Capabilities and Challenges of Natural Attenuation in the Subsurface: Lessons from the U. S. Geological Survey Toxics Substances Hydrology Program

By Barbara A. Bekins, Arthur L. Baehr, Isabelle M. Cozzarelli, Hedeff I. Essaid, Sheridan K. Haack, Ronald W. Harvey, Allen M. Shapiro, James A. Smith, Richard L. Smith

ABSTRACT

Natural attenuation has been used as a practical method to dispose of wastes throughout human history. However the presumption of natural attenuation as a strategy for disposal of wastes has had serious consequences for human health and the environment in the twentieth century. It is now clear that the environment has a limited ability to assimilate wastes and that this ability depends on the nature and quantity of the waste compounds and the characteristics of the subsurface. Thus, increasing our knowledge of the capabilities and limitations of natural attenuation is of high priority. To this end, the U.S. Geological Survey Toxic Substances Hydrology Program (Toxics Program) conducts studies on the fate of contaminants in the natural environment. Results from the Toxics Program research sites have documented the effectiveness of a variety of individual processes that together contribute to natural attenuation in the subsurface. The site studies also indicate that many challenges remain in our efforts to understand the effectiveness of natural attenuation. These include spatial heterogeneity and slow, rate-limiting processes that result in long time frames for cleanup. The subsurface microbial populations that catalyze biotransformation reactions also are poorly understood. However, recent results have yielded insights into controls on the spatial and temporal distribution of the various microorganisms. Quantitative models have been used successfully at several sites for estimating the relative contribution of each natural attenuation process to the overall mass loss. Future research is needed that targets gaps in our understanding of compound-specific behavior, subsurface microbial ecology, and uncertainties associated with heterogeneities and long time frames.