapid detection of different hydrocarbon classes. The bacterial biosensors were optimized for the assessment of hydrocarbon compounds. Development of quantitative structure-activity relationships (QSARs) increased understanding of their responses to hydrocarbons and other petroleum-derived compounds. The three biological methods were then used to monitor the nutrient-assisted bioremediation of oil-spiked soils in two separate microcosm experiments: (1) remediation of four crude oils and one refined oil compared using bacterial biosensors, and (2) effects of diesel on three different soil types, and effects of three refined oils on one soil type, assessed using Biolog and PLFA profiling. All three biological test methods were sensitive to the oil contamination levels applied in the microcosm experiments. Individual species (biosensor tests) gave different responses to different oils; however, the community-level responses showed no differences. The microbial communities of the three different soil types could still be distinguished after 14-15 weeks of hydrocarbon contamination. The community response of the contaminated soils had changed from that of the control soils after four weeks of bioremediation, and did not return in similarity to the control over the course of the experiment.

Animal infestations of water distribution systems are a cause of considerable concern for the water industry. The appearance of an animal at a consumer's tap often gives rise to complaints. This study, in Anglian Water, Oundle Division, Northamptonshire, U.K. investigated the ecology of the infesting community and its management. Mains sampling used sequential flushes at the hydrant and a model for animal removal was constructed. Tap sampling collected animals from filters at the tap and an index of emergence (density in a day's consumption) and degree of infestation (based on density and the ability to cause complaints) were devised. Reservoir sampling used a perspex trap to sample the water column and sediments. Methods to determine the environmental and operational characteristics of the system were also devised. Community clustering on the basis of animal density determined five distinct community types. Operational and environmental parameters did not coincide with any of the types, though correlations indicated that highest densities occurred at sites further (in time) from treatment with a low turnover time. Sites fed by slow sand filters were not recolonised by chironomid larvae nor to the degree of other sites by other taxa. No successional sequence was found and it was concluded that animals recolonised on the basis of a 'competitive lottery'. The relationship between density at the tap and in the main was considered and seasonal changes in the degree of infestation between sites confirmed that water from rapid gravity filtration leads to more severe animal problems, particularly during the summer. Benthic animals penetrated treatment in low numbers, but reservoirs near treatment were dominated by limnetic animals. Many died and became an indirect food source for infesting animals, but some survived and colonised pipes. Reservoirs at the extremes of the system were not influenced by these taxa and were 'extensions' of the distribution system.

Diagnosing faults in wastewater treatment, like diagnosis of most problems, requires bi-directional plausible reasoning. This means that both predictive (from causes to symptoms) and diagnostic (from symptoms to causes) inferences have to be made, depending on the evidence available, in reasoning for the final diagnosis. The use of computer technology for the purpose of diagnosing faults in the wastewater process has been explored, and a rule-based expert system was initiated. It was found that such an approach has serious limitations in its ability to reason bi-directionally, which makes it unsuitable for diagnosing tasks under the conditions of uncertainty. The probabilistic approach known as Bayesian Belief Networks (BBNS) was then critically reviewed, and was found to be well-suited for diagnosis under uncertainty. The theory and application of BBNs are outlined. A full-scale BBN for the diagnosis of faults in a wastewater treatment plant based on the activated sludge system has been developed in this research. Results from the BBN show good agreement with the predictions of wastewater experts. It can be concluded that the BBNs are far superior to rule-based systems based on certainty factors in their ability to diagnose faults and predict systems in complex operating systems having inherently uncertain behaviour.

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.

In recent years, freshwater fish farmers have come under increasing pressure from the Water Authorities to control the quality of their farm effluents. This project aimed to investigate methods of treating aquacultural effluent in an efficient and cost-effective manner, and to incorporate the knowledge gained into an Expert System which could then be used in an advice service to farmers. From the results of this research it was established that sedimentation and the use of low pollution diets are the only cost effective methods of controlling the quality of fish farm effluents. Settlement has been extensively investigated and it was found that the removal of suspended solids in a settlement pond is only likely to be effective if the inlet solids concentration is in excess of 8 mg/litre. The probability of good settlement can be enhanced by keeping the ratio of length/retention time (a form of mean fluid velocity) below 4.0 metres/minute. The removal of BOD requires inlet solids concentrations in excess of 20 mg/litre to be effective, and this is seldom attained on commercial fish farms. Settlement, generally, does not remove appreciable quantities of ammonia from effluents, but algae can absorb ammonia by nutrient uptake under certain conditions. The use of low pollution, high performance diets gives pollutant yields which are low when compared with published figures obtained by many previous workers. Two Expert Systems were constructed, both of which diagnose possible causes of poor effluent quality on fish farms and suggest solutions. The first system uses knowledge gained from a literature review and the second employs the knowledge obtained from this project's experimental work. Consent details for over 100 fish farms were obtained from the public registers kept by the Water Authorities. Large variations in policy from one Authority to the next were found. These data have been compiled in a computer file for ease of comparison.