Adaptation in hindsight: dynamics and drivers shaping urban wastewater systems.

Well-planned urban infrastructure should meet critical loads during its design lifetime. In order to proceed with design, engineers are forced to make numerous assumptions with very little supporting information about the development of various drivers. For the wastewater sector, these drivers include the future amount and composition of the generated wastewater, effluent requirements, technologies, prices of inputs such as energy or chemicals, and the value of outputs produced such as nutrients for fertilizer use. When planning wastewater systems, there is a lack of methods to address discrepancies between the timescales at which fundamental changes in these drivers can occur, and the long physical life expectancy of infrastructure (on the order of 25-80 years). To explore these discrepancies, we take a hindsight perspective of the long-term development of wastewater infrastructure and assess the stability of assumptions made during previous designs. Repeatedly we find that the drivers influencing wastewater loads, environmental requirements or technological innovation can change at smaller timescales than the infrastructure design lifetime, often in less than a decade. Our analysis shows that i) built infrastructure is continuously confronted with challenges it was not conceived for, ii) significant adaptation occurs during a structure's lifetime, and iii) "muddling-through" is the pre-dominant strategy for adaptive management. As a consequence, we argue, there is a need to explore robust design strategies which require the systematic use of scenario planning methods and instruments to increase operational, structural, managerial, institutional and financial flexibility. Hindsight studies, such as this one, may inform the development of robust design strategies and assist in the transition to more explicit forms of adaptive management for urban infrastructures.

Intermittent operation of ultra-low pressure ultrafiltration for decentralized drinking water treatment.

River water was treated by ultrafiltration at a relatively low transmembrane pressure (40 mbar). As observed before, flux stabilization occurred after several days of operation although no back-flushing or cross flow was applied. Interruptions in flux were applied by temporary offset of the transmembrane pressure. After restoration of the transmembrane pressure, the initial flux was higher than the stable flux level, and the flux recovery depended on the standstill time. Furthermore, if a short cross flow was applied after standstill, the flux was restored to an even higher level. In all cases, the flux decreased again during operation to reach finally the same stable level as before standstill. In order to evaluate the influence of intermittent operation as practiced for water treatment on a household level, daily interruptions of flux were applied. An optimum of total daily water production rate was obtained at 21 h of operation and 3 h of standstill per day. A model was developed which can describe the impact of intermittent operation on the flux depending on the duration of the standstill and operating periods. This enables the prediction of production capacity of the system operated intermittently. The flux increase during standstill could be explained by a relaxation and expansion of the biofouling layer, while the higher flux after forward-flushing was caused by this layer being partially sloughed off. Household water treatment with the process presented here will generally be operated on a discontinuous basis. The results show that such operation schemes do not compromise the permeability of the system, but actually lead to higher fluxes after standstill.

Automatic reactor model synthesis with genetic programming.

Successful modeling of wastewater treatment plant (WWTP) processes requires an accurate description of the plant hydraulics. Common methods such as tracer experiments are difficult and costly and thus have limited applicability in practice; engineers are often forced to rely on their experience only. An implementation of grammar-based genetic programming with an encoding to represent hydraulic reactor models as program trees should fill this gap: The encoding enables the algorithm to construct arbitrary reactor models compatible with common software used for WWTP modeling by linking building blocks, such as continuous stirred-tank reactors. Discharge measurements and influent and effluent concentrations are the only required inputs. As shown in a synthetic example, the technique can be used to identify a set of reactor models that perform equally well. Instead of being guided by experience, the most suitable model can now be chosen by the engineer from the set. In a second example, temperature measurements at the influent and effluent of a primary clarifier are used to generate a reactor model. A virtual tracer experiment performed on the reactor model has good agreement with a tracer experiment performed on-site.

Is modeling of biological wastewater treatment a mature technology?

Based on three case studies it is demonstrated that the application of mathematical models in biological wastewater treatment has not yet reached its full potential. Model structure uncertainty and correlation of uncertain parameter values make up the first case. The combination of biokinetic and hydraulic models relates to the second case. The evolution of a full scale plant over its life expectancy is the frame for the third case. This paper was initially presented as a discussion starter and thus is raising questions rather than providing answers.

Modelling biocide leaching from facades.

Biocides leach from facades during rain events and subsequently enter the aquatic environment with storm water. Little is known about the losses of an entire settlement, since most studies referred to wash-off experiments conducted under laboratory conditions. Their results show a fast decrease of concentrations in the beginning, which subsequently slows down. The aim of this study is to develop a simple model to understand the mechanisms leading to these losses as well as to simulate losses under various rainfall and application conditions. We developed a four-box model based on the knowledge gained from fits of an exponential function to an existing experimental data set of a wash-off experiment. The model consists of two mobile stocks from which biocides are washed off during a rain event. These mobile stocks are supplied with biocides from storage stocks by diffusion-type processes. The model accurately reproduced the measured data of wash-off during single cycles as well as peak wash-offs over all cycles. Our model results for diuron losses showed that a large proportion (∼ 70%) of the applied biocides are still in the stocks even after a rain volume corresponding to several years (1100 mm y(-1), Swiss Plateau). Applications to realistic outdoor conditions showed that losses can not be neglected for urban environments and that knowledge about the amount of rainfall turned into runoff and the decay constants of the biocides in the facades are crucial. The model increased our understanding of the processes leading to the observed dynamic in laboratory experiments and was used to simulate losses for various rainfall and application conditions.

Identification of industrial wastewater by clustering wastewater treatment plant influent ultraviolet visible spectra.

A procedure is proposed which allows the detection of industrial discharge events at the inlet of a wastewater treatment plant without the need for measurements performed at the industry, for special equipment and for exact knowledge of the industrial sewage. By performing UV/Vis measurements at the inlet of a plant and analyzing them with a two-staged clustering method consisting of the self-organizing map algorithm and the Ward clustering method, typical sewage clusters can be found. In an experiment performed at a mid-sized Swiss plant, one cluster of a cluster model with five clusters could be attributed to an industrial laundry. Out of 95 laundry discharging events measured in a validation period, 93 were correctly detected by the proposed algorithm, two were false positives and five were false negatives.

Using reactive tracers to detect flow field anomalies in water treatment reactors.

The hydraulics of water and wastewater treatment reactors has a major impact on their performance and control. The residence time distribution as a measure for the hydraulics represents macroscopic mixing in an integrated way with no spatial information. However, with regard to optimal sensor location for process control and for process optimisation measures, spatial information about macro-mixing is helpful. Spatially distributed measurements of reactive tracers can provide this information. In this paper we generally discuss how reactive tracers can be used to detect and characterize distinct large scale flow structures. It is shown that tracer substances are particularly suited if their reaction time scale is similar to the time scale of the large scale flow structure. For nitrifying activated sludge systems, ammonium is identified to be a suitable tracer. In a comprehensive experimental study at a real aeration tank, two distinct large scale flow features were identified by distributed ammonium measurements. Flow velocity measurements using acoustic Doppler velocimetry clearly supported the nature of these flow field anomalies. Ion-selective electrodes are a well suited device for ammonium measurements providing the temporal resolution that is needed for such an analysis.

Effects of aeration patterns on the flow field in wastewater aeration tanks.

Due to the high energy input of aeration, the spatial distribution of air diffusers largely determines the flow field in aeration tanks. This has consequences on the efficiency of the aeration system, the performance of the aeration tank and on tank operation and control. This paper deals with these effects applying both Computational Fluid Dynamics (CFD) enhanced with a biokinetic model and full scale validation using velocity and reactive tracer measurements with high temporal and spatial resolution. It is shown that small changes in the diffuser arrangement drastically change the overall flow field. Using different aeration patterns in the same tank may lead to large scale instabilities in the flow field that lower plant performance and produce strong variations in concentration signals impeding their use for plant control. CFD is a valuable tool to analyze the interaction of flow field and aeration and their effects on plant performance and operation. But, in complex flow situations experimental validation is needed and strongly suggested.

Global sensitivity analysis in wastewater treatment plant model applications: prioritizing sources of uncertainty.

This study demonstrates the usefulness of global sensitivity analysis in wastewater treatment plant (WWTP) design to prioritize sources of uncertainty and quantify their impact on performance criteria. The study, which is performed with the Benchmark Simulation Model no. 1 plant design, complements a previous paper on input uncertainty characterisation and propagation (Sin et al., 2009). A sampling-based sensitivity analysis is conducted to compute standardized regression coefficients. It was found that this method is able to decompose satisfactorily the variance of plant performance criteria (with R(2) > 0.9) for effluent concentrations, sludge production and energy demand. This high extent of linearity means that the plant performance criteria can be described as linear functions of the model inputs under the defined plant conditions. In effect, the system of coupled ordinary differential equations can be replaced by multivariate linear models, which can be used as surrogate models. The importance ranking based on the sensitivity measures demonstrates that the most influential factors involve ash content and influent inert particulate COD among others, largely responsible for the uncertainty in predicting sludge production and effluent ammonium concentration. While these results were in agreement with process knowledge, the added value is that the global sensitivity methods can quantify the contribution of the variance of significant parameters, e.g., ash content explains 70% of the variance in sludge production. Further the importance of formulating appropriate sensitivity analysis scenarios that match the purpose of the model application needs to be highlighted. Overall, the global sensitivity analysis proved a powerful tool for explaining and quantifying uncertainties as well as providing insight into devising useful ways for reducing uncertainties in the plant performance. This information can help engineers design robust WWTP plants.

Struvite precipitation from urine - Influencing factors on particle size.

Struvite crystallisation is a fast and reliable phosphorus removal and recovery process for concentrated waste streams - such as hydrolysed human urine. In order to optimise P-elimination efficiency, it is beneficial to obtain larger particle sizes: they are easier to separate and less prone to wash-out than smaller particles. This paper presents the results of a study on the effect of process parameters on particle size in a single step struvite precipitation. Crystals formed in batch experiments with real hydrolysed urine were shown to have an average size of >90 microm at pH 9 and 20 degrees C. This is reduced to 45 microm when changing stirrer type. Particle size increases with lower supersaturation. The results showed that under otherwise constant conditions, particle size decreases with lower temperature and has a minimum between pH 9 and 10. Deviating trends are observed at pH <8. Struvite formation in a CSTR (continuously stirred tank reactor) process was shown to be a reliable stable process that does not require any pH control. A method based on conductivity measurement is presented to estimate ionic strength, which is needed for equilibrium calculations.

Nitrification and me - a subjective review.

Based on the subjective experience of the author it is discussed how the nitrification processes served as an important basis for the development of today's understanding and mathematical models for many wastewater treatment processes (activated sludge, biofilm reactors) and self-purification processes in rivers. Besides being an important process for the protection of receiving waters, nitrification served as a proxy for the understanding of the behavior of a narrowly defined group of microorganisms growing on known substrates under environmental conditions. Until the upcoming of readily available microbial genetic techniques, nitrification was the single most studied microbial process in environmental engineering.

Compartmental models for continuous flow reactors derived from CFD simulations.

Reactor modeling is of major interest in environmental technology. In this context, new contaminants with higher degradation requirements increase the importance of reactor hydraulics. CFD (Computational Fluid Dynamics) may meet this challenge but is expensive for everyday use. In this paper, we provide research and practice with a methodology designed to automatically reduce the complexity of such a high-dimensional flow model to a compartmental model. The derivation is based on the concentration field of a reacting species which is included in the steady state CFD simulation. While still capturing the most important flow features, the compartmental model is fast, easy to use, and open for process modeling with yet unknown compounds. The inherent overestimation of diffusion by compartmental models has been corrected by locally adjusting turbulent fluxes. We successfully applied the methodology to the ozonation process and experimentally verified it with tracer experiments. The loss of information was quantified as a deviation from CFD performance prediction for different reactions. With increasing discretisation of the compartmental model, these deviations diminish. General advice on the necessary discretisation is given.

Uncertainty analysis in WWTP model applications: a critical discussion using an example from design.

This study focuses on uncertainty analysis of WWTP models and analyzes the issue of framing and how it affects the interpretation of uncertainty analysis results. As a case study, the prediction of uncertainty involved in model-based design of a wastewater treatment plant is studied. The Monte Carlo procedure is used for uncertainty estimation, for which the input uncertainty is quantified through expert elicitation and the sampling is performed using the Latin hypercube method. Three scenarios from engineering practice are selected to examine the issue of framing: (1) uncertainty due to stoichiometric, biokinetic and influent parameters; (2) uncertainty due to hydraulic behaviour of the plant and mass transfer parameters; (3) uncertainty due to the combination of (1) and (2). The results demonstrate that depending on the way the uncertainty analysis is framed, the estimated uncertainty of design performance criteria differs significantly. The implication for the practical applications of uncertainty analysis in the wastewater industry is profound: (i) as the uncertainty analysis results are specific to the framing used, the results must be interpreted within the context of that framing; and (ii) the framing must be crafted according to the particular purpose of uncertainty analysis/model application. Finally, it needs to be emphasised that uncertainty analysis is no doubt a powerful tool for model-based design among others, however clear guidelines for good uncertainty analysis in wastewater engineering practice are needed.

Global sensitivity analysis for model-based prediction of oxidative micropollutant transformation during drinking water treatment.

This study quantifies the uncertainty involved in predicting micropollutant oxidation during drinking water ozonation in a pilot plant reactor. The analysis is conducted for geosmin, methyl tert-butyl ether (MTBE), isopropylmethoxypyrazine (IPMP), bezafibrate, beta-cyclocitral and ciprofloxazin. These compounds are representative for a wide range of substances with second order rate constants between 0.1 and 1.9x10(4)M(-1)s(-1) for the reaction with ozone and between 2x10(9) and 8x10(9)M(-1)s(-1) for the reaction with OH-radicals. Uncertainty ranges are derived for second order rate constants, hydraulic parameters, flow- and ozone concentration data, and water characteristic parameters. The uncertain model factors are propagated via Monte Carlo simulation and the resulting probability distributions of the relative residual micropollutant concentrations are assessed. The importance of factors in determining model output variance is quantified using Extended Fourier Amplitude Sensitivity Testing (Extended-FAST). For substances that react slowly with ozone (MTBE, IPMP, geosmin) the water characteristic R(ct)-value (ratio of ozone- to OH-radical concentration) is the most influential factor explaining 80% of the output variance. In the case of bezafibrate the R(ct)-value and the second order rate constant for the reaction with ozone each contribute about 30% to the output variance. For beta-cyclocitral and ciprofloxazin (fast reacting with ozone) the second order rate constant for the reaction with ozone and the hydraulic model structure become the dominating sources of uncertainty.

Underestimation of uncertainty in statistical regression of environmental models: influence of model structure uncertainty.

As the quality of sensors increases, systematic discrepancies between measurements and model outputs become more apparent Applying regression type analysis in these cases leads to autocorrelated residuals, biased parameter estimates, and underestimation of uncertainty. This paper examines how parameter estimates are affected by model structure uncertainty for an application from wastewater treatment. A Monod model is fitted to synthetic data generated by a reference system exhibiting predefined Tessier kinetics and a known error process. A range of methods are suggested to test if the resulting residuals fulfill the IID (independent and identically distributed)-requirement visual examination of time series, autocorrelation, and partial autocorrelation functions, the Jarque-Bera normality test, the Runs test for independence, and the BDS test for IID. The tests are shown to perform well at low measurement noise but not at higher levels of noise where transferring the parameter estimates gained from a batch system leads to erroneous estimation of steady state concentrations in a completely stirred tank reactor. Additional diagnostics are suggested which include second order autocorrelation functions of the residuals in the case of a single experiment and examination of moving averages of residuals in the case of multiple experiments.

Sources of parameter uncertainty in predicting treatment performance: the case of preozonation in drinking water engineering.

This study investigates the factors that determine parameter uncertainty when applying predefined, existing models to predict the performance of a full scale treatment system from environmental engineering. The analysis is performed for ozonation of surface water, a technology applied in drinking water treatment for disinfection and oxidation of micropollutants. The pseudo first order rate constant of ozone decay k(O3) is characterized as a time dependent parameter and estimated from data obtained from three experimental setups representing upscaling stages in engineering design. To obtain meaningful uncertainty estimates, various factors need to be acknowledged: uncertainty about the model structure, uncertainty of other model parameters, uncertainty due to non-representative sampling, and errors in chemical analysis. It is concluded that an on-site automated sequencing batch reactor is best suited for estimating kinetics during operation of the full scale system. Furthermore, the transferability of information in upscaling from laboratory experiments to the full scale system is found to be critical. Although uncertainty analysis enhances the understanding of the system, it is also shown to be a subjective process depending on the knowledge and assumptions of the modeler and the availability and quality of data.

Uncertainty in prediction of disinfection performance.

Predicting the disinfection performance of a full-scale reactor in drinking water treatment is associated with considerable uncertainty. In view of quantitative risk analysis, this study assesses the uncertainty involved in predicting inactivation of Cryptosporidium parvum oocysts for an ozone reactor treating lake water. A micromodel is suggested which quantifies inactivation by stochastic sampling from density distributions of ozone exposure and lethal ozone dose. The ozone exposure distribution is computed with a tank in series model that is derived from tracer data and measurements of flow, ozone concentration and ozone decay. The distribution of lethal ozone doses is computed with a delayed Chick-Watson model which was calibrated by Sivaganesan and Marinas [2005. Development of a Ct equation taking into consideration the effect of Lot variability on the inactivation of Cryptosporidium parvum oocysts with ozone. Water Res. 39(11), 2429-2437] utilizing a large number of inactivation studies. Parameter uncertainty is propagated with Monte Carlo simulation and the probability of attaining given inactivation levels is assessed. Regional sensitivity analysis based on variance decomposition ranks the influence of parameters in determining the variance of the model result. The lethal dose model turns out to be responsible for over 90% of the output variance. The entire analysis is re-run for three exemplary scenarios to assess the robustness of the results in view of changing inputs, differing operational parameters or revised assumptions about the appropriate model. We argue that the suggested micromodel is a versatile approach for characterization of disinfection reactors. The scheme developed for uncertainty assessment is optimal for model diagnostics and effectively supports the management of uncertainty.

Estimating sewer leakage from continuous tracer experiments.

Direct measurements of sewer leakage with continuous dosing of tracers are often considered too imprecise for practical applications. However, no mathematical framework for data analysis is reported in literature. In this paper, we present an improved experimental design and data analysis procedure together with a comprehensive framework for uncertainty assessment. Test runs in a 700 m-long watertight sewer showed no significant bias and a very high precision of the methodology. The standard error in the results was assessed to 2.6% of the labeled flow with a simplified model. It could be reduced to 1.2% when a dynamic data analysis procedure was applied. The major error contribution was caused by transient transport phenomena, which suggests that careful choosing of the experimental time is more important than the choice of a very specific tracer substance. Although the method is not intended to replace traditional CCTV inspections, it can provide complementary information for rational rehabilitation planning.

Discrete event simulation for exploring strategies: an urban water management case.

This paper presents a model structure aimed at offering an overview of the various elements of a strategy and exploring their multidimensional effects through time in an efficient way. It treats a strategy as a set of discrete events planned to achieve a certain strategic goal and develops a new form of causal networks as an interfacing component between decision makers and environment models, e.g., life cycle inventory and material flow models. The causal network receives a strategic plan as input in a discrete manner and then outputs the updated parameter sets to the subsequent environmental models. Accordingly, the potential dynamic evolution of environmental systems caused by various strategies can be stepwise simulated. It enables a way to incorporate discontinuous change in models for environmental strategy analysis, and enhances the interpretability and extendibility of a complex model by its cellular constructs. It is exemplified using an urban water management case in Kunming, a major city in Southwest China. By utilizing the presented method, the case study modeled the cross-scale interdependencies of the urban drainage system and regional water balance systems, and evaluated the effectiveness of various strategies for improving the situation of Dianchi Lake.

The behaviour of pharmaceuticals and heavy metals during struvite precipitation in urine.

Separating urine from wastewater at the source reduces the costs of extensive wastewater treatment. Recovering the nutrients from urine and reusing them for agricultural purposes adds resource saving to the benefits. Phosphate can be recovered in the form of struvite (magnesium ammonium phosphate). In this paper, the behaviour of pharmaceuticals and heavy metals during the precipitation of struvite in urine is studied. When precipitating struvite in urine spiked with hormones and non-ionic, acidic and basic pharmaceuticals, the hormones and pharmaceuticals remain in solution for more than 98%. For heavy metals, initial experiments were performed to study metal solubility in urine. Solubility is shown to be affected by the chemical conditions of stored and therefore hydrolysed urine. Thermodynamic modelling reveals low or very low equilibrium solute concentrations for cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni) and lead (Pb). Experiments confirmed Cd, Cu and Pb carbonate and hydroxide precipitation upon metal addition in stored urine with a reaction half-life of ca. 7 days. For all metals considered, the maximum specific metal concentrations per gram phosphate or nitrogen showed to be typically several orders of magnitudes lower in urine than in commercially available fertilizers and manure. Heavy metals in struvite precipitated from normal stored urine could not be detected. Phosphate recovery from urine over struvite precipitation is shown to render a product free from most organic micropollutants and containing only a fraction of the already low amounts of heavy metals in urine.