Perspectives on modelling micropollutants in wastewater treatment plants

Clouzot, Ludiwine; Choubert, J.M.; Cloutier, Frédéric; Goel, Rajeev; Love, Nancy G.; Melcer, Henryk; Ort, Christoph; Patureau, Dominique; Plósz, Benedek G.; Pomies, Maxime; Vanrolleghem, Peter A.

doi:10.2166/wst.2013.272

Cited by 32 publications

(17 citation statements)

References 4 publications

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“…In a review on STP models for organic xenobiotics, Clouzot et al () highlighted that different modeling approaches are preferable for research, practice (plant design) and regulatory purposes. Multimedia box models, such as SimpleTreat and the STP model (Bock et al ) have been the most widely applied in regulatory risk assessment, following the development of the fugacity concept.…”

Section: Discussionmentioning

confidence: 99%

Evolution of the sewage treatment plant model SimpleTreat: Use of realistic biodegradability tests in probabilistic model simulations

Franco

Struijs

Gouin

et al. 2013

Integr Environ Assess Manag

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Given the large number of chemicals under regulatory scrutiny, models play a crucial role in the screening phase of the environmental risk assessment. The sewage treatment plant (STP) model SimpleTreat 3.1 is routinely applied as part of the European Union System for the Evaluation of Substances to estimate the fate and elimination of organic chemicals discharged via sewage. SimpleTreat estimates tend to be conservative and therefore only useful for lower-tier assessments. A probabilistic version of SimpleTreat was built on the updated version of the model (SimpleTreat 3.2, presented in a parallel article in this issue), embracing likeliest as well as worst-case conditions in a statistically robust way. Probabilistic parameters representing the variability of sewage characteristics, STP design, and operational parameters were based on actual STP conditions for activated sludge plants in Europe. An evaluation study was carried out for 4 chemicals with distinct sorption and biodegradability profiles: tonalide, triclosan, trimethoprim, and linear alkylbenzene sulfonate. Simulations incorporated information on biodegradability simulation studies with activated sludge (OECD 314B and OECD 303A tests). Good agreement for both median values and variability ranges was observed between model estimates and monitoring data. The uncertainty analysis highlighted the importance of refined data on partitioning and biodegradability in activated sludge to achieve realistic estimates. The study indicates that the best strategy to refine the exposure assessment of down-the-drain chemicals is by integrating higher-tier laboratory data with probabilistic STP simulations and, if possible, by comparing them with monitoring data for validation.

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Section: Discussionmentioning

confidence: 99%

Evolution of the sewage treatment plant model SimpleTreat: Use of realistic biodegradability tests in probabilistic model simulations

Franco

Struijs

Gouin

et al. 2013

Integr Environ Assess Manag

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“… Should sulphur compounds be considered in performance evaluation and in what way?  Do we consider ecotoxicity to evaluate micropollutant removal (Clouzot et al, 2013)?…”

Section: New Evaluation Criteriamentioning

confidence: 99%

“…competition for iron), which of the (bio)chemical S-related oxido-reduction processes need to be included and how is the competition with methanogens to be modelled?  Do the traditional micropollutant fate models (volatilization, sorption, photolysis, biodegradation) that only require standard chemical properties (Henry coefficients, Kow, …) suffice or are dedicated models necessary for each micropollutant (Clouzot et al, 2013)?…”

Section: New Chemical and Biochemical Transformationsmentioning

confidence: 99%

Population balance models: a useful complementary modelling framework for future WWTP modelling

Torfs

Ducoste

Vanrolleghem

et al. 2014

Water Science and Technology

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Population balance models (PBMs) represent a powerful modelling framework for the description of the dynamics of properties that are characterised by distributions. This distribution of properties under transient conditions has been demonstrated in many chemical engineering applications. Modelling efforts of several current and future unit processes in wastewater treatment plants could potentially benefit from this framework, especially when distributed dynamics have a significant impact on the overall unit process performance. In these cases, current models that rely on average properties cannot sufficiently capture the true behaviour and even lead to completely wrong conclusions. Examples of distributed properties are bubble size, floc size, crystal size or granule size. In these cases, PBMs can be used to develop new knowledge that can be embedded in our current models to improve their predictive capability. Hence, PBMs should be regarded as a complementary modelling framework to biokinetic models. This paper provides an overview of current applications, future potential and limitations of PBMs in the field of wastewater treatment modelling, thereby looking over the fence to other scientific disciplines.

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“…Ternes 1998) have promoted a substantial amount of research regarding the extension of ASMs to describe micropollutants (Clouzot et al 2013;Plósz et al 2013b). By micropollutants we mean compounds such as pharmaceuticals, personal care products, and biocides which are found in the environment in low concentrations (μg/L or ng/L).…”

Section: Introductionmentioning

confidence: 99%

Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies

Snip

Flores-Alsina

Aymerich

et al. 2016

Science of The Total Environment

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The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2-4h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The 'traditional' variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities.

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Perspectives on modelling micropollutants in wastewater treatment plants

Cited by 32 publications

References 4 publications

Evolution of the sewage treatment plant model SimpleTreat: Use of realistic biodegradability tests in probabilistic model simulations

Evolution of the sewage treatment plant model SimpleTreat: Use of realistic biodegradability tests in probabilistic model simulations

Population balance models: a useful complementary modelling framework for future WWTP modelling

Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies

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