Science Inventory

A sensitivity analysis of a human exposure model using the Sobol method

Citation:

Langstaff, J., G. Glen, C. Holder, S. Graham, AND K. Isaacs. A sensitivity analysis of a human exposure model using the Sobol method. Stochastic Environmental Research and Risk Assessment . Springer-Verlag, BERLIN-HEIDELBERG, Germany, 36:3945-3960, (2022). https://doi.org/10.1007/s00477-022-02238-7

Impact/Purpose:

The Air Pollutants Exposure Model (APEX; U.S. EPA 2019a, b), developed and maintained by the U.S. Environmental Protection Agency (EPA), is a stochastic population-based inhalation exposure model that can used to simulate behaviors, home environments, and exposures associated with ambient pollutant concentrations for a simulated population of thousands of individuals. The continued use of the model in regulatory applications necessitates regular efforts by EPA to update datasets and probability distributions associated with its inputs, including those describing human behaviors and the indoor environment, to ensure relevance to current conditions and ultimate defensibility of simulation results. However, EPA must prioritize these efforts to input variables that most greatly influence characterization of the variability of exposures across the population.

Description:

The Air Pollutants Exposure Model (APEX) is a stochastic population-based inhalation exposure model which (along with its earlier version called pNEM) has been used by the U.S. Environmental Protection Agency (EPA) for over 30 years for assessment of human exposure to airborne pollutants. This study describes the application of a variance decomposition-based sensitivity analysis using the Sobol method to elucidate the key APEX inputs and processes that affect variability in exposure and dose for the simulated population. Understanding APEX’s sensitivities to these inputs helps not only the model user but also the EPA in prioritizing limited resources towards data-collection and analysis efforts for the most influential variables, in order to maintain the quality and defensibility of the simulation results. This analysis examines exposure to ozone of children ages 5–18 years. The results show that selection of activity diaries and microenvironmental parameters (including air-exchange rate and decay rate) are the most influential to estimated exposure and dose, their aggregate main-effect indices (MEIs) equaling 0.818 (out of a maximum of 1.0) for daily-average ozone exposure and 0.469 for daily-average inhaled ozone dose. The modeled person’s home location, sampled from national Census data, has a modest influence on exposure (MEI = 0.079 for daily averages), while age, sex, and body mass, also sampled from Census and other survey data, have modest influences on inhaled dose (aggregate MEI = 0.307). The sensitivity analysis also plays a quality-assurance role by evaluating the sensitivities against our knowledge of the physical properties of the model.

Record Details:

Record Type:DOCUMENT( JOURNAL/ PEER REVIEWED JOURNAL)
Product Published Date:11/01/2022
Record Last Revised:01/05/2023
OMB Category:Other
Record ID: 356758