Whether sustainability can, or should, be defined in a practical operational sense, it is clear that the emergence of such a notion has prompted what seems to be a profound re-thinking of whether our society, economic system, and technology are as we would wish them to be. Sustainable development, clean technology, life-cycle analysis, pollution prevention, and so on, are expressions of a willingness to leave no stone unturned, as it were, in the search for what would be appropriate. With respect to the design and operation of a city's wastewater infrastructure, in particular, this search is characterised by a seeming explosion in the possible combinations of appropriate technologies, gross uncertainty about how novel technologies - only now emerging - might perform in the very long term, and a continuing absence of specific criteria of sustainability for determining the grounds on which any candidate technology might be preferred over another. The paper introduces a simple computational procedure for generating and screening candidate combinations of unit-process technologies for an urban wastewater infrastructure. This is based on the use of Monte Carlo simulation, with the identification of those specific technologies (and combinations thereof) that appear to have the greatest probability of being selected for use under different, possibly evolving, criteria of sustainability. Application of the procedure is illustrated with respect to just a part of this infrastructure, i.e., the wastewater treatment plant.

The scope for modelling the behaviour of pollutants in the aquatic environment is now immense. In many practical applications there are effectively no computational constraints on what is possible. There is accordingly an increasing need for a set of principles of modelling that in some respects may well be different from those applicable when conceptualisation, the accuracy of the numerical solution scheme, and the inadequacies of an overly simplified model structure, were the issues of the day. Given the availability of increasingly comprehensive software, the user of a model is increasingly likely to be accelerated into a position where the issue of model calibration (identification) is an immediate problem. From the practical point of view of needing to make a decision on the control of a pollutant, the problem of identification may, or may not, be avoided. It is argued that a consistent approach to establishing whether such identification is necessary depends on establishing the significance, or otherwise, of model uncertainty. Identifying the model against field data does not have merely the goal of yielding “best” estimates of the unknown coefficients (parameters) appearing in the given model structure. It may also serve the purpose of identifying and modifying the uncertainty attaching to the model as a description of observed behaviour, which uncertainty will then be propagated forward in any predictions made with the model.

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The impacts of combined sewage discharges on river water quality are studied using the MCSTR (Multiple Continuously Stirred Tank Reactor) dynamic model. The potential for applying this model in a real-time context is demonstrated as a tool to support decisions regarding treatment plant operating during storm events, when it is often not possible to sustain full treatment of the incoming sewage flow. Discharges to the River Cam of treated and untreated urban wastewaters from Cambridge and the Cambridge Sewage Works are addressed as a hypothetical case study. Alternative treatment strategies are defined for improving receiving water quality and assessed through simulated water quality downstream of the discharge; the state variables of the model include the concentrations of biochemical oxygen demand, ammoniacal- and nitrate-nitrogen, and dissolved oxygen (chlorophyll-a concentrations are also calculated, but not considered herein). Strategies are assessed and ranked according to the reduction in maximum pollutant concentration (or the increase in minimum concentration, in the case of dissolved oxygen) promoted by each alternative, relative to conventional operation. The consequences of discharging overflows at an alternative position in the river, rather than together with the treatment-plant effluent, are also evaluated. Run times fr the MCSTR model are of the order of just a few minutes (at most), thus allowing the potential for its use in real time as a decision-support aid.

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