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.

A diverse array of environmental data from Ohio were placed into a geographical information system (GIS). This GIS allowed for the investigation of approaches and paradigms currently advocated for ecological risk assessment. The paradigm of chemical mixture additivity was investigated in this project. Toxic units (toxic unit ) concentration of a chemical in an organism/chemical concentration causing a specified effect) for 12 organic and 11 metal contaminants were calculated from 2878 fish samples collected at 1010 sites throughout the state of Ohio. Additive analysis of TUs for organic chemicals based on regulatory-based protective limits (toxicity reference value ) USEPA water quality criterion*bioconcentration factor) overpredicted adverse effects to individual fish and fish communities. However, addition of organic chemical molar units did not overpredict adverse effects, thus, supporting the concept of baseline toxicity. Molar units of organic chemicals with diverse modes of action may be added together, so long as they are at concentrations below levels deemed protective of most species (e.g., 95%, water quality criterion). Analysis of metal TUs benchmarked against regulatorybased limits overpredicted adverse effects, whereas benchmark concentrations from population response (survival, growth, reproduction) data from the literature and Ohio reference site fish community responses corresponded better to field observations. Of the factors analyzed, habitat quality is the best single predictor of fish community integrity in Ohio, not body burdens of metals or organic chemicals.

Previous studies have shown that significant environmental changes are the result of human activities such as urbanization occurring at the spatial scale of landscapes. The challenge faced by many planners today is how to understand such relationships in order to support integrated watershed planning and management. Although many mathematical models have been developed to simulate the chemical transport process in a river, few are actually used in watershed assessment and management. Recently, incorporating analytical models into GIS platforms has emerged as a promising research area attracting planners and other resource managers. In this paper we present a GIS-based river water quality model (GIS-ROUT) to predict chemistry changes in river water as a result of sewage discharge changes in a watershed. Integration of spatial data, GIS, and analytical models in GIS-ROUT makes it possible to examine the dynamic linkages between water quality and human activities in a watershed. Furthermore, the user-friendly interface of the model allows its users to concentrate on the planning issues, such as examining the “What if…” questions related to different development scenarios. The study not only contributes to the application of GIS and water quality models in planning, but it also provides a comprehensive view of the watershed that can help government agencies and other stakeholders to make informed decisions.

Abstract-The relationship of multiple factors, such as instream habitat, drainage area, gradient, cumulative effluent, conventional pollutants, and chemical mixtures, to fish communities was explored at the subbasin, basin, and state level within the state of Ohio, USA. Two approaches were used: bottom-up, which focused on subbasin-and basin-level relationships within the Great Miami River, Ohio, and top-down, focusing on relationships across the entire state. Data were provided by the Ohio Environmental Protection Agency and the U.S. Environmental Protection Agency. These data were integrated via a geographical information system. Multiple linear regression was used to determine the strength of stressor-response relationships. The greatest amount of variation of the index of biotic integrity (IBI) and selected metrics was addressed at the subbasin level, followed by the basin and state level, respectively. Overall, habitat factors were the best predictors and positively related to the IBI and number of fish species. Chemical factors, such as cumulative effluent, metals, ammonia, and biochemical oxygen demand, were consistently observed as negative, moderating factors for IBI and fish taxa richness and were the best predictors of the percent of fish observed with deformities, fin erosions, lesions, and tumors.