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Using Variations of the Well-Mixed Room (WMR) Model to Assess Chemical Exposures

Abstract No:

1722 

Abstract Type:

Student Poster 

Authors:

K Babik1, G Ramachandran2

Institutions:

1Johns Hopkins School of Public Health, Baltimore, MD, 2Johns Hopkins, Baltimore, MD

Presenter:

Kelsey Babik, MPH  
Johns Hopkins School of Public Health

Faculty Advisor:

Gurumurthy Ramachandran, PhD, CIH  
Johns Hopkins

Description:

This project focuses on improving current predictive occupational inhalation exposure models by adding variables for exposure controls in order to predict exposure to various chemicals in workplace scenarios. It is a first in a series of projects with the primary goals of developing better predictive exposure models to decrease uncertainty in exposure assessments and to expand use of the models to multiple industries and workplace scenarios using aerosols.

Situation/Problem:

Models for predicting inhalation exposures in occupational settings are critical for comprehensive exposure assessments as measuring every exposure may not be feasible. However, most models such as the well-mixed room (WMR) and near field-far field (NF-FF) models are not widely used for reasons including a perceived lack of "validation," a woeful lack of guidance on model use in real-world workplace scenarios, concerns about models accounting for all relevant variables (structural uncertainty), poor knowledge of values of model input variables (parameter uncertainty), and unknown precision in estimates. Furthermore, predictive mathematical inhalation models in use are incredibly simplistic - focused on predicting occupational exposures to only gases and vapors and lacking variables accounting for exposure controls (e.g. local exhaust ventilation and recirculating air).

Methods:

A highly-controlled exposure chamber (25.7 m3) was characterized by comparing (via log-linear regression) manually set air flows rates read from a flowmeter (0 – 20 m3/min) to measured air exchange rates collected from concentration decay data generated from the emission of acetone and toluene. Variables for engineering controls (e.g. efficiency of local exhaust ventilation and recirculating air) were added to the standard WMR model then evaluated under controlled conditions in a series of chamber studies using acetone and toluene. This design allowed for model inputs to be accurately measured and controlled, generating over 100 pairs of measured and modeled exposure estimates. By varying conditions (generation rate (G: 0.25 - 1 mg/min); air flow rate (Q: 1 - 12 m3/min); efficiency of collection with LEV (ε L: 0.5 – 1.0)) in the chamber one at a time, model performance with different engineering controls across a range of conditions was evaluated. Measured and modeled exposure estimates were compared using linear regression.

Results / Conclusions:

Chamber characterization with the decay studies indicated that the flowmeter provides a very good approximation of the true air exchange rate the chamber is set at (r2 = 0.834). Model performance for the standard WMR model was excellent, with almost perfect agreement between the modeled and measured concentrations (r2 = 0.992). Most percent difference values between the measure and modeled values were less than 10% (average percent difference = 8.12%). Comparison of the modeled solvent concentrations and those measured in the LEV had positive degree of agreement (r2 = 0.6). These results support the use of the WMR model adjusted to include variables for engineering controls to help guide exposure risk management decision making. They also suggest that evaluating these models using aerosols holds much promise.

Primary Topic:

Exposure Assessment Strategies

Secondary Topics:

Aerosols
Engineering Controls and Ventilation

Co-Authors

Please add your co-authors below. Co-authors are listed for professional courtesy and will not be communicated with regarding the decision notification or any on-site logistics, if accepted. Only the primary presenter listed is expected to attend and present the content on-site.

Darpan Das, PhD; Yuan Shao, PhD, CIH; Gurumurthy Ramachandran, PhD, CIH

Acknowledgements and References

List any additional people who worked on the project or provided guidance and support along with details on the role they played in the research. (Please include first name, last name, organization, city, state and country).

Shaun TechSupport; Draeger, Inc.; Telford, PA; USA
Instrumental in trouble shooting PID sensor issues.

Eric, Harley, and Emma; Baltimore, MD; USA
Moral support.

Practical Application

How will this help advance the science of IH/OH?

While models have been developed for workplace exposure to chemicals, bioaerosols, particulate matter, and specific workplace scenarios, there are no models that incorporate the impacts of exposure controls. It is innovative because (1) it improves upon currently used exposure assessment tools; (2) it examines different workplace scenarios using various engineering controls; and (3) it provides a much needed decision framework for professionals in the exposure assessment field to choose a model that best fits their needs. This research is the first in a series of projects aimed at improving currently used exposure modeling tools for predicting worker exposure to hazards. Next steps include evaluating the models for exposure to aerosols by including variables for particle dynamics (e.g. gravitational settling, impaction, and eddy and Brownian diffusion) into the models.