Optimal influent N-to-P ratio for stable microalgal cultivation in water treatment and nutrient recovery.

Species specific nitrogen-to-phosphorus molar ratio (NPR) has been suggested for green microalgae. Algae can store nitrogen and phosphorus, suggesting that the optimum feed concentration dynamically changes as function of the nutrient storage. We assessed the effect of varying influent NPR on microalgal cultivation in terms of microbial community stability, effluent quality and biokinetics. Mixed green microalgae (Chlorella sorokiniana and Scenedesmus sp.) and a monoculture of Chlorella sp. were cultivated in continuous laboratory-scale reactors treating used water. An innovative image analysis tool, developed in this study, was used to track microbial community changes. Diatoms proliferated as influent NPR decreased, and were outcompeted once cultivation conditions were restored to the optimal NPR range. Low NPR operation resulted in decrease in phosphorus removal, biomass concentration and effluent nitrogen concentration. ASM-A kinetic model simulation results agreed well with operational data in the absence of diatoms. The failure to predict operational data in the presence of diatoms suggest differences in microbial activity that can significantly influence nutrient recovery in photobioreactors (PBR). No contamination occurred during Chlorella sp. monoculture cultivation with varying NPRs. Low NPR operation resulted in decrease in biomass concentration, effluent nitrogen concentration and nitrogen quota. The ASM-A model was calibrated for the monoculture and the simulations could predict the experimental data in continuous operation using a single parameter subset, suggesting stable biokinetics under the different NPR conditions. Results show that controlling the influent NPR is effective to maintain the algal community composition in PBR, thereby ensuring effective nutrients uptake.

Occurrence, characterisation and fate of (nano)particulate Ti and Ag in two Norwegian wastewater treatment plants.

Due to their widespread application in consumer products, elemental titanium (e.g., titanium dioxide, TiO2) and silver (Ag), also in nanoparticulate form, are increasingly released from households and industrial facilities to urban wastewater treatment plants (WWTPs). A seven-day sampling campaign was conducted in two full-scale WWTPs in Trondheim (Norway) employing only primary treatment. We assessed the occurrence and elimination of Ti and Ag, and conducted size-based fractionation using sequential filtration of influent samples to separate particulate, colloidal and dissolved fractions. Eight-hour composite influent samples were collected to assess diurnal variations in total Ti and Ag influx. Measured influent Ti concentrations (up to 290 µg L-1) were significantly higher than Ag (<0.15-2.1 µg L-1), being mostly associated with suspended solids (>0.7 µm). Removal efficiencies ≥70% were observed for both elements, requiring for one WWTP to account for the high Ti content (∼2 g L-1) in the flocculant. Nano- and micron-sized Ti particles were observed with scanning transmission electron microscopy (STEM) in influent, effluent and biosolids, while Ag nanoparticles were detected in biosolids only. Diurnal profiles of influent Ti were correlated to flow and pollutant concentration patterns (especially total suspended solids), with peaks during the morning and/or evening and minima at night, indicating household discharges as predominant source. Irregular profiles were exhibited by influent Ag, with periodic concentration spikes suggesting short-term discharges from one or few point sources (e.g., industry). Influent Ti and Ag dynamics were reproduced using a disturbance scenario generator model, and we estimated per capita loads of Ti (42-45 mg cap-1 d-1) and Ag (0.11 mg cap-1 d-1) from households as well as additional Ag load (14-22 g d-1) from point discharge. This is the first study to experimentally and mathematically describe short-term release dynamics and dry-weather sources of emissions of Ti and Ag in municipal WWTPs and receiving environments.

Reactor staging influences microbial community composition and diversity of denitrifying MBBRs- Implications on pharmaceutical removal.

The subdivision of biofilm reactor in two or more stages (i.e., reactor staging) represents an option for process optimisation of biological treatment. In our previous work, we showed that the gradient of influent organic substrate availability (induced by the staging) can influence the microbial activity (i.e., denitrification and pharmaceutical biotransformation kinetics) of a denitrifying three-stage Moving Bed Biofilm Reactor (MBBR) system. However, it is unclear whether staging and thus the long-term exposure to varying organic carbon type and loading influences the microbial community structure and diversity. In this study, we investigated biofilm structure and diversity in the three-stage MBBR system (S) compared to a single-stage configuration (U) and their relationship with microbial functions. Results from 16S rRNA amplicon libraries revealed a significantly higher microbial richness in the staged MBBR (at 99% sequence similarity) compared to single-stage MBBR. A more even and diverse microbial community was selected in the last stage of S (S3), likely due to exposure to carbon limitation during continuous-flow operation. A core of OTUs was shared in both systems, consisting of Burkholderiales, Xanthomonadales, Flavobacteriales and Sphingobacteriales, while MBBR staging selected for specific taxa (i.e., Candidate division WS6 and Deinococcales). Results from quantitative PCR (qPCR) showed that S3 exhibited the lowest abundance of 16S rRNA but the highest abundance of atypical nosZ, suggesting a selection of microbes with more diverse N-metabolism (i.e., incomplete denitrifiers) in the stage exposed to the lowest carbon availability. A positive correlation (p < 0.05) was observed between removal rate constants of several pharmaceuticals with abundance of relevant denitrifying genes, but not with biodiversity. Despite the previously suggested positive relationship between microbial diversity and functionality in macrobial and microbial ecosystems, this was not observed in the current study, indicating a need to further investigate structure-function relationships for denitrifying systems.

The impact of temperature on the transformation of illicit drug biomarkers in wastewater.

Temperature is one of the key factors, influencing the transformation kinetics of organic chemicals. In the context of wastewater-based epidemiology, however, temperature differences among sewer catchments and within the same catchment (due to, e.g., seasonal variations) have been neglected to date as a factor influencing the estimation of illicit drug consumption. In this study, we assessed the influence of temperature on the transformation of biomarkers in wastewater and its ensuing implications on the back-calculation of chemical consumption rate in urban catchments using the example of selected illicit drugs. Literature data, obtained in laboratory-scale experiments, on the stability of drug biomarkers in untreated wastewater at trace levels was systematically reviewed, and transformation rates obtained at different temperatures were collected. Arrhenius-based equations were fitted to empirical data and identified to describe the transformation of selected cocaine and morphine biomarkers at applicability temperature range (from 2-9 °C to 30-31 °C), with estimated exponential Arrhenius coefficients between 1.04 and 1.18. These empirically-derived relationships were used to assess the influence of temperature on the transformation of drug biomarkers during in-sewer transport and its effect on the back-calculation of drug consumption rate in hypothetical urban catchment scenario simulations. Up to 4-fold increase in removal efficiency was estimated when wastewater temperature increased from 15 °C to 25 °C. Findings from this study can help reducing the uncertainty intrinsic to wastewater-based epidemiology studies, and will be beneficial in comparing chemical consumption estimates from different catchments worldwide.

A systematic model identification method for chemical transformation pathways - the case of heroin biomarkers in wastewater.

This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology.

Transformation and Sorption of Illicit Drug Biomarkers in Sewer Biofilms.

In-sewer transformation of drug biomarkers (excreted parent drugs and metabolites) can be influenced by the presence of biomass in suspended form as well as attached to sewer walls (biofilms). Biofilms are likely the most abundant and biologically active biomass fraction in sewers. In this study, 16 drug biomarkers were selected, including the parent forms and the major human metabolites of mephedrone, methadone, cocaine, heroin, codeine, and tetrahydrocannabinol (THC). Transformation and sorption of these substances were assessed in targeted batch experiments using laboratory-scale biofilm reactors operated under aerobic and anaerobic conditions. A one-dimensional model was developed to simulate diffusive transport, abiotic and biotic transformation, and partitioning of drug biomarkers. Model calibration to experimental results allowed estimating biotransformation rate constants in sewer biofilms, which were compared to those obtained for suspended biomass. Our results suggest that sewer biofilms can enhance the biotransformation kinetics of most selected compounds. Through scenario simulations, we demonstrated that the estimation of biotransformation rate constants in biofilm can be significantly biased if the boundary layer thickness is not accurately estimated. This study complements our previous investigation on the transformation and sorption of drug biomarkers in the presence of only suspended biomass in untreated sewage. A better understanding of the role of sewer biofilms-also relative to the in-sewer suspended solids-and improved prediction of associated fate processes can result in more accurate estimation of daily drug consumption in urban areas in wastewater-based epidemiological assessments.

Removal of pharmaceuticals in pre-denitrifying MBBR - Influence of organic substrate availability in single- and three-stage configurations.

Due to the limited efficiency of conventional biological treatment, innovative solutions are being explored to improve the removal of trace organic chemicals in wastewater. Controlling biomass exposure to growth substrate represents an appealing option for process optimization, as substrate availability likely impacts microbial activity, hence organic trace chemical removal. This study investigated the elimination of pharmaceuticals in pre-denitrifying moving bed biofilm reactors (MBBRs), where biofilm exposure to different organic substrate loading and composition was controlled by reactor staging. A three-stage MBBR and a single-stage reference MBBR (with the same operating volume and filling ratio) were operated under continuous-flow conditions (18 months). Two sets of batch experiments (day 100 and 471) were performed to quantify and compare pharmaceutical removal and denitrification kinetics in the different MBBRs. Experimental results revealed the possible influence of retransformation (e.g., from conjugated metabolites) and enantioselectivity on the removal of selected pharmaceuticals. In the second set of experiments, specific trends in denitrification and biotransformation kinetics were observed, with highest and lowest rates/rate constants in the first (S1) and the last (S3) staged sub-reactors, respectively. These observations were confirmed by removal efficiency data obtained during continuous-flow operation, with limited removal (<10%) of recalcitrant pharmaceuticals and highest removal in S1 within the three-stage MBBR. Notably, biotransformation rate constants obtained for non-recalcitrant pharmaceuticals correlated with mean specific denitrification rates, maximum specific growth rates and observed growth yield values. Overall, these findings suggest that: (i) the long-term exposure to tiered substrate accessibility in the three-stage configuration shaped the denitrification and biotransformation capacity of biofilms, with significant reduction under substrate limitation; (ii) biotransformation of pharmaceuticals may have occurred as a result of cometabolism by heterotrophic denitrifying bacteria.

Diffusion and sorption of organic micropollutants in biofilms with varying thicknesses.

Solid-liquid partitioning is one of the main fate processes determining the removal of micropollutants in wastewater. Little is known on the sorption of micropollutants in biofilms, where molecular diffusion may significantly influence partitioning kinetics. In this study, the diffusion and the sorption of 23 micropollutants were investigated in novel moving bed biofilm reactor (MBBR) carriers with controlled biofilm thickness (50, 200 and 500 µm) using targeted batch experiments (initial concentration = 1 µg L-1, for X-ray contrast media 15 µg L-1) and mathematical modelling. We assessed the influence of biofilm thickness and density on the dimensionless effective diffusivity coefficient f (equal to the biofilm-to-aqueous diffusivity ratio) and the distribution coefficient Kd,eq (L g-1). Sorption was significant only for eight positively charged micropollutants (atenolol, metoprolol, propranolol, citalopram, venlafaxine, erythromycin, clarithromycin and roxithromycin), revealing the importance of electrostatic interactions with solids. Sorption equilibria were likely not reached within the duration of batch experiments (4 h), particularly for the thickest biofilm, requiring the calculation of the distribution coefficient Kd,eq based on the approximation of the asymptotic equilibrium concentration (t > 4 h). Kd,eq values increased with increasing biofilm thickness for all sorptive micropollutants (except atenolol), possibly due to higher porosity and accessible surface area in the thickest biofilm. Positive correlations between Kd,eq and micropollutant properties (polarity and molecular size descriptors) were identified but not for all biofilm thicknesses, thus confirming the challenge of improving predictive sorption models for positively charged compounds. A diffusion-sorption model was developed and calibrated against experimental data, and estimated f values also increased with increasing biofilm thickness. This indicates that diffusion in thin biofilms may be strongly limited (f â‰ª 0.1) by the high biomass density (reduced porosity).

Wastewater-based epidemiology to assess pan-European pesticide exposure.

Human biomonitoring, i.e. the determination of chemicals and/or their metabolites in human specimens, is the most common and potent tool for assessing human exposure to pesticides, but it suffers from limitations such as high costs and biases in sampling. Wastewater-based epidemiology (WBE) is an innovative approach based on the chemical analysis of specific human metabolic excretion products (biomarkers) in wastewater, and provides objective and real-time information on xenobiotics directly or indirectly ingested by a population. This study applied the WBE approach for the first time to evaluate human exposure to pesticides in eight cities across Europe. 24 h-composite wastewater samples were collected from the main wastewater treatment plants and analyzed for urinary metabolites of three classes of pesticides, namely triazines, organophosphates and pyrethroids, by liquid chromatography-tandem mass spectrometry. The mass loads (mg/day/1000 inhabitants) were highest for organophosphates and lowest for triazines. Different patterns were observed among the cities and for the various classes of pesticides. Population weighted loads of specific biomarkers indicated higher exposure in Castellon, Milan, Copenhagen and Bristol for pyrethroids, and in Castellon, Bristol and Zurich for organophosphates. The lowest mass loads (mg/day/1000 inhabitants) were found in Utrecht and Oslo. These results were in agreement with several national statistics related to pesticides exposure such as pesticides sales. The daily intake of pyrethroids was estimated in each city and it was found to exceed the acceptable daily intake (ADI) only in one city (Castellon, Spain). This was the first large-scale application of WBE to monitor population exposure to pesticides. The results indicated that WBE can give new information about the "average exposure" of the population to pesticides, and is a useful complementary biomonitoring tool to study population-wide exposure to pesticides.

Intermittent Aeration Suppresses Nitrite-Oxidizing Bacteria in Membrane-Aerated Biofilms: A Model-Based Explanation.

Autotrophic ammonium oxidation in membrane-aerated biofilm reactors (MABRs) can make treatment of ammonium-rich wastewaters more energy-efficient, especially within the context of short-cut ammonium removal. The challenge is to exclusively enrich ammonium-oxidizing bacteria (AOB). To achieve nitritation, strategies to suppress nitrite-oxidizing bacteria (NOB) are needed, which are ideally grounded on an understanding of underlying mechanisms. In this study, a nitrifying MABR was operated under intermittent aeration. During eight months of operation, AOB dominated, while NOB were suppressed. On the basis of dissolved oxygen (DO), ammonium, nitrite, and nitrate profiles within the biofilm and in the bulk, a 1-dimensional nitrifying biofilm model was developed and calibrated. The model was utilized to explore the potential mechanisms of NOB suppression associated with intermittent aeration, considering DO limitation, direct pH effects on enzymatic activities, and indirect pH effects on activity via substrate speciation. The model predicted strong periodic shifts in the spatial gradients of DO, pH, free ammonia, and free nitrous acid, associated with aerated and nonaerated phases. NOB suppression during intermittent aeration was mostly explained by periodic inhibition caused by free ammonia due to periodic transient pH upshifts. Dissolved oxygen limitation did not govern NOB suppression. Different intermittent aeration strategies were then evaluated for nitritation success in intermittently aerated MABRs: both aeration intermittency and duration were effective control parameters.

Transformation and Sorption of Illicit Drug Biomarkers in Sewer Systems: Understanding the Role of Suspended Solids in Raw Wastewater.

Sewer pipelines, although primarily designed for sewage transport, can also be considered as bioreactors. In-sewer processes may lead to significant variations of chemical loadings from source release points to the treatment plant influent. In this study, we assessed in-sewer utilization of growth substrates (primary metabolic processes) and transformation of illicit drug biomarkers (secondary metabolic processes) by suspended biomass. Sixteen drug biomarkers were targeted, including mephedrone, methadone, cocaine, heroin, codeine, and tetrahydrocannabinol (THC) and their major human metabolites. Batch experiments were performed under aerobic and anaerobic conditions using raw wastewater. Abiotic biomarker transformation and partitioning to suspended solids and reactor wall were separately investigated under both redox conditions. A process model was identified by combining and extending the Wastewater Aerobic/anaerobic Transformations in Sewers (WATS) model and Activated Sludge Model for Xenobiotics (ASM-X). Kinetic and stoichiometric model parameters were estimated using experimental data via the Bayesian optimization method DREAM(ZS). Results suggest that biomarker transformation significantly differs from aerobic to anaerobic conditions, and abiotic conversion is the dominant mechanism for many of the selected substances. Notably, an explicit description of biomass growth during batch experiments was crucial to avoid significant overestimation (up to 385%) of aerobic biotransformation rate constants. Predictions of in-sewer transformation provided here can reduce the uncertainty in the estimation of drug consumption as part of wastewater-based epidemiological studies.

Short-sludge age EBPR process - Microbial and biochemical process characterisation during reactor start-up and operation.

The new paradigm for used water treatment suggests the use of short solid retention times (SRT) to minimize organic substrate mineralization and to maximize resource recovery. However, little is known about the microbes and the underlying biogeochemical mechanisms driving these short-SRT systems. In this paper, we report the start-up and operation of a short-SRT enhanced biological phosphorus removal (EBPR) system operated as a sequencing batch reactor (SBR) fed with preclarified municipal wastewater, which is supplemented with propionate. The microbial community was analysed via 16S rRNA amplicon sequencing. During start-up (SRT = 8 d), the EBPR was removing up to 99% of the influent phosphate and completely oxidized the incoming ammonia. Furthermore, the sludge showed excellent settling properties. However, once the SRT was shifted to 3.5 days nitrification was inhibited and bacteria of the Thiothrix taxon proliferated in the reactor, thereby leading to filamentous bulking (sludge volume index up to SVI = 1100 mL/g). Phosphorus removal deteriorated during this period, likely due to the out-competition of polyphosphate accumulating organisms (PAO) by sulphate reducing bacteria (SRB). Subsequently, SRB activity was suppressed by reducing the anaerobic SRT from 1.2 day to 0.68 day, with a consequent rapid SVI decrease to ∼200 ml/g. The short-SRT EBPR effectively removed phosphate and nitrification was mitigated at SRT = 3 days and oxygen levels ranging from 2 to 3 mg/L.

Biofilm Thickness Influences Biodiversity in Nitrifying MBBRs-Implications on Micropollutant Removal.

In biofilm systems for wastewater treatment (e.g., moving bed biofilms reactors-MBBRs) biofilm thickness is typically not under direct control. Nevertheless, biofilm thickness is likely to have a profound effect on the microbial diversity and activity, as a result of diffusion limitation and thus substrate penetration in the biofilm. In this study, we investigated the impact of biofilm thickness on nitrification and on the removal of more than 20 organic micropollutants in laboratory-scale nitrifying MBBRs. We used novel carriers (Z-carriers, AnoxKaldnes) that allowed controlling biofilm thickness at 50, 200, 300, 400, and 500 µm. The impact of biofilm thickness on microbial community was assessed via 16S rRNA gene amplicon sequencing and ammonia monooxygenase (amoA) abundance quantification through quantitative PCR (qPCR). Results from batch experiments and microbial analysis showed that (i) the thickest biofilm (500 µm) presented the highest specific biotransformation rate constants (kbio, L g(-1) d(-1)) for 14 out of 22 micropollutants; (ii) biofilm thickness positively associated with biodiversity, which was suggested as the main factor for the observed enhancement of kbio; (iii) the thinnest biofilm (50 µm) exhibited the highest nitrification rate (gN d(-1) g(-1)), amoA gene abundance and kbio values for some of the most recalcitrant micropollutants (i.e., diclofenac and targeted sulfonamides). Although thin biofilms favored nitrification activity and the removal of some micropollutants, treatment systems based on thicker biofilms should be considered to enhance the elimination of a broad spectrum of micropollutants.

Removal of Antibiotics in Biological Wastewater Treatment Systems-A Critical Assessment Using the Activated Sludge Modeling Framework for Xenobiotics (ASM-X).

Many scientific studies present removal efficiencies for pharmaceuticals in laboratory-, pilot-, and full-scale wastewater treatment plants, based on observations that may be impacted by theoretical and methodological approaches used. In this Critical Review, we evaluated factors influencing observed removal efficiencies of three antibiotics (sulfamethoxazole, ciprofloxacin, tetracycline) in pilot- and full-scale biological treatment systems. Factors assessed include (i) retransformation to parent pharmaceuticals from e.g., conjugated metabolites and analogues, (ii) solid retention time (SRT), (iii) fractions sorbed onto solids, and (iv) dynamics in influent and effluent loading. A recently developed methodology was used, relying on the comparison of removal efficiency predictions (obtained with the Activated Sludge Model for Xenobiotics (ASM-X)) with representative measured data from literature. By applying this methodology, we demonstrated that (a) the elimination of sulfamethoxazole may be significantly underestimated when not considering retransformation from conjugated metabolites, depending on the type (urban or hospital) and size of upstream catchments; (b) operation at extended SRT may enhance antibiotic removal, as shown for sulfamethoxazole; (c) not accounting for fractions sorbed in influent and effluent solids may cause slight underestimation of ciprofloxacin removal efficiency. Using tetracycline as example substance, we ultimately evaluated implications of effluent dynamics and retransformation on environmental exposure and risk prediction.

Towards a consensus-based biokinetic model for green microalgae - The ASM-A.

Cultivation of microalgae in open ponds and closed photobioreactors (PBRs) using wastewater resources offers an opportunity for biochemical nutrient recovery. Effective reactor system design and process control of PBRs requires process models. Several models with different complexities have been developed to predict microalgal growth. However, none of these models can effectively describe all the relevant processes when microalgal growth is coupled with nutrient removal and recovery from wastewaters. Here, we present a mathematical model developed to simulate green microalgal growth (ASM-A) using the systematic approach of the activated sludge modelling (ASM) framework. The process model - identified based on a literature review and using new experimental data - accounts for factors influencing photoautotrophic and heterotrophic microalgal growth, nutrient uptake and storage (i.e. Droop model) and decay of microalgae. Model parameters were estimated using laboratory-scale batch and sequenced batch experiments using the novel Latin Hypercube Sampling based Simplex (LHSS) method. The model was evaluated using independent data obtained in a 24-L PBR operated in sequenced batch mode. Identifiability of the model was assessed. The model can effectively describe microalgal biomass growth, ammonia and phosphate concentrations as well as the phosphorus storage using a set of average parameter values estimated with the experimental data. A statistical analysis of simulation and measured data suggests that culture history and substrate availability can introduce significant variability on parameter values for predicting the reaction rates for bulk nitrate and the intracellularly stored nitrogen state-variables, thereby requiring scenario specific model calibration. ASM-A was identified using standard cultivation medium and it can provide a platform for extensions accounting for factors influencing algal growth and nutrient storage using wastewater resources.

Life cycle assessment as development and decision support tool for wastewater resource recovery technology.

Life cycle assessment (LCA) has been increasingly used in the field of wastewater treatment where the focus has been to identify environmental trade-offs of current technologies. In a novel approach, we use LCA to support early stage research and development of a biochemical system for wastewater resource recovery. The freshwater and nutrient content of wastewater are recognized as potential valuable resources that can be recovered for beneficial reuse. Both recovery and reuse are intended to address existing environmental concerns, for example, water scarcity and use of non-renewable phosphorus. However, the resource recovery may come at the cost of unintended environmental impacts. One promising recovery system, referred to as TRENS, consists of an enhanced biological phosphorus removal and recovery system (EBP2R) connected to a photobioreactor. Based on a simulation of a full-scale nutrient and water recovery system in its potential operating environment, we assess the potential environmental impacts of such a system using the EASETECH model. In the simulation, recovered water and nutrients are used in scenarios of agricultural irrigation-fertilization and aquifer recharge. In these scenarios, TRENS reduces global warming up to 15% and marine eutrophication impacts up to 9% compared to conventional treatment. This is due to the recovery and reuse of nutrient resources, primarily nitrogen. The key environmental concerns obtained through the LCA are linked to increased human toxicity impacts from the chosen end use of wastewater recovery products. The toxicity impacts are from both heavy metals release associated with land application of recovered nutrients and production of AlCl3, which is required for advanced wastewater treatment prior to aquifer recharge. Perturbation analysis of the LCA pinpointed nutrient substitution and heavy metals content of algae biofertilizer as critical areas for further research if the performance of nutrient recovery systems such as TRENS is to be better characterized. Our study provides valuable feedback to the TRENS developers and identifies the importance of system expansion to include impacts outside the immediate nutrient recovery system itself. The study also show for the first time the successful evaluation of urban-to-agricultural water systems in EASETECH.

iCFD: Interpreted Computational Fluid Dynamics - Degeneration of CFD to one-dimensional advection-dispersion models using statistical experimental design - The secondary clarifier.

The present study aims at using statistically designed computational fluid dynamics (CFD) simulations as numerical experiments for the identification of one-dimensional (1-D) advection-dispersion models - computationally light tools, used e.g., as sub-models in systems analysis. The objective is to develop a new 1-D framework, referred to as interpreted CFD (iCFD) models, in which statistical meta-models are used to calculate the pseudo-dispersion coefficient (D) as a function of design and flow boundary conditions. The method - presented in a straightforward and transparent way - is illustrated using the example of a circular secondary settling tank (SST). First, the significant design and flow factors are screened out by applying the statistical method of two-level fractional factorial design of experiments. Second, based on the number of significant factors identified through the factor screening study and system understanding, 50 different sets of design and flow conditions are selected using Latin Hypercube Sampling (LHS). The boundary condition sets are imposed on a 2-D axi-symmetrical CFD simulation model of the SST. In the framework, to degenerate the 2-D model structure, CFD model outputs are approximated by the 1-D model through the calibration of three different model structures for D. Correlation equations for the D parameter then are identified as a function of the selected design and flow boundary conditions (meta-models), and their accuracy is evaluated against D values estimated in each numerical experiment. The evaluation and validation of the iCFD model structure is carried out using scenario simulation results obtained with parameters sampled from the corners of the LHS experimental region. For the studied SST, additional iCFD model development was carried out in terms of (i) assessing different density current sub-models; (ii) implementation of a combined flocculation, hindered, transient and compression settling velocity function; and (iii) assessment of modelling the onset of transient and compression settling. Furthermore, the optimal level of model discretization both in 2-D and 1-D was undertaken. Results suggest that the iCFD model developed for the SST through the proposed methodology is able to predict solid distribution with high accuracy - taking a reasonable computational effort - when compared to multi-dimensional numerical experiments, under a wide range of flow and design conditions. iCFD tools could play a crucial role in reliably predicting systems' performance under normal and shock events.

From consumption to harvest: Environmental fate prediction of excreted ionizable trace organic chemicals.

Excreted trace organic chemicals, e.g., pharmaceuticals and biocides, typically undergo incomplete elimination in municipal wastewater treatment plants (WWTPs) and are released to surface water via treated effluents and to agricultural soils through sludge amendment and/or irrigation with freshwater or reclaimed wastewater. Recent research has shown the tendency for these substances to accumulate in food crops. In this study, we developed and applied a simulation tool to predict the fate of three ionizable trace chemicals (triclosan-TCS, furosemide-FUR, ciprofloxacin-CIP) from human consumption/excretion up to the accumulation in soil and plant, following field amendment with sewage sludge or irrigation with river water (assuming dilution of WWTP effluent). The simulation tool combines the SimpleTreat model modified for fate prediction of ionizable chemicals in a generic WWTP and a recently developed dynamic soil-plant uptake model. The simulation tool was tested using country-specific (e.g., consumption/emission rates, precipitation and temperature) input data. A Monte Carlo-based approach was adopted to account for the uncertainty associated to physico-chemical and biokinetic model parameters. Results obtained in this study suggest significant accumulation of TCS and CIP in sewage sludge (1.4-2.8 mg kgDW(-1)) as compared to FUR (0.02-0.11 mg kgDW(-1)). For the latter substance, more than half of the influent load (60.1%-72.5%) was estimated to be discharged via WWTP effluent. Specific emission rates (g ha(-1) a(-1)) of FUR to soil via either sludge application or irrigation were up to 300 times lower than for TCS and CIP. Nevertheless, high translocation potential to wheat was predicted for FUR, reaching concentrations up to 4.3 µg kgDW(-1) in grain. Irrigation was found to enhance the relative translocation of FUR to plant (45.3%-48.9% of emission to soil), as compared to sludge application (21.9%-27.6%). A comparison with peer-reviewed literature showed that model predictions were close to experimental data for elimination in WWTP, concentrations in sewage and sludge and bioconcentration factors (BCFs) in plant tissues, which showed however a large variability. The simulation tool presented here can thus be useful for priority setting and for the estimation of human exposure to trace chemicals via intake of food crops.

Microthrix parvicella abundance associates with activated sludge settling velocity and rheology - Quantifying and modelling filamentous bulking.

The objective of this work is to identify relevant settling velocity and rheology model parameters and to assess the underlying filamentous microbial community characteristics that can influence the solids mixing and transport in secondary settling tanks. Parameter values for hindered, transient and compression settling velocity functions were estimated by carrying out biweekly batch settling tests using a novel column setup through a four-month long measurement campaign. To estimate viscosity model parameters, rheological experiments were carried out on the same sludge sample using a rotational viscometer. Quantitative fluorescence in-situ hybridisation (qFISH) analysis, targeting Microthrix parvicella and phylum Chloroflexi, was used. This study finds that M. parvicella - predominantly residing inside the microbial flocs in our samples - can significantly influence secondary settling through altering the hindered settling velocity and yield stress parameter. Strikingly, this is not the case for Chloroflexi, occurring in more than double the abundance of M. parvicella, and forming filaments primarily protruding from the flocs. The transient and compression settling parameters show a comparably high variability, and no significant association with filamentous abundance. A two-dimensional, axi-symmetrical computational fluid dynamics (CFD) model was used to assess calibration scenarios to model filamentous bulking. Our results suggest that model predictions can significantly benefit from explicitly accounting for filamentous bulking by calibrating the hindered settling velocity function. Furthermore, accounting for the transient and compression settling velocity in the computational domain is crucial to improve model accuracy when modelling filamentous bulking. However, the case-specific calibration of transient and compression settling parameters as well as yield stress is not necessary, and an average parameter set - obtained under bulking and good settling conditions - can be used.

Factors influencing sorption of ciprofloxacin onto activated sludge: experimental assessment and modelling implications.

Many of the pharmaceuticals and personal care products occurring in municipal sewage are ionizing substances, and their partitioning behaviour is affected by ionic interactions with solid matrices. In activated sludge systems, such interactions have currently not been adequately understood and described, particularly for zwitterionic chemicals. Here we present an assessment of the effects of pH and iron salt dosing on the sorption of ciprofloxacin onto activated sludge using laboratory experiments and full-scale fate modelling. Experimental results were described with Freundlich isotherms and showed that non-linear sorption occurred under all the conditions tested. The greatest sorption potential was measured at pH=7.4, at which ciprofloxacin is speciated mostly as zwitterion. Iron salt dosing increased sorption under aerobic and, to a lesser extent, anoxic conditions, whereas no effect was registered under anaerobic conditions. The activated sludge model for xenobiotics (ASM-X) was extended with Freundlich-based sorption kinetics and used to predict the fate of ciprofloxacin in a wastewater treatment plant (WWTP). Scenario simulations, using experimental Freundlich parameters, were used to identify whether the assessed factors caused a significant increase of aqueous ciprofloxacin concentration in full-scale bioreactors. Simulation results suggest that a pH increase, rather than a reduction in iron salt dosing, could be responsible for a systematic deterioration of sorption of ciprofloxacin in the WWTP.