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exposures throughout the range of exposure levels, sufficiently large epidemi-
ologic studies that incorporate state-of-the-art analytic methods are needed (see
the section “Applications of Biomarkers to Human Health Studies”). But, even
when biologic effects are not evident (and in special cases of hormesis when
there are potentially beneficial effects), the challenge for EPA is to provide
meaningful and relevant information to potentially affected parties.
It is now possible, while testing for emerging contaminants of interest and
their metabolites, to monitor the effluent of a publicly owned wastewater-
treatment plant and determine trace quantities and metabolites of substances—
such as pharmaceuticals (licit and illicit), personal-care products, and hormones
(natural and synthetic)—that are being used and disposed of or excreted by peo-
ple in each town (Zegura et al. 2009; Jean et al. 2012; Neng and Nogueira 2012).
The mass emission factors per capita can be calculated for the chemicals without
determining individual household use. However, without better knowledge of
the environmental and human health risks of such low-dose exposures, the ad-
vanced detection capabilities do not necessarily help the agency to interpret the
results or to protect human health and the environment more effectively. One
example is mercury. On one hand, from a toxicologic standpoint, mercury is one
of the most studied elements (Schober et al. 2003; Jones et al. 2010). On the
other hand, it is still difficult to make a conclusive assessment of the health ef-
fects of mercury emitted into the environment (EPA 2011a). Finding cost-
effective research opportunities for connecting data on environmental chemicals
with environmental and health outcomes can contribute to an increase in knowl-
edge and can inform policy.
Fate and Transport of Chemicals in the Environment
EPA has long been recognized as a leader in developing computer models
of the fate and transport of chemical contaminants in the environment, a key
component in constructing models of human exposure and health outcomes, as
well as in source attribution for ecologic and human endpoints. It develops and
supports models for both scientific purposes and application in environmental
management. Although many of its models are well established and now backed
by years of application experience, EPA and the broader environmental-
modeling community face challenges to improve spatial and temporal resolu-
tion, to account for stochastic environmental behaviors and for modeling uncer-
tainties, to improve the characterization of transfers between environmental me-
dia (air, surface water, groundwater, and soil), and to account for feedback
between contaminant concentrations and environmental behavior (for example,
the effects of such short-lived radiative-forcing agents as ozone and aerosols
have on climate change). Furthermore, sources, properties, and behaviors of
some contaminants remain poorly understood, even after years of study. EPA
also faces significant challenges and opportunities for integrating models with
data from new monitoring systems through data assimilation and inverse model-
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