L10: Exposure Modeling in Practice: Ventilation Performance and Model Selection for Bayesian Decision Analysis

Ernest N. Morial New Orleans Convention Center 

Exposure modeling remains underutilized in occupational exposure assessment for a variety of reasons including uncertainty in parameter estimation and lack of guidance on model selection. This session presents complementary doctoral dissertation research from the University of Minnesota and Johns Hopkins University designed to address these gaps through improved ventilation parameter estimation, model validation studies, and a structured framework for selecting appropriate models in practice and for Bayesian Decision Analysis (BDA). The session will begin by demonstrating the importance of local exhaust ventilation capture efficiency for exposure control and by quantifying capture efficiency under controlled experimental conditions. Results from controlled chamber experiments quantifying how capture velocity, cross draft velocity, hood angle, and source distance influence local exhaust ventilation capture efficiency will be presented. These findings provide new insight into how operating conditions affect contaminant capture at the source and help explain why ventilation systems that meet ACGIH design criteria may still fail to adequately control exposures in real workplaces. Results from controlled chamber experiments evaluating contaminant generation rates and local exhaust ventilation (LEV) capture efficiencies will be presented and translated into practical recommendations for applying variations of the Well-Mixed Room (WMR) and Near-Field/Far-Field (NF/FF) models. These findings help clarify how engineering controls and contaminant release characteristics inform model inputs and predicted exposure concentrations. This session will also demonstrate how validated modeling outputs can be incorporated as informed priors within a BDA framework to strengthen exposure judgments when monitoring data are limited. Attendees will be introduced to the newly developed Exposure Decision Dashboard tool for constructing modeling-based priors and integrating them with professional judgment and monitoring data. This presentation provides actionable guidance for improved evaluation of engineering controls and exposure assessment techniques.

Upon completion, the participant will be able to:
• Evaluate how operating conditions such as capture velocity, cross draft velocity, hood angle, and source distance influence local exhaust ventilation capture efficiency.
• Interpret how variations in local exhaust ventilation performance affect contaminant capture and exposure control.
• Use exposure models in exposure assessment through interpreting performance studies of the models.
• Select models that are appropriate to their given exposure scenario under conditions of uncertainty in parameters.
• Improve exposure judgments by incorporating exposure modeling into Bayesian Decision Analysis (BDA) through the use of the newly developed Occupational Exposure Decision Dashboard tool. 

Engineering Controls and Ventilation

Exposure Assessment

Education and training

Exposure Assessment

Ventilation

In-person

Student and Early Career Professional Track

Professional