Exposure Database Improvements for Indoor Air Model Validation

Abstract

Recent research in the area of public health has identified indoor and near-field exposure as typically dominant sources of airborne, low level exposure to people. Historically, the answer to the specific questions about, “What is in this building's or that room's air?” have been accomplished by direct measurement of the airborne concentrations. If an exposure study's sample (i.e., the number of different locations of the same class) is large enough and representative enough, then some conclusions can be made about the exposures in that class of building. Given finite resources, one could argue that we will probably never have sufficient means to adequately sample (by direct measurement) the universe of human exposures for each microenvironment of importance. The scientific community will continue to investigate and seek to understand and develop the science of contaminant source generation and control. As part of this critical activity, mathematical exposure models need to be developed and used as simulators of reality; unfortunately, this type of exposure assessment is underutilized. Mathematical exposure models use quantified independent variables as inputs to provide predicted airborne concentration as the output. Most industrial hygiene and public health studies and subsequent databases have concentrated heavily on measuring only the dependent variable of airborne concentration. For the most part, these studies have not quantified and captured the independent or predictor variables that influenced the concentration level. Thus, we have not been able to use these databases to answer important questions about the drivers of airborne concentration or their control, making model validation and development difficult. This article attempts to identify the critical predictor elements of airborne contaminant generation and control and present them in order of importance to the model building process. Capturing and matching predictor variables with their airborne concentration level outcomes and saving them in databases will be significantly more arduous and costly than current practice. If the studies are properly planned and executed, the rewards of these additional data should outweigh the costs, and the process should ultimately become cost effective as model sophistication and validation improve and model reliability grows. Jayjock, M.A.; Hawkins, N.C.: Exposure Database Improvements for Indoor Air Model Validation. Appl. Occup. Environ. Hyg. 10(4):379–382; 1995.