5023.0: Wednesday, October 24, 2001 - Board 1

Abstract #25396

A GIS-Based Monte Carlo Approach to Identify Spatial Relationship between High Indoor Radon Concentrations and Seismic Activities at local level

Joey Zhou, PhD1, Igor Lacan, MS2, Kai-Shen Liu, PhD2, and Jed Waldman, PhD2. (1) Office of Healthy Homes and Lead Hazard Control, U.S. Department of Housing and Urban Development, 451 7th Street SW(P3206), Washington, DC DC 20410, 202/755-1785, joey_zhou@hud.gov, (2) Environmental Health Laboratory Branch, California Department of Health Services, 2151 Berkeley Way, Berkeley, CA 94704

Radon levels emanating from soils and measured in groundwater are believed to be associated with seismic activity. Seismic-related anomalies in airborne radon concentration were observed prior to the Kobe earthquake in January 1995. There have been attempts to establish radon as a precursor of earthquakes, but these have been inconclusive. This study concerned the spatial relationship between high indoor radon concentrations and seismic activities in Ventura and in portions of Northwestern Los Angeles county of California. California Department of Health Services measured annual average indoor radon concentrations for 862 homes in this area from 1991 to 1992. Location data of earthquakes larger than 4 Richter scale that occurred in the study area from 1973 to 1997 was obtained from the United States Geological Survey. The hypothesis was that the spatial distribution of the 124 homes with radon concentration larger than 2 pCi/l is associated with spatial distribution of large earthquakes. A new GIS-based Monte Carlo approach was developed to study the relationship of the two spatial distributions, and a statistically significant spatial relationship was found with P < 0.0001. However, the causal relationship or mechanism is unclear. It is a challenge to many who attempt to move beyond descriptive use of GIS in data management, data visualization, and map generation etc. This study demonstrated that GIS and statistical techniques can be linked effectively to support analytical spatial analysis.

Learning Objectives: N/A

Keywords: Geographic Information Systems, Radiation

Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

The 129th Annual Meeting of APHA