Center for Environmental and Health Effects of PFAS

项目总结 意义：在过去的几十年里，我们对环境的不利影响的理解 人类健康的质量已经大幅提高，需要监测和预测的工具 环境完整性。因此，已经做出努力，有效地监测和调节潜力 环境压力源，以改善环境健康，如环保局的环境质量指数。 当前的环境建模工具试图对地理空间数据进行建模以预测环境质量(EQ)， 包括汇总不同环境领域的监测和社会人口数据。 创新性/独特性：然而，当前的建模工具不能提供全面和动态的 对环境和人类健康进行建模预测。环境完整性框架(EIF)寻求 来填补这些空白。拟议的研究旨在建立一个全面的框架，提供可更新的 通过整合几个环境领域和包括其他领域的EQ和人类健康预测 全氟辛烷磺酸等环境污染物。监督机器学习(ML)或“白盒”方法将 生成可解释的情商预测，并可通过交互式仪表板访问广泛的用户群。一个 将成立由社区利益相关者和SRP研究人员组成的EIF发展咨询小组，以 进一步努力确定风险社区最关心的问题的优先顺序，并保持 在EIF发展过程中的信任和透明度。这一广泛的用户基础将包括研究人员和风险 在现场监测情商的评估员。此外，我们还寻求让年轻人参与EIF的高危社区 研讨会中的仪表板，以促进环境健康方面的科学素养和交流 问题。 我们的目标是： 1.融合先进和创新的集成机器学习(ML)技术 集成和可互操作的建模环境完整性预测。使用多个ML 集成方法中的模型为组合环境生成特定领域的EQ分数 高危网站的完整性评分。全氟辛烷磺酸浓度与污染物的关系 不同领域的暴露水平将被整合到综合评分和 建模框架(EIF)。 2.优先进行人类健康风险成果和方法的翻译传播 在NIEHS翻译研究框架内。关注沟通和社区 通过与SRP研究人员和社区利益相关者的合作，参与EIF的部分。 通过向处于风险中的社区通报PFAS和 与环境质量有关的问题在其当地社区。

PROJECT SUMMARY Significance: Over the past several decades, our understanding of the adverse effects of environmental quality on human health has risen substantially, and there is a need for tools that monitor and predict environmental integrity. As a result, efforts have been made to effectively monitor and regulate potential environmental stressors to improve environmental health, such as the EPA’s Environmental Quality Index. Current environmental modeling tools try to model geospatial data to predict environmental quality (EQ), including aggregating different environmental domain monitoring and sociodemographic data. Innovation/Uniqueness: However, current modeling tools do not provide comprehensive and dynamic modeling predictions for environmental and human health. The Environmental Integrity Framework (EIF) seeks to fill these gaps. The proposed research aims to build a comprehensive framework that provides updateable EQ and human health predictions by integrating several environmental domains and including other environmental contaminants such as PFAS. Supervised machine learning (ML) or “white-box” methods will generate interpretable EQ predictions and be accessible via interactive dashboards to a broad user base. An EIF development Advisory group composed of community stakeholders and SRP researchers will be formed to further our efforts to prioritize what at-risk communities are concerned about the most and uphold a level of trust and transparency during the development of the EIF. This wide userbase will include researchers and risk assessors monitoring EQ at a site. Also, we seek to engage youth in at-risk communities with the EIF dashboard in workshops to promote science literacy and communication regarding environmental health issues. Our aims are to: 1. Incorporate advanced and innovative ensemble machine learning (ML) techniques for integrative and interoperable modeling Environmental Integrity Predictions. Employ several ML models in an ensemble approach to generate domain-specific EQ scores for a combined Environmental Integrity Score for at-risk sites. The relationships of the PFAS concentrations and contaminant exposure levels across the different domains will be integrated into a comprehensive scoring and modeling framework (EIF). 2. Prioritize Translational Dissemination of human health risks outcomes and methodologies within the NIEHS Translational Research Framework. Focus on the communication and community engagement portion of the EIF via collaborations with SRP researchers and community stakeholders. Focus on establishing trust and transparency by informing at-risk communities on PFAS and environmental quality-related issues in their local communities.