化学物質のリスク評価のための水系暴露解析モデル

DOI Web Site 参考文献27件
  • 石川 百合子
    独立行政法人　産業技術総合研究所　化学物質リスク管理研究センター　生態リスク解析チーム
  • 東海 明宏
    独立行政法人　産業技術総合研究所　化学物質リスク管理研究センター　水圏環境評価チーム

書誌事項

タイトル別名
  • Watershed Model for Chemical Risk Assessment in Japan
  • カガク ブッシツ ノ リスク ヒョウカ ノ タメ ノ スイケイ バクロ カイセキ モデル

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説明

The AIST-SHANEL (AIST-Standardized Hydrology-based AssessmeNt tool for chemical Exposure Load) software was developed to estimate detailed and accurate exposure predictions of concentrations of chemicals at watershed areas for use within chemical risk assessment and management in Japan. This software covers the area more than 50% population in Japan. The main technical 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 distributions in exposure concentrations of chemicals with chemical emissions and river discharge. For estimating concentrations of chemicals in surface water on the 1km scale per day or month, 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 river discharge and the emission amounts of chemicals at each grid. The discharge 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. Required input parameters in this model were emission data, sewage removal rate and basic chemical-specific properties for the chemical of interest. Here this model was applied to Tama River and predicted the exposure concentrations of some representative chemicals, for example 4-nonylphenol, in terms of ecological risk and risk reduction measures. In this way, this model will enables us to conduct ecological risk assessment in aquatic systems and will be expected as chemical risk assessment tool.

収録刊行物

  • 資源と素材

    資源と素材 122 (9), 433-441, 2006

    一般社団法人 資源・素材学会

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