ATSDR/MESL Exposure-Dose Reconstruction Program

ATSDR/MESL 暴露剂量重建计划

• 批准号: 8106441
• 负责人: MUSTAFA M ARAL
• 金额: $ 50万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2013-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8106441
• 关键词: ATSDR; MESL; Exposure; Dose; Reconstruction

Description (Provided by Applicant) A comprehensive study is proposed to aid in understanding the fate and transport processes that affect the migration of contaminants in the environment. The other goal is the development of innovative risk-based methodologies and computational tools, which can be used to simulate the migration patterns of contaminants in the environment. For this purpose, we propose the development of modular computational tools, which will include mechanistic models for surface water, groundwater, air and other engineered conduit pathways. The proposed modular models will be linked to risk-based exposure analysis models using probability and possibility theory. The mechanistic modeling will be used to understand and link the effect of environmental factors on spatial and temporal spread of contaminants in the environment. The risk-based exposure analysis will be used to quantify the health risk through standard exposure pathways, and will be based on variability and uncertainty in input parameters as well as uncertainty in conceptual models developed. The goal is to provide managers and health scientists, risk-based modular simulation tools to evaluate contaminant concentration levels in the environment at the human contact level so that preventative measures and decisions can be tied to transparent and reproducible data.

描述（申请人提供） 建议进行一项全面的研究，以帮助了解影响污染物在环境中迁移的归宿和迁移过程。另一个目标是开发创新的基于风险的方法和计算工具，可用于模拟环境中污染物的迁移模式。为此，我们建议开发模块化计算工具，其中包括地表水，地下水，空气和其他工程管道路径的机械模型。拟议的模块模型将与使用概率和可能性理论的基于风险的风险分析模型相联系。机械建模将用于理解和链接的影响，环境因素对污染物在环境中的空间和时间的传播。基于风险的接触分析将用于通过标准接触途径量化健康风险，并将基于输入参数的可变性和不确定性以及所开发的概念模型的不确定性。其目标是为管理人员和健康科学家提供基于风险的模块化模拟工具，以评估人类接触水平的环境中的污染物浓度水平，以便将预防措施和决策与透明和可重复的数据联系起来。