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Important Physiological Parameters and Physical Activity Data for Evaluating Exposure Modeling Performance: a Synthesis
Citation:
McCurdy, T. Important Physiological Parameters and Physical Activity Data for Evaluating Exposure Modeling Performance: a Synthesis. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-15/175, 2015.
Impact/Purpose:
The National Exposure Research Laboratory’s (NERL’s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA’s mission to protect human health and the environment. HEASD’s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA’s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
The purpose of this report is to develop a database of physiological parameters needed for understanding and evaluating performance of the APEX and SHEDS exposure/intake dose rate model used by the Environmental Protection Agency (EPA) as part of its regulatory activities. The APEX model is the Air Pollution Exposure Model and SHEDS is the Stochastic Human Exposure and Dose Simulation model. APEX is used by both EPA’s Office of Air Quality Planning and Standards (OAQPS) and the National Exposure Research Laboratory (NERL) in EPA’s Office of Research and Development (ORD), while SHEDS mostly is used by ORD. Both models have been used by non-EPA organizationsIn spirit this paper follows the intent of Data Sources Available for Modeling Exposures in Older Adults (McCurdy, 2011) but is expanded to include all age groups. Because a nationally-applicable repository of data for physiological factors does not exist, we looked to the clinical nutrition and exercise physiology literatures for relevant information on age- and gender-specific variables used in our exposure models. Much of these data come from “panel” or convenience studies of specific population subgroups, many of them focused on people with a health or weight issue. Since these studies—especially newer ones--often include a similar age/gender control group of approximately the same sample size (n), information for “normal” subjects also is reported. Control groups generally are defined to be subjects with no known health/weight issues relevant to the topic being investigated, which of course is not the same as having no physical problem(s) or being healthy, although study authors often labeled them as such. In our data tables, we generally used the original author(s) delineation of the tested groups that was identified even though the designations often were not precise. Combining data from disparate studies having different objectives and using a variety of protocols and subjects results in considerable uncertainty regarding general applicability of the information gathered. A formal meta-analysis of the data often is attempted in that situation (Egger & Smith, 1997; Egger et al., 1997), but that is not possible to undertake at the present time. Perhaps this compilation can become the basis for such an effort, since it provides for each study its group mean, standard deviation (where possible), and its sample size. (Where only standard errors of the estimate [SE] are provided in an article, they are converted to SD by multiplying by square root of the sample size: SD = SE * √ n.) Additional information would have to be obtained for each study, however, in order to undertake a complete meta-analysis of data contained in this report.