Evaluation of SimpleTreat 4.0: Simulations of pharmaceutical removal in wastewater treatment plant facilities

Lautz, L.S.; Struijs, Jeroen N.; Nolte, Tom M.; Breure, A.M.; Grinten, E. van der; Meent, Dik van de; Zelm, Rosalie van

doi:10.1016/j.chemosphere.2016.10.123

Cited by 40 publications

(23 citation statements)

References 64 publications

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“…Overall, the ability of standard process models to accurately predict the influence of specific parameters on REs across many chemicals and WWTPs remains to be proved. In a recent study on RE of pharmaceuticals, the predicted effluent concentrations from SimpleTreat were within a factor of 10 compared to actual measurements 18 .…”

Section: Introductionmentioning

confidence: 80%

“…For surfactants and fragrances, the search was carried out on ISI Web of Knowledge, PubMed, and Google Scholar without geographical and time constraints. For pharmaceuticals, the dataset gathered by Lautz, et al 18 was used as a basis and updated with an additional search in Web of Science, PubMed and Google Scholar. The search strings used for both searches are provided in the supplementary information (SI, S1).…”

Section: 1mentioning

confidence: 99%

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Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Douziech

Conesa

Benítez‐López

et al. 2018

Environ. Sci.: Processes Impacts

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Large variations in removal efficiencies (REs) of chemicals have been reported for monitoring studies of activated sludge wastewater treatment plants (WWTPs). In this work, we conducted a meta-analysis on REs (1539 data points) for a set of 209 chemicals consisting of fragrances, surfactants, and pharmaceuticals in order to assess the drivers of the variability relating to inherent properties of the chemicals and operational parameters of activated sludge WWTPs. For a reduced dataset (n = 542), we developed a mixed-effect model (meta-regression) to explore the observed variability in REs for the chemicals using three chemical specific factors and four WWTP-related parameters. The overall removal efficiency of the set of chemicals was 82.1% (95% CI 75.2-87.1%, N = 1539). Our model accounted for 17% of the total variability in REs, while the process-based model SimpleTreat did not perform better than the average of the measured REs. We identified that, after accounting for other factors potentially influencing RE, readily biodegradable compounds were better removed than non-readily biodegradable ones. Further, we showed that REs increased with increasing sludge retention times (SRTs), especially for non-readily biodegradable compounds. Finally, our model highlighted a decrease in RE with increasing K. The counterintuitive relationship to K stresses the need for a better understanding of electrochemical interactions influencing the RE of ionisable chemicals. In addition, we highlighted the need to improve the modelling of chemicals that undergo deconjugation when predicting RE. Our meta-analysis represents a first step in better explaining the observed variability in measured REs of chemicals. It can be of particular help to prioritize the improvements required in existing process-based models to predict removal efficiencies of chemicals in WWTPs.

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Section: Introductionmentioning

confidence: 80%

Section: 1mentioning

confidence: 99%

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Douziech

Conesa

Benítez‐López

et al. 2018

Environ. Sci.: Processes Impacts

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“…102,103 The SimpleTreat model 4.0 104 has been updated to include improved handling of ionizable organics, thus providing a tool for estimating chemical fate and behaviour in domestic sewage and activated sludge systems for down-thedrain chemicals, such as pharmaceuticals. 105…”

Section: Emission Datamentioning

confidence: 99%

Environmental fate and exposure models: advances and challenges in 21^st century chemical risk assessment

Guardo

Gouin

MacLeod

et al. 2018

Environ. Sci.: Processes Impacts

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Environmental fate and exposure models are a powerful means to integrate information on chemicals, their partitioning and degradation behaviour, the environmental scenario and the emissions in order to compile a picture of chemical distribution and fluxes in the multimedia environment. A 1995 pioneering book, resulting from a series of workshops among model developers and users, reported the main advantages and identified needs for research in the field of multimedia fate models. Considerable efforts were devoted to their improvement in the past 25 years and many aspects were refined; notably the inclusion of nanomaterials among the modelled substances, the development of models at different spatial and temporal scales, the estimation of chemical properties and emission data, the incorporation of additional environmental media and processes, the integration of sensitivity and uncertainty analysis in the simulations. However, some challenging issues remain and require research efforts and attention: the need of methods to estimate partition coefficients for polar and ionizable chemical in the environment, a better description of bioavailability in different environments as well as the requirement of injecting more ecological realism in exposure predictions to account for the diversity of ecosystem structures and functions in risk assessment. Finally, to transfer new scientific developments into the realm of regulatory risk assessment, we propose the formation of expert groups that compare, discuss and recommend model modifications and updates and help develop practical tools for risk assessment.

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“…The estimation program “STPWIN,” however, which is part of the USEPA's EPISuit, predicts the removal of a chemical in a typical activated sludge‐based WWTP and can, thus, be used for modeling the EoL phase of PhACs reaching WWTPs (i.e., for estimating API emissions released into the environment with WWTP effluents). Similarly, the model “SimpleTreat 4.0” provided by the Dutch National Institute for Public Health and the Environment (RIVM) estimates chemical emissions from WWTPs and has already been tested for modeling API removal at WWTPs (Lautz et al ). The actual problem is, thus, not the absence of models or approaches but the lack of an agreed‐upon and scientifically robust scheme within the LCA and pharmaceutical community, according to which API emissions during the use and EoL phase of the thousands of worldwide available human drugs can be determined.…”

Section: Recommendationsmentioning

confidence: 99%

Modeling pharmaceutical emissions and their toxicity‐related effects in life cycle assessment (LCA): A review

Emara¹,

Lehmann²,

Siegert³

et al. 2018

Integr Envir Assess & Manag

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Over the last few decades, worldwide detection of active pharmaceutical ingredients (APIs) in aquatic environments and the associated toxicological effects on wildlife and human health have become a matter of public and scientific debate. While life cycle assessment (LCA) and life cycle impact assessment (LCIA) models are increasingly used to assess the potential eco-and human-toxicological effects of chemical emissions, few studies have looked into the issue of modeling pharmaceutical emissions specifically and their toxicity-related effects in an LCA context. This paper reviews the state of the art to inventory and characterize API emissions in LCA with the goal to identify relevant gaps and challenges. A search for 208 environmentally relevant APIs in 2 life cycle inventory (LCI) databases revealed a meager representation of this group of chemicals. Similarly, the LCIA model USEtox was found to include characterization factors (CFs) for less than 60 APIs. First approaches to model API emissions in LCA were identified on the basis of an examination of 40 LCA case studies in the pharmaceutical sector and in the field of wastewater treatment. Moreover, CFs for 79 additional APIs, expressing their ecotoxicity and/or human toxicity potential, were gathered from literature. An analysis of the variability of API-CFs in different LCIA models showed a variation of about 2-3 orders of magnitude. Based on the review results, 3 main gaps in the modeling and characterization of API emissions in an LCA context were identified: (1) incomplete modeling of API flows and API emissions along the life cycle of human pharmaceuticals, especially during their use and end-of-life phase, (2) limited API coverage in existing LCIA toxicity models, and (3) missing pharma-specific impact pathways (e.g., endocrine disruption and antibiotic resistance) in existing LCIA models. Recommendations to tackle these gaps are provided, and priority action steps are discussed. Integr Environ Assess Manag 2019;15:6-18. C 2018 SETAC

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Evaluation of SimpleTreat 4.0: Simulations of pharmaceutical removal in wastewater treatment plant facilities

Cited by 40 publications

References 64 publications

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Environmental fate and exposure models: advances and challenges in 21^st century chemical risk assessment

Modeling pharmaceutical emissions and their toxicity‐related effects in life cycle assessment (LCA): A review

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Evaluation of SimpleTreat 4.0: Simulations of pharmaceutical removal in wastewater treatment plant facilities

Cited by 40 publications

References 64 publications

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Quantifying variability in removal efficiencies of chemicals in activated sludge wastewater treatment plants – a meta-analytical approach

Environmental fate and exposure models: advances and challenges in 21st century chemical risk assessment

Modeling pharmaceutical emissions and their toxicity‐related effects in life cycle assessment (LCA): A review

Contact Info

Product

Resources

About

Environmental fate and exposure models: advances and challenges in 21^st century chemical risk assessment