# Exposure Characterization and Modeling Core

> **NIH NIH P30** · JOHNS HOPKINS UNIVERSITY · 2022 · $205,668

## Abstract

A. ABSTRACT
The Exposure Characterization and Modeling Core (ECMC) is critical to the ability of the Center for
Community Health: Addressing Regional Maryland Environmental Determinants of Disease (CHARMED) to
achieve its central goals of fostering innovative interdisciplinary research in environmental health and translating
knowledge into action to address community environmental challenges in the Maryland region. The Specific
Aims of the ECMC are to (1) Provide resources and support for planned and ongoing research projects by Center
investigators, including pilot projects and projects related to Center initiatives; (2) Provide expert consultation
and guidance for CHARMED investigators and community members related to measurement needs, statistical
data analysis, and results interpretation; (3) Develop new technologies, modeling frameworks, and statistical
methodologies for Center needs. The ECMC will be the central source for exposure assessment support for
ongoing NIEHS and Center Pilot projects. ECMC experts will also serve as scientific mentors of the Community
Engagement Scholars and participate in community-engaged science. The ECMC will lower the financial and
technical barriers for translation of CHARMED research findings, and this important support would not be
available were it not for the Center grant mechanism.

## Key facts

- **NIH application ID:** 10394478
- **Project number:** 1P30ES032756-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Gurumurthy Ramachandran
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $205,668
- **Award type:** 1
- **Project period:** 2022-06-24 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10394478

## Citation

> US National Institutes of Health, RePORTER application 10394478, Exposure Characterization and Modeling Core (1P30ES032756-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10394478. Licensed CC0.

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