# Evaluating Cumulative Environmental Exposure to Metals and Non-metals and Community-level Health Using Geospatial Modeling and Personal Exposure Assessment

> **NIH NIH P50** · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · 2024 · $280,102

## Abstract

Project Summary
Communities that practice traditional land-based cultures are intimately connected to the environment due to
cultural, spiritual, and traditional practices. While our Center’s previous work demonstrated varying degrees of
metal exposure among members of Navajo Nation, Cheyenne River Sioux (CRST) and Apsaálooke (Crow),
single exposure pathway (e.g. water) investigations were not sufficient to explain individual-level exposure and
adverse health outcomes. Moreover, there remains limited knowledge on other chemical exposure sources
prominent on Tribal lands, such as trash burning, that may lead to accumulation of microplastics, volatile, and
semi-volatile compounds in the environment and lead human exposures. This research project will address the
challenge of integrating multiple exposure routes for Native communities through modeling combined
environmental exposure potential. We will adapt an existing GIS-based multi-criteria decision analysis
approach that can integrate air, water, and soil pathways previously used by our group. Our modeling
framework enables integration of novel soil, water, air, and plant data on microplastics and other chemicals
produced by low-temperature trashing combustion. This research approach is innovative because it
investigates combined exposures on tribal lands, including microplastics and combusted plastic by-products
against a background of high metal exposures. We will (1) develop predictive and validate combined
environmental exposure models based on GIS-based multi-criteria decision analysis, which considers chemical
sources, topography, infrastructure, and land-use practices; (2) ground truth model predictions through
measurement of combined exposures for people and cultural resources (such as livestock and plants) using
silicone wristbands, human biomonitoring, and immunology studies; and (3) collect community-scale health
survey data to begin assessing exposure :disease relationships (e.g., cancer, autoimmunity, and
cardiovascular disease) in collaboration with RP3. SA1 will generate the first combined environmental
exposure spatial products for Apsaálooke (Crow) and Crow and CRST reservation lands and a refined product
for NN. These spatial products will support environmental health research among Tribal communities and
provide policy-makers with critical information to address Tribal health disparities. The studies proposed in SA2
provide important ground truthing of the GIS-based model through individual-level chemical and immune
marker measurements. This will inform policy makers about the scope and extent of combined environmental
exposures in their communities and allow us to consider the contributions of combined chemical exposures to
associations with metals that we have observed in participant samples from these communities. The health
survey data will provide data to validate and substantiate health disparities at individual community scale for
tribal community partners, and at a sca...

## Key facts

- **NIH application ID:** 10808165
- **Project number:** 5P50MD015706-10
- **Recipient organization:** UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
- **Principal Investigator:** Joseph Hamilton Hoover
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $280,102
- **Award type:** 5
- **Project period:** 2015-08-01 → 2026-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10808165

## Citation

> US National Institutes of Health, RePORTER application 10808165, Evaluating Cumulative Environmental Exposure to Metals and Non-metals and Community-level Health Using Geospatial Modeling and Personal Exposure Assessment (5P50MD015706-10). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10808165. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*