You are here:
PREDICTING PARTICULATE (PM-10) FREQUENCY DISTRIBUTIONS FOR URBAN POPULATIONS USING A RANDOM COMPONENT SUPERPOSITION MODEL (RCS) MODEL
Citation:
Ott, W R., L A. Wallace, AND D T. Mage. PREDICTING PARTICULATE (PM-10) FREQUENCY DISTRIBUTIONS FOR URBAN POPULATIONS USING A RANDOM COMPONENT SUPERPOSITION MODEL (RCS) MODEL. Presented at PM 2000 AWMA Conference, Charleston, SC, January 24-28, 2000.
Impact/Purpose:
The main objective is to investigate human exposure to fine and coarse particles (and PAHs) from several important sources such as cooking, woodsmoke, and household cleaning. A second objective is to investigate the observed increased personal exposure (compared to indoor air concentrations measured by a fixed monitor) to particles: the so-called "personal cloud," that has been observed in many occupational and some environmental studies. A third objective is to incorporate the findings into a mass-balance indoor air quality model.
Description:
Health risk evaluations usually require the frequency distribution of personal exposures of a given population. For particles, personal exposure field studies have been conducted in only a few urban areas, such as Riverside, CA; Philipsburg, NJ; and Toronto, Ontario. This paper explores the concept that these exposure data, however limited, may allow prediction of distributions for other urban areas where personal exposure field studies have not yet been: carried out. To test this concept, a statistical model was developed that treats the indoor and outdoor components of concentration as independent random variables that are added together by the principle of superposition, or a random component superposition (RCS) model. The ambient concentrations entering indoors from outdoors are modified by a dimensionless attenuation coefficient. This model is consistent with our physical understanding of the factors affecting indoor particle concentrations and personal exposures, and it allows the incremental exposure distributions from different cities to be compared. The RCS model assumes that personal exposure is the sum of two independent random variables, one caused by personal activities and one caused by outdoor concentrations infiltrating indoors. The model predicts that the correlation coefficient r between the personal exposures and ambient concentrations will follow a specific nonlinear equation when the personal exposures of n persons are averaged together on a given date. The experimental data show reasonably good agreement with the predictions of this model. Since this model flows from our physical understanding of the process and the properties of the empirical data, it may be a suitable tool for predicting population exposure frequency distributions in other urban areas where exposure field studies have not yet been conducted.
This work has been funded in part by the United States Environmental Protection Agency. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.