Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

SRA 2013 Annual Meeting Abstracts
T4-B.3 Teuschler, LK*; Rice, GE; Mumtaz, M; Hertzberg, RC;
U.S. Environmental Protection Agency;
teuschler.linda@epa.gov
Adapting chemical mixture risk assessment methods to
assess chemical and non-chemical stressor combinations
Chemical mixture risk assessment methods are routinely used
to estimate joint toxicity of co-occurring chemicals. The impact
of nonchemical stressors on mixture risk has rarely been
addressed. To assess combinations of chemical and
nonchemical stressors, along with protective buffers,
component-based approaches may be applicable, depending on
the toxic action among diverse stressors. Such methods are
feasible but use simple depictions of complex biological
processes. Proposed approaches include: response addition
(RA) and effects addition (EA), assuming toxicological
independence; dose addition in the form of a hazard index (HI),
assuming similar toxicity; and the qualitative Weight of
Evidence (WOE) method to evaluate joint toxicity. Under RA,
the incremental probabilistic risks (e.g., of heart attack)
developed for each individual stressor are summed; under EA,
the incremental changes in a measured effect (e.g., diastolic
blood pressure) are summed to estimate the total change in
that effect, where both approaches are constrained to small
incremental changes. Using the HI, hazard quotients are
summed or subtracted, as appropriate, for the same effect (e.g.,
hypertension from particulate matter, lead, omega 3 fatty acids,
stress). The WOE method indicates the impact of each
nonchemical stressor/buffer on the toxicity of each chemical
and the strength of available evidence (e.g., impacts of
diabetes/omega 3 fatty acids on heart disease from particulate
matter); criteria are needed to make these evaluations. The
uncertainties in combined stressor assessments should be
articulated, and the valid exposure ranges for the assessment
should be described, along with the influence of each stressor
on the analysis. Although these methods need to be more fully
developed, they show promise for application in cumulative risk
assessments. (The views expressed in this abstract are those of
the authors and do not necessarily reflect the views or policies
of the U.S. EPA.)
T2-I.4 Thekdi, SA; University of Richmond;
sthekdi@richmond.edu
Risk-based investment for prison infrastructure systems
By 2018, the Bureau of Prisons network is expected to operate
at 45% above inmate capacity (GAO 2012). Limited research
has evaluated the management of prison infrastructure systems
from a risk-based perspective. Overcrowding may result in
avoidable government expenditures, degraded security,
sanitary concerns for inmates, and inadequate rehabilitation
programming. Traditionally, demand planning and management
for prison infrastructure have been addressed through prison
sentencing in court systems. However, this work addresses the
risks associated with prison management through the
evaluation of rehabilitation programs, investments in
capacities, and other program analysis methods. A multicriteria
analysis is used to prioritize investments to reduce
vulnerabilities of prison systems.
M4-A.2 Thomas, D; US EPA; thomas.david@epa.gov
Metabolism and the toxicity of arsenic
Chronic exposure to inorganic arsenic in environmental and
occupational settings has been associated with increased
cancer risk. Exposure to inorganic arsenic is also strongly
implicated as a causative factor for a number of non-cancer
health effects. Ingested or inhaled arsenic undergoes extensive
biotransformation. In humans and many other species,
inorganic arsenic and its mono- and di-methylated metabolites
are excreted in urine after exposure to inorganic arsenic.
Enzymatically catalyzed formation of methylated metabolites of
arsenic produces an array of metabolites that contain arsenic in
either the trivalent or pentavalent oxidation state. Production of
methylated metabolites containing trivalent arsenic is
problematic because these species are highly reactive. These
metabolites are more potent cytotoxins and genotoxins than
inorganic arsenic. Capacity to activate arsenic by methylation is
influenced by a variety of biological and behavioral factors. For
example, interindividual variation in genotypes for arsenic (+3
oxidation state) methyltransferase affects the arsenic
methylation phenotype and disease susceptibility phenotypes.
Although most studies have focused on characterization of
exposure in populations that consume drinking water
containing inorganic arsenic, in some cases food can be a
significant source of exposure to this metalloid. In foods,
arsenic can be present in inorganic or methylated forms or in
complex organic forms. Linkages among the metabolism of
these arsenicals have not been elucidated, although they may
be important to assessing aggregate exposure to this toxin.
(This abstract does not reflect US EPA policy.)
P.37 Timofeev, A.A.*; Sterin, A.M.; RIHMI-WDC;
arseni@developitbest.com
Identifying regional features of temperature variability
using cluster analysis and quantile regression applied to
the daily surface level observations
It is very important to assess extreme climate variability, which
may cause unwanted weather anomalies. With appropriate
usage, this information effectively allows reducing possible
losses caused by extreme weather events. As we’ve shown in
our previous presentations, quantile regression does not have
drawbacks of traditional approaches, and provides
comprehensive data, describing changes in statistical
distribution across full range of quantiles. However, the more
detailed information we get, the more difficult it gets to analyze
and interpret. In our early research we’ve moved from linear
process diagrams to colored cross sections to visualize quantile
trend values, obtained in one location or distributed vertically
along radiosonde soundings made from same weather station.
Both methods are very effective for separate examination of
limited number of weather stations. This time we introduced
new approach - use vectors of quantiles as an input for cluster
analysis. Then plot resulting clusters on a map as simple
colored marks. Thus we can see if there are evidently similar
points (weather stations) and how their similarity in changes of
variability matches geographical Locations. We can use any
part of distribution we are interested in just by making
appropriate selection of quantiles. For example for extreme
events analysis, top section of the distribution should be
interesting. Using that approach, we’ve obtained very
interesting results, with highly pronounced regional similarities
of distribution changes in certain parts of statistical distribution
of surface temperature. Thus quantile regression, combined
with cluster analysis on it’s results provides comprehensive
information about climate variability changes projected onto a
geographical map.
December 8-11, 2013 - Baltimore, MD