The energy-water-food nexus: strategic analysis of technologies for transforming the urban metabolism.

Urban areas are considered net consumers of materials and energy, attracting these from the surrounding hinterland and other parts of the planet. The way these flows are transformed and returned to the environment by the city is important for addressing questions of sustainability and the effect of human behavior on the metabolism of the city. The present work explores these questions with the use of systems analysis, specifically in the form of a Multi-sectoral Systems Analysis (MSA), a tool for research and for supporting decision-making for policy and investment. The application of MSA is illustrated in the context of Greater London, with these three objectives: (a) estimating resource fluxes (nutrients, water and energy) entering, leaving and circulating within the city-watershed system; (b) revealing the synergies and antagonisms resulting from various combinations of water-sector innovations; and (c) estimating the economic benefits associated with implementing these technologies, from the point of view of production of fertilizer and energy, and the reduction of greenhouse gases. Results show that the selection of the best technological innovation depends on which resource is the focus for improvement. Urine separation can potentially recover 47% of the nitrogen in the food consumed in London, with revenue of $33 M per annum from fertilizer production. Collecting food waste in sewers together with growing algae in wastewater treatment plants could beneficially increase the amount of carbon release from renewable energy by 66%, with potential annual revenues of $58 M from fuel production.

Modeling phosphorus in the upper Etowah River basin: identifying sources under uncertainty.

The Uniform Covering by Probabilistic Rejection (UCPR) algorithm was used, in conjunction with the Soil and Water Assessment Tool (SWAT) model, to identify P loads from point source and nonpoint source polluters in the upper Etowah River basin (UERB) in Georgia. The key findings of the research are as follows. The mean absolute error was preferred over the root mean square error as a search criterion for the UCPR algorithm when water quality observations are scarce. The undocumented P load from point sources in the UERB was consistently estimated as about 43 kg/d by the proposed method; but the method was not able to identify the broiler litter application rate to the poultry/beef operation pastures. Point sources (both documented and undocumented), poultry/beef operation pastures, and forests are the three major contributors of P. During 1992-1996, on average they accounted for 36.4, 31.7, and 17.2% of P load from the UERB, respectively.

A random search methodology for examining parametric uncertainty in water quality models.

The advent of the modern high-speed digital computer has tremendously enhanced the utility of Monte Carlo methods for evaluating complex environmental simulation models. In particular, random searching is becoming popular, as thousands of model runs can now be executed quickly and with minimal effort. Indeed, the issues of computational burden and inefficiency, hitherto the bane of random searching, are now receding. This paper presents one such method, uniform covering by probabilistic rejection (UCPR), which combines a pure random search with a probabilistic rejection algorithm that significantly enhances its efficiency. Using nearest-neighbor distances, an ensemble of points in a predefined parameter sampling domain migrates to locate and define a final distribution of optimal parameter vectors, thus providing a realistic depiction of parameter uncertainty. In a prototypical case study of the Oconee River (Georgia, USA), UCPR and regionalized sensitivity analysis, are employed for identifying the parameters of sediment-transport-associated nutrient dynamics, a dynamic river water quality model. Results indicate the existence of a complex interactive parameter structure, evidenced by multiple sets of optimal points widely dispersed over a broad domain of feasible parameter values.

Towards a synthesis of data-based and theory-based models of environmental systems.

A two-pronged approach to interpreting field data through the use of models is presented. This approach builds upon both data- and theory-based models and their associated methods of system identification. It seeks to overcome their respective limitations: that theory-based models are not unambiguously identifiable from the observations, while a well identified data-based model may not be capable of a satisfactory theoretical interpretation. The purpose of the approach is thereby to gain a deeper understanding of complex environmental systems. Recursive methods of time-series analysis are used to identify the data-based models and the modified recursive prediction error algorithm is employed for parameter estimation of the theory-based models. The results of these identification exercises for the two classes of models can be compared in terms of the macro-parameters of the studied system's time constant and steady-state gain. Two case studies are presented to illustrate the overall performance of the two-pronged approach. It is found that: (1) more is to be gained through the joint application of the two classes of models than the exclusive use of either; (2) to some extent, identifying the structure and estimating the parameters of one type of model can be improved by recourse to the corresponding results for the other; and (3) reconciliation of the results from identifying the two classes of model in the parameter space has significant advantages over the more familiar process of evaluating a model's performance in the terms of its (observed) state space features.

Uncertainty and precaution in environmental management.

In this paper, two different visions of the relationship between science and policy are contrasted with one another: the "modern" vision and the "precautionary" vision. Conditions which must apply in order to invoke the Precautionary Principle are presented, as are some of the main challenges posed by the principle. The following central question remains: If scientific certainty cannot be provided, what may then justify regulatory interventions, and what degree of intervention is justifiable? The notion of "quality of information" is explored, and it is emphasized that there can be no absolute definition of good or bad quality. Collective judgments of quality are only possible through deliberation on the characteristics of the information, and on the relevance of the information to the policy context. Reference to a relative criterion therefore seems inevitable and legal complexities are to be expected. Uncertainty is presented as a multidimensional concept, reaching far beyond the conventional statistical interpretation of the concept. Of critical importance is the development of methods for assessing qualitative categories of uncertainty. Model quality assessment should observe the following rationale: identify a model that is suited to the purpose, yet bears some reasonable resemblance to the "real" phenomena. In this context, "purpose" relates to the policy and societal contexts in which the assessment results are to be used. It is therefore increasingly agreed that judgment of the quality of assessments necessarily involves the participation of non-modellers and non-scientists. A challenging final question is: How to use uncertainty information in policy contexts? More research is required in order to answer this question.

On the development and application of a continuous-discrete recursive prediction error algorithm.

Recursive state and parameter reconstruction is a well-established field in control theory. In the current paper we derive a continuous-discrete version of recursive prediction error algorithm and apply the filter in an environmental and biological setting as a possible alternative to the well-known extended Kalman filter. The framework from which the derivation is started is the so-called 'innovations-format' of the (continuous time) system model, including (discrete time) measurements. After the algorithm has been motivated and derived, it is subsequently applied to hypothetical and 'real-life' case studies including reconstruction of biokinetic parameters and parameters characterizing the dynamics of a river in the United Kingdom. Advantages and characteristics of the method are discussed.

Fault detection in a real-time monitoring network for water quality in the lagoon of Venice (Italy).

In the context of monitoring water quality in natural ecosystems in real time, on-line data quality control is a very important issue for effective system surveillance and for optimizing maintenance of the monitoring network. This paper presents some applications of recursive state-parameter estimation algorithms to real-time detection of signal drift in high-frequency observations. Two continuous-discrete recursive estimation schemes, namely the Extended Kalman Filter and the Recursive Prediction Error algorithm, were applied to assuring the quality of the dissolved oxygen (DO) time series, as obtained from the Lagoon of Venice (Italy) during August 2002, through the real-time monitoring network of the Magistrato alle Acque (the Venice Water Authority). Results demonstrate the effectiveness of the methodology in early detection of a probable drift in the DO signal. Comparison of these results with those obtained from the application of a related recursive scheme (a Dynamic Linear Regression procedure) suggests the strong benefits of approaching the problem of on-line data quality control with several (not merely a single) independent such estimation methods.

Transforming data into information.

In spite of a long history of automated instruments being deployed in the water industry, only recently has the difficulty of extracting timely insights from high-grade, high-volume data sets become an important problem. Put simply, it is now relatively easy to be "data-rich", much less easy to become information-rich". Whether the availability of so many data arises from "technological push" or the "demand pull" of practical problem solving is not the subject of discussion. The paper focuses instead on two issues: first, an outline of a methodological framework, based largely on the algorithms of (on-line) recursive estimation and involving a sequence of transformations to which the data can be subjected; and second, presentation and discussion of the results of applying these transformations in a case study of a biological system of wastewater treatment. The principal conclusion is that the difficulty of transforming data into information may lie not so much in coping with the high sampling intensity enabled by automated monitoring networks, but in coming to terms with the complexity of the higher-order, multi-variable character of the data sets, i.e., in interpreting the interactions among many contemporaneously measured quantities.

Criteria for assessment of the operational potential of the urban wastewater system.

Application of real-time control (RTC) is one possible measure to increase the performance of the urban wastewater system. However, the potential and the benefits of control depend strongly on the characteristics of the individual site under question. Conventionally, to evaluate this potential, a detailed feasibility study had to be carried out. In some cases, such a study may well conclude that, for the given site, real-time control does not have any significant potential, thus resulting in unnecessarily having spent precious resources for a detailed study. It would be desirable to have a methodology that allows simple, and cost-effective, screening of sites for which the analysis of real-time control may be beneficial. Earlier research led to the provision of an easy-to-apply scoring system which allows a quick assessment of the RTC potential of controlling flow in sewer systems. However, since this procedure does not take into account water quality aspects, or the treatment plant or the receiving water body, it cannot be used for assessing the potential of RTC of the complete system, let alone for integrated RTC. This paper describes the first part of an on-going project which aims at establishing an enhanced procedure for assessing the real-time control potential for the entire urban wastewater system. After providing a definition of the term "RTC potential", a large number of (partly hypothetical) case studies (varying a number of key parameters of the wastewater system) is simulated, using the simulation tool SYNOPSIS. For each of these sites, a number of real-time control algorithms are developed and optimised, following a general procedure, which allows for local, global and integrated scenarios to be considered. Analysis of the results reveals those system parameters which are of particular significance to the RTC potential of urban wastewater systems. These are discussed and assessed in this paper. Furthermore, the results of a simulation study are provided which indicate a clear potential of integrated control even for many case studies for which local control provides hardly any benefits. Subsequent studies will complement the simulation study by comparison with a number of real case studies in various countries.

Operational control of storm sewage at an activated sludge process.

Operational control of storm sewage at a wastewater treatment plant has attracted intensive concern over the last decade in the context of river basin management. The focus is on the exploitation of the full capacity of the wastewater treatment plant in attenuating storm sewage, and minimizing a direct storm sewage bypass to the river. Attention is particularly paid to the surge of storm water on the activated sludge process. Based on two typical rain events, this paper discusses the performance of several practical controllers in achieving an optimal effluent performance under storm loadings, without risking internal biomass stability and sludge overflow. The control algorithms tested include various controls of recycle rate, step-feed and step-sludge. Prediction errors of influent characteristics and process responses are also under consideration in the assessment. The results illustrate well the desirability, effectiveness and robustness of the tested controllers.

Parameter optimisation of real-time control strategies for urban wastewater systems.

Real-time control (RTC) of wastewater systems has been a topic of research and application for over two decades. Attempts so far have mainly focused on one of the parts of the urban wastewater system: either the sewer system, or the treatment plant or the river. Approaches to integrate these subsystems and considering them jointly for control purposes have been pursued only recently. Control of the systems aims at pursuing one (or several concomitant) objectives, which are expressed, for example, in terms of overflow volumes, loads, effluent concentrations, receiving water quality or monetary costs, to name just a few. This paper provides a general and formal definition of the problem to define a real time control algorithm for a given urban wastewater system. A general mathematical optimization problem is formulated, which describes the task of finding an (in some sense) optimum control algorithm. Since this optimization problem is, in the general case, highly non-linear with only limited information available about the objective function itself, optimization methods appropriate for this type of problem are identified. Here, the similarity of the problem to find a control algorithm and of the parameter estimation problem common in mathematical modelling becomes apparent. Hence, methods (and problems encountered) in parameter estimation can be transferred to the problem of determining optimum RTC algorithms. This parallelism is outlined in the paper. As an application of the parameterisation and optimization of control strategies, integrated control of an urban wastewater system is discussed. Since the analysis of integrated control as just described poses certain requirements on a simulation engine, a novel modelling tool, called SYNOPSIS, is utilized here. This simulation tool, comprising of modules simulating water quantity and quality processes in all parts of the urban wastewater system, is embedded into a suite of optimization procedures. An integrated RTC algorithm for the urban wastewater system is formulated, the parameters of which are optimized using various global optimization routines. Comparison of their efficiency indicates good performance for the Controlled Random Search and for the genetic algorithms. The findings suggest that integrated control can indeed lead to an increase in performance of the urban wastewater system. These results appear to be encouraging and justify further work. Areas for further development are identified in the final section of the paper.

Identification of model structure for aquatic ecosystems using regionalized sensitivity analysis.

The Regionalized Sensitivity Analysis (RSA) was developed in 1978, for identifying critical unknown processes in poorly defined systems, thus directing the focus of further scientific investigations. Here, we demonstrate its application to model structure identification, by ranking the constituent hypotheses and identifying the critical elements for progressive revision of the model. Our case study is Lake Oglethorpe--a small monomictic impoundment in South-eastern Georgia, USA. Recent studies indicate that the warm temperate regional climate affords an extended growing season--typically from March to October--which promotes bacterial productivity in the lake. The result is a summer food web dominated by microbial processes, in contrast to the conventional phytoplankton-dominated food chains typically observed in the cold temperate lakes of Europe and North America. Starting with a simple phytoplankton-based food web model and a qualitative definition of system behaviour, we use the RSA procedure to establish the critical role of bacteria-mediated decomposition in Lake Oglethorpe, thus justifying the inclusion of microbial processes. Further analysis reveals the importance of size-dependent selective consumption of phytoplankton and bacteria. Finally, we discuss important practical implications of this novel application of the RSA regarding sampling efficiency and statistical robustness.

Ranking stormwater control strategies under uncertainty: the River Cam case study.

Monte Carlo simulations taking uncertainty in model parameters into account were performed on a river water quality model. The simulation results were used to rank wastewater treatment plant control strategies according to their impacts on river water quality. This impact is estimated by the maximum ammonium concentration and by the duration of dissolved oxygen concentration below 4 g/m3 at the downstream boundary of the system. The strategies were classified according to the previous criteria using 4 ranking methods, one of them being based on the concept of stochastic dominance. Results are presented for a case study based on a 10 km stretch of the River Cam as it passes through the city of Cambridge in Eastern England. It was found that ranking was robust in face of uncertainty in the parameter values for the control strategies considered as being superior in terms of river water quality impacts.

Development and evaluation of a mathematical model for the study of sediment-related water quality issues.

A mathematical model (Sediment-Transport-Associated Nutrient Dynamics-STAND) has been developed for the study of sediment-associated water quality issues. The model is intended to simulate changes of water composition associated with sediment behavior. It has a 3-level structure. The first level accounts for the hydraulics of open-channel flow. The second computes sediment transport potential and actual rates based on the information provided by the first level. A non-equilibrium approach is used. In the third level, changes of nutrient concentrations along a studied river are computed with the consideration of nutrient transport, adsorption/desorption, and release. In order to calibrate the model, field data were collected from the Oconee River, a major tributary of the Altamaha River in Georgia, USA. Two stations, approximately 17 km distant from each other, were established along the river for the purpose of data collection. Observations of the river's hydraulics, suspended sediment, and water quality (mainly orthophosphate, nitrate, temperature, specific conductivity, oxidation-reduction potential, dissolved oxygen, and pH) were collected at the two stations. Another data set collected along a major tributary of the Yellow River in China was also used for calibration of the model's hydraulics and sediment transport parts. Calibration and validation results are encouraging, which suggests STAND may be a useful tool for the thorough study and understanding of nutrient dynamics associated with sediment behaviour.

Abortion, obtained and denied: research approaches.

PIP: Research proposals and papers from the Second Workshop on Abortion, Obtained and Denied; Research Approaches to Outcomes were summarized. Major research proposals included: mortality and morbidity, mental health and related considerations, abortion and family planning, and socioeconomic factors. Papers reviewed were: Somatic consequences of abortion (Tietze), outcomes of induced abortion: medical-clinical view (Cushner). Therapeutic abortion; a liason psychiatrist's perspective (Wolf), Children born after abortion requests are denied (Pohlman), Abortion and fertility regulation in the socialist countries of central and eastern Europe (David and Russo), Demographic Consequences of abortion (Brackett), Induced abortion and family planning (Tyler, Asher, and Freeman), Induced abortion and contraception: Theorectical considerations (Moore), and Socioeconomic outcomes of present abortion regulations and practices (Muller).^ieng