PH16 Ecological Risk Assessment and Landscape Ecological Modeling
Exhibit Hall
8:00 AM - Thursday
(PH189) Development of the watershed model AIST-SHANEL for estimating chemical concentrations in Japan.
Ishikawa, Y1, Tokai, A1, Nakanishi, J1, 1 National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, JAPAN
ABSTRACT- The objective of the AIST-SHANEL model is to develop detailed and accurate exposure predictions of concentrations of chemicals at watershed area level for use within chemical risk assessment and risk management in Japan. The main focus is on the temporal and spatial fate of chemicals in surface water, together with emissions from point sources of various industry and sewage treatment plants and non-point sources of households and agricultural land. Therefore, it is necessary to account for spatial and temporal variability in chemical emissions, river flows and landscape characteristics. For estimating concentrations of chemicals in surface water on the 1km scale per day, a given watershed area was segmented into 1km grids, including 3 compartments of surface water, sediment, surface soil. The exposure concentrations in the watershed area were predicted based on the rate of flow and the emission amounts of chemicals at each grid. The rate of flow was estimated from precipitation amounts in the watershed area. The emission amounts of chemicals based on the PRTR in Japan were allocated to 1km grids within the watershed area using industrial manufacture data, sewerage area data and land use data. The attenuation mechanisms included advection, diffusion, sorption/desorption, settling and resuspension of suspended solids, diffusive exchange between compartments, and biodegradation. Input parameters included a description of each environmental media, emission rates, and chemical-specific properties and degradation rates. This watershed model was applied to Tama River, one of the urban rivers in Tokyo, and predicted the exposure concentrations of some representative chemicals, for example 4-nonylphenol. The predicted concentrations fell within one order of magnitude of measurement data. This model will be useful and user-friendly for chemical risk assessment tool.
Key words: exposure concentrations, watershed model, emission amounts of chemicals, chemical risk assessment