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Effect of Activity Patterns on Radon Exposure

An important goal of those concerned with air pollution is to estimate the time-weighted average exposure to various indoor air pollutants. Various studies have measured the concentration of pollutant for indoor environments, but without regard to the presence of humans. Other studies have used diaries and other records to evaluate the activity patterns relative to various environments, indoor and outdoor. In this presentation I will show how the results of these studies can be combined analytically to provide estimates of the distribution of human exposures to radon. This is a big step beyond the earlier approaches, which have used random sampling to computationally estimate the exposure distribution (Ref. 1). As an example, I will use residential radon concentrations and activity patterns associated with people in the State of California. For both populations, homes and people, there have been detailed studies made using samples of size larger than 3,000. The relatively large sample size was needed to consider various strata. For homes, they considered such things as housing type, climate, and geologic province. For people, they considered such things age, gender and income level. The salient points of these studies will be reviewed. In both cases state officials arc satisfied that the obtained distributions accurately reflect the populations. I focus on the residential environment because it is the environment where we spend the most time and the one where people receive their largest dose of natural radiation (from radon, of course). The procedure presented is to evaluate the exposure distribution by averaging over all home concentration levels and all human activity patterns. It reduces to a type of convolution of these two distributions. The home radon concentration levels are well approximated as a lognormal distribution and the at-home fraction of a day may be approximated as a histogram. Corrections are made for the concentrations because people spend the nighttime hours at home when radon levels are twice their day-average recorded levels. With these assumptions, an analytic formula for the daily human exposure distribution per unit radon (pCi-daylliter) may be analytically calculated. It is a single peak distribution which is broader that a lognormal distribution. Albeit, the arithmetic average for California is a modest 0.9 pCi/L with less than one percent of population predicted to have a time-weighted exposure greater than 4.0 pCi/L. Because the procedure is analytic, it may be evaluated using ubiquitous spreadsheet software requiring minimal computer resources. It would be interesting to compare the recent updates to the California activity patterns with those found in Iowa (reported at last year's AARST meeting). Nevertheless, it is not believed that activity patterns vary significantly from state to state, and therefore, Californian patterns can be used to estimate human exposure for radon in other states.