Identification of behavioural model input data sets for WWTP uncertainty analysis.

Uncertainty analysis is important for wastewater treatment plant (WWTP) model applications. An important aspect of uncertainty analysis is the identification and proper quantification of sources of uncertainty. In this contribution, a methodology to identify an ensemble of behavioural model representations (combinations of input data, model structure and parameter values) is presented and evaluated. The outcome is a multivariate conditional distribution of input data that is used for generating samples of likely inputs (such as Monte Carlo input samples) to perform WWTP model uncertainty analysis. This article presents an approach to verify uncertainty distributions of input data (otherwise often assumed) by using historical observations and actual plant data.

Evaluation of environmental impacts for future influent scenarios using a model-based approach.

Changes in dilution of wastewater to a treatment plant due to infiltration or surface runoff can have a great impact on treatment process performance. This paper presents a model-based approach in which realistic influent scenarios are generated and used as inputs to a dynamic plant-wide process model of the wastewater treatment plant. The simulated operation is subsequently evaluated using life-cycle assessment (LCA) to quantify the environmental impacts of the future influent scenarios. The results show that increased infiltration led to higher environmental impact per kg nitrogen removed. The increase in surface runoff had a minor impact.

Plant-wide model-based analysis of iron dosage strategies for chemical phosphorus removal in wastewater treatment systems.

Stringent phosphorus discharge standards (i.e. 0.15-0.3 g P.m-3) in the Baltic area will compel wastewater treatment practice to augment enhanced biological phosphorus removal (EBPR) with chemical precipitation using metal salts. This study examines control of iron chemical dosing for phosphorus removal under dynamic loading conditions to optimize operational aspects of a membrane biological reactor (MBR) pilot plant. An upgraded version of the Benchmark Simulation Model No. 2 (BSM2) with an improved physico-chemical framework (PCF) is used to develop a plant-wide model for the pilot plant. The PCF consists of an equilibrium approach describing ion speciation and pairing, kinetic minerals precipitation (such as hydrous ferric oxides (HFO) and FePO4) as well as adsorption and co-precipitation. Model performance is assessed against data sets from the pilot plant, evaluating the capability to describe water and sludge lines across the treatment process under steady-state operation. Simulated phosphorus differed as little as 5-10% (relative) from measured phosphorus, indicating that the model was representative of reality. The study also shows that environmental factors such as pH, as well operating conditions such as Fe/P molar ratios (1, 1.5 and 2), influence the concentration of dissolved phosphate in the effluent. The time constant of simultaneous precipitation in the calibrated model, due to a step change decrease/increase in FeSO4 dosage, was found to be roughly 5 days, indicating a slow dynamic response due to a multi-step process involving dissolution, oxidation, precipitation, aging, adsorption and co-precipitation. The persistence effect of accumulated iron-precipitates (HFO particulates) in the activated sludge seemed important for phosphorus removal, and therefore solids retention time plays a crucial role according to the model. The aerobic tank was deemed to be the most suitable dosing location for FeSO4 addition, due to high dissolved oxygen levels and good mixing conditions. Finally, dynamic model-based analyses show the benefits of using automatic control when dosing chemicals.

Dynamic modelling of nitrous oxide emissions from three Swedish sludge liquor treatment systems.

The objective of this paper is to model the dynamics and validate the results of nitrous oxide (N2O) emissions from three Swedish nitrifying/denitrifying, nitritation and anammox systems treating real anaerobic digester sludge liquor. The Activated Sludge Model No. 1 is extended to describe N2O production by both heterotrophic and autotrophic denitrification. In addition, mass transfer equations are implemented to characterize the dynamics of N2O in the water and the gas phases. The biochemical model is simulated and validated for two hydraulic patterns: (1) a sequencing batch reactor; and (2) a moving-bed biofilm reactor. Results show that the calibrated model is partly capable of reproducing the behaviour of N2O as well as the nitritation/nitrification/denitrification dynamics. However, the results emphasize that additional work is required before N2O emissions from sludge liquor treatment plants can be generally predicted with high certainty by simulations. Continued efforts should focus on determining the switching conditions for different N2O formation pathways and, if full-scale data are used, more detailed modelling of the measurement devices might improve the conclusions that can be drawn.

Uncertainty-based calibration and prediction with a stormwater surface accumulation-washoff model based on coverage of sampled Zn, Cu, Pb and Cd field data.

A dynamic conceptual and lumped accumulation wash-off model (SEWSYS) is uncertainty-calibrated with Zn, Cu, Pb and Cd field data from an intensive, detailed monitoring campaign. We use the generalized linear uncertainty estimation (GLUE) technique in combination with the Metropolis algorithm, which allows identifying a range of behavioral model parameter sets. The small catchment size and nearness of the rain gauge justified excluding the hydrological model parameters from the uncertainty assessment. Uniform, closed prior distributions were heuristically specified for the dry and wet removal parameters, which allowed using an open not specified uniform prior for the dry deposition parameter. We used an exponential likelihood function based on the sum of squared errors between observed and simulated event masses and adjusted a scaling factor to cover 95% of the observations within the empirical 95% model prediction bounds. A positive correlation between the dry deposition and the dry (wind) removal rates was revealed as well as a negative correlation between the wet removal (wash-off) rate and the ratio between the dry deposition and wind removal rates, which determines the maximum pool of accumulated metal available on the conceptual catchment surface. Forward Monte Carlo analysis based on the posterior parameter sets covered 95% of the observed event mean concentrations, and 95% prediction quantiles for site mean concentrations were estimated to 470 µg/l ± 20% for Zn, 295 µg/l ± 40% for Cu, 20 µg/l ± 80% for Pb and 0.6 µg/l ± 35% for Cd. This uncertainty-based calibration procedure adequately describes the prediction uncertainty conditioned on the used model and data, but seasonal and site-to-site variation is not considered, i.e. predicting metal concentrations in stormwater runoff from gauged as well as ungauged catchments with the SEWSYS model is generally more uncertain than the indicated numbers.

Examining the biodegradation of endocrine disrupting bisphenol A and nonylphenol in WWTPs.

The aim of this work was to examine biodegradation of the endocrine disrupting chemicals bisphenol A (BPA) and nonylphenol (NP) in activated sludge. Experiments were performed in a pilot wastewater treatment plant (WWTP) in Copenhagen, Denmark. During standard operation the BPA concentration was halved whereas the NP concentration was unchanged. Step-addition experiments showed that biomass adaptation to increased BPA and NP concentrations took 10 to more than 40 days depending on temperature, hydraulic retention time, and pre-exposure of the biomass. Mass-balance experiments showed that above 99% of the dosed BPA and 90% of the dosed NP is removed by biodegradation at steady-state. Batch experiments showed that BPA biodegradation occur solely under aerobic conditions. The work is believed to add vital knowledge to our understanding of parameters and processes governing biodegradation of EDCs in WWTPs.

Modelling as a tool when interpreting biodegradation of micro pollutants in activated sludge systems.

The aims of the present work were to improve the biodegradation of the endocrine disrupting micro pollutant, bisphenol A (BPA), used as model compound in an activated sludge system and to underline the importance of modelling the system. Previous results have shown that BPA mainly is degraded under aerobic conditions. Therefore the aerobic phase time in the BioDenitro process of the activated sludge system was increased from 50% to 70%. The hypothesis was that this would improve the biodegradation of BPA. Both the influent and the effluent concentrations of BPA in the experiment dropped significantly after increasing the aerobic time. From simulations with a growth-based biological/physical/chemical process model it was concluded that although the simulated effluent concentration of BPA was independent of the influent concentration at steady-state, the observed drop in effluent concentrations probably was caused by either a larger specific biomass to influent BPA ratio, improved biodegradation related to the increased aerobic phase time, or a combination of the two. Thereby it was not possibly to determine if the increase in aerobic phase time improved the biodegradation of BPA. The work underlines the importance of combining experimental results with modelling when interpreting results from biodegradation experiments with fluctuating influent concentrations of micro pollutants.

How uncertain is model-based prediction of copper loads in stormwater runoff?

In this paper, we conduct a systematic analysis of the uncertainty related with estimating the total load of pollution (copper) from a separate stormwater drainage system, conditioned on a specific combination of input data, a dynamic conceptual pollutant accumulation-washout model and measurements (runoff volumes and pollutant masses). We use the generalized likelihood uncertainty estimation (GLUE) methodology and generate posterior parameter distributions that result in model outputs encompassing a significant number of the highly variable measurements. Given the applied pollution accumulation-washout model and a total of 57 measurements during one month, the total predicted copper masses can be predicted within a range of +/-50% of the median value. The message is that this relatively large uncertainty should be acknowledged in connection with posting statements about micropollutant loads as estimated from dynamic models, even when calibrated with on-site concentration data.

Comparative uncertainty analysis of copper loads in stormwater systems using GLUE and grey-box modeling.

In this paper two attempts to assess the uncertainty involved with model predictions of copper loads from stormwater systems are made. In the first attempt, the GLUE methodology is applied to derive model parameter sets that result in model outputs encompassing a significant number of the measurements. In the second attempt the conceptual model is reformulated to a grey-box model followed by parameter estimation. Given data from an extensive measurement campaign, the two methods suggest that the output of the stormwater pollution model is associated with significant uncertainty. With the proposed model and input data, the GLUE analysis show that the total sampled copper mass can be predicted within a range of +/-50% of the median value (385 g), whereas the grey-box analysis showed a prediction uncertainty of less than +/-30%. Future work will clarify the pros and cons of the two methods and furthermore explore to what extent the estimation can be improved by modifying the underlying accumulation-washout model.

Integrated modelling of two xenobiotic organic compounds.

This paper presents a dynamic mathematical model that describes the fate and transport of two selected xenobiotic organic compounds (XOCs) in a simplified representation of an integrated urban wastewater system. A simulation study, where the xenobiotics bisphenol A and pyrene are used as reference compounds, is carried out. Sorption and specific biological degradation processes are integrated with standardised water process models to model the fate of both compounds. Simulated mass flows of the two compounds during one dry weather day and one wet weather day are compared for realistic influent flow rate and concentration profiles. The wet weather day induces resuspension of stored sediments, which increases the pollutant load on the downstream system. The potential of the model to elucidate important phenomena related to origin and fate of the model compounds is demonstrated.