# Flexible Bayesian Hierarchical Models for Estimating Inhalation Exposures

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $371,209

## Abstract

Project Summary/Abstract
 We propose to develop innovative statistical tools for melding exposure models and observational data aris-
ing from measurements of concentrations in controlled chamber conditions. As a first step, we will construct
a rich dataset of exposure scenarios in laboratory exposure chambers and real workplace settings, contain-
ing data on exposure determinants such as contaminant generation and ventilation rates and exposure mea-
surements. We will develop a comprehensive and computationally feasible Bayesian statistical framework for
melding the physical exposure models with experimental data from the workplace to effectively account for the
sources of uncertainty and produce reliable statistical inference (estimation and predictions). We will employ a
Bayesian framework to validate physical models from monitoring data. Our framework will also include formal
statistical measures for validating models with observed field data. We do so by assessing how adequately the
models capture features and patterns in the monitoring data, applying sensitivity analysis to the choice of priors,
and choosing or selecting a model among a set of competing models. We will also develop and disseminate a
user-friendly statistical software package that will enable researchers to implement the proposed methods for a
wide variety of physical models to analyze their data in a seamless and convenient manner. Upon successful
completion of the project, our developments will allow researchers and exposure managers to systematically
evaluate retrospective exposure, to predict current and future exposure in the absence of the working process
or operation, and to estimate exposure with only a small number of air samples with possibly high variability.
With only a few monitoring data points, our Bayesian melding framework will provide more precise estimates of
exposure than monitoring. With advances in computational methods and inexpensive software implementation,
we purport to exalt formal modeling to an indispensable position in the exposure assessors' armory.

## Key facts

- **NIH application ID:** 10060746
- **Project number:** 5R01ES030210-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Sudipto Banerjee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $371,209
- **Award type:** 5
- **Project period:** 2018-12-15 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10060746

## Citation

> US National Institutes of Health, RePORTER application 10060746, Flexible Bayesian Hierarchical Models for Estimating Inhalation Exposures (5R01ES030210-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10060746. Licensed CC0.

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