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Summary: River water quality models can be valuable tools for the assessment and management of receiving water body quality. However, such water quality models require accurate model calibration in order to specify model parameters. Reliable model calibration requires an extensive array of water quality data that are generally rare and resource-intensive, both economically and in terms of human resources, to collect. In the case of small rivers, such data are scarce due to the fact that these rivers are generally considered too insignificant, from a practical and economic viewpoint, to justify the investment of such considerable time and resources. As a consequence, the literature contains very few studies on the water quality modelling for small rivers, and such studies as have been published are fairly limited in scope. In this paper, a simplified river water quality model is presented. The model is an extension of the Streeter–Phelps model and takes into account the physico-chemical and biological processes most relevant to modelling the quality of receiving water bodies (i.e., degradation of dissolved carbonaceous substances, ammonium oxidation, algal uptake and denitrification, dissolved oxygen balance, including depletion by degradation processes and supply by physical reaeration and photosynthetic production). The model has been applied to an Italian case study, the Oreto river (IT), which has been the object of an Italian research project aimed at assessing the river’s water quality. For this reason, several monitoring campaigns have been previously carried out in order to collect water quantity and quality data on this river system. In particular, twelve river cross sections were monitored, and both flow and water quality data were collected for each cross section. The results of the calibrated model show satisfactory agreement with the measured data and results reveal important differences between the parameters used to model small rivers as compared to those for large rivers. Additionally, these results appear related to the different roles played by physical–chemical–biological processes in small rivers as compared to larger systems. [Copyright &y& Elsevier]

Numerical modelling can be a useful tool to assess a receiving water body's quality state. Indeed, the use of mathematical models in river water quality management has become a common practice to show the cause-effect relationship between emissions and water body quality and to design as well as assess the effectiveness of mitigation measures. In the present study, a hydrodynamic river water quality model is presented. The model consists of a quantity and a quality sub-model. The quantity sub-model is based on the Saint Venant equations. The solution of the Saint Venant equations is obtained by means of an explicit scheme based on space-time conservation. The method considers the unification of space and time and the enforcement of flux conservation in both space and time. On the other hand, the quality sub-model is based on the advection dispersion equation. Particularly, the principle of upstream weighting applied to finite difference methods is employed. This method enable us to reduce the numerical dispersion avoiding oscillation phenomena. The optimal weighting coefficient was calculated on the basis of the mesh Peclet number. Regarding the quality processes, the model takes into account the main physical/chemical processes; these are degradation of dissolved carbonaceous substances, ammonium oxidation, algal uptake and denitrification, dissolved oxygen balance, including depletion by degradation processes and supply by physical reaeration and photosynthetic production. To properly simulate the river water quality, four state variables were considered: DO, BOD, NH4, and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project for which quantity and quality data were gathered. A sensitivity analysis of the model output compared to the model input or parameters was carried out. [ABSTRACT FROM AUTHOR]

Summary: The evaluation of urban stormwater quality is of relevant importance for urban drainage, and mathematical models may be of great interest in this respect. To date, several detailed mathematical models are available to predict stormwater quantity–quality characteristics in urban drainage systems. However, only a few models take sewer sediments into account, considering their cohesive-like properties that influence the build-up process of the pollutant load. Furthermore, the model data requirements, especially for the quality aspects, are extensive, which limit their applicability and affect model results with large uncertainty. Uncertainty analysis provides a measure or index regarding the significance and the accuracy of the results obtained by mathematical modelling and is therefore of high interest. Nevertheless, only few studies have been carried out in the urban drainage field, and very few deal with water quality issues. One of the main reasons for this lack of research is the computational burden required by detailed models that preserve this analysis and generally require several Monte Carlo simulation runs. A possible to this problem may be the adoption of simplified parsimonious models that generally require shorter computational times. In this context, this paper presents a parsimonious conceptual model for the evaluation of the pollutant load in-sewers. The model contains two modules: a hydrological and hydraulic module that calculates the hydrographs at the inlet and at the outlet of the sewer system, and a solid transfer module that calculates the pollutographs. The cohesive properties of sewer sediments were carefully considered. Further, the effectiveness of the innovative sewer sediment modelling approach has been verified by taking into account the uncertainty assessed according to the GLUE methodology. The model has been tested using experimental quantity–quality data gathered in two Italian catchments, Fossolo (Bologna) and Parco d’Orlèans (Palermo). [Copyright &y& Elsevier]

Water quality modelling is of crucial importance for the assessment of physical, chemical, and biological changes in water bodies. Mathematical approaches to water modelling have become more prevalent over recent years. Different model types ranging from detailed physical models to simplified conceptual models are available. Actually, a possible middle ground between detailed and simplified models may be parsimonious models that represent the simplest approach that fits the application. The appropriate modelling approach depends on the research goal as well as on data available for correct model application. When there is inadequate data, it is mandatory to focus on a simple river water quality model rather than detailed ones. The study presents a parsimonious river water quality model to evaluate the propagation of pollutants in natural rivers. The model is made up of two sub-models: a quantity one and a quality one. The model employs a river schematisation that considers different stretches according to the geometric characteristics and to the gradient of the river bed. Each stretch is represented with a conceptual model of a series of linear channels and reservoirs. The channels determine the delay in the pollution wave and the reservoirs cause its dispersion. To assess the river water quality, the model employs four state variables: DO, BOD, NH4, and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project in which quantity and quality data were gathered. A sensitivity analysis of the model output to the model input or parameters was done based on the Generalised Likelihood Uncertainty Estimation methodology. The results demonstrate the suitability of such a model as a tool for river water quality management. [ABSTRACT FROM AUTHOR]

Sewer systems convey mostly dry weather flow, coming from domestic and industrial sanitary sewage as well as infiltration flow, and stormwater due to meteoric precipitations. Traditionally, in urban drainage two types of sewer systems are adopted: separate and combined sewers. The former convey dry and wet weather flow separately into two different networks, while the latter convey dry and wet weather flow together. Which is the best solution in terms of costbenefit analysis still remains a controversial subject. The present study was aimed at comparing the pollution loads discharged to receiving bodies by Wastewater Treatment Plant (WWTP) and Combined Sewer Overflow (CSO) for different kinds of sewer systems (combined and separate). To accomplish this objective, a comparison between the two systems was carried out using results from simulations of catchments characterised by different dimensions, population densities and water supply rate. The analysis was based on a parsimonious mathematical model able to simulate the sewer system as well as the WWTP during both dry and wet weather. The rain series employed for the simulations was six years long. Several pollutants, both dissolved and particulate, were modelled. The results confirmed the uncertainties in the choice of one system versus the other, emphasising the concept that case-by-case solutions have to be undertaken. Further, the compared systems showed different responses in terms of effectiveness in reducing the discharged mass to the RWB in relation to the particular pollutant taken into account. [ABSTRACT FROM AUTHOR]

Abstract: In the last few years, the use of mathematical models in WasteWater Treatment Plant (WWTP) processes has become a common way to predict WWTP behaviour. However, mathematical models generally demand advanced input for their implementation that must be evaluated by an extensive data-gathering campaign, which cannot always be carried out. This fact, together with the intrinsic complexity of the model structure, leads to model results that may be very uncertain. Quantification of the uncertainty is imperative. However, despite the importance of uncertainty quantification, only few studies have been carried out in the wastewater treatment field, and those studies only included a few of the sources of model uncertainty. Seeking the development of the area, the paper presents the uncertainty assessment of a mathematical model simulating biological nitrogen and phosphorus removal. The uncertainty assessment was conducted according to the Generalised Likelihood Uncertainty Estimation (GLUE) methodology that has been scarcely applied in wastewater field. The model was based on activated-sludge models 1 (ASM) and 2 (ASM2). Different approaches can be used for uncertainty analysis. The GLUE methodology requires a large number of Monte Carlo simulations in which a random sampling of individual parameters drawn from probability distributions is used to determine a set of parameter values. Using this approach, model reliability was evaluated based on its capacity to globally limit the uncertainty. The method was applied to a large full-scale WWTP for which quantity and quality data was gathered. The analysis enabled to gain useful insights for WWTP modelling identifying the crucial aspects where higher uncertainty rely and where therefore, more efforts should be provided in terms of both data gathering and modelling practises. [Copyright &y& Elsevier]

Abstract: Mathematical modelling of membrane bioreactors (MBRs) for wastewater treatment has targeted either the biological processes (treatment quality target) as well as various aspects of system management. However, the high number of interactions among the involved physical–chemical processes, hampers a straightforward mathematical modelling. This circumstance is much more emphasized for submerged MBR systems where the membrane is immersed in an aeration tank. One of the main crucial points that prevents a comprehensive understanding is the interpretation of the fouling phenomenon and its connections with the biological processes. An overall mathematical model for MBR has not been completely established yet. Indeed, existing MBR models are generally focused only on some of the involved processes (e.g. biological, physical, chemical, etc.) to analyze limited MBR aspects. However, a comprehensive integrated mathematical approach aimed at an in-depth analysis is warmly recommended in order to optimize the whole MBR system. Seeking the development of the area, the paper presents an integrated MBR model that simulates and takes into account both biological and physical processes. An innovative calibration protocol was applied to evaluate the model parameter values as well. This protocol is based on a preliminary global sensitivity analysis to reduce the number of model parameters to be calibrated to the most influential ones. The calibration protocol consists of a novel step-wise Monte Carlo based calibration of the subset of influential parameters. The key point of the step-wise procedure is that calibration is carried out for sub-groups of variables instead of solving a complex multi-objective function. The model results compare fairly well with the experimental results of a MBR pilot plant. [Copyright &y& Elsevier]

Abstract: A mathematical model for the simulation of physical-biological organic removal by means of a membrane bioreactor (MBR) has been previously developed and tested. This paper presents an analysis of the uncertainty of the MBR model. Particularly, the research explores the applicability of the Generalised Likelihood Uncertainty Estimation (GLUE) methodology that is one of the most widely used methods for investigating the uncertainties in the hydrology and that now on is spreading in other research field. For the application of the GLUE methodology, several Monte Carlo simulations have been run varying the all model influential parameters simultaneously. The model was applied to an MBR pilot plant located at the Acqua dei Corsari WWTP (Palermo, IT) where water quality data were gathered. In particular, the MBR pilot plant consists of a lab-scale hollow fibre membrane module in submerged configuration. The GLUE methodology enabled us to gain useful insight about the robustness of the model approach. Particularly, the results showed that the biological process is influenced mainly by the parameters characterising the formation and degradation of Soluble Microbial Products, whereas the fouling phenomenon is mainly influenced by the backwashing efficiency. The application of the GLUE methodology shows that the model considered for the MBR simulation is somehow too simple in order to predict plants performances. Indeed, GLUE enabled us to identify the main model components that needs to be improved and where much attention has to be paid both in terms of model algorithms and quality data to be gathered. This studies confirmed the suitability of the GLUE methodology as a powerful tool for simplified screening methodology to assess the uncertainty also in the field of wastewater treatment. [ABSTRACT FROM AUTHOR]

Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution evaluation. Eight models were compared based on the results of a case study in which there was limited data available for calibration. Uncertainty analysis was then conducted using three different methods: the Bayesian Monte Carlo method, the GLUE pseudo-Bayesian method and the GLUE method revised by means of a formal distribution of residuals between the model and measured data (GLUE_f). The uncertainty assessment of the models enabled evaluation of the advantages and limitations of the three methodologies adopted. The models were then tested using the quantity–quality data gathered for the Fossolo catchment in Bologna, Italy. The results revealed that all of the models evaluated here provided good calibration results, even if the model reliability (in terms of related uncertainty) varied, which suggests the adoption of a specific modelling approach with respect to the others. Additionally, a comparison of uncertainty analysis approaches showed that, regarding the models evaluated here, the classical Bayesian method is more effective at discriminating models according to their uncertainty, but the GLUE approach performs similarly when it is based on the same founding assumptions as the Bayesian method. [Copyright &y& Elsevier]

Summary: Over the last few years, the use of mathematical models has gained importance in urban drainage system management; indeed, such models enable the combined analysis of different components that constitute a drainage system; the sewer system, wastewater treatment plant and the receiving water body. The effectiveness of an integrated approach has been widely demonstrated in the past and is presented in the EU Water Framework Directive, which also introduces a new point of view regarding the water quality management of the whole system, requiring a global analysis at the river basin scale for pollutant sources. However, integrated urban drainage models introduce several uncertain factors that are due to the high complexity level of the adopted approaches as well as to the lack of data for a robust model calibration. This paper presents the uncertainty assessment of an integrated urban drainage model developed in previous studies by means of the Generalized Likelihood Uncertainty Estimation methodology. The analysis has been applied to an experimental catchment in Bologna (Italy) which covers a part of the Bologna sewer network and a reach of the Savena River. [Copyright &y& Elsevier]

Abstract: The paper presents the setting up of a mathematical model for membrane bioreactor able to simulate physical-biological wastewater organic removal. The model is basically divided into two sub-models: the first sub-model is basically devoted for the simulation of the biological features and the second one for the physical processes. In particular regarding the biological aspects, the ASM concept has been employed. On the other hand, the physical processes have been modelled considering the deep-bed theory taking into account not only the effect of the physical membrane filtration but also the cake layer effect. This latter operates as a biological membrane leading to a further reduction of the effluent COD. The model was applied to a SMBR pilot plant characterized by hollow fibre membrane module in submerged configuration. The SMBR was fed by raw wastewater collected from the Palermo (IT) WWTP and it was in operation for a total period of 130 days. During the whole experimentation the TSS was maintained meanly constant with periodic sludge withdrawal, in order to analyse the role of cake layer on organic removal. The model results are interesting and confirm the importance of cake deposition in the filtration process. The developed model can be employed as a useful tool in optimizing operation conditions as well as design issues for SMBR systems. [Copyright &y& Elsevier]

Abstract: The paper presents a 1D mathematical model for the simulation of the percolation fluxes throughout a landfill for municipal solid waste (MSW). Specifically, the model was based on mass balance equations, that enable simulation of the formation of perched leachate zones in a landfill for MSW. The model considers the landfill divided in several layers evaluating the inflow to and outflow from each layer as well as the continuous moisture distribution. The infiltration flow was evaluated by means of the Darcy’s law for an unsaturated porous medium, while the moisture distribution evaluation has been carried out on the basis of the theory of the vertically distributed unsaturated flow. The solution of the model has been obtained by means of the finite difference method. The model has been applied to a semi-idealized landfill located in Palermo landfill (Bellolampo). Specifically, field measurements were conducted to determine the relationship between waste density and applied vertical strain. This relationship was then used to relate vertical strain to waste porosity. The inflow rate to the system was simulated via a synthetic hyetograph whose characteristics have been identified in a previous hydrologic study. Three simulations, each with a different initial moisture content, were conducted. The model results showed a different response of the landfill in terms both of flow rates throughout the landfill and moisture profile. Indeed, the initial moisture content drastically influenced not only the formation of perched leachate zones but also their extension. The model can be a useful tool in predicting potential for the formation of perched leachate zones. [Copyright &y& Elsevier]

Abstract: The paper presents the results of a study in which the uncertainty levels associated with a detailed and a simplified/parsimonious sewer sediment modelling approach have been compared. The detailed approach used an Infoworks CS sewer network model combined with a user developed sediment transport code and the simplified approach used a conceptual sewer flow and quality model. The two approaches have been applied to a single case study sewer network and the simulation results compared. The case study was selected as moderate storm events had occurred during a 2year rainfall and sewer flow monitoring period. Flooding had been observed and this was thought to be caused by significant solids accumulation in the sewer network. As a result sediment deposit measurements were carried out over a 6month period. Model simulations were made of this period and predictions obtained of sediment deposit location and depth. The uncertainty analysis of both modelling approaches was carried out using Monte Carlo based computational methods. This was a limitation for the detailed approach with regards to computational time. Use of the simplified model was not constrained by this issue and so a more conventional assessment of the uncertainty was possible. The simplified approach, due to its structure, only provided a temporal estimate of uncertainty at the final section of the catchment. The detailed approach enabled an assessment of uncertainty at an individual pipe scale but only at the end of the simulation period. A comparison of the uncertainty estimations from both methods at the final section of the catchment and the end of the simulation period indicated comparable values of predicted uncertainty. Therefore a complementary use of both approaches would allow reasonably comparable estimations of levels of uncertainty at both a spatial and temporal scale. The use of such modelling approaches may provide a useful decision-making tool for sewer system management. [Copyright &y& Elsevier]

Abstract: Over the last two decades, Membrane Bioreactors (MBRs) emerged even more for wastewater treatment, ensuring high removal efficiencies as well as very small footprint requirements. Indeed, in this kind of process, a modification in biomass activity and viability can exist compared to that of a CAS process. In this context, respirometric analysis represents a reliable tool in order to evaluate the actual biomass kinetic parameters, to insert in mathematical models in the design phase, as well as to monitor the biomass viability, especially when these processes are operated with high SRT values. The paper presents some results of respirometric techniques applied for the characterisation of wastewater and biomass activity in a pilot UCT-MBR plant for nutrient removal, operating with high SRT. In particular, the respirometric tests were specifically aimed at investigating heterotrophic and autotrophic bacterial activity. The pilot plant was built at Palermo WWTP and consisted of three reactors: anaerobic, anoxic and aerobic, followed by an aerobic compartment containing two submerged hollow fibre membrane modules with typical recycling lines. The kinetic parameters for heterotrophic bacteria resulted lower respect to the CAS; regarding the nitrifying bacteria, the kinetic constants were in the range of CAS, suggesting a good nitrification activity. [Copyright &y& Elsevier]