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Biopolimer materials, such as polyhydroxyalkanoates (PHA), are essential to restrain the use of petroleum-based plastic material fostered by European Union regulation. Despite the recent development regarding the production of PHA, the scale-up of sewage sludge-based technology is still in its infancy since the literature lacks studies on the environmental impact of the process. This work aims to stand as a pioneer study reporting the direct greenhouse gas (GHG) emissions and carbon footprint (CF) of two sewage sludge-based PHA production strategies. The two strategies, aerobic dynamic feeding (ADF) and aerobic/anoxic enrichment (AE/AN), were monitored and compared based on the system's efficiency in removing carbon and nutrients, PHA's production and productivity, nitrous oxide direct emissions and the CF. The produced PHA accounted for 38.21 g PHA g−1 volatile suspended solids (VSS) % and 14.54 g PHA g−1 VSS %, for ADF and AE/AN, respectively. The N 2 O emissions were lower for ADF than the AE/AN enrichment, 0.39 N 2 O–N L−1 and 0.98 N 2 O–N L−1, respectively, as was the CF, which accounted for 3.56 kg CO 2 day−1 and 6.91 kg CO 2 day−1 for ADF and AE/AN respectively. This innovative study provides valuable insights into comparing the two above-mentioned strategies and a pilot for structuring and designing future studies comprehensively considering the environmental consequences of the process in future life cycle assessments. [Display omitted] • ADF and AE/AN were compared to assess the environmental impact of producing PHA. • ADF reached 38.21 % w/w of PHA produced, while AE/AN only achieved 14.54 % w/w. • AE/AN showed an increased amount of N 2 O emitted, up to 0.98 mg N 2 O–N L−1. • AE/AN almost doubled ADF's carbon footprint (CF), reaching 13.09 kg CO 2 day−1. • CF proves that a trade-off between efficiency and sustainability is yet to be found. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on polyhydroxyalkanoate (PHA) production from sewage sludge. Greenhouse gas (GHG) emissions were monitored for the first time to assess the impact of climate change and environmental sustainability. The pilot plant was composed of a fermenter with a membrane and two biological reactors (namely, selection and accumulation). Results showed that despite a low organic loading rate (namely, 0.06 kg BOD kg SS−1 day−1), a good PHA yield was obtained (namely, 0.37 g PHA/g volatile fatty acids), confirming that sewage sludge can be a suitable feedstock. GHG emissions were 3.85E-04 g CO 2 eq/g and 32.40 g CO 2 eq/g, direct and indirect, respectively. Results provided valuable insights in view of finding a trade-off between PHA production and GHG emissions to prove the PHA production process as an effective solution for biosolids disposal at a low carbon footprint. [Display omitted] • Sewage sludge acidogenic fermentation at low carbon footprint was performed. • High COD, ammonia and phosphate removal was achieved. • Final PHA concentration of 0.37 g PHA/g VSS was obtained. • N 2 O emission factor accounted for less than 1 % of total nitrogen. • N 2 O direct and indirect emissions were 3.85E-04 and 32.40 g CO2eq/g treated sludge. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A comprehensive protocol for polyhydroxyalkanoates extraction is presented. • Purification post-treatment was performed. • The highest recovery yield (73%) was obtained when using NH 4 -Laurate. • The molecular weights of both biopolymers were low. Abstract A new protocol for polyhydroxyalkanoates (PHA) extraction from mixed microbial cultures (MMCs) is proposed. PHA-accumulating capacity of the MMC was selected in a sequencing batch reactor (SBR) fed with a synthetic effluent emulating a fermented oil mill wastewater (OMW). The highest recovery yield and purity (74 ± 8% and 100 ± 5%, respectively) was obtained when using NH 4 -Laurate for which operating conditions of the extraction process such as temperature, concentration and contact time were optimized. Best conditions for PHA extraction from MMC turned to be: i) a pre-treatment with NaClO at 85 °C with 1 h of contact time, followed by ii) a treatment with lauric acid in a ratio acid lauric to biomass of 2:1 and 3 h of contact time. [ABSTRACT FROM AUTHOR]

Abstract A new model for accounting carbon and energy footprint of wastewater treatment plants (WWTPs) is proposed. The model quantifies direct and indirect greenhouse gas (GHG) emissions related to biological and physical processes of a WWTP. The model takes into account several innovative aspects with respect to already available literature models: i. kinetic/mass-balances; ii. nitrification as a two-step process; iii. nitrous oxide (N 2 O) formation during nitrification and denitrification both in dissolved and off-gas forms. A full-scale application has been performed by adopting the case study of a real WWTP. A scenario analysis was performed to quantify the influence of: composition of inflow wastewater (scenario 1), operating conditions (scenario 2), and oxygen transfer efficiency (scenario 3). Results have underlined the key role of the ratio between influent biodegradable carbon and nitrogen concentration on influencing direct and indirect GHG emissions. Direct GHG emissions increase from 0.49 to 0.63 kgCO 2 eq m−3 with the decrease of the influent ratio of the readily biodegradable carbon and organic and ammonia nitrogen. The increase of the influent organic and ammonia nitrogen favours the daily production of active ammonia oxidization biomass. The simultaneous variation of the investigated factors has amplified direct and indirect GHG emissions to a maximum value of 0.94 and 0.24 kgCO 2eq m−3, respectively. Graphical abstract Image 10216 Highlights • A new plant-wide model including greenhouse gases is presented. • Mechanisms of nitrification and denitrification are simulated in a comprehensive way. • The new model is applied to a real case study. • Several scenarios are detected to envisage model features. • The model is a promising tool for design and manage wastewater treatment plant. [ABSTRACT FROM AUTHOR]

Abstract Stricter effluent limits, water shortage conditions, land availability requires today even more the needs of advanced wastewater treatments. Attractive solutions come from membrane bioreactors (MBR), Integrated Fixed Film Activated Sludge (IFAS) or combinations (i.e., IFAS-MBRs). One crucial aspect for the applicability of this overall new technology, compared to the conventional activated sludge systems, is the lack of knowledge for design and manage (e.g., kinetic constants, optimal operative conditions etc.). In view of the above frame, the aim of the present study was to assess the kinetic and stoichiometric parameters of bacterial species in MBRs by means of respirometric techniques. Plant configurations, operational conditions and wastewater features (domestic/industrial) were analysed. Four different MBR plants were investigated: i) sequencing batch MBR subjected to a gradual salinity increase; ii) pre-denitrification MBR treating saline wastewater contaminated by hydrocarbons; iii) University of Cape Town (UCT) MBR treating domestic wastewater subjected to a carbon-to-nitrogen (C/N) ratio variation; iiii) UCT- IFAS -MBR treating domestic wastewater. Results show a significant influence on biomass respiratory activity from both plant configurations and operational conditions. The salinity increase severely affected the activity of autotrophic species, while heterotrophic community was mainly influenced by the C/N variation. Moreover, it was observed a specialization in the IFAS-MBR configuration, with the suspended biomass more affine to organic matter, whilst biofilm in the nitrification process. The respirometric analysis confirmed to be an effective tool for the evaluation of the biomass kinetic and stoichiometric parameters. The results of this study can be useful for the application of mathematical models in the design phase and for the monitoring of biomass viability during plant operations. Graphical abstract Image 1 Highlights • The study aim was to provide new insights on biokinetic behaviour of MBRs. • Respirometry was used to assess the kinetic and stoichiometric coefficients. • Heterotrophs highly suffered low C/N ratio with increased membrane fouling. • Salinity highly affected the autotrophic activity increasing the pore blocking. • It was observed a specialization of suspended and attached biomass in the IFAS-MBR. [ABSTRACT FROM AUTHOR]

Abstract This research elucidates the pollutants (nutrients and carbon) removal performance and nitrous oxide (N 2 O) emissions of two pilot plants. Specifically, a University of Cape Town (UCT) Membrane Bioreactor (MBR) plant and an Integrated Fixed Film Activated Sludge (IFAS)-UCT-MBR plant were investigated. The plants were fed with real wastewater augmented with acetate and glycerol in order to control the influent carbon nitrogen ratio (C/N). The short-term effect of the inlet C/N ratio variation (C/N = 5 mgCOD/mgN and C/N = 10 mgCOD/mgN) on the behaviour of both plants was investigated. The results showed that the IFAS-UCT-MBR configuration provided the best performance in terms of pollutants removal at the two investigated C/N ratios. Furthermore, the lowest N 2 O emission (with respect to the influent nitrogen) was observed in the IFAS-UCT-MBR configuration, thus suggesting a potential beneficial effect of the biofilm in the emission reduction. However, the membrane of the IFAS-UCT-MBR showed a greater fouling tendency compared to the UCT-MBR configuration. This result, likely related to the biofilm detached from carriers, could seriously affect the indirect GreenHouse Gas emissions due to the increase of the energy requirement for permeate extraction with the increase of membrane fouling. Graphical abstract Image Highlights • A full comparison of UCT-MBR vs IFAS-UCT-MBR is reported. • Two COD/N ratios were investigated in both systems. • IFAS-UCT-MBR configuration provided the best pollutant removal efficiency. • IFAS-UCT-MBR configuration showed lower N 2 O emission. • A greater tendency of membrane fouling was noticed for the IFAS system. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Two mathematical models (Model I and Model II) are compared. • Model I considers N 2 O production only during denitrification. • Model II considers the ammonia-oxidizing bacteria formation pathways for N 2 O. • Model II has a better capability of reproducing the measured data. • Model II provides a global reduction in the uncertainty of the predictions. Abstract This paper compares two mathematical models (Model I and Model II) to predict greenhouse gases emission from a University Cape Town (UCT) – membrane bioreactor (MBR) plant. Model I considers N 2 O production only during denitrification. Model II takes into account the ammonia-oxidizing bacteria (AOB) formation pathways for N 2 O. Both models were calibrated adopting real data. Model comparison was performed in terms of (i) sensitivity analysis (ii) best fit and (iii) model prediction uncertainty. On average 6% of factors of Model I and 9% of Model II resulted to be important. In terms of best fit, Model II had a better capability of reproducing the measured data. The average efficiency related to the N 2 O model outputs was equal to 0.33 and 0.38 for Model I and Model II, respectively. On average, 73% (Model I) and 86% (Model II) of measured data lay inside the uncertainty bands. [ABSTRACT FROM AUTHOR]

An integrated membrane bioreactor (MBR) model was previously proposed and tested. The model provides a comprehensive and detailed description of the nitrogen biological removal processes with respect to up-to-date literature. This paper presents a sensitivity and uncertainty analysis aimed at identifying the key factors affecting the variability of the model predictions. The Standardized Regression Coefficients (SRC) method was adopted for the sensitivity analysis. The uncertainty analysis was employed by running Monte Carlo simulations by varying only the value of the key factors affecting the model outputs. The sensitivity analysis combined with the uncertainty analysis applied here enabled to gain useful insights about the robustness of the model. By means of the SRC method 45 model factors (of 122) were selected as important. The results obtained here allowed to investigate the advantage of a detailed description of the nitrogen transformation bioprocesses (nitrification/denitrification) in terms of model accuracy and uncertainty bandwidth. The model allows to simulate the intermediate product during nitrification/denitrification, thus providing the possibility to control the nitrogen compounds that favour the formation of nitrous oxide. [ABSTRACT FROM AUTHOR]

• A literature review on biological processes in MBR models is presented. • There are still gaps in the literature due to EPS complex nature and multiple origins. • SMP and EPS modelling data are derived from an experimental estimation. • Accurate estimation of SMP and EPS can contribute to optimizing membrane fouling. A mathematical correlation between biomass kinetic and membrane fouling can improve the understanding and spread of Membrane Bioreactor (MBR) technology, especially in solving the membrane fouling issues. On this behalf, this paper, produced by the International Water Association (IWA) Task Group on Membrane modelling and control, reviews the current state-of-the-art regarding the modelling of kinetic processes of biomass, focusing on modelling production and utilization of soluble microbial products (SMP) and extracellular polymeric substances (EPS). The key findings of this work show that the new conceptual approaches focus on the role of different bacterial groups in the formation and degradation of SMP/EPS. Even though several studies have been published regarding SMP modelling, there still needs to be more information due to the highly complicated SMP nature to facilitate the accurate modelling of membrane fouling. The EPS group has seldom been addressed in the literature, probably due to the knowledge deficiency concerning the triggers for production and degradation pathways in MBR systems, which require further efforts. Finally, the successful model applications showed that proper estimation of SMP and EPS by modelling approaches could optimise membrane fouling, which can influence the MBR energy consumption, operating costs, and greenhouse gas emissions. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Biological nutrient removal was operated at different solids (SRT) and hydraulic retention times (HRT) in order to assess their influence on nitrous oxide (N2O) emission from a hybrid moving‐bed membrane bioreactor. The observed results show that the N2O production decreased when the SRT/HRT was decreased. The maximum N2O gaseous concentration was measured in the aerobic reactor at the end of phase I, and it decreased through phases II and III. From mass balances over the reactors of the system, the aerated (aerobic and membrane) reactors were the largest producers of N2O, showing that the greater part of N2O was produced during the nitrification process. [ABSTRACT FROM AUTHOR]

As sources of greenhouse gas (GHG) emissions, integrated urban drainage systems (IUDSs) (i.e., sewer systems, wastewater treatment plants and receiving water bodies) contribute to climate change. This paper, produced by the International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, reviews the state-of-the-art and modelling tools developed recently to understand and manage GHG emissions from IUDS. Further, open problems and research gaps are discussed and a framework for handling GHG emissions from IUDSs is presented. The literature review reveals that there is a need to strengthen already available mathematical models for IUDS to take GHG into account. [ABSTRACT FROM AUTHOR]

In this study a University of Cape Town (UCT) Integrated Fixed Film Activated Sludge (IFAS) Membrane BioReactor (MBR) wastewater treatment plant was monitored in terms of nitrous oxide (N 2 O) emissions. The short term effect on the N 2 O emission due to the influent carbon-to-nitrogen (C/N) ratio variation (C/N ratios of 2, 5 and 10 gCOD/gN) was evaluated. Since in a previous study, the effect of the C/N ratio was studied in the same system without biofilm (UCT-MBR configuration) the main aim here was to investigate the role of biofilms on N 2 O emissions. Under all the investigated C/N ratios, the N 2 O fluxes and the average emission factors were lower than that of previous studies with no biofilm presence. The total average N 2 O emission was 0.5% of the influent nitrogen with biofilm (IFAS system) and 3.5% without biofilm. This result emphasizes the potential role of the biofilms in attenuating the N 2 O emissions especially in the case of stress conditions (i.e., low C/N influent ratios). An increase of N 2 O flux from the anoxic reactor (till 28 mgN 2 O m −2 h −1 ) occurred at the lowest influent C/N tested (2 gCOD/gN - phase III). At C/N equal to 2 gCOD/gN the anoxic reactor was the main source of N 2 O, contributing 45% of all produced N 2 O. This result was attributed to an incomplete denitrification caused by a lack of organic carbon and a slight increase of dissolved oxygen concentration. [ABSTRACT FROM AUTHOR]

A comprehensive integrated membrane bioreactor (MBR) model for wastewater treatment is here proposed. The model quantifies the main biological and physical processes. The model describes the biological removal of organic matter, nitrogen and phosphorus including greenhouse gases (carbon dioxide, CO 2 and nitrous oxide, N 2 O). The model takes into account the following main innovative aspects jointly: i. Two-step nitrification process; ii. N 2 O formation due to ammonia-oxidizing bacteria as a product of the hydroxylamine oxidation (NH 2 OH) and of the nitrite (NO 2 − ) reduction; iii. Soluble microbial product (SMP) formation/degradation due to microbial growth and endogenous respiration; iv. Interlink between SMP and membrane fouling. The model was calibrated by employing a detailed calibration protocol and data from a University Cape Town (UCT) – MBR pilot plant. The key processes contributing to the N 2 O formation were properly described (total efficiency related to the calibrated model equal to 0.55). Results suggested that the incomplete hydroxylamine oxidation and the heterotrophic denitrification were the predominant processes influencing the N 2 O production. The model was able to describe the membrane fouling as demonstrated by the high efficiency (0.92) for the resistance state variable. This result confirms the importance in the modelling approach of considering both biological and physical processes jointly. [ABSTRACT FROM AUTHOR]

The aim of the present paper was to investigate the effect of solids (SRT) and hydraulic (HRT) retention time on Integrated Fixed Film Activated Sludge (IFAS) University of Cape Town (UCT) membrane Bioreactor (MBR). In particular, three different pairs of SRT and HRT values were analysed, namely, Phase I 56 d/30 h, Phase II 31 d/15 h and Phase III 7 d/13 h. The short-term effect of these three SRT/HRT conditions was assessed by analysing several system performance indicators: organic carbon and biological nutrient (nitrogen and phosphorus) removal, biomass respiratory activity, activated sludge filtration properties and membrane fouling. The results showed that the decrease of SRT/HRT had a positive influence on system performance. Specifically, the IFAS-UCT-MBR showed excellent removal of organic matter - highest value (99%) at the shortest SRT/HRT (7 d/13 h). Also, the increase in organic loading rate resulting from the decrease of SRT and HRT led to improved nitrogen removal due to higher N removal by sludge wasting requiring less N removal (as N 2 ) by denitrification. Complete nitrification of influent ammonia was achieved at all three SRT/HRT phases, guaranteed by the presence of biofilm carriers in the aerobic reactor, which ensured a higher media SRT than suspended biomass SRT. The increase of the organic loading rate and decrease in SRT led also to a higher heterotrophic activity as demonstrated by the respirometric batch tests, which is due to the increasing active biomass fraction of the volatile suspended solids as SRT decreases. The SRT/HRT decrease over the three phases resulted in an overall increase of the Extracellular Polymeric Substance concentration, which caused an increase in membrane fouling. [ABSTRACT FROM AUTHOR]

The influence of the main operational variables on N 2 O emissions from an Integrated Fixed Film Activated Sludge University of Cape Town membrane Bioreactor pilot plant was studied. Nine operational cycles (total duration: 340 days) were investigated by varying the value of the mixed liquor sludge retention time (SRT) (Cycles 1–3), the feeding ratio between carbon and nitrogen (C/N) (Cycles 4–6) and simultaneously the hydraulic retention time (HRT) and the SRT (Cycles 7–9). Results show a huge variability of the N 2 O concentration in liquid and off-gas samples (ranged from 10 −1 μg N 2 O-N L −1 to 10 3 μg N 2 O-N L −1 ). The maximum N 2 O concentration (1228 μg N 2 O-N L −1 ) in the off-gas samples occurred in the anoxic reactor at the lowest C/N value confirming that unbalanced C/N promotes the N 2 O emission during denitrification. The aerated reactors (aerobic and MBR) have been the major N 2 O emitters during all the three Phases. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the nitrous oxide (N 2 O) emissions from a moving bed based Integrated Fixed Film Activated Sludge (IFAS) - membrane bioreactor (MBR) pilot plant, designed according to the University of Cape Town (UCT) layout. The experimental campaign had a duration of 110 days and was characterized by three different sludge retention time (SRT) values (∞, 30 d and 15 d). Results highlighted that N 2 O concentrations decreased when the biofilm concentrations increased within the aerobic reactor. Results have shown an increase of N 2 O with the decrease of SRT. Specifically, an increase of N 2 O-N emission factor occurred with the decrease of the SRT (0.13%, 0.21% and 0.76% of influent nitrogen for SRT = ∞, SRT = 30 d and SRT = 15 d, respectively). Moreover, the MBR tank resulted the key emission source (up to 70% of the total N 2 O emission during SRT = ∞ period) whereas the highest N 2 O production occurred in the anoxic reactor. Moreover, N 2 O concentrations measured in the permeate flow were not negligible, thus highlighting its potential detrimental contribution for the receiving water body. The role of each plant reactor as N 2 O-N producer/consumer varies with the SRT variation, indeed the aerobic reactor was a N 2 O consumer at SRT = ∞ and a producer at SRT = 30 d. [ABSTRACT FROM AUTHOR]

The paper presents the sensitivity and uncertainty analysis of an integrated urban drainage model which includes micropollutants. Specifically, a bespoke integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole – SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Overall, five scenarios, each characterized by different uncertainty combinations of sub-systems (i.e., SS, WWTP and RWB), have been considered applying, for the sensitivity analysis, the Extended-FAST method in order to select the key factors affecting the RWB quality and to design a reliable/useful experimental campaign. Results have demonstrated that sensitivity analysis is a powerful tool for increasing operator confidence in the modelling results. The approach adopted here can be used for blocking some non-identifiable factors, thus wisely modifying the structure of the model and reducing the related uncertainty. The model factors related to the SS have been found to be the most relevant factors affecting the SMX modeling in the RWB when all model factors (scenario 1) or model factors of SS (scenarios 2 and 3) are varied. If the only factors related to the WWTP are changed (scenarios 4 and 5), the SMX concentration in the RWB is mainly influenced (till to 95% influence of the total variance for S SMX,max ) by the aerobic sorption coefficient. A progressive uncertainty reduction from the upstream to downstream was found for the soluble fraction of SMX in the RWB. [ABSTRACT FROM AUTHOR]

The present study explores the interlinkages among the operational variables of a University of Cape Town (UCT) Integrated Fixed Film Activated Sludge (IFAS) membrane bioreactor (MBR) pilot plant. Specifically, dedicated experimental tests were carried out with the final aim to find-out a constitutive relationship among operational costs (OCs), effluent quality index (EQI), effluent fines (EF). Greenhouse gas (GHG) emissions were also included in the study. Results showed that the EQI increases at low flow rate likely due to the dissolved oxygen (DO) limitation in the biological processes. Direct GHGs increase with the increasing of the air flow due to the anoxic N 2 O contribution. Irreversible membrane fouling reduce from 98% to 85% at the air flow rate of 0.57 m 3 h −1 and 2.56 m 3 h −1 , respectively. However, the increase of the air flow rate leads to the increase of the N 2 O–N flux emitted from the MBR (from 40% to 80%). [ABSTRACT FROM AUTHOR]

In this paper, an experimental campaign was carried out on a University of Cape Town Integrated Fixed Film Activated Sludge Membrane Bioreactor (UCT-IFAS-MBR) pilot plant. The aim of the study was to evaluate the effect of the influent C/N ratio on the system performance in terms of organic carbon, nitrogen and phosphorus removal, biomass viability (through respirometry), activated sludge features and membrane filtration properties. The experiments were organized into three phases, characterized by a variation of the C/N ratio (namely, Phase I: C/N = 5, Phase II: C/N = 10; Phase III: C/N = 2). The results highlighted that the system performance was significantly affected by C/N ratio. The removal efficiencies were satisfactory for C/N ratio equal to 10 and 5, with average removal in Phases I and II of 98–98%, 53–69% and 67–87% for COD, nitrogen and phosphorus, respectively. Conversely, with a C/N ratio of 2, a significant worsening of the pilot plant performance was observed, with average COD, nitrogen and phosphorus removal equal to 70%, 44% and 26%, respectively, much lower compared to the previous phases. Respirometry highlighted a significant decrease of bacterial activity when the C/N was reduced to 2, even if the biofilm seemed to be more resilient in terms of activity compared to the activated sludge. [ABSTRACT FROM AUTHOR]

In this study a new mathematical model to quantify greenhouse gas emissions (namely, carbon dioxide and nitrous oxide) from membrane bioreactors (MBRs) is presented. The model has been adopted to predict the key processes of a pilot plant with pre-denitrification MBR scheme, filled with domestic and saline wastewater. The model was calibrated by adopting an advanced protocol based on an extensive dataset. In terms of nitrous oxide, the results show that an important role is played by the half saturation coefficients related to nitrogen removal processes and the model factors affecting the oxygen transfer rate in the aerobic and MBR tanks. Uncertainty analysis showed that for the gaseous model outputs 88–93% of the measured data lays inside the confidence bands showing an accurate model prediction. [ABSTRACT FROM AUTHOR]

The paper reports the results of an experimental campaign carried out on a University of Cape Town (UCT) integrated fixed-film activated sludge (IFAS) membrane bioreactor (MBR) pilot plant. The pilot plant was analysed in terms of chemical oxygen demand (COD) and nutrients removal, kinetic/stoichiometric parameters, membrane fouling and sludge dewaterability. Moreover, the cultivable bacterial community structure was also analysed. The pilot plant showed excellent COD removal efficiency throughout experiments, with average value higher than 98%, despite the slight variations of the influent wastewater. The achieved nitrification efficiency was close to 98% for most of the experiments, suggesting that the biofilm in the aerobic compartment might have sustained the complete nitrification of the influent ammonia, even for concentrations higher than 100 mg L −1 . The irreversible resistance due to superficial cake deposition was the mechanism that mostly affected the membrane fouling. Moreover, it was noticed an increase of the resistance due pore blocking likely due to the increase of the EPS Bound fraction that could derive by biofilm detachment. The bacterial strains isolated from aerobic tank are wastewater bacteria known for exhibiting efficient heterotrophic nitrification–aerobic denitrification and producing biofilm. [ABSTRACT FROM AUTHOR]

The effect of the carbon-to-nitrogen (C/N) ratio in the influent on the nitrous oxide (N 2 O) emission from a University of Cape Town Membrane BioReactor pilot plant was investigated. The membrane was located in a separate tank to single out the production of N 2 O due to the biological processes from N 2 O stripping as a result of the extra aeration needed for the mitigation of membrane fouling. The experimental campaign was divided into two phases, each characterized by a different C/N ratio (namely, 10 and 5 mgCOD/mgTN, Phase I and Phase II, respectively). The decrease of the C/N ratio promoted the increase of N 2 O emissions in both gaseous and dissolved phases, mainly due to a decreased nitrification/denitrification capacity of the system. The highest N 2 O concentration in the dissolved phase was found in the permeate. This result suggests that the dissolved N 2 O in the permeate stream discharged from a MBR cannot be neglected. The total N 2 O emission was approximately of 0.01% and 0.1% of the total influent nitrogen load for the Phase I and Phase II, respectively. The findings suggest that the C/N ratio represents an indirect cause of N 2 O emission; the low C/N value (Phase II) led to the increase of pH and free ammonia causing a stress effect on the growth of nitrifying species increasing the N 2 O emission. [ABSTRACT FROM AUTHOR]

The present paper reports the results of a nitrous oxide (N 2 O) production investigation in a moving bed based integrated fixed film activated sludge (IFAS) membrane bioreactor (MBR) pilot plant designed in accordance with the University of Cape Town layout for biological phosphorous removal. Gaseous and liquid samples were collected in order to measure the gaseous as well as the dissolved concentration of N 2 O. Furthermore, the gas flow rate from each reactor was measured and the gas flux was estimated. The results confirmed that the anoxic reactor represents the main source of nitrous oxide production. A significant production of N 2 O was, however, also found in the anaerobic reactor, thus indicating a probable occurrence of the denitrifying phosphate accumulating organism activity. The highest N 2 O fluxes were emitted from the aerated reactors (3.09 g N 2 O N m −2 h −1 and 9.87 g N 2 O N m −2 h −1 , aerobic and MBR tank, respectively). The emission factor highlighted that only 1% of the total treated nitrogen was emitted from the pilot plant. Furthermore, the measured N 2 O concentrations in the permeate flow were comparable with other reactors. Nitrous oxide mass balances outlined a moderate production also in the MBR reactor despite the low hydraulic retention time. On the other hand, the mass balance showed that in the aerobic reactor a constant consumption of nitrous oxide (up to almost 15 mg N 2 O h −1 ) took place, due to the high amount of stripped gas. [ABSTRACT FROM AUTHOR]

The joint effect of wastewater salinity and hydrocarbons on nitrous oxide emission was investigated. The membrane bioreactor pilot plant was operated with two phases: i. biomass acclimation by increasing salinity from 10 gNaCl L −1 to 20 gNaCl L −1 (Phase I); ii. hydrocarbons dosing at 20 mg L −1 with a constant salt concentration of 20 gNaCl L −1 (Phase II). The Phase I revealed a relationship between nitrous oxide emissions and salinity. During the end of the Phase I, the activity of nitrifiers started to recover, indicating a partial acclimatization. During the Phase II, the hydrocarbon shock induced a temporary inhibition of the biomass with the suppression of nitrous oxide emissions. The results revealed that the oxic tank was the major source of nitrous oxide emission, likely due to the gas stripping by aeration. The joint effect of salinity and hydrocarbons was found to be crucial for the production of nitrous oxide. [ABSTRACT FROM AUTHOR]

The paper reports the results of nitrous oxide (N 2 O) emissions from aerobic and anoxic tank of a Sequential Batch Membrane Bioreactor (SB-MBR) pilot plant. The influence of salinity variation on N 2 O emission was analyzed by gradually increasing the inlet salt concentration from 0 to 10 g NaCl L −1 . The observed results showed that the N 2 O concentration of the gaseous samples was strongly influenced by the salt concentration. This result was likely related to a worsening of the nitrification activity due to the effect of salinity on autotrophic bacteria. Dissolved oxygen concentration and salinity were found to be the key factors affecting N 2 O concentration in the gaseous samples withdrawn from the anoxic tank. Despite the fact that the N 2 O concentration in the anoxic tank was higher than in the aerobic one, it was found that the aerobic tank emitted around 25 times more N 2 O than the anoxic one. [ABSTRACT FROM AUTHOR]

The effect of the carbon-to-nitrogen (C/N) ratio variation in a University of Cape Town Membrane bioreactor (UCT-MBR) was investigated. The experimental campaign was divided into two phases, each characterized by a different C/N ratio (namely, 10 and 5, Phase I and Phase II, respectively). The UCT-MBR pilot plant was analysed in terms of carbon and nutrients removal, biomass respiratory activity, activated sludge features and membrane fouling. The results highlighted that the nutrients removal was significantly affected by the decrease of the C/N ratio during the Phase II. The biological carbon removal was also affected by the low C/N value during the Phase II. Indeed, the average biological COD removal efficiency was equal to 72.9% during the Phase II, while the average value was 82.8% in the Phase I. The respirometric batch test suggested that both heterotrophic and autotrophic species were severely affected by the lower C/N ratio in the Phase II. Moreover, a decrease of the membrane filtration properties was observed during the Phase II, mainly due to the worsening of the activated sludge features, which enhanced the increase of SMP production. [ABSTRACT FROM AUTHOR]

This paper presents the sensitivity and uncertainty analysis of a mathematical model for greenhouse gas emission (GHG) and energy consumption assessment in wastewater treatment plants. A sensitivity analysis was carried out (using two different methods) to determine which model factors have the greatest effect on the predicted values of the GHG production. Further, an uncertainty analysis was carried out to quantify the uncertainty of the key model outputs, such as carbon dioxide production from activated sludge treatment. The results show that influent fractionation factors, which characterize influent composition, have an important role on direct and indirect GHGs production and emission. Moreover, model factors related to the aerobic biomass growth show a relevant influence on GHGs in terms of emission from off-site power generation (mCO2eq;PG). Further, model factors related to the autotrophic biomass growth were found to strongly interact with other factors especially in modeling mCO2eq;PG. Finally, nitrous oxide (N2O) emission associated with the effluent has the highest uncertainty, suggesting the need for a mechanistic model for N2O production in biological treatment. [ABSTRACT FROM AUTHOR]

In this work, a sequential batch membrane bioreactor pilot plant is investigated to analyze the effect of a gradual increase in salinity on carbon and nutrient removal, membrane fouling and biomass kinetic parameters. The salinity was increased by 2 g NaCl L −1 per week up to 10 g NaCl L −1 . The total COD removal efficiency was quite high (93%) throughout the experiment. A gradual biomass acclimation to the salinity level was observed during the experiment, highlighting the good recovery capabilities of the system. Nitrification was also influenced by the increase in salinity, with a slight decrease in nitrification efficiency (the lowest value was obtained at 10 g NaCl L −1 due to lower nitrifier activity). Irreversible cake deposition was the predominant fouling mechanism observed during the experiment. Respirometric tests exhibited a stress effect due to salinity, with a reduction in the respiration rates observed (from 8.85 mgO 2 L −1 h −1 to 4 mgO 2 L −1 h −1 ). [ABSTRACT FROM AUTHOR]

The paper reports the main results of an experimental campaign performed on a membrane bioreactor pilot plant designed to treat synthetic shipboard slops. The experimental campaign was divided into two phases: salinity acclimation up to 20 g NaCl L −1 (Phase I) and hydrocarbon (diesel fuel) dosing (Phase II). The observed results show that the carbon removal was not severely affected by the wastewater features. Conversely, respirometric tests showed that nitrification was strongly affected by the salinity (33% of nitrification efficiency at 20 g NaCl L −1 – Phase I) as a result of the salinity in the autotrophic biomass. Moreover, the sludge viscosity increased during Phase II due to the wastewater composition, leading to an increase in the membrane resistance, and severe degradation of the sludge dewaterability was also observed. Indeed, the capillary suction time increased by a factor of 3 times compared with that of Phase I. [ABSTRACT FROM AUTHOR]

Nitrous oxide, carbon dioxide and methane are greenhouse gases (GHG) emitted from wastewater treatment that contribute to its carbon footprint. As a result of the increasing awareness of GHG emissions from wastewater treatment plants (WWTPs), new modelling, design, and operational tools have been developed to address and reduce GHG emissions at the plant-wide scale and beyond. This paper reviews the state-of-the-art and the recently developed tools used to understand and manage GHG emissions from WWTPs, and discusses open problems and research gaps. The literature review reveals that knowledge on the processes related to N 2 O formation, especially due to autotrophic biomass, is still incomplete. The literature review shows also that a plant-wide modelling approach that includes GHG is the best option for the understanding how to reduce the carbon footprint of WWTPs. Indeed, several studies have confirmed that a wide vision of the WWPTs has to be considered in order to make them more sustainable as possible. Mechanistic dynamic models were demonstrated as the most comprehensive and reliable tools for GHG assessment. Very few plant-wide GHG modelling studies have been applied to real WWTPs due to the huge difficulties related to data availability and the model complexity. For further improvement in GHG plant-wide modelling and to favour its use at large real scale, knowledge of the mechanisms involved in GHG formation and release, and data acquisition must be enhanced. [ABSTRACT FROM AUTHOR]

Sensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important ( factor prioritisation ) and non-influential ( factor fixing ) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality–quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiarities, applicability, and reliability of the three methods is presented. Moreover, a graphical Venn diagram based classification scheme and a precise terminology for better identifying important, interacting and non-influential factors for each method is proposed. In terms of convergence, it was shown that sensitivity indices related to factors of the quantity model achieve convergence faster. Results for the Morris screening method deviated considerably from the other methods. Factors related to the quality model require a much higher number of simulations than the number suggested in literature for achieving convergence with this method. In fact, the results have shown that the term “screening” is improperly used as the method may exclude important factors from further analysis. Moreover, for the presented application the convergence analysis shows more stable sensitivity coefficients for the Extended-FAST method compared to SRC and Morris screening. Substantial agreement in terms of factor fixing was found between the Morris screening and Extended FAST methods. In general, the water quality related factors exhibited more important interactions than factors related to water quantity. Furthermore, in contrast to water quantity model outputs, water quality model outputs were found to be characterised by high non-linearity. [ABSTRACT FROM AUTHOR]

A new mathematical model able to quantify greenhouse gas (GHG) emissions in terms of carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) for a Membrane Bioreactor (MBR) is presented. The proposed mathematical model is of the Activated Sludge Model (ASM) family and takes into account simultaneously both biological and physical processes (e.g., membrane fouling). An analysis of the key factors and sources of uncertainty influencing GHG emissions is also presented. Specifically, the standardized regression coefficient, the Extended-FAST and a Monte Carlo based method are employed for assessing model factors which influence three performance indicators: effluent quality index, operational costs and GHGs. Model factors are classified as important, non-influential and interacting. The model is applied to a University Cape Town–MBR pilot plant which was object of an extensive field gathering campaign. The results reveal that model factors related to nitrogen transformation and membrane separation processes play a central role in the uncertainty of GHG estimation. Model factors that are associated with physical processes exhibit large first-order and total-order effects, which emphasises the importance of a holistic approach that jointly considers biological and physical processes. Furthermore, the membrane has a key role in GHG emissions as a result of the cake layer thickness which in turns influences the mass of substrate retained by the membrane and, thus, the biological process in the MBR. The results show that a modeller should not exclude the role of phosphorus in the contribution of accumulating organisms during the prediction of GHGs due to the high interaction of N 2 O. The results reveal that the uncertainty in the emission factors for CO 2 is higher than the uncertainty in the emission factors for N 2 O (namely, 2.2 and 0.17%, respectively). [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: Three global sensitivity analysis (GSA) methods are applied and compared to assess the most relevant processes occurring in wastewater treatment systems. In particular, the Standardised Regression Coefficients, Morris Screening and Extended-FAST methods are applied to a complex integrated membrane bioreactor (MBR) model considering 21 model outputs and 79 model factors. The three methods are applied with numerical settings as suggested in literature. The main objective considered is to classify important factors (factors prioritisation) as well as non-influential factors (factors fixing). The performance is assessed by comparing the most reliable method (Extended-FAST), by means of proposed criteria, with the two other methods. In particular, similarity to results obtained from Extended-FAST is assessed for sensitivity indices, for the ranking of sensitivity indices, for the classification into important/non-influential factors and for the method's ability to detect interaction among factors and to provide results in a reasonable time. It was found that the computationally less expensive SRC method was applied outside its range of applicability (R 2) = (0.3–0.6) < 0.7. Still, the SRC produced a ranking of important factors similar to Extended-FAST. For some variables significant interactions among the factors were revealed by computing the total effect indices STi using Extended-FAST. This means that to obtain reliable variance decomposition and to detect and quantify interactions among the factors, the use of the Extended-FAST is recommended. Regarding the comparison between Morris screening and Extended-FAST a poor agreement was found. In particular, the Morris screening overestimated the number of both important and non-influential factors compared to Extended-FAST for the analysed case study. [Copyright &y& Elsevier]

Abstract: 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=9min) 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Climate is one of the most important factors leading to changes in the hydrologic and environmental characteristics of river basins. When considering water quality, the natural factors should be weighed against anthropogenic factors (such as urbanization, increased population, and higher water demand) that may increase or decrease the effect of climatic modifications. Any prediction of future climatic and anthropogenic scenarios is affected by uncertainty and the modelling tools that are used for evaluating their impact on receiving water bodies. The present paper is aimed at investigating the complexity of such analyses and the uncertainty related to future impact predictions based on limited databases. Specifically, the study is focused on quantifying the impact of urbanization and social changes coupled with climate on river water quality under uncertainty. The study was carried out at a small catchment scale (less than ), an analysis that has been lacking from the field so far. The analysis took into account both anthropogenic change and climate determined from observed data. Because of inherent uncertainty in the model caused by the complexity of the model and its calibration, the analysis was performed considering the effect of such uncertainty on reliability by means of Monte Carlo simulations. This analysis has been applied to a monitored semiurbanized basin in Sicily (Italy). The study showed that the proposed analyses can useful for planners and urban managers; however, the uncertainty connected with mathematical modeling and small available databases has to be taken into account because it can affect decision making. The impacts of anthropogenic changes and climate can interact, thus requiring that such factors be jointly analyzed. A larger effort in monitoring and characterization of natural and urban water systems is needed to increase the reliability of models and to improve the effectiveness of decision making based on such models. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

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: 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: 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. [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]

In integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones depending 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 simulation output from upstream models is transferred to the downstream ones as input. The overall uncertainty can differ from the simple sum of uncertainties generated in each sub-model, depending on well-known uncertainty accumulation problems. The present paper aims to study the uncertainty propagation throughout an integrated urban water-quality model. At this scope, a parsimonious bespoke integrated model has been used allowing analysis of the combinative effect between different sub-models. Particularly, the data and parameter uncertainty have been assessed and compared by means of the variance decomposition concept. The integrated model and the methodology for the uncertainty decomposition have been applied to a complex integrated catchment: the Nocella basin (Italy). The results show that uncertainty contribution due to the model structure is higher with respect to the other sources of uncertainty. [ABSTRACT FROM AUTHOR]

Every model is, by definition, a simplification of the system under investigation. Although it would be desirable to reduce the gap between the simulated and the observed behaviors of the system to zero, this reduction is generally impossible owing to the unavoidable uncertainties inherent in any modeling procedure. Uncertainty analyses can provide useful insights into the best model approach to be used for obtaining results with a high level of significance and reliability. The evaluation of parameter uncertainties is necessary for calibration and for estimating the impact of these uncertainties on model performance. In this context, the uncertainty of a river water-quality model developed in previous studies is presented. The main goal is to gain insights into the modeling approaches concerning small rivers. Previous works generally focused on modeling the large river while neglecting the small one. Following a model calibration, the model uncertainty has been assessed by means of the generalized likelihood uncertainty estimation (GLUE). The results showed that the biological process related to the biological oxygen demand (BOD) removal influenced mainly by the parameters characterizing deoxygenation and nitrogen removal. On the other hand, the biological processes related to nitrogen removal were influenced not only by the parameters related to the nitrogen removal but also to oxygen concentration. The application of the GLUE methodology shows that the river quality model considered is suitable for simulating the important processes involved. Uncertainty bounds showed different amplitudes with respect to the pollutant species considered. In particular, the oxygen uncertainty bounds were narrower with respect to the other model components suggesting much attention must be paid to both model algorithms and quality data to be gathered. The study confirmed the suitability of the GLUE methodology as a powerful tool as a simplified screening methodology to assess uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: Urban sources of water pollution have often been cited as the primary cause of poor water quality in receiving water bodies (RWB), and recently many studies have been conducted to investigate both continuous sources, such as wastewater-treatment plant (WWTP) effluents, and intermittent sources, such as combined sewer overflows (CSOs). An urban drainage system must be considered jointly, i.e., by means of an integrated approach. However, although the benefits of an integrated approach have been widely demonstrated, several aspects have prevented its wide application, such as the scarcity of field data for not only the input and output variables but also parameters that govern intermediate stages of the system, which are useful for robust calibration. These factors, along with the high complexity level of the currently adopted approaches, introduce uncertainties in the modelling process that are not always identifiable. In this study, the identifiability analysis was applied to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two WWTPs and has a small river as the RWB. The system was simulated by employing an integrated model developed in previous studies. The main goal of the study was to assess the right number of parameters that can be estimated on the basis of data-source availability. A preliminary sensitivity analysis was undertaken to reduce the model parameters to the most sensitive ones. Subsequently, the identifiability analysis was carried out by progressively considering new data sources and assessing the added value provided by each of them. In the process, several identifiability methods were compared and some new techniques were proposed for reducing subjectivity of the analysis. The study showed the potential of the identifiability analysis for selecting the most relevant parameters in the model, thus allowing for model simplification, and in assessing the impact of data sources for model reliability, thus guiding the analyst in the design of future monitoring campaigns. Further, the analysis showed some critical points in integrated urban drainage modelling, such as the interaction between water quality processes on the catchment and in the sewer, that can prevent the identifiability of some of the related parameters. [Copyright &y& Elsevier]

Summary: 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. [ABSTRACT FROM AUTHOR]