Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene
Kyle P. Messier, Yasuyuki Akita, Marc L. Serre
Department of Environmental Science and Engineering, University of North Carolina, Chapel Hill, NC 27599
Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments.
We hypothesize that GIS techniques can be integrated to improve estimates of concentration exposure in epidemiological studies. A novel framework is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME).
A contaminant source LUR model was developed that assesses the range of influence of point sources on groundwater Tetrachloroethylene. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function.
We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners and Resource Conservation and Recovery (RCRA) sites at short ranges. The integration of the LUR model as mean trend in BME results in a 9.1% decrease in cross validation mean square error compared to BME with a constant mean trend.
Combining multistage address geocoding, LUR, and BME can produce more accurate estimates of contaminant exposure for exposure assessments and epidemiological studies.
The Development of these maps was supported by the UNC Superfund Research Program-Research Translation Core (P42-ES005948), with funding from the National Institute of Environmental Health Sciences