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.