Search

In the last twenty years, the scientific world has paid particular care towards the problems that involve the environment. Accordingly, several researches were developed to describe phenomena that take place during both wet and dry periods and to increase the knowledge in this field. In particular, attention was addressed towards the problems linked with receiving water body pollution because of the impact of rain water in the urban environment. In order to obtain a good description of the problem, it is important to analyse both quantity and quality aspects connected with all the transformation phases that characterise the urban water cycle. Today, according to this point, integrated modelling approach is spreading, aiming to find solutions to improve the quality characteristics of the receiving water body. Because several models are connected together for analysing the fate of pollutants from the sources on the urban catchment to the final recipient, classical problems connected with the selection and calibration of parameters are amplified by the complexity of the modelling approach increasing the uncertainty and reducing the reliability connected with a model's application. For this reason, a parsimonious integrated modelling approach has been developed and its uncertainty has been evaluated adopting the well known GLUE framework. For the purpose of the study, the uncertainty analysis has been applied to a “semi–hypothetic” case study obtained connecting Fossolo catchment (Bologna – Italy) to the Oreto river near Palermo (Italy).

Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models that characterise technical or natural systems. In the field of wastewater modelling, most of the recent applications of GSA use either regression-based methods, which require close to linear relationships between the model outputs and model factors, or screening methods, which only yield qualitative results. However, due to the characteristics of membrane bioreactors (MBR) (non-linear kinetics, complexity, etc.) there is an interest to adequately quantify the effects of non-linearity and interactions. This can be achieved with variance-based sensitivity analysis methods. In this paper, the Extended Fourier Amplitude Sensitivity Testing (Extended-FAST) method is applied to an integrated activated sludge model (ASM2d) for an MBR system including microbial product formation and physical separation processes. Twenty-one model outputs located throughout the different sections of the bioreactor and 79 model factors are considered. Significant interactions among the model factors are found. Contrary to previous GSA studies for ASM models, we find the relationship between variables and factors to be non-linear and non-additive. By analysing the pattern of the variance decomposition along the plant, the model factors having the highest variance contributions were identified. This study demonstrates the usefulness of variance-based methods in membrane bioreactor modelling where, due to the presence of membranes and different operating conditions than those typically found in conventional activated sludge systems, several highly non-linear effects are present. Further, the obtained results highlight the relevant role played by the modelling approach for MBR taking into account simultaneously biological and physical processes.

Membrane bioreactors (MBR) are being increasingly used for wastewater treatment. Mathematical modeling of MBR systems plays a key role in order to better explain their characteristics. Several MBR models have been presented in the literature focusing on different aspects: biological models, models which include soluble microbial products (SMP), physical models able to describe the membrane fouling and integrated models which couple the SMP models with the physical models. However, only a few integrated models have been developed which take into account the relationships between membrane fouling and biological processes. With respect to biological phosphorus removal in MBR systems, due to the complexity of the process, practical use of the models is still limited. There is a vast knowledge (and consequently vast amount of data) on nutrient removal for conventional-activated sludge systems but only limited information on phosphorus removal for MBRs. Calibration of these complex integrated models still remains the main bottleneck to their employment. The paper presents an integrated mathematical model able to simultaneously describe biological phosphorus removal, SMP formation/degradation and physical processes which also include the removal of organic matter. The model has been calibrated with data collected in a UCT-MBR pilot plant, located at the Palermo wastewater treatment plant, applying a modified version of a recently developed calibration protocol. The calibrated model provides acceptable correspondence with experimental data and can be considered a useful tool for MBR design and operation.

Membrane fouling represents one of the major issues for a membrane bioreactor (MBR). Membrane fouling and high aeration requirements (for inducing shear stress to limit fouling) make MBR operation economically demanding due to high energy costs. Although several studies on MBR fouling have been performed, comprehensive knowledge on how to reduce membrane fouling and consequently save energy is still lacking. An integrated mathematical model for MBR is applied to a University of Cape Town membrane bioreactor with the final aim to reduce the energy costs. In particular, the influence of the aeration intensity, the duration of filtration/backwashing cycles, and the number of membrane cleanings are investigated. Five scenarios are analyzed and compared, each implementing different operating conditions. The features of the analyzed scenarios are quantified by employing Monte Carlo simulations and performance indices partially drawn from literature. The results provide insights about the role played by the main physical/chemical/biological processes in view of a system optimization. As expected, MBR operation at low air flow rate (qa) leads to a substantial reduction of the operational costs (specifically, 20% with respect to the suggested manufacturers ones in terms of qa). Despite such a reduction of qa, a good effluent quality is also obtained as an effect of a high biological cake thickness. Results also show that the values of filtration time (Tf) higher than those suggested by manufacturers (e.g., Tf=9 min) can be used to increase effluent quality. This study demonstrates that both energy savings and effluent quality can be improved by varying the operational variables with respect to those of the suggested manufacture. One of the main insights gained from this study is that the values of the operating variables (i.e., qa, Tb and Tf) suggested by the manufactures can be changed to obtain a system that still respects high effluent quality and is characterized by lower economical cost. The proposed modeling approach can be an useful tool for the optimization of the operating conditions in order to reduce the operational costs for MBR systems.

Engineers are increasingly called to deal with practical problems related to water resources management, risk analysis, environmental engineering and water pollution. Providing a way forward to solve the above questions requires setting up mathematical models that are affected by uncertainty, that in many cases is relevant. In fact, the theories that are employed for providing solutions to engineer’s problems are not exact sciences: even if it is methodologically rigorous, it is incapable of producing precise results, for the presence of inherent randomness that translates in indeterminacy and therefore uncertainty. To cope with uncertainty is a challenge for scientists and practitioners, that requires the application of proper procedures for model identification, parameter calibration, hypothesis testing, model testing (also called model validation) and finally uncertainty assessment. As a matter of fact, uncertainty estimation in hydrological surface and subsurface modelling is today one of the most important subfields of hydrology, according to the numerous contributions presented by the recent scientific literature.

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 2 year 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 6 month 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.

The performance of a membrane bioreactor (MBR), and mechanisms of fouling formation, may differ due to the start-up. Therefore, the start-up can constitute an aspect that critically influences MBR performance during its lifespan. Indeed, the start-up can influence the mechanisms of membrane fouling, which is of paramount importance in an MBR. In order to gain insights on the effects of the start-up, both experimental and mathematical modelling studies were carried out on an MBR pilot plant. The MBR pilot plant constituted of a hollow fibre membrane module, in a submerged configuration, was fed by real wastewater. Two experimental periods were carried out, lasting 65 days each, characterised by two different MBR start-ups: without inoculum of activated sludge, and with inoculum of activated sludge. An integrated MBR mathematical model was applied to the two periods in order to combine the insights gained from the experimental survey. The MBR model simulates both biological and physical processes simultaneously, taking into account the interactions between them. Two MBR model calibrations were carried out for the two different experimental start-ups. Model calibration was carried out by means of an innovative calibration protocol, based on the global sensitivity analysis, and the Generalised Likelihood Uncertainty Estimation. Thus, two sets of model parameters were assessed and compared. Both experimental and mathematical model results were analysed to quantify the effects of the two start-ups on membrane fouling. The results showed only slight differences in terms of total pollutant removal efficiency. However, relevant differences appeared with respect to the fouling phenomenon between the two start-ups. The results demonstrated that the best choice, in terms of the start-up of MBR systems, comes from a compromise between minimisation of the fouling issues and high system performance.

Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the model’s structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.

Identifiability analysis enables the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology enables one to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for setting up measuring campaigns for integrated water quality modelling. Particularly, by means of the identifiability analysis, the information about the location and the number of the monitoring stations in the integrated system required for assessing a specific group of model parameters were gained. The analysis has been applied to a real, partially urbanised, catchment containing two sewer systems, two wastewater treatment plants and a river. Several scenarios of measuring campaigns have been considered; each scenario was characterised by different monitoring station locations for the gathering of quantity and quality data. The results enabled us to assess the maximum number of model parameters quantifiable for each scenario i.e. for each data set. The methodology resulted to be a powerful tool for designing measuring campaign for integrated water quality modelling. Indeed, the crucial cross sections throughout the integrated wastewater system were detected optimizing both human and economic efforts in the gathering of field data. Further, a connection between the data set and the number of model parameters effectively assessable has been established leading to much more reliable model results.

In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box–Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box–Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the residuals distribution. If residuals are not normally distributed, the uncertainty is over-estimated if Box–Cox transformation is not applied or non-calibrated parameter is used.

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.

In the recent years, there has been an increasing interest in the development of hybrid reactors, especially in the up-grading of existing activated sludge plants that are no longer able to comply with concentration limits established by regulatory agencies. In such systems the biomass grows both as suspended flocs and as biofilm. In this way, it is possible to obtain a higher biomass concentration in the reactor, but without any significant increase of the load to the final clarifier. The paper presents the setting-up of a dynamic mathematical model aimed at quantitatively describing the biokinetic processes occurring in a hybrid moving bed biofilm reactor (HMBBR), and, more in general, in integrated fixed-film activated sludge (IFAS) processes, as well as to compare the simulation results with measured data from a HMBBR pilot plant built at the Norwegian University of Science and Technology in Trondheim (Norway). Particularly, the pilot plant consisted of three aerobic tanks in series; the first and third aerobic reactors were pure suspended biomass systems, while the second aerobic reactor was filled with the AnoxKaldnes™ K1 carriers for biofilm development. The mathematical model consists of two connected models for the simulation of both suspended biomass and biofilm. Biochemical conversions are evaluated according to the well known matrix notation used in the Activated Sludge Model No. 1 (ASM1) for both attached and suspended biomass and, in addition to biochemical conversion, the model contains the simulation of particulate detachment from the biofilm into the bulk liquid. The results showed an overall good agreement between measured and simulated data, for both biofilm and suspended biomass, with a good reproduction of dynamic processes in the hybrid moving bed pilot plant, and they are encouraging for further developments.

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.

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.

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.

Summary Mathematical models are of common use in urban drainage, and they are increasingly being applied to support decisions about design and alternative management strategies. In this context, uncertainty analysis is of undoubted necessity in urban drainage modelling. However, despite the crucial role played by uncertainty quantification, several methodological aspects need to be clarified and deserve further investigation, especially in water quality modelling. One of them is related to the “a priori” hypotheses involved in the uncertainty analysis. Such hypotheses are usually condensed in “a priori” distributions assessing the most likely values for model parameters. This paper explores Bayesian uncertainty estimation methods investigating the influence of the choice of these prior distributions. The research aims at gaining insights in the selection of the prior distribution and the effect the user-defined choice has on the reliability of the uncertainty analysis results. To accomplish this, an urban stormwater quality model developed in previous studies has been employed. The model has been applied to the Fossolo catchment (Italy), for which both quantity and quality data were available. The results show that a uniform distribution should be applied whenever no information is available for specific parameters describing the case study. The use of weak information (mostly coming from literature or other model applications) should be avoided because it can lead to wrong estimations of uncertainty in modelling results. Model parameter related hypotheses would be better dropped in these cases.

Quantification of uncertainty is of paramount interest in integrated urban drainage water quality modelling. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. However, the state of knowledge regarding uncertainties in urban drainage models is poor. In the case of integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating the sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones in a manner dependent on the model structure, the estimation of parameters and the availability and uncertainty of measurements in the different parts of the system. Uncertainty basically propagates throughout a chain of models in which the simulation output from upstream models is transferred to the downstream ones as input. The Variance Decomposition Approach tracks uncertainty propagation commonly assuming that the correlation among error sources is negligible. In complex environmental models, the overall uncertainty can differ significantly from the simple sum of uncertainties generated in each sub-model, showing the well-known uncertainty accumulation problems due to non-linearity in the model and correlation among the sources of uncertainty. This work discusses the importance of such issues in the application of a complex integrated urban drainage model with the aim of evaluating the applicability of Variance Decomposition Approach. The integrated model and the methodology for the uncertainty decomposition were then applied to a complex integrated catchment: the Nocella basin (Italy). The results showed that the Variance Decomposition Approach can be a powerful tool for uncertainty analysis, but a possible correlation among uncertainty sources should be considered because it can greatly affect the analysis.

The continuous growth of urban areas and the increasing public awareness of the environmental impacts of storm water have raised interest on the quality of the receiving water bodies. In the past two decades, many efforts have been directed at improving urban drainage systems by introducing mitigation measures to limit the negative environmental impacts of storm water. These mitigation measures are generally called best management practices (BMPs), sustainable urban drainage systems, or low impact developments, and they include practices such as infiltration and storage tanks that reduce the peak flow and retain some of the polluting materials. Choosing the best mitigation measure is still a controversial topic. To gain insight on the best technique, this study compares different distributed and centralized urban storm-water management techniques, including infiltration and storage facilities. The main objective of this study is to use modeling to assess the effects of the different urban drainage techniques. To this end, a homemade model that was developed in previous studies is applied. This model enables us to simulate both combined sewer systems and ancillary structures such as storm tanks or infiltration trenches to determine water quantity and quality characteristics. A long-term simulation is employed to account for the effects of sediments in BMPs, which generally reduce the hydraulic capacity. The results allow us to draw some conclusions on the peculiarities of BMP techniques, on the possibility of integrating different techniques for improving efficiency, and on BMP maintenance planning.

In the past three decades, scientific research has focused on the preservation of water resources, and in particular, on the polluting impact of urban areas on natural water bodies. One approach to this research has involved the development of tools to describe the phenomena that take place on the urban catchment during both wet and dry periods. Research has demonstrated the importance of the integrated analysis of all the transformation phases that characterise the delivery and treatment of urban water pollutants from source to outfall. With this aim, numerous integrated urban drainage models have been developed to analyse the fate of pollution from urban catchments to the final receiving waters, simulating several physical and chemical processes. Such modelling approaches require calibration, and for this reason, researchers have tried to address two opposing needs: the need for reliable representation of complex systems, and the need to employ parsimonious approaches to cope with the usually insufficient, especially for urban sources, water quality data. The present paper discusses the application of a bespoke model to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two wastewater treatment plants, and has a small river as the receiving water body. The paper describes the monitoring approach that was used for model calibration, presents some interesting considerations about the monitoring needs for integrated modelling applications, and provides initial results useful for identifying the most relevant polluting sources.

Urban water quality management often requires use of numerical models allowing the evaluation of the cause–effect relationship between the input(s) (i.e. rainfall, pollutant concentrations on catchment surface and in sewer system) and the resulting water quality response. The conventional approach to the system (i.e. sewer system, wastewater treatment plant and receiving water body), considering each component separately, does not enable optimisation of the whole system. However, recent gains in understanding and modelling make it possible to represent the system as a whole and optimise its overall performance. Indeed, integrated urban drainage modelling is of growing interest for tools to cope with Water Framework Directive requirements. Two different approaches can be employed for modelling the whole urban drainage system: detailed and simplified. Each has its advantages and disadvantages. Specifically, detailed approaches can offer a higher level of reliability in the model results, but can be very time consuming from the computational point of view. Simplified approaches are faster but may lead to greater model uncertainty due to an over-simplification. To gain insight into the above problem, two different modelling approaches have been compared with respect to their uncertainty. The first urban drainage integrated model approach uses the Saint-Venant equations and the 1D advection-dispersion equations, for the quantity and for the quality aspects, respectively. The second model approach consists of the simplified reservoir model. The analysis used a parsimonious bespoke model developed in previous studies. For the uncertainty analysis, the Generalised Likelihood Uncertainty Estimation (GLUE) procedure was used. Model reliability was evaluated on the basis of capacity of globally limiting the uncertainty. Both models have a good capability to fit the experimental data, suggesting that all adopted approaches are equivalent both for quantity and quality. The detailed model approach is more robust and presents less uncertainty in terms of uncertainty bands. On the other hand, the simplified river water quality model approach shows higher uncertainty and may be unsuitable for receiving water body quality assessment.

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’Orleans (Palermo).

The objective of this paper is the definition of a methodology to evaluate the impact of the temporal resolution of rainfall measurements in urban drainage modelling applications. More specifically the effect of the temporal resolution on urban water quality modelling is detected analysing the uncertainty of the response of rainfall–runoff modelling. Analyses have been carried out using historical rainfall–discharge data collected for the Fossolo catchment (Bologna, Italy). According to the methodology, the historical rainfall data are taken as a reference, and resampled data have been obtained through a rescaling procedure with variable temporal windows. The shape comparison between ‘true’ and rescaled rainfall data has been carried out using a non-dimensional accuracy index. Monte Carlo simulations have been carried out applying a parsimonious urban water quality model, using the recorded data and the resampled events. The results of the simulations were used to derive the cumulative probabilities of quantity and quality model outputs (peak discharges, flow volume, peak concentrations and pollutant mass) conditioned on the observation according to the GLUE (Generalized Likelihood Uncertainty Estimation) methodology. The results showed that when coarser rainfall information is available, the model calibration process is still efficient even if modelling uncertainty progressively increases especially with regards to water quality aspects.

In recent years, limitations linked to traditional urban drainage schemes have been pointed out and new approaches are developing introducing more natural methods for retaining and/or disposing of stormwater. These mitigation measures are generally called Best Management Practices or Sustainable Urban Drainage System and they include practices such as infiltration and storage tanks in order to reduce the peak flow and retain part of the polluting components. The introduction of such practices in urban drainage systems entails an upgrade of existing modelling frameworks in order to evaluate their efficiency in mitigating the impact of urban drainage systems on receiving water bodies. While storage tank modelling approaches are quite well documented in literature, some gaps are still present about infiltration facilities mainly dependent on the complexity of the involved physical processes. In this study, a simplified conceptual modelling approach for the simulation of the infiltration trenches is presented. The model enables to assess the performance of infiltration trenches. The main goal is to develop a model that can be employed for the assessment of the mitigation efficiency of infiltration trenches in an integrated urban drainage context. Particular care was given to the simulation of infiltration structures considering the performance reduction due to clogging phenomena. The proposed model has been compared with other simplified modelling approaches and with a physically based model adopted as benchmark. The model performed better compared to other approaches considering both unclogged facilities and the effect of clogging. On the basis of a long-term simulation of six years of rain data, the performance and the effectiveness of an infiltration trench measure are assessed. The study confirmed the important role played by the clogging phenomenon on such infiltration structures.

Until now the majority of sewer networks in Italy have been built and managed considering small as well as local wastewater treatment plants (WWTPs). However, pushed by Italian and EU regulations, centralised and large WWTPs are becoming more common in Italy. One major technical aspect caused by the changing from local to centralised WWTPs is the development of significant in-sewer processes. These processes are also emphasized by the building of long interceptor sewers characterized by high hydraulic retention times and absence of tributary sewers. In this paper a model derived from the well known WATS model (Wastewater Aerobic/anaerobic Transformation in Sewers) has been set up and applied to an Italian case study located in Palermo. To assure a good degree of reliability the model needs a number of parameters that can be measured or calibrated by the combined use of field data and laboratory analyses. To support the planning of the experimental activity a sensitivity analysis to assess the influence of each model parameter has been carried out. Indeed, the sensitivity analysis allowed identifying the most significant model parameters. As a matter of the fact, by means of sensitivity analysis it was possible not only to pin down the most important model parameters but also to address the field survey towards the most sensitive model parameters in order to optimize the model application. The results are interesting and enabled to reduce the number of parameters to be calibrated from 24 to 14. More specifically, COD fractions, YHw and μH showed a strong influence on the model results addressing the field campaign to an accurate evaluation of their value.

Over the last decade new technologies are emerging even more for wastewater treatment. Among the new technologies, a recent possible solution regards Moving Bed Biofilm Reactors (MBBRs) that represent an effective alternative to conventional processes. More specifically such systems consist in the introduction of plastic elements inside the aerobic reactor as carrier material for the growth of attached biomass. Recently, one of the mostly used alternatives is to couple the Moving Bed Biofilm Reactor (MBBR) process with the conventional activated sludge process, and the resulting process is usually called HMBBR (Hybrid MBBR). In the MBBR process the biofilm grows attached on small plastic elements that are kept in constant motion throughout the entire volume of the reactor. Indeed, in such a system, a competition between the two biomasses, suspended and attached, can arise for the availability of the substrates, leading, as a consequence, to a modification in the biokinetic parameters of the two biomasses, compared to that of a pure suspended or attached biomass process. This paper presents the first results of a study aimed at estimating the kinetic heterotrophic constants in a HMBBR pilot plant using respirometric techniques. The pilot plant was built at the Acqua dei Corsari (Palermo) wastewater treatment plant and consisted of two parallel lines realized in a pre-anoxic scheme, in one of which the carrier material was added to the aerobic reactor with a filling ratio of 30%.

In urban drainage, new computational possibilities have supported the development of new integrated approaches aimed at joint water quantity and quality analysis of the whole urban drainage system. Although the benefit of an integrated approach has been widely demonstrated, to date, several aspects prevent its applicability such as scarce availability of field data if compared with model complexity. These aspects sometimes prevent the correct estimation of parameters thus leading to large uncertainty in modelling response. This is a typical parameter identifiability problem that is discussed in the present paper evaluating the effect of identifiability procedures in increasing operator confidence in modelling results. The methodology presented has been applied to a home-made integrated urban drainage model that has been calibrated/validated considering field data collected in the Savena experimental catchment (Bologna, Italy). The results demonstrate the effectiveness of the identifiability analysis in obtaining a tool for urban integrated modelling applications and field data gathering campaigns.

Over the last few years there has been a growing attention regarding the receiving water body quality state. As a matter of a fact, the Directive 91/271 of the European Union (EU) replaced the “emission standard” concept that fixes discharge limits depending on polluting emission characteristics, with the “stream standard” concept that fixes discharge limits for each polluting substance depending on self-depurative characteristics of the RWB. In this context, several WWTPs need to be upgraded in order to meet stricter effluent limits. The need of WWTP upgrading was also emphasized by the growing urbanization that have led, in most cases, to get overloaded WWTP due to an overcoming of the maximum WWTP capacity. In order to upgrade existing WWTP basically two main possibilities can be chosen: building new tanks or modify the WWTP by introducing new technologies such as the HMBBR systems. In this paper, such latter possibility was explored and as a case study an existing Italian WWTP (Acqua dei Corsari) located in Palermo (IT) was analysed. The main goal was to test the effectiveness of HMBBR systems with respect to the WWTP upgrading. The survey was carried out by means of model simulation and an HMBBR pilot plant. This latter was employed for the evaluation of the model parameters as well as kinetic coefficients for the HMBBR. The model results are encouraging towards the WWTP upgrading by means of HMBBR. As a matter of fact, the model simulation results showed that the WWTP maximum capacity can be upgraded from 480,000 up to one million PE.

A semi-empirical mechanistic model able to simulate the dynamics of a stabilization reservoir was developed incorporating both settling of particulate components and chemical/biological processes. Several factors affecting the reservoir effluent quality were taken into account: hydraulics and hydrology, solar radiation, atmospheric reaeration, algae, zooplankton, organic matter, pathogen bacteria, and sediment-water interaction. The model quantifies the specific influence of each factor on effluent quality, evaluating the correlation between the different considered factors. State variables included in the model were: algae, dissolved oxygen, organic matter, zooplankton and indicator bacteria. The model was transferred into a computational code in order to provide a useful and versatile tool for water resource planning management issues. The model was verified by comparing simulated results with full-scale data collected from a small reservoir (Sicily, IT) filled with partially treated wastewater. The reservoir has a volume of 11,000 m3, a maximum depth of 6.3 m and a mean depth of about 5 m. The monitoring period lasted four months during which the reservoir operated in different hydraulics conditions: as a standard batch reactor and as a continuous flow reactor. The model was able to reproduce the behaviour of the principal simulated parameters thus representing a potential tool for the management and performance optimization of these peculiar storage/treatment systems.

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.

In recent years there has been an increasing interest in the development of hybrid biofilm reactors, especially in the upgrading of existing WWTP that are no longer able to respect concentration limits. In fact, today's challenge is the achievement of a good aquatic state for the receiving water bodies according to the Water Framework Directive requirements, which indeed limit even more the continuous emissions, i.e. coming from WWTP. This paper presents the setting up of a mathematical model for the simulation of a hybrid MBBR system; the model calibration/validation has been carried out considering a field gathering campaign on an experimental pilot plant. The main goal is to gain insight about MBBR processes attempting to overcome main shortcomings in particular referring to the modelling aspects. The model is made up of two connected sub-models for the simulation of the suspended and attached biomass. The model is mainly based on the concepts of the activated sludge model No. 1 (ASM1) for the description of the biokinetic process both for the suspended and for the attached biomass. The results show a good agreement between predicted and observed values both for the attached and for the suspended biomass moreover they are encouraging for further researches.

Urban stormwater quality modelling has become a fundamental issue in the evaluation of the receiving water bodies' quality state. Laboratory study as well as field campaigns have widely demonstrated that combined sewer sediments present cohesive-like properties which increase their resistance to erosion. However, only few models take sewer sediments into account considering their rheological properties. In the present paper different sewer sediment erosion models have been tested and their uncertainties have been assessed. The main goal was to discriminate the algorithms with respect to their robustness and the reduction of uncertainty. In order to accomplish such objective the GLUE methodology has been used. The model has been tested using the quantity-quality data gathered for the Fossolo catchment (Bologna, Italy). Results show a general tendency of sewer sediment erosion models to become overparameterised when trying to fit the complexity of the physical processes thus increasing modelling uncertainty.

La necessità di adeguare e/o ampliare gli esistenti impianti biologici di trattamento delle acque reflue, sia urbane che industriali, sia per potenziarne la capacità depurativa che per ottemperare al rispetto di limiti allo scarico per gli effluenti trattati sempre più restrittivi, si scontra sempre più di frequente con la difficoltà a reperire adeguate aree di espansione degli impianti. Pertanto, negli ultimi decenni sono state proposte nuove tecnologie atte a ridurre al massimo il volume dei reattori biologici, o incrementando la concentrazione di biomassa presente nel sistema e la capacità dello stadio finale del trattamento biologico, ossia quello di separazione solido-liquido, come nel caso dei sistemi MBR (Membrane BioReactor), oppure fornendo adeguati mezzi di supporto, ad elevata superficie specifica di attecchimento, sui quali fare sviluppare una notevole quantità di biomassa in forma di biofilm, come nel caso dei sistemi MBBR (Moving Bed Biofilm Reactor). Tali soluzioni, certamente di grande interesse tecnico e scientifico, trovano però spesso difficoltà di applicazione per i notevoli costi di investimento e di esercizio. In particolare, con rifermento ai costi di esercizio, basta soltanto pensare alle basse capacità di trasferimento di ossigeno che i sistemi di aerazione mostrano nei due sopracitati processi: fattore  assai ridotto per l’elevata concentrazione di biomassa nei sistemi MBR e aerazione a bolle medio-grosse nei sistemi MBBR.

I fanghi costituiscono, come è noto, l’inevitabile prodotto di risulta dei processi attuati lungo la linea acque del ciclo di trattamento degli impianti di depurazione delle acque reflue. I dati più recentemente pubblicati dall’Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA) indicano, in Italia, una produzione di fanghi nel 2015 di oltre 3 milioni di tonnellate, destinata ulteriormente ad aumentare via via che andrà crescendo la percentuale di reflui avviati alla depurazione, ma anche in conseguenza del progressivo restringimento degli standard di qualità degli effluenti trattati, imposti dai provvedimenti normativi in materia. Ovviamente, la suddetta cospicua produzione di fanghi comporta sia elevati costi dei relativi trattamento e smaltimento, che talvolta possono addirittura raggiungere il 50% di quelli necessari per la gestione complessiva degli impianti di depurazione (Andreoli et al., 2007), ma anche difficoltà sempre crescenti nell’individuare soluzioni per il loro smaltimento finale. In tali condizioni, destano estremo interesse le soluzioni tecniche e tecnologiche volte alla riduzione della produzione di fango ma anche al miglioramento della sua qualità, al fine di facilitarne lo smaltimento o, ancora meglio, consentire il recupero dell’energia e della materia ad esso associate. Nel seguito si riporta una sintetica illustrazione delle principali soluzioni che possono essere all’uopo adottate, prestando particolare attenzione agli interventi riguardanti il fango biologico, che, tipicamente, rappresenta l’aliquota preponderante.

We analyze the benefits of a practical application of Low Impact Development (LID) structures in an urbanized area in terms of reduced flooding volumes and peak flow. LID techniques are fairly economic and easy to implement solutions for the hydraulic restoration and improvement of the efficiency of existing urban drainage networks. The case study is the New Rome Fair, a recent urban development where flooding problems have been observed in the last few years. The study illustrates the potential of LID structures together with a dual system, and their impact on flooding occurrences, for two different Return Periods, highlighting the need for attentive and feasible applications of such structures in a heavily urbanized environment, harmoniously with the related architectural aspects.

The main goal of this study was the evaluation of the excess sludge reduction in a MBR for biological nitrogen removal (BNR) through the implementation of the Oxic-Settling-Anaerobic (OSA) process. For this purpose, a MBR pilot plant (42 L volume) was realized according to a pre-denitrification scheme. The whole experimentation was divided into two periods, named Period 1 and Period 2, respectively. In Period 1 the pilot plant was started-up and the excess sludge production was evaluated. In Period 2 the plant configuration was partially modified by inserting an anaerobic reactor into the return activated sludge (RAS) line to realize an OSA configuration. In Period 1, the Yobs resulted equal to 0.39 gVSS g−1CODremoved, in accordance with the reference values for MBR plants reported in the literature (Wang et al. 2013). Similarly, all the kinetic and stoichiometric parameters, for both autotrophic and heterotrophic biomass, resulted in line with those reported in a MBR with a pre-denitrification scheme (Lubello et al. 2009). In contrast, in Period 2 the Yobs showed a significant decrease, reaching a pseudo steady-state value of 0.17 gVSS g−1CODremoved at the end of the experiments, highlighting a reduction of 55% compared to Period 1.

La scelta del ciclo di trattamento rappresenta la prima, e probabilmente più delicata, opzione della procedura che porta alla redazione del progetto di un impianto di trattamento delle acque e, più in particolare, di un impianto di depurazione. Essa è influenzata da numerosi fattori, sostanzialmente riconducibili alle seguenti categorie: le caratteristiche quali-quantitative del refluo (portata; inquinanti e relative concentrazioni); la morfologia e le caratteristiche ambientali del sito ove dell’impianto (a cominciare dall’estensione della superficie disponibile); l’organizzazione e le risorse della struttura deputata a presiedere alla gestione ed all’esercizio dell’opera; la tipologia e la sensibilità del corpo idrico ricettore nonché il livello di salvaguardia per esso desiderato e le modalità di scarico nello stesso dell’effluente depurato; i sistemi di smaltimento del fango; l’eventuale possibilità di riutilizzo del refluo trattato e, comunque, le disposizioni normative che ne disciplinano la qualità; il livello tecnologico delle soluzioni tecniche (di processo e impiantistiche) adottabili. Negli ultimi anni sono stati soprattutto i fattori attinenti alle ultime tre categorie quelli in cui si sono avute le più importanti novità, determinando ripercussioni sui criteri alla base della scelta del ciclo di trattamento secondo un rapporto consequenziale di causa-effetto. Nel lavoro, dopo brevi ricapitolazioni dell’evoluzione che ha avuto la normativa nel nostro Paese e dei progressi tecnologici che hanno interessato i principali processi depurativi, in particolare biologici, si valuta la loro influenza sulla scelta del ciclo di trattamento, considerando i casi sia di impianti da realizzare ex-novo, che di quelli da sottoporre ad interventi di potenziamento e/o adeguamento.