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.

项目总结