# Bringing Modern Data Science Tools to Bear on Environmental Mixtures

> **NIH NIH R01** · RICE UNIVERSITY · 2020 · $1

## Abstract

Project Summary: Bringing Modern Data Science Tools to Bear on Environmental Mixtures
Environmental exposures often cumulate in particular geographies, and the nature of the complex mixtures
that characterize these exposures remains understudied. In addition, adverse environmental exposures often
occur in communities facing multiple social stressors such as deteriorating housing, inadequate access to
health care, poor schools, high unemployment, crime, and poverty – all of which may compound the effects of
environmental exposures.
Our central objective is to develop new data architecture, statistical, and machine learning methods to
assess how exposure to environmental mixtures shapes educational outcomes in the presence or
absence of social stress. We focus on air pollution mixtures, childhood lead exposure, and social stressors.
We will implement our proposed work in North Carolina (NC), a state characterized by diverse environmental
features, industrial activities, and airsheds typified by varying pollution emission sources and resulting pollutant
mixtures.
To accomplish this central objective, we will first develop, document, and disseminate methods for building
space-time environmental and social data architectures. We will implement this for all of NC, incorporating data
on air pollution, lead exposure risk, and social exposures from 1990-2015+ (dataset 1). Second, we will refine
methods for linking unrelated datasets to build a space-time child movement and outcome data architecture
(dataset 2). Third, we will connect exposures (dataset 1) and outcomes (dataset 2) data via shared geography
and temporality into a single, comprehensive geodatabase. Fourth, we will implement increasingly complex
methods to assess the effect of environmental mixtures in the presence or absence of social stressors on early
childhood educational outcomes. We will document and disseminate all of the underlying methodological work
via public website.
The proposed work leverages a rich array of data resources already available to the investigators (with some
significantly post-processed) and allows tracking of children across space and time. Our team brings tools from
modern data science (hierarchical Bayesian methods with variable selection, spatial point process models,
machine learning) to bear on the critical question of how environmental mixtures shape child outcomes directly
and differentially in the presence of social stress.

## Key facts

- **NIH application ID:** 9882999
- **Project number:** 5R01ES028819-03
- **Recipient organization:** RICE UNIVERSITY
- **Principal Investigator:** Marie Lynn Miranda
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2018-02-01 → 2020-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9882999

## Citation

> US National Institutes of Health, RePORTER application 9882999, Bringing Modern Data Science Tools to Bear on Environmental Mixtures (5R01ES028819-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9882999. Licensed CC0.

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