This approach therefore results in a topolgy of indoor radon for the state or region for which the analysis is performed, or even for the U.S. as a whole if applied across the country. The purpose of developing this topology is to identify more reliably the areas having indoor concentrations that are substantially higher than average. Monitoring and remedial efforts could then be focussed on these areas, resulting in relatively rapid help to the occupants of houses that have very high levels of indoor radon.
Our project has been devoted to developing these analytical tools
and to identifying the data needed to perform the anlyses effectively.
In the course of this research effort, we have of course performed specific
analyses for several states, selected because of their particular levels
or distribution of radon concentrations or because of the relative availability
of suitable predictive information. We have also performed analyses using
a national data set and, perhaps more importantly for practical purposes,
performed a series of regional analyses that include most of the 48 contiguous
states, based on several national datasets that are available. (Cf. a map
of the United States. [after it is normalized to long-term concentrations]
see image below) In both state and regional analyses, we have often found
that our stastistical models account for about 80% of the variability in
mean county concentrations, or more precisely in the logarithm of the county
geometric mean indoor concentrations - a level of success that corresponds
to predicted county means that are relatively certain.
Predicted Geometric Mean (GM) Indoor Radon Concentration by State. These GMs are for annual-average living-area (AALA) indoor radon concentrations and were estimated based on a correlation of two national databases, one having AALA concentrations from about 5000 homes selected nationally and the other having about 40,000 short-term (several day) screening measurements (usually taken in the basement if there was one). The AALA data are reasonable measures of the concentrations that occupants are actually exposed to annually, but there are many more screening measurements. The correlation between the two permits use of the screening data to make better estimates of state of local AALA concentrations. With regard to the state estimates, note that there is considerable local variability in indoor concentrations within states and smaller areas.
The Indoor Environment Program, part of the Environmental Energy Technologies Division, at Lawrence Berkeley National Laboratory has since 1977 had an active indoor radon group, devoted to investigating the origin, behavior, concentrations, and control of radon in homes and other buildings. This group's efforts have included, not only experimental studies, but also development of theoretical models for transport of radon from the (usually) soil source into the indoor environment. But, based on its interest in exposures to and behavior of indoor radon, the indoor radon group began several years ago to investigate statistical correlations between indoor radon and physical factors and how they might be used to provide predictors of local concentrations that are superior to those from the monitoring data themselves or from various types of radon "potential" maps, including those incorporating results from available physical models. Research personnel have included scientists from the Indoor Environment Program, faculty from the Statistics Department of the University of California, Berkeley, and students from various departments of the University. This project has been viewed as one of the more important environmental research efforts at Lawrence Berkeley National Laboratory.
The radon research group in the Geologic Division of the U.S.
Geological Survey previously developed a set of geologic radon potential
books and associated maps in cooperation with the Environmental Protection
Agency. It also investigated various questions of radon generation and
transport, such as the relationship of soil-gas radon concentrations to
surficial uranium concentrations, soil-gas transport through porous media,
and geochemical influences on radon emanation.
Various monitoring efforts demonstrate that the concentration distribution of indoor radon is approximately lognormal both on a national scale and on a local scale, but the mean concentration varies substantially from one locale to another, as does the fraction of houses having concentrations exceeding various levels of concern, such as 4, 10, or 20 pCi/l. Homes with high indoor concentrations tend to "cluster" geographically, suggesting that systematic identification of "high-radon" areas would increase the efficiency and speed of identification of high-radon homes. For example, preliminary analysis by LBNL of the national indoor radon concentration distribution and of the variance of geometric means (GMs) with area, together with the distribution of surficial radium concentrations (estimated from the aerial gamma-ray data of the National Uranium Resource Evaluation (NURE)) and data on infiltration rates, suggests that approximately 90% of 20-pCi/l homes might be found among only 10% of areas (or, more specifically, housing groups, such as those associated with the population in a census tract).
This project is aimed at using various classes of information, including monitoring data and information on pertinent physical factors, in a self-consistent analytical framework for predicting local indoor radon concentrations by county or smaller geographic areas. The project is intended both to develop the analytical methods, through development of demonstration models for slected states or regions, and also to provide these tools in a usable form for other scientists and in particular for State agencies and other entities that wish to use them for help in identifying high-radon areas.