Superfund Research Program
Use of Space/Time Analysis For Improved Exposure Assessment
Predicting the transport of contaminants through a subsurface environment is important for estimating contaminant exposures in humans, especially when real-life exposure data are limited. Models that incorporate the complexities of a subsurface environment can provide the most meaningful estimates of human exposures. However, the subsurface environment is difficult to model mathematically and computationally as this system is heterogeneous in composition and dynamic in nature.
One of the complexities of modeling contaminant transport in a subsurface environment is the variability of the environment's physical structure. Underground space can be composed of a variety of materials that range from loosely connected sand to compact clay. The arrangement of these materials varies greatly, not only within a given subsurface system, but also between different subsurface environments.
Another complexity of modeling a subsurface environment is that natural processes - such as precipitation, groundwater flow, and contaminant distribution - develop over space and time. Modeling these complicated spatiotemporal patterns is challenging but very important for evaluating potential human risk to subsurface contamination.
Scientists at the University of North Carolina have developed an innovative mathematical model to account for the variability and dynamic nature of subsurface systems. This model treats space and time as a unified continuum which translates into a more realistic representation of groundwater flow and contaminant transport than previous mapping techniques that are based only on spatial factors.
The spatiotemporal model offers distinct advantages over traditional mathematical models. To begin, it offers the flexibility of using various physical knowledge bases: for example, the model can accommodate samples or actual measurements obtained at different times and spatial locations, as well as probabilistic data and measurement errors. The spatiotemporal model also reduces the number of required actual measurements. Thus, optimal predictions of exposures to environmental contaminants can be generated from a limited amount of real-life data. This is significant because actual measurements are usually sparse in many exposure assessment situations.
With these improvements in subsurface modeling and mapping, better estimates of human exposures in a composite space-time framework are now possible. Based on these methods of stochastic spatiotemporal exposure analysis, the group is the first to generate spatiotemporal maps of groundwater contaminant exposures and the associated health effects.
The most significant aspect of this research is the development of an integrated environmental health paradigm that accounts for spatiotemporal natural uncertainties and biological variabilities. The two important features of this paradigm are summarized by the words holistic and stochastic. The justification for a holistic approach stems from the fact that exposure-health effect processes form an integrated whole that is more important than each one of the processes individually. A stochastic approach is necessary because of the various uncertainties involved at every stage. It is believed that this holistic-stochastic method of space-time analysis will become an indispensable tool for exposure estimation and risk assessment.
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To learn more about this research, please refer to the following sources:
- Christakos G, Bogaert P. 1996. Spatiotemporal analysis of springwater ion processes derived from measurements at the Dyle Basin in Belgium. IEEE Transactions on Geoscience and Remote Sensing 34(3):626-642.
- Christakos G, Hristopulos D. 1996. Stochastic indicators for waste site characterization. Water Resour Res 32(8):2563-2578.
- Christakos G. 1991. A theory of spatiotemporal random fields and its application to space-time data processing. IEEE Trans Syst Man Cybern 21(4):861-875.
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