Center for Environmental and Health Effects of PFAS

PFAS 环境与健康影响中心

• 批准号: 10477630
• 负责人: CAROLYN J MATTINGLY
• 金额: $ 5万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-24 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10477630
• 关键词: Adverse effects; Collaborations; Communication; Communities; Data; Development; Educational workshop; Environmental Health; Environmental Monitoring; Environmental Pollution; Health; Human; Machine Learning; Methodology; Methods; Modeling; Monitor; National Institute of Environmental Health Sciences; Outcome; Research; Research Personnel; Risk; Science; Signal Recognition Particle; Site; Techniques; Translational Research; Trust; Update; Youth; base; community engagement; dashboard; environmental stressor; improved; indexing; innovation; interoperability; literacy; machine learning model; predictive modeling; sociodemographics; supervised learning; tool

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.

项目概要 意义：在过去的几十年里，我们对环境不利影响的认识不断加深。 人类健康质量大幅提高，需要监测和预测的工具 环境完整性。因此，已做出努力，有效监测和监管潜在的 改善环境健康的环境压力源，例如 EPA 的环境质量指数。 当前的环境建模工具尝试对地理空间数据进行建模来预测环境质量（EQ）， 包括汇总不同环境领域的监测和社会人口统计数据。 创新/独特性：然而，当前的建模工具不提供全面和动态的 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 通过整合多个环境领域并包括其他领域来进行情商和人类健康预测 PFAS 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 EQ 预测，并可通过交互式仪表板供广泛的用户群访问。一个 由社区利益相关者和SRP研究人员组成的EIF发展咨询小组将成立 我们进一步努力优先考虑高风险社区最关心的问题，并维持一定程度的关注 EIF 开发过程中的信任和透明度。这个广泛的用户群将包括研究人员和风险 评估员在现场监控情商。此外，我们还寻求通过 EIF 吸引高危社区的年轻人参与 讲习班中的仪表板，以促进有关环境健康的科学素养和交流 问题。 我们的目标是： 1. 结合先进和创新的集成机器学习 (ML) 技术 集成且可互操作的环境完整性预测建模。雇用多个 ML 采用集成方法的模型为组合环境生成特定领域的 EQ 分数 有风险站点的完整性评分。 PFAS浓度与污染物的关系 不同领域的暴露水平将被纳入综合评分和 建模框架（EIF）。 2. 优先考虑人类健康风险结果和方法的转化传播 在 NIEHS 转化研究框架内。注重沟通和社区 通过与 SRP 研究人员和社区利益相关者的合作，参与 EIF 的参与部分。 通过向高危社区通报 PFAS 和 当地社区的环境质量相关问题。