# Center for Environmental and Health Effects of PFAS

> **NIH NIH P42** · NORTH CAROLINA STATE UNIVERSITY RALEIGH · 2022 · $50,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10477630
- **Project number:** 3P42ES031009-03S1
- **Recipient organization:** NORTH CAROLINA STATE UNIVERSITY RALEIGH
- **Principal Investigator:** Carolyn J. Mattingly
- **Activity code:** P42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $50,000
- **Award type:** 3
- **Project period:** 2022-02-24 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10477630

## Citation

> US National Institutes of Health, RePORTER application 10477630, Center for Environmental and Health Effects of PFAS (3P42ES031009-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10477630. Licensed CC0.

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