Literature DB >> 27156081

iSTREEM(®) : An approach for broad-scale in-stream exposure assessment of "down-the-drain" chemicals.

Katherine E Kapo1, Paul C DeLeo2, Raghu Vamshi3, Christopher M Holmes3, Darci Ferrer2, Scott D Dyer4, Xinhao Wang5, Charlotte White-Hull4,6.   

Abstract

The "in-stream exposure model" iSTREEM(®) , a Web-based model made freely available to the public by the American Cleaning Institute, provides a means to estimate concentrations of "down-the-drain" chemicals in effluent, receiving waters, and drinking water intakes across national and regional scales under mean annual and low-flow conditions. We provide an overview of the evolution and utility of the iSTREEM model as a screening-level risk assessment tool relevant for down-the-drain products. The spatial nature of the model, integrating point locations of facilities along a hydrologic network, provides a powerful framework to assess environmental exposure and risk in a spatial context. A case study compared national distributions of modeled concentrations of the fragrance 1,3,4,6,7,8-Hexahydro-4,6,6,7,8,8,-hexamethylcyclopenta-γ-2-benzopyran (HHCB) and the insect repellent N,N-Diethyl-m-toluamide (DEET) to available monitoring data at comparable flow conditions. The iSTREEM low-flow model results yielded a conservative distribution of values, whereas the mean-flow model results more closely resembled the concentration distribution of monitoring data. We demonstrate how model results can be used to construct a conservative estimation of the distribution of chemical concentrations for effluents and streams leading to the derivation of a predicted environmental concentration (PEC) using the high end of the concentration distribution (e.g., 90th percentile). Data requirements, assumptions, and applications of iSTREEM are discussed in the context of other down-the-drain modeling approaches to enhance understanding of comparative advantages and uncertainties for prospective users interested in exposure modeling for ecological risk assessment. Integr Environ Assess Manag 2016;12:782-792.
© 2016 SETAC. © 2016 SETAC.

Entities:  

Keywords:  Effluent; Exposure model; Geographic information systems; Risk assessment; Wastewater

Mesh:

Substances:

Year:  2016        PMID: 27156081     DOI: 10.1002/ieam.1793

Source DB:  PubMed          Journal:  Integr Environ Assess Manag        ISSN: 1551-3777            Impact factor:   2.992


  5 in total

1.  Assessment of Non-Occupational 1,4-Dioxane Exposure Pathways from Drinking Water and Product Use.

Authors:  Daniel Dawson; Hunter Fisher; Abigail E Noble; Qingyu Meng; Anne Cooper Doherty; Yuko Sakano; Daniel Vallero; Rogelio Tornero-Velez; Elaine A Cohen Hubal
Journal:  Environ Sci Technol       Date:  2022-04-05       Impact factor: 11.357

2.  Ecological Risk Assessment of Pharmaceuticals in the Transboundary Vecht River (Germany and The Netherlands).

Authors:  Daniel J Duarte; Gunnar Niebaum; Volker Lämmchen; Eri van Heijnsbergen; Rik Oldenkamp; Lucia Hernández-Leal; Heike Schmitt; Ad M J Ragas; Jörg Klasmeier
Journal:  Environ Toxicol Chem       Date:  2021-05-28       Impact factor: 4.218

Review 3.  A Review of the Environmental Fate and Effects of Acesulfame-Potassium.

Authors:  Kerry Belton; Edward Schaefer; Patrick D Guiney
Journal:  Integr Environ Assess Manag       Date:  2020-04-10       Impact factor: 2.992

4.  A High-Resolution Spatial Model to Predict Exposure to Pharmaceuticals in European Surface Waters: ePiE.

Authors:  Rik Oldenkamp; Selwyn Hoeks; Mirza Čengić; Valerio Barbarossa; Emily E Burns; Alistair B A Boxall; Ad M J Ragas
Journal:  Environ Sci Technol       Date:  2018-10-22       Impact factor: 9.028

5.  Validation of AIST-SHANEL Model Based on Spatiotemporally Extensive Monitoring Data of Linear Alkylbenzene Sulfonate in Japan: Toward a Better Strategy on Deriving Predicted Environmental Concentrations.

Authors:  Tohru Nishioka; Yuichi Iwasaki; Yuriko Ishikawa; Masayuki Yamane; Osamu Morita; Hiroshi Honda
Journal:  Integr Environ Assess Manag       Date:  2019-08-09       Impact factor: 2.992

  5 in total

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