Data for developing metamodels to assess the fate, transport, and bioaccumulation of organic chemicals in rivers. Chemicals have log Kow ranging from 3 to 14, and rivers have mean annual discharges ranging from 1.09 to 3240 m3/s.

This dataset was developed to demonstrate how metamodels of high resolution, process-based models that simulate the fate, transport, and bioaccumulation of organic chemicals in rivers can be developed and applied for screening level exposure assessments. Chemicals of concern are assumed to be released from point sources (e.g., wastewater treatment works) and have log n-octanol/water partition coefficients (log Kow) ranging from 3 to 14. The rivers of concern have mean annual discharges ranging from 1.09 to 3240 m3/s. Five existing USEPA models are used to generate the required databases. The Chemical Transformation Simulator (CTS) and the High Throughput Stochastic Human Exposure and Dose Simulation Model (SHEDS-HT) are used to estimate the pchem properties and loading rates of the chemicals of interest, respectively. Using these data, the dissolved and total water concentrations, and total sediment concentrations for each chemical-loading combination in the rivers of interest are simulated using the Exposure Analysis Modeling System (EXAMS). Lastly, the Kow-based Aquatic BioAccumulation Model (KABAM) and the Bioaccumulation and Aquatic System Simulator (BASS) are used to estimate BCFs of periphyton and phytoplankton and BAFs of benthic invertebrates, zooplankton, and fish in the rivers of interest. Using these BCFs and BAFs and EXAMS dissolved water concentrations, expected whole-body concentrations of exposed fish and invertebrates are calculated for each chemical-loading-river combination. This dataset is associated with the following publication: Barber, C., K. Isaacs, and C. Stevens. Developing and applying metamodels of high resolution process-based simulations for high throughput exposure assessment of organic chemicals in riverine ecosystems. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 605606: 471-481, (2017).

[
  "020:00"
]

[
  "020:095"
]

[
  "https://doi.org/10.1016/j.scitotenv.2017.06.198"
]