separately, and asked to write down words that they associate with them. The secondgroup was also given the words uncertainty and risk, separately, but asked todraw images associated with them. The last group was given a group of images, andasked to check whether it is associated with risk or uncertainty. The study aims tounderstand how the public visually ties risk and uncertainty to concrete objects. Theresults suggest that individuals tie abstract concepts to concrete objects that are eithergeographically available or experienced.P.112 Erraguntla NK, Sielken RL, Valdez-Flores C, Grant RL; neeraja.erraguntla@tceq.texas.govTexas Commission on Environmental QualityAN UPDATED INHALATION UNIT RISK FACTOR FOR ARSENIC ANDINORGANIC ARSENIC COMPOUNDS BASED ON A META-ANALYSISOF EPIDEMIOLOGY STUDIESThe United States Environmental Protection Agency (USEPA) developed aninhalation unit risk factor (URF) of 4.3 x 10-3 per μg/m3 <strong>for</strong> arsenic in 1984 <strong>for</strong> excesslung cancer mortality based on epidemiological studies of workers at two smelters:the Asarco smelter in Tacoma, Washington and the Anaconda smelter in Montana.Since the USEPA assessment, new studies have been published and exposureestimates were updated at the Asarco and Anaconda smelters and additional years offollow-up evaluated. The Texas Commission on Environmental Quality (TCEQ) hasdeveloped an inhalation URF <strong>for</strong> lung cancer mortality from exposures to arsenicand inorganic arsenic compounds based on a newer epidemiology study of Swedishworkers and the updates of the Asarco and Anaconda epidemiology studies. Usingmeta-analysis on the URFs from these three epidemiology cohort studies, the finalinhalation URF is 1.5×10-4 per μg/m3. The de minimis concentration level (i.e., airconcentration at 1 in 100,000 excess lung cancer mortality) is 0.067 µg/m3. This valuewill be used to evaluate ambient air monitoring data so the general public in Texas isprotected against adverse health effects from chronic exposure to arsenic.W3-H.1 Ertem M, Bier VM; bier@engr.wisc.eduUniversity of Wisconsin-MadisonDEFENDER-ATTACKER MODEL FOR COMPUTER NETWORK SE-CURITYWe propose a general defender-attacker model <strong>for</strong> security of computer networks,using attack graphs to represent the possible attacker strategies and defenderoptions. The defender’s objective is to maximize the security of the network undera limited budget. In the literature, most network-interdiction models allow the attackeronly one attempt (assuming that the attacker is captured and disabled aftera single failure); other models allow multiple attempts, but assume that any subsequentattempt begins at the point in the network where the previous attempt failed.These models are not appropriate <strong>for</strong> computer security, where the attacker could be94operating from the safety of a <strong>for</strong>eign country, and the cost of starting over with acompletely different attack strategy may be quite low. To represent the ability of theattacker to launch multiple attempts, we represent the attacker’s success or failure onany one arc of the attack graph probabilistically, and <strong>for</strong>mulate the resulting securityproblem as a multiple-stage stochastic network-interdiction problem. In the resultinggame, a non-myopic defender anticipates both the attacker’s strategy choices, andthe probability of success or failure, and chooses a single defensive strategy (i.e., aset of arcs in the attack graph to be protected) by which to defend against multipleattempted attacks. The attacker then launches an optimal attack against the system,based on knowledge of which arcs have been protected. If the attacker fails at thefirst attempt, a second-stage optimal attack strategy is chosen, based on a revised attackgraph showing which arcs have already been successfully traversed (now assumedto have success probabilities of 1), and which arc led to failure of the first-stage attack(now assumed to have a success probability of 0). We solve the resulting stochasticoptimizationproblem using two-stage stochastic optimization with recourse.M2-J.3 Evans AM, Rice G, Teuschler LK, Wright JM; evans.amandam@epa.govAssociation of Schools of Public Health, National Center <strong>for</strong> Environmental Assessment, Officeof Research and Development, US Environmental Protection AgencyA CUMULATIVE EXPOSURE ASSESSMENT OF NOISE AND VOLA-TILE ORGANIC COMPOUNDSAlthough humans are exposed daily to multiple chemical, biological, and physicalstressors, epidemiological studies and risk assessments typically examine them individually.Occupational studies show that concurrent exposure to elevated levels ofnoise and organic solvents (e.g. toluene, ethyl benzene, styrene, xylenes) have a synergisticeffect on hearing loss. In this research, we characterize concurrent exposuresof volatile organic compounds (VOCs) and noise among population subsets in theurban area, San Francisco County, CA. Demographic variables, personal VOC measurementsand exposure factors were extracted from the 1999-2000 National Healthand Nutrition Examination Survey dataset. 2000 census block group population demographics(aggregated by race- gender-educational attainment) were extracted fromthe National Historical Geographic In<strong>for</strong>mation System <strong>for</strong> all census block groupswithin the county. Personal exposure measurements of 10 VOCs (benzene, toluene,ethylbenzene, m,p-xylene, and o-xylene, 1,4-dichlorobenzene, chloro<strong>for</strong>m, trichlorethylene,tetrachloroethylene, and methyl tert-butyl ether) were available <strong>for</strong> 851 individualsfrom passive samplers worn <strong>for</strong> 48-72 hours. Census block group level noiseexposures were estimated using ArcGIS from a noise map of San Francisco (Seto etal., 2007) based on traffic-induced noise levels estimated from the Federal HighwayAdministration’s Traffic Noise Model 2.5 model. Exposure differences were detectedby races gender, and education <strong>for</strong> noise and VOCs. Hazard Indices (based on permissibleexposure levels in occupational settings and inhalation reference concentra-
tions) were < 1 <strong>for</strong> sub-populations with highest 90th percentile ethylbenzene andtoluene exposures. This case study addresses an important research need and exemplifiessome of the data gaps that may impact cumulative exposure assessment ef<strong>for</strong>ts.The views expressed in this abstract are those of the authors and do not necessarilyreflect the views or policies of the US EPA.P.79 Evensen DT; dte6@cornell.eduCornell UniversityA NEW YORK (OR PENNSYLVANIA) STATE OF MIND: CROSS-STATEDIFFERENCES IN PRINT MEDIA COVERAGE OF DRILLING FORNATURAL GAS IN THE MARCELLUS SHALE REGIONHydraulic fracturing <strong>for</strong> natural gas in shale <strong>for</strong>mations throughout the UnitedStates is a rapidly proliferating means of fossil fuel extraction. The injection ofmillions of gallons of water deep into the ground to release natural gas offers potentiallylucrative economic gain <strong>for</strong> individuals leasing land and collecting royalties,but also creates potential <strong>for</strong> environmental hazards and threats to social well being(e.g., municipal services, infrastructure, community character) in areas subject to largeamounts of drilling. We conducted a content analysis of newspaper media coverageof hydraulic fracturing in four major local newspapers in the northern tier of Pennsylvaniaand the southern tier of New York. We examined the extent to which mediacoverage framed issues related to drilling as environmental, economic, and/or socialissues. We then analyzed the valence with which these issues were discussed. Coveragein Pennsylvania focused substantially more on economic issues and contained amuch higher percentage of positively valenced articles compared with coverage inNew York. NY coverage focused substantially more on environmental issues andcontained a much higher percentage of negative or mixed valence articles comparedwith PA coverage. We discuss several possible explanations <strong>for</strong> these notable differencesand offer implications <strong>for</strong> risk communication.T3-E.3 Evers EG, Horneman ML, Berk PA, Van Leusden FM, De Jonge R; eric.evers@rivm.nlNational Institute <strong>for</strong> Public Health, Bilthoven, The NetherlandsA CAMPYLOBACTER QMRA (QUANTITATIVE MICROBIOLOGICALRISK ASSESSMENT) FOR PETTING ZOOSThe relative importance of petting zoos <strong>for</strong> transmission of Campylobacter tohumans as an example direct contact route was estimated while including the effectof interventions. For this, a mathematical model including variability was built describingthe transmission of Campylobacter in animal feces from the various animalspecies and fences and the playground to humans. This transmission involves visitorstouching these so-called carriers, subsequently touching their lips and possibly ingestingcampylobacters. Many data were not available and there<strong>for</strong>e extensive field andlaboratory research was done to fulfill the needs. The distribution of fecal contaminationon all carriers was measured by swabbing in ten petting zoos, using Escherichiacoli as an indicator. The transmission rate from carrier to hand and from hand to lipwas measured using pre-applied cow feces to which E. coli WG5 was added as anindicator. Carrier-hand and hand-lip touching frequencies were estimated by in total13 days of observations of visitors by two observers at two petting zoos. Combiningthe exposure distributions with a Beta-Poisson dose response function gives estimationsof the probability of illness after a petting zoo visit due to Campylobacter of4.94E-5 <strong>for</strong> children and 8.63E-6 <strong>for</strong> adults. For the whole of the Netherlands in ayear this implies 232 and 38 cases <strong>for</strong> children and adults, respectively, so 270 casesin total. Comparison with the 12,000 Campylobacter cases due to consumption ofchicken filet as estimated by a previous QMRA shows that petting zoos are not animportant transmission route <strong>for</strong> Campylobacter. Cleaning the fences proves to be anadvisable intervention, as a scenario of 90% reduction of contamination gives an 84% reduction in the number of petting zoo Campylobacter cases. Cleaning only goatfences gives a 71% reduction, whereas the playground plays a minor role. The modelcan easily be adapted <strong>for</strong> other fecally transmitted pathogens.W3-E.1 Fanaselle WL, Dennis S, Oryang D, Pouillot R, Van Doren J; Wendy.Fanaselle@fda.hhs.govFood and Drug Administration, Center <strong>for</strong> Food Safety and NutritionSITE VISITS: A NOVEL MEANS OF FILLING-IN THE DATA GAPSAssessing and then managing risks has always been an integral part of foodsafety throughout history. Quantitative microbial risk assessment techniques arehelping to advance the scientific basis of our food safety regulation. The accuracy ofthese risk assessments is dependent on the amount of data available to develop therisk assessment models. However, the high degree of variability in the food supplysystem makes filling those data gaps very difficult and challenging, requiring new andcreative means <strong>for</strong> increasing our knowledge of the system to be modeled in a riskassessment. The U.S. Food and Drug Administration’s Center <strong>for</strong> Food Safety andApplied Nutrition (CFSAN) continues to seek new methods to obtain this data andfill valuable data gaps needed <strong>for</strong> food safety risk assessments that have the desiredlevel of accuracy to assist with decision-making. One of the more novel, successfulmethods involves bringing the risk assessment team to visit all phases of the foodsupply system, including locations involved in food preparation, processing, manufacturing,harvesting, and consumption. For example, site visits were made to a cruiseship, a U.S. navy ship, produce processing plants, produce farm, spice processing andmanufacturing plants, dairy processing plants, and retail delis to help the teams developan understanding of means of microbial transmission, and the pros and consof potential mitigation strategies. Each site visit produced expected and unexpectedlessons learned. The biggest surprise to the teams was the value of the unexpectedlessons learned in filling risk assessment data gaps, and helping the teams better un-95
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