Human Health Risk Assessment Work Plan. Red Rock Road, Sutherlin, Oregon. SLR Ref:

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1 Human Health Risk Assessment Work Plan Red Rock Road, Sutherlin, Oregon SLR Ref: November, 2014

2 HUMAN HEALTH RISK ASSESSMENT WORK PLAN RED ROCK ROAD, SUTHERLIN, OREGON Prepared for: GSI Water Solutions, Inc. 55 SW Yamhill Street Portland, Oregon This document has been prepared by SLR International Corporation. The material and data in this report were prepared under the supervision and direction of the undersigned. Jeffery A. Peterson, PhD Principal Environmental Scientist Amanda Bailey Associate Risk Assessment Scientist

3 CONTENTS ACRONYMS... iv 1. INTRODUCTION Risk Assessment Approaches Objectives BACKGROUND Site Description Previous Environmental Investigations Site Assessment (December 1995) Preliminary Assessment (May 1999) Site Assessment (October 2000) Investigation Data Report (September 2004) Focused Feasibility Study (2006) Inventory of Current Conditions and Land Uses (2008) Interim Remedial Action (2009) Sampling and Bioaccessibility Assessment (2009) Interim Remedial Action Assessment, Bioaccessibility Review and Updated Human Health Risk Assessment (2011) Interim Remedial Action Assessment Arsenic Bioaccessibility Reviews Updated Human Health Risk Assessment Hot Spot Determination Ecological Assessment (2011) Technical Memorandum: Land Use and Risk Exposure Scenario Summary, Red Rock Road, Sutherlin, Oregon (2014) Technical Memorandum: Project Work Plan for Red Rock Road, Sutherlin, Oregon third Revision (2014) CONCEPTUAL SITE MODEL Source Fate and Transport Human Exposure Routes Human Exposure Scenarios EXPOSURE ASSESMENT Target Population Gender Distribution Age Distribution Approach for Estimating Exposure Over an Individual s Life Variability and Uncertainty Exposure Concentration PRA Distribution DRA Exposure Point Concentration i

4 CONTENTS (CONTINUED) Variability and Uncertainty Body Weight PRA Distributions DRA Point Estimate Variability and Uncertainty Exposure Duration PRA Distribution DRA Point Estimate Variability and Uncertainty Exposure Frequency PRA Distribution DRA Point Estimate Variability and Uncertainty Exposure Time PRA Distribution DRA Point Estimate Variability and Uncertainty Soil Ingestion Rate PRA Distribution DRA Point Estimate Variability and Uncertainty Gastrointestinal Absorption Factor Particulate Emission Factor TOXICITY ASSESSMENT Oral Reference Dose Inhalation Reference Concentration Oral Slope Factor Inhalation Unit Risk Factor RISK CHARACTERIZATION Deterministic Risk Assessment Hazard Quotients Lifetime Excess Cancer Risk Probabilistic Risk Assessment Hazard Quotients Lifetime Excess Cancer Risk Sensitivity Analyses REFERENCES ii

5 CONTENTS (CONTINUED) FIGURES Figure 1 Red Rock Road Overview Map Figure 2 Human Health Conceptual Site Model Figure 3 Zip Code TABLES Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Exposure Scenarios Exposure Parameters Age Distribution Arsenic Concentrations in Red Rock Road Soil Body Weight Distributions and Constants by Gender and Age Exposure Duration Distributions by Age Range APPENDICES Appendix A ProUCL Output for Arsenic Concentrations in Soil iii

6 ACRONYMS ABS gi ADC ap ADD ADD si AT c AT nc ATSDR BW BW a BW c CF C s CSM CTE DEQ DRA ED ED a ED c ED r EF EF hd EPA EPC ET FFS F v GSI HHRA HQ HQ ap HQ si gastrointestinal absorption factor distribution of average daily concentration from inhalation of particulates average daily dose average daily dose from soil ingestion averaging time for a carcinogen averaging time for a noncarcinogen Agency for Toxic Substances and Disease Registry body weight adult body weight child body weight conversion factor concentration in soil conceptual site model central tendency exposure Oregon Department of Environmental Quality deterministic risk assessment exposure duration adult exposure duration child exposure duration resident exposure duration exposure frequency exposure frequency in hours per day U.S. Environmental Protection Agency exposure point concentration exposure time focused feasibility study fraction of vegetables from contaminated site GSI Water Solutions human health risk assessment hazard quotient hazard quotient from inhalation of particulates hazard quotient from soil ingestion iv

7 ACRONYMS (CONTINUED) hr hour hr/d hours per day IRIS EPA s Integrated Risk Information System IRS soil ingestion rate IRS adj IRS a IRS c IUR LECR LECR ap LECR si mg/m 3 mg/d mg/kg MP NHANES II NHAPS PEF PRA age-adjusted soil ingestion rate adult soil ingestion rate child soil ingestion rate inhalation unit risk factor lifetime excess cancer risk lifetime excess cancer risk from particulate inhalation lifetime excess cancer risk from soil ingestion milligrams per cubic meter milligrams per day milligrams per kilogram milepost Second National Health and Nutrition Survey National Human Activity Pattern Survey particulate emission factor probabilistic risk assessment ProUCL ProUCL Version RB relative bioavailability RBALP Relative Bioavailability Leaching Procedure RBC risk-based concentration RfC inhalation reference concentration RfD oral reference dose RME Reasonable Maximum Exposure RRR Red Rock Road SD standard deviation SF slope factor SLR SLR International Corporation START Superfund Technical Assessment and Response Team TERA Toxicology Excellence for Risk Assessment v

8 ACRONYMS (CONTINUED) UCL upper confidence limit vi

9 1. INTRODUCTION In contract with GSI Water Solutions (GSI) and on behalf of Weyerhaeuser, SLR International Corporation (SLR) has prepared this Human Health Risk Assessment (HHRA) Work Plan for the Red Rock Road (RRR) in Douglas County near Sutherlin, Oregon. This HHRA Work Plan was prepared at the request of the Oregon Department of Environmental Quality (DEQ). The RRR is an approximately 17-mile long former railroad grade, and material from the former Bonanza mine was used to construct the railroad. Rock from the former Bonanza mine has elevated concentrations of some naturally occurring metals, including arsenic. For more than a decade, a variety of environmental investigations have been performed to evaluate metals in RRR soil and gravel (GSI, 2011a; 2014b). An HHRA was completed for RRR in 2004 (CH2M Hill, 2004), and using additional information regarding the bioavailability of arsenic, an updated HHRA was completed in 2011 (GSI, 2011a). These previous HHRAs evaluated generic DEQ human exposure scenarios intended to apply to any site in Oregon. Subsequently, DEQ developed site-specific human exposure scenarios using information regarding land use near RRR and how local residents may contact soil of RRR (DEQ, 2012). The purpose of this Work Plan is to describe the methods that will be used to evaluate human health risks associated with DEQ s site-specific exposure scenarios for the RRR site. 1.1 RISK ASSESSMENT APPROACHES The most common method used to evaluate risks that contaminants may pose to human health is a deterministic risk assessment (DRA), and DEQ has developed several guidance documents for DRA (DEQ, 2000; 2003a; 2010). DEQ risk-based concentrations (RBCs) used to evaluate many contaminated sites in Oregon are based on DRA methods (DEQ, 2003a). In DRA, the variables used in risk calculations (e.g., chemical concentrations, human exposure factors, chemical toxicity estimates) are represented by single values (point estimates). Known natural variability in exposure parameters (e.g., measured variability in chemical concentrations, body weight, contact rates, etc.), and uncertainty in these parameters, are not considered in risk calculations. Instead, variability and uncertainty are typically addressed by using conservative (health protective) assumptions to select point estimates for various variables. The end result of using conservative (i.e., biased) point estimates for input parameters is that final risk estimates will be biased high (risks are overestimated), but the degree to which risk estimates will be biased is largely unknown. The primary advantage of DRA is that risk estimates can be made using simple algebra, although more complex mathematics may sometimes be used to estimate point values for a particular input. Probabilistic risk assessment (PRA) uses techniques that allow formal consideration of both variability and uncertainty in risk estimates. Under PRA, probability distributions are used for one or more variables instead of point estimates. For example, instead of using a single value to represent the body weight of humans, a distribution based on the measured natural variability in body weight of a population is used. PRA produces a distribution of risk estimates, and offers a more complete characterization of potential risks. Furthermore, the roles of variability and uncertainty in risk parameters can be better evaluated using PRA. 1

10 This Work Plan presents methods for evaluating risks that RRR soil may pose to human health under the DEQ s site-specific exposure scenarios using both DRA and PRA. Previous risk assessments for RRR (CH2M Hill, 2004; GSI, 2011a) used DRA, but did not evaluate all of the site-specific exposure scenarios. None of the previous risk evaluations used PRA. To promote efficiency, the HHRA will be performed in a tiered manner. Initial risk estimates will be made using DRA methods. Scenarios with risk estimates based on DRA methods that are above DEQ target risk levels will be further evaluated using PRA methods. Based on preliminary estimates, DRA risk estimates for some residential land use scenarios are likely to be above target risk levels. Therefore, this Work Plan describes a PRA approach to estimate risks for various types of residents. 1.2 OBJECTIVES The objectives of this Work Plan are as follows: Describe scenarios by which people may contact soil from RRR. Describe DRA methods that will be used to estimate potential risks that RRR soil may pose to human health. Describe PRA methods that will be used to estimate potential risks that RRR soil may pose to human health. 2

11 2. BACKGROUND This section presents background information regarding RRR, such as the nature of the road, and findings of some relevant previous investigations. Much of this background information has been detailed in previous reports (GSI, 2011a; 2014b). 2.1 SITE DESCRIPTION The RRR site is a former railroad grade constructed, in part, with materials from the nearby Bonanza cinnabar mine. RRR is approximately 17 miles in length (Figure 1). The western 10 miles of the railroad grade was constructed in the early 1900s by Roach Timber Company. The Roach Timber portion of railroad grade right-of-way was purchased by Weyerhaeuser in the late 1940s. Subsequently, this railroad grade was extended 7 miles east by Weyerhaeuser for construction of a logging railroad that was completed in After the railroad shut down in 1966, the railroad grade right-of-way reverted back to the original owners. Currently, Weyerhaeuser owns six parcels of property totaling approximately 2.5 miles of the original railroad grade. The condition of the former railroad grade varies along its 17-mile length. Most of the RRR is covered with vegetation or has been covered with gravel. The most westerly 0.38 mile and a section from Milepost (MP) 0.85 to MP 1.0 of RRR have been obliterated by residential and commercial development. From MP 0.4 to MP 0.85, a gravel cap was placed over the road in 1999 and re-graveled in Spot repairs were made in In 2009, as an interim remedy, the DEQ placed gravel caps on 12 properties (including the spot repairs) between MP 0.4 and MP PREVIOUS ENVIRONMENTAL INVESTIGATIONS Several evaluations have been conducted along RRR. Documents regarding site characterization and interim remedial actions are summarized below SITE ASSESSMENT (DECEMBER 1995) In response to inquiries regarding the source of fill material in RRR, DEQ collected two surface soil samples of RRR material near State Street, in the City of Sutherlin, Oregon. Samples were analyzed for Primary Pollutant Metals, but did not include testing for arsenic. Results from this evaluation were not incorporated into later assessments of RRR PRELIMINARY ASSESSMENT (MAY 1999) The U.S. Environmental Protection Agency s (EPA) Superfund Technical Assessment and Response Team (START) completed a preliminary assessment in May This work was documented in a report titled Red Rock Road Site Preliminary Assessment Sutherlin, Oregon (E & E, 1999). No soil samples were collected for this assessment. 3

12 2.2.3 SITE ASSESSMENT (OCTOBER 2000) EPA s START team conducted a site inspection in May This work was documented in a report titled Red Rock Road-Sutherlin Site Inspection Report (E & E, 2000). Soil samples were collected from RRR, nearby surface soils, and creek sediment. Some sample results and findings from this report were incorporated into later RRR evaluations INVESTIGATION DATA REPORT (SEPTEMBER 2004) A report, titled Investigation Data Report and Human Health Risk Assessment (CH2M HILL, 2004), summarized 2001 sampling and laboratory test results. Thirty-three surface soil samples were collected along the 17-mile length of RRR. In areas that had been covered with gravel, samples of the gravel cap and the underlying RRR soil were collected. Background soil samples were collected at nine locations along the RRR alignment. Split samples were collected and tested by DEQ at four locations. A HHRA was completed using DRA to determine the potential risks that RRR soil may pose to people under current and future site conditions. The HHRA evaluated DEQ s generic human exposure scenarios. Based on bioaccessibility measures, the relative bioavailability (RB) of arsenic in RRR soil was estimated at 3.3 percent. RB represents the amount of arsenic absorbed from soil compared to the amount absorbed from food or water in the tests that were used to estimate the toxicity of arsenic. These RB estimates were subsequently modified based on additional testing (see Section 2.2.8). The primary risk estimates were as follows: Potential risk associated with exposure to natural background soil (not RRR) was above the DEQ s 1x10-6 excess lifetime cancer risk threshold, but below the 1x10-4 DEQ hot spot threshold. The risk estimate associated with exposure to the gravel cap on portions of RRR was lower than the background soil risk estimate, but above the 1x10-6 DEQ cancer risk threshold. The risk estimate for RRR soil was above the 1x10-6 DEQ cancer risk threshold, but below the 1x10-4 DEQ hot spot threshold. This risk assessment was not approved by DEQ FOCUSED FEASIBILITY STUDY (2006) A focused feasibility study (FFS) was prepared to facilitate the selection and approval of a remedial action alternative for the Weyerhaeuser properties located on RRR. The study and addendum were titled Focused Feasibility Study Red Rock Road Site Sutherlin, Oregon (CH2M HILL, 2006a; 2006b). Remedial technologies were screened and those retained were assembled into remedial alternatives that were evaluated against DEQ balancing factors. Alternatives evaluated were: (1) no action, (2) administrative controls to address long-term maintenance and future land use issues, (3) gravel cap consisting of a 6-inch-thick layer of crushed rock covering the side slopes 4

13 and top of RRR, (4) vegetative cover consisting of naturally occurring and planted vegetation, (5) topsoil and vegetative cover consisting of a 4-inch-thick layer of topsoil and planted grass, and (6) fencing and signing that would be placed on both sides of RRR. The FFS concluded that capping and fencing alternatives were protective at a reasonable cost and were compatible with existing structures and future land use. An addendum to the FFS identified specific remedies selected for each Weyerhaeuser property at the RRR site. Specific recommended remedies were to maintain existing vegetative and gravel covers on Weyerhaeuser properties. The FFS was not approved by DEQ INVENTORY OF CURRENT CONDITIONS AND LAND USES (2008) In 2008, a detailed evaluation was conducted to assess the physical condition, and determine current users, of RRR for each property. The report was prepared in two parts (A and B). Part A was prepared by DEQ and inventoried properties for the western 10 miles of RRR, while Part B was prepared by Weyerhaeuser and inventoried properties for the eastern 7 miles of RRR (CH2M HILL and DEQ, 2008). Ten properties on the western 10 miles and two properties on the eastern 7 miles were identified as having the highest potential for direct human contact with RRR soils. The list of properties with the highest potential for human exposure was later updated to include additional properties as part of the interim remedial action discussed below INTERIM REMEDIAL ACTION (2009) An interim remedy was completed that consisted of gravel caps constructed on 12 of the properties identified as having the greatest potential for human exposure, to serve as a barrier to RRR soils. The interim remedy was driven by risk calculations completed by DEQ using a RB of 80 percent, as conservatively recommended by EPA Risk Assessment Guidance and a residential exposure scenario, which indicated human health risk was above the 1x10-4 DEQ hot spot threshold. The caps were constructed in January 2009 and September Design drawings and specifications for the interim remedy were included in the Interim Cleanup Action Implementation Report Red Rock Road ECSI No Sutherlin, Oregon (ASA, 2009) SAMPLING AND BIOACCESSIBILITY ASSESSMENT (2009) In 2009, 38 samples of RRR soil and 3 background soil samples were collected from 11 properties identified as having a high potential for human exposure. In addition, 20 soil samples were collected adjacent to RRR in a grid layout from a single property to assess the spatial distribution of arsenic near RRR. All of the soil samples first were tested for arsenic. Fourteen of the samples then were tested using a Relative Bioavailability Leaching Procedure (RBALP) that measured the fraction of total arsenic in RRR soils that might be liberated from the soils in the gastrointestinal tract following ingestion. The RBALP was developed by a consortium of EPA Region 8, industry, and academic scientists. The RBALP testing found that the relative bioavailability ranged from 2.4 to 7.0 percent of the total arsenic in the soil for the samples tested. The test methods and findings were submitted to DEQ in a memorandum (CH2M HILL, 2009). 5

14 Arsenic speciation testing was conducted on five soil samples using electron microprobe analysis testing methods. The form of arsenic is one line of evidence in determining if the arsenic in RRR soils is available for biological uptake through ingestion. Ingestion was found to be the dominant exposure pathway in the 2004 risk assessment (CH2M HILL, 2004). The study found a single phase of iron oxide to be the predominant form of arsenic in the soil. The mineralogy report for these samples (Drexler, 2009) indicated that the relative arsenic mass present in this iron oxide phase ranged from 86 percent to 99 percent across the samples evaluated. Arsenic associated with this form of iron oxide has been found to have low bioavailability when fed to swine and primates (Drexler, 2009). To evaluate the potential impact on native soils near RRR, 25 soil samples were collected in a 5 by 5 sampling grid at 30-foot spacing between sample points. The grid was oriented so that 10 sample locations were on each side of the road alignment and 5 sample locations were located on the road. Surface soil samples were collected from 0 to 6-inches below ground surface. The samples were collected near MP 10 on the Johns property, where the former railroad alignment passes through pasture land. The arsenic concentrations in the soil samples adjacent to the road are similar to the background soil sampling test results, suggesting that a significant mass of arsenic has not migrated from RRR to adjacent soils. However, because each sample included the uppermost 6-inch soil layer, it is unclear if the effects of a thin layer of RRR dust on the soil surface could have been observed. A separate investigation was performed to better characterize the potential effects of RRR dust migration (see Section ) INTERIM REMEDIAL ACTION ASSESSMENT, BIOACCESSIBILITY REVIEW AND UPDATED HUMAN HEALTH RISK ASSESSMENT (2011) An assessment of the existing gravel caps, a review of the bioaccessibility testing, and an updated HHRA were conducted by GSI in Results are presented in the Red Rock Road Data Report and Updated Risk Assessment (GSI, 2011a). This report was reviewed by DEQ; comments were incorporated into a final version Interim Remedial Action Assessment The objective of the gravel cap assessment was to confirm the ability of the gravel cap to serve over time as a barrier to the underlying RRR fill. The work included collecting 19 soil samples from the gravel cap material placed over RRR soil in 2007 and The arsenic concentrations were consistently low and in the range of typical arsenic concentrations found in Oregon. These test results confirmed that the gravel covers continue to serve as a barrier to limit human exposure to the RRR soils Arsenic Bioaccessibility Reviews At the request of Weyerhaeuser, Exponent conducted an independent review of the arsenic bioaccessibility testing completed in The work included an overview of animal studies on arsenic bioavailability, a review of RBALP test methods and correlations with animal studies, and conclusions on the arsenic RB for RRR soils. A conclusion from the independent review was that the arsenic RB for the RRR soils is less than 10 percent and possibly much lower (consistent with the RRR soil extraction test results indicating a RB of 2.4 to 7.0 percent). 6

15 Toxicology Excellence for Risk Assessment (TERA) completed a second independent review at the request of DEQ. TERA reviewed the 2009 arsenic bioaccessibility evaluation and Exponent s review. TERA concurred with conclusions made by Exponent that a RB for RRR soils of 10 percent is acceptable to use for assessing human health risk (TERA, 2011) Updated Human Health Risk Assessment The 2004 HHRA was updated to address changed conditions at the site, incorporate a revised RB of 10 percent, and include the substantially larger database for arsenic in RRR soil. Because most other elements of the 2004 HHRA remained up-to-date and consistent with DEQ DRA guidance, only the risk estimates for arsenic were updated. The findings from the updated risk assessment were as follows: Potential risk associated with exposure to natural background concentrations of arsenic in soil was above the DEQ 1x10-6 cancer risk threshold, but below the 1x10-4 DEQ hot spot threshold for the residential and occupational exposure scenarios. Risks associated with exposure to the gravel cap on RRR were much lower than background soil risk estimates for the residential, occupational, and recreational exposure scenarios. Potential risk associated with exposure to RRR soil was above the 1x10-6 DEQ cancer risk threshold, and at, but not above, the 1x10-4 DEQ hot spot threshold for the residential, occupational, and recreational exposure scenarios Hot Spot Determination An evaluation was completed to determine if areas of the RRR site could be defined as hot spots based on DEQ s criteria for soil that is highly concentrated, highly mobile, or not reliably contained. The conclusion from the evaluation was that there are no hot spot conditions on RRR ECOLOGICAL ASSESSMENT (2011) An ecological assessment, titled Red Rock Road Level I Ecological Scoping Assessment (October 2011), was completed by GSI, to determine the ecological impacts that RRR presents to the environment (GSI, 2011b). The ecological assessment addressed habitat conditions, ecological receptors, and exposure pathways in accordance with DEQ guidance. The report concluded that there was no evidence of impact to plants or wildlife and there was little indication of road material entering into streams where fish and other aquatic animals are present. The report recommended that no further ecological evaluations are required TECHNICAL MEMORANDUM: LAND USE AND RISK EXPOSURE SCENARIO SUMMARY, RED ROCK ROAD, SUTHERLIN, OREGON (2014) This technical memorandum (GSI, 2014a) provided the basis for determining current and future land use for the properties adjacent to RRR. The memorandum summarized site-specific 7

16 conditions regarding land use laws, zoning restrictions, and proximity of housing and commercial buildings to RRR. Land use and property use trends since the railroad was dismantled were used to predict future land use patterns over the next 30 years. These current and predicted future conditions will be used in the Risk Assessment and Feasibility Study for the RRR project TECHNICAL MEMORANDUM: PROJECT WORK PLAN FOR RED ROCK ROAD, SUTHERLIN, OREGON THIRD REVISION (2014) This work plan (GSI, 2014b) summarized past work and outlined additional work for the RRR project. Specifically, the work plan presented additional details for a proposed probabilistic risk assessment and a sampling and analysis plan to characterize migration of dust from RRR. The results of the dust migration study were recently summarized in a draft technical memorandum titled Dust Study Summary for Red Rock Road, Sutherlin, Oregon, Technical Memorandum DRAFT (GSI, 2014c). 8

17 3. CONCEPTUAL SITE MODEL The conceptual site model (CSM) summarizes known or suspected sources of contamination, fate and transport processes that affect the distribution of contamination, and mechanisms by which human receptors may contact impacted environmental media. Four elements are required to establish a complete exposure pathway: 1) a source and mechanism of chemical release to the environment, 2) an environmental transport medium for a released chemical, 3) a point of potential contact with the impacted medium (referred to as the exposure point), and 4) an exposure route (e.g., soil ingestion) at the exposure point. The human health CSM is shown in Figure 2, and elements of the CSM are discussed below. 3.1 SOURCE Material from the Bonanza cinnabar mine that was used as ballast for the former RRR railroad is the source of arsenic in soil. Although mercury is also present in RRR soil, arsenic is the primary chemical of potential concern. The 2004 HHRA found that potential risks to residents exposed to mercury in soil of RRR over a lifetime are over two orders of magnitude below the DEQ acceptable risk level (CH2M Hill, 2004). Given the minimal potential health risks associated with potential exposure to mercury, this HHRA Work Plan focuses only on arsenic. 3.2 FATE AND TRANSPORT The primary theoretical mechanisms that can affect fate and transport of arsenic in RRR soil include partitioning (leaching) to porewater, migration from porewater to groundwater, advection and dispersion in groundwater, sorption to the underlying natural soil matrix, and wind-mediated emission and dispersion of small soil particles (dust). The relative importance of these processes in structuring the dynamics of arsenic fate and transport varies, depending on the solubility of arsenic in soil, the dynamics of groundwater flow, wind speeds, and other factors. Some plants may accumulate low levels of arsenic by root uptake from soil. Animals that then consume these plants could then be exposed to arsenic in their diet. With the exception of dust generation, the above fate and transport processes are expected to be minimal for the RRR site. For example, leaching tests found that potential leaching of arsenic from RRR soil into groundwater is low (CH2M Hill, 2004). These results are consistent with findings of various bioavailability evaluations which also found that arsenic in RRR soil has relatively low solubility (Drexler, 2009; GSI, 2011a). Similarly, DEQ (2003b) found that arsenic is unlikely to significantly accumulate in home-grown garden plants. Although a previous study found little evidence that migration of dust from RRR is a significant transport process (CH2M HILL, 2009), this study had some important limitations. Therefore, an additional dust migration study was performed to better evaluate potential migration of dust from RRR. Results of this study will be used to identify human exposure scenarios where inhalation of dust from RRR may be a significant exposure route (GSI, 2014c). 9

18 3.3 HUMAN EXPOSURE ROUTES The primary routes by which people may be exposed to arsenic in RRR soil include incidental ingestion of soil, and inhalation of airborne dust particles. Although there are few data regarding dermal absorption of arsenic from soil, the Agency for Toxic Substances and Disease Registry (ATSDR) estimates that dermal absorption of inorganic arsenic is sufficiently low that this potential exposure route is unlikely to represent a health concern (ATSDR, 2007). As described in greater detail below, the proposed DRA and PRA will quantitatively evaluate risks associated with potential soil ingestion and inhalation exposures. Potential dermal exposures will be assessed in an uncertainty evaluation. Although there is considerable uncertainty regarding potential dermal absorption of inorganic arsenic in soil, the weight of available evidence suggests that dermal absorption of arsenic from RRR soil is likely to be minimal. As mentioned previously, the solubility of arsenic in RRR soil is low (CH2M Hill, 2004). The dissolved form of arsenic has the greatest potential to cross the skin barrier and enter the body. Given the low inherent solubility of arsenic in RRR soil, dermal absorption is expected to be very low relative to potential ingestion and inhalation exposures. Dermal absorption of chemicals from soil is determined, in part, by the propensity for soil to adhere to the skin. Several studies have found that skin adherence varies as a function of particle size with relatively large particles adhering to skin less than small particles (EPA, 2011). One study found that for dry or moderately moist soils, most particles that adhered to the skin were less than 63 micrometers (µm) in diameter (EPA, 2011). RRR is mainly comprised of packed gravel. Based on soil sieve tests, only 12 percent of the RRR soil surface is made up of particles smaller than 75 µm (GSI, 2014c). Given that the RRR surface is compacted and only a small fraction of particles are less than 75 µm in diameter, particle adherence to the skin is expected to be low and dermal absorption of arsenic from soil is expected to be minimal relative to other potential exposure routes (e.g., incidental soil ingestion). Lowney et al. (1997) studied the dermal absorption of arsenic that was applied to the abdomens of monkeys. They found that dermal absorption of soluble arsenic in solution was about 5 percent, similar to results of a previous study of soil spiked with soluble arsenic (Wester et al., 1993) that currently represents the basis for EPA s default arsenic dermal absorption value (EPA, 2004). However, dermal absorption from soil with high concentrations of weathered arsenic (not spiked soluble arsenic) was minimal and could not be distinguished from background (e.g., absorption of natural arsenic from food). Dermal absorption of arsenic from soil is likely negligible, in part, because arsenic in soil is often complexed with minerals and has low solubility (Lowney et al., 1997). Studies of people exposed to arsenic in well water have also shown low dermal absorption. For example, when people that have high levels of arsenic in well water are provided alternative drinking water with low arsenic levels, their body burdens of arsenic return to normal (Post, 2003). Although these people continue to bathe in water with high levels of arsenic, dermal absorption of arsenic is minimal. Given that dermal absorption of arsenic from soil (and water) appears to be minimal, there are few data that can be used to accurately estimate dermal uptake from soil, and dermal risk assessment methods involve considerable uncertainty, potential dermal absorption of arsenic from RRR soil will be assessed in the uncertainty evaluation. Any quantitative risk estimates 10

19 would simply be a function of whatever uncertain assumptions are made regarding dermal uptake processes. Potential risks associated with oral and inhalation exposures to arsenic in soil are expected to be far greater than those associated with dermal exposures. 3.4 HUMAN EXPOSURE SCENARIOS DEQ (2012) identified six scenarios by which people may have significant exposure to RRR soil: Primary Residential, Secondary Residential, Tertiary Residential, Timberlands, Recreational, and Construction/Utility Worker. Some of these exposure scenarios were not directly evaluated in previous HHRAs (e.g., Secondary Residential). Therefore, life stages and exposure routes associated with these exposure scenarios are described below. Based on a comprehensive review of properties near the RRR, these DEQ exposure scenarios have been amended to include a description of how both land use and proximity to RRR are likely to influence people s exposure to RRR soil and dust. DEQ defined a Primary Residential scenario as one with a residential component in the parcel s zoning, and where the home is located close to RRR and interaction with RRR is common (or likely in the future). This scenario has been subdivided into two categories based on the assumed prevalence of RRR soil within the residence, which is likely a function of distance of the home from RRR. Results of the RRR dust migration study will be used to determine which of these scenarios apply to a specific property or location (GSI, 2014c). Also, the DEQ Tertiary Residential scenario was largely redundant with the DEQ Secondary Residential scenario, and was therefore eliminated (Table 1). The Timberlands and Construction Worker scenarios are based on adult occupational exposures. The residential scenarios involve people who may be exposed over a lifetime at a residence, and involve multiple life stages (e.g., children and adults). The Recreational scenario also involves multiple life stages. Some of the site-specific exposure scenarios are similar to DEQ s default exposure scenarios (e.g., Timberlands, Construction/Utility Worker) or others that were previously evaluated (CH2M Hill, 2004; GSI, 2011a), and these scenarios will be evaluated using either previous HHRA results or generic DEQ RBCs. Some of the residential scenarios are significantly different from those evaluated previously, and these scenarios will be evaluated using DRA and PRA methods. Details regarding the site-specific exposure scenarios are as follows: Residence On Road This scenario has a residential home on or immediately adjacent to RRR. The home is close enough to RRR that dust from RRR regularly penetrates the home, and soil from RRR is regularly tracked into the home. Under this scenario, residents of all age classes are routinely exposed to RRR soil throughout the day. Because RRR soil/dust is assumed to be pervasive in the home, the scenario assumes that essentially all of the soil/dust that people incidentally ingest or inhale comes from RRR. Residence Near Road Under this scenario, the residential home is located sufficiently far from RRR that dust or soil from RRR does not penetrate the home. Residents are exposed to RRR soil when they perform outdoor activities, including outdoor home recreation (e.g., children playing), on or near RRR. Residents of all age classes are exposed to RRR soil. This scenario 11

20 assumes that essentially all outdoor activities performed by a resident lead to contact with RRR soil and dust. Resident Worker DEQ defined a Secondary Residential scenario as one with a strong agricultural component. Properties falling within this scenario will be zoned Farm Forest, Rural Residential, or Timber Reserve, and RRR will be used to access agricultural lands or ranch lands on a less than daily basis. The residence will not be located close to RRR and there will be limiting factors in the zoning that would not allow or that would make it difficult to place a residence in close proximity to RRR. This scenario assumes that RRR is sufficiently far from a residential home that dust from RRR rarely penetrates the home, and that residents do not routinely contact RRR whenever they are outdoors. Instead, this scenario assumes that residents may contact RRR when they perform outdoor work (farming, ranching) on or near RRR away from the home. Because residents will mainly contact RRR soil while performing outdoor work, this scenario has been termed the Resident Worker scenario. Also, because the primary activities that lead to contact are related to work, and young children (<12 years old) do not typically perform regular work, this scenario focuses on residents age 12 years and older. Timberlands This scenario applies to properties that are predominantly zoned Farm Forest or Timber Reserve, where no residence is currently on the property, and where zoning would either preclude or make it very difficult to allow a residence on the parcel in the future. Any exposure to RRR soil is assumed to occur as a result of work-related activities on these properties. It is assumed that exposure to RRR soil would occur during intense forestry activities that typically take place in a short period of time (e.g., several weeks of daily access on the RRR during a logging operation). DEQ s generic occupational worker scenario for adults (DEQ, 2010) may be used as a surrogate for these parcels. Recreational This scenario was established in the 2004 human health risk assessment for the RRR, and includes publicly-owned and privately-owned properties where there is recreational use of a trail on top of the RRR. The most apparent area for this type of scenario is the trail running from the former Sutherlin Creek crossing at the east end to the festival grounds at the west end. Some of this area is paved. Exposure to RRR soil under this scenario is assumed to be limited to several times a week during the summer months. Some of the timberlands may also include this scenario. The 2004 human health risk assessment focused on estimating risks to youth that may recreate on RRR (CH2M Hill, 2004). Construction/Utility Worker This scenario applies to properties where construction workers or utility workers may contact RRR. The scenario would likely apply to properties within the City boundaries of Sutherlin, but could include construction or utility work for other sites further up the watershed having to do with roads, bridges, or utilities. Again, DEQ s generic construction worker scenario for adults (DEQ, 2003a) may be used as a surrogate for these parcels. In summary, the residential scenarios identified by DEQ (Primary Resident and Secondary Resident) were not explicitly evaluated in previous HHRAs for RRR (CH2M Hill, 2004; GSI, 2011a). The remaining sections of this Work Plan focus on the DRA and PRA methods that will be used to evaluate residential land use scenarios (e.g., Residence On Road, Residence Near Road, and Resident Worker). It is anticipated that either previous risk assessment results (GSI, 2011a) or generic DEQ RBCs will be used to evaluate the Timberlands, Recreational, and Construction/Utility Worker exposure scenarios. Furthermore, because risk estimates based on 12

21 DRA methods will likely be above DEQ target risk levels, PRA methods are described for the various residential scenarios. 13

22 4. EXPOSURE ASSESMENT This section describes the approach that will be used to estimate the magnitude, frequency, and duration of human exposures to arsenic in RRR soil. The target population being assessed is discussed along with characteristics of this target population such as age and gender structure. Assumed soil contact rates (e.g., soil ingestion rates), soil concentrations, exposure frequency, and exposure duration are defined. All of this information is used to estimate the dose of arsenic that people may receive from soil contact. As mentioned previously, DRA uses point estimates of each exposure factor, while PRA uses information regarding the distribution of values for each factor. Most of the proposed PRA exposure factor distributions were derived from DEQ s Guidance for Use of Probabilistic Analysis in Human Health Risk Assessments (DEQ, 1998). Most of the data used by DEQ to develop the exposure factor distributions in DEQ s 1998 PRA guidance are similar to those in the EPA s most recent Exposure Factors Handbook (EPA, 2011). Therefore, in general, it appears that the DEQ exposure factor distributions adequately represent current understanding of potential human exposures. PRA can quantitatively evaluate the influence of variability and uncertainty on risk estimates. Variability is the inherent heterogeneity within a population (e.g., natural differences in body size, mobility rates, ingestion rates, inhalation rates, etc. between individuals), and can be better defined, but not reduced, through further study. Uncertainty is due to lack of knowledge of an exposure factor or exposure process. In theory, uncertainty can be reduced through further study. Three general types of uncertainty in risk assessment include (EPA, 2001): Scenario Uncertainty Errors from incomplete definition of the risk scenario to be evaluated (e.g., incomplete definition of the conceptual site model). Model Uncertainty Limitations in the models used to characterize exposure processes. Parameter Uncertainty Errors in the values or distributions used for an exposure factor (e.g., errors in sampling and measurement). To promote transparency, an attempt is made in this Work Plan to consider the roles that both variability and uncertainty play in shaping assumed exposure factor distributions. When possible, the potential effects of Scenario Uncertainty, Model Uncertainty, and Parameter Uncertainty are discussed for exposure factor distributions. Uncertainty is qualitatively classified as low, medium, or high. DRA uses point estimates of each exposure factor. Typically, two types of exposures are evaluated for each receptor (DEQ, 2010): a Reasonable Maximum Exposure (RME) value that represents a high end exposure (often a 90 th percentile from the distribution of an exposure factor), and a Central Tendency Exposure (CTE) value that represents the average exposure (often a mean or median from the exposure factor distribution). DEQ (2010) RME and CTE point estimates are proposed for most exposure factors (Table 2) for the DRA. An attempt is made to consider the rationale for each RME and CTE value along with the role that variability and uncertainty played in shaping point estimates. However, in several 14

23 cases, it is difficult to reconstruct the data and evaluation that ultimately led to a particular point value. The rationale for various point estimates is rarely discussed in DEQ guidance (DEQ, 2010), and studies cited to support some point values are vague. Potential cancer and noncancer effects are estimated using different methods. For noncarcinogens, it is assumed that compensatory processes (e.g., detoxification) prevent the expression of adverse effects unless the dose a person is exposed to on a chronic (long-term) basis exceeds a threshold that overwhelms natural defense mechanisms (EPA, 1989a). This chronic threshold dose is termed the reference dose. The reference dose is expressed as an average daily dose (adjusted for body weight) that a person may contact for more than 10 percent of their life. If the chronic dose a person is exposed to exceeds the reference dose, it is inferred that the exposure may lead to unacceptable risks (EPA, 1989a). Note that other than ensuring that the assumed exposure duration is of a chronic nature, exposure duration plays little role in noncancer risk estimates. For this reason, when both children and adults are in the focal population (i.e., residential exposure scenarios), noncancer risks are typically estimated for only children in a DRA (DEQ, 2010). Children tend to have higher contact rates when adjusted for body size (e.g., soil ingestion rate), and thus have higher potential risk estimates. For carcinogens, EPA assumes that any exposure to a carcinogen has a finite probability of causing a cancer (EPA, 1989a). No threshold exposure is assumed for carcinogenesis. Instead, it is typically assumed that the likelihood of developing cancer increases as a linear function of the total dose a person experiences over their life. Exposure duration plays an important role in cancer risk estimates. DRA for residential receptors typically assumes that exposure occurs in childhood and extends into adulthood (DEQ, 2010). Age-adjusted intake rates that account for differing contact rates, body size, and exposure durations between children and adults are used in DRA cancer risk estimates for residents (DEQ, 2010). One of the most common methods for performing PRA is Monte Carlo simulations. A Monte Carlo simulation integrates various exposure factors to derive an exposure for a hypothetical individual of the focal population. For each individual, exposure values are randomly selected from probability distributions for each of several exposure factors. This process is repeated many times (typically about 10,000) to estimate exposures for numerous hypothetical individuals in the population. The end result is a distribution of potential exposures (and/or risk estimates) representative of the entire population. Typically, several basic characteristics are defined for each probability distribution. These characteristics include the relative probability of exposure values, central tendency values (e.g., mean, median, or mode), shape of the distribution (e.g., skewness, multimodality), and boundary conditions for the probability distribution. For example, there are finite limits to human body size, and body size distributions are typically bounded by estimates of the minimum and maximum size of an individual in a particular age class. Proposed exposure factor distributions are given in Table 2 and further explained below. Each exposure factor distribution should be independent of others, or if there are dependencies between variables, these dependencies should be quantified. Most of the exposure factors described below appear to be independent of each other. For example, adult body weight is likely independent of soil contact rate because large adults are unlikely to have behaviors that lead to more or less incidental soil ingestion than small adults. Similarly, exposure duration 15

24 (number of years a person may be exposed to RRR soil) appears to be independent of exposure frequency (days per year a person contacts RRR soil) because people that live in a home for a relatively long period are unlikely to behave in a way that leads to more or less frequent soil contact than people who live in homes for shorter durations. The proposed PRA will be performed using the Crystal Ball, Fusion Edition release software program. This software program uses Monte Carlo simulations to estimate risks. 4.1 TARGET POPULATION The proposed focal population for the HHRA is the group of people that live within the zip code that includes Sutherlin, Oregon (97479). As shown in Figure 3, this zip code includes the City of Sutherlin and extends several miles to the east. As a result, this zip code appears to include most of the properties near RRR census data from the US Census Bureau for the zip code are proposed to define the age and gender characteristics of the focal population (US Census Bureau, 2010). These census data represent a readily available source of information describing a relevant group of people that live near the RRR site. The zip code unit appears to be the smallest geographic area for which census data are available, and this concept of the focal population appears more relevant than larger areas such as the county, state, or country GENDER DISTRIBUTION Gender can play a role in shaping exposure because, on average, males tend to be larger than females. Exposure doses are expressed as a function of body weights (see Section 6), so if all other exposure factors are identical, large-bodied individuals would have relatively lower exposure doses than small-bodied people. Body weight also tends to change with age, and DEQ provides body weight distributions for both males and females of different age classes. In order to incorporate the full range of body weight information into the PRA, a gender distribution was identified that can be used to determine the age-based distribution from which to select body weights for the focal population. The 2010 census information discussed previously is proposed to identify the gender distribution for the population in the zip code that includes Sutherlin, Oregon (Table 2) AGE DISTRIBUTION Age also plays a role in shaping potential exposures because body weight tends to change with age, some contact rates vary as a function of age, and exposure duration may vary with age due to developmental and behavioral patterns that change over a person s lifetime. For example, children tend to have higher soil ingestion rates than adults, and young adults tend to be more mobile (i.e., relocate more frequently) than older adults. The age distribution describes the composition of various age classes in the focal population. DEQ provides distributions for some exposure factors by age class. Again, the 2010 census data for zip code is proposed as the source of the age distribution for the focal population (Table 3). 16

25 Under the Residence On Road and Residence Near Road scenarios, it is assumed that exposure to RRR soil can begin shortly after birth. The Resident Worker scenario assumes that exposure to RRR soil occurs during work-related activities that start when an individual is at least 12 years old. This starting age is convenient because some of the DEQ distributions based on age class have age 12 as the start of an age class (DEQ, 1998). For practical reasons, the age distribution of local residents was capped at 60 (Tables 2 and 3) APPROACH FOR ESTIMATING EXPOSURE OVER AN INDIVIDUAL S LIFE The various residential exposure scenarios involve a population comprised of individuals of different ages and genders. Because several exposure factors change as a function of age, it is important to account for the variability in exposures that may occur over an individual s life. Below is a general approach proposed to account for variability in exposure associated with age and gender for a residential receptor. 1. Randomly select the age of a hypothetical person for which exposure will be estimated from the age distribution of residents living in the zip code that includes Sutherlin, Oregon. 2. Randomly select the gender of the hypothetical person from the distribution of residents. 3. Randomly select the exposure duration for the person based on age from DEQ s agespecific distributions for time spent in a residence. Exposure durations vary by age class. 4. Randomly select an age- and gender-specific starting body weight from the relevant DEQ body weight distributions. Body weight varies as a function of gender and age. 5. If the exposure duration happens to start in the juvenile phase, it will be important to account for natural changes in body weight as a person ages. The details of this step are further described in the body weight section below (Section 4.3). 6. Include the other exposure factor distributions for the age range being evaluated, if relevant. Most of the additional exposure factor distributions are not age-specific and apply to hypothetical receptors of all ages. 7. Sum exposures over relevant age classes and estimate risk for a particular individual. 8. Repeat the above process 10,000 times, and create a risk distribution for the entire population of local residents VARIABILITY AND UNCERTAINTY The age and gender distributions primarily reflect variability of people within the zip code as reported in the 2010 census. Because the census is intended to be a comprehensive accounting of all individuals in the population (not a sample), the reported variation is likely to be relatively reliable and repeatable. 17

26 There are several sources of uncertainty in the definition of the target population, and to a lesser extent in the age and gender distributions for the population. For example, the actual group of people that regularly contact RRR soil is unknown, and the population that may contact RRR soil at some point in the future is unknowable. Therefore, assumptions must be made to define the population likely to contact soil from RRR. The concept proposed here is to use the population of individuals within the zip code to represent people likely to contact RRR soil. It is reasonable to think that the people that live within the City of Sutherlin zip code are representative of the subset of people that live, work, or recreate near RRR. Relative to alternative concepts of the focal population with readily available data (e.g., population in the county, state, or country), scenario uncertainty regarding the zip code focal population is considered low. A conceptually similar process is used under conventional DRA approaches (EPA, 1989a; DEQ, 2010) to identify the focal population(s) for HHRA. However, in DRA, the focal population is conceptual in nature and rarely described in a quantitative or semi-quantitative manner. Often, multiple subsets of the general population are defined in a broad sense and evaluated separately. Because DRA requires the use of point estimates of exposure factors, simplifying assumptions typically require that the focal population is reduced to an abstraction. For example, DEQ default point estimates typically define all children in the focal population as the group of people between birth and 6 years of age, all adults as people above 18 years of age, and both hypothetical groups are evaluated separately (DEQ, 2010). Default exposure estimates are typically based on data collected around the nation (DEQ, 2010), and very little of a conventional DRA reflects local or site-specific conditions. Relative to the proposed PRA approach, scenario uncertainty for DRA is considered high. Proposed age and gender distributions may involve moderate to low model and parameter uncertainty. Model uncertainty would be primarily limited to age distributions (not gender), and could arise by the creation of age classes and certain boundary conditions. For example, the Resident Worker scenario assumes that work-related exposure to outdoor soil begins when individuals reach age 12 (it is assumed that younger people do not work outdoors), and this boundary condition introduces uncertainty. Parameter uncertainty may arise through errors in collection and reporting of age and gender information. Relative to other types of exposure factors, overall uncertainty in age and gender distributions is considered low. Also, it appears that age and gender distributions are unbiased. With a few potential exceptions, gender is not explicitly considered in conventional DRA (DEQ, 2010), and age is only considered in a general manner for some exposure factors (i.e., soil ingestion rate). As a result, scenario uncertainty regarding the effects of gender and age on soil exposures is high under conventional DRA. The child group evaluated in DRA includes children under age 7 years, and this group was selected because children in this age group are estimated to have high soil exposures (EPA, 1991a). Therefore, the age variable for children used in DRA is biased and likely overestimates exposure for children in general. 4.2 EXPOSURE CONCENTRATION The exposure concentration factor describes the concentration(s) of arsenic in soil of RRR that a person may contact. Results from studies of arsenic concentrations in RRR soil include data 18

27 used in the 2004 HHRA (CH2M Hill, 2004) along with data collected in 2009 (CH2M Hill, 2009). These soil results are presented in Table 4. Previous evaluations have found no evidence of significant spatial variation in arsenic concentrations between different sections of the RRR (CH2M Hill, 2004). Instead, it appears that arsenic concentrations in the mine tailings used to construct the RRR are relatively uniform. Therefore, all sample results from the RRR will be pooled to estimate exposure concentrations of arsenic in RRR soil. Consistent with the approach used in the 2004 HHRA (CH2M Hill, 2004), in cases where a primary and duplicate sample were collected, the average of the two will be used as the concentration value PRA DISTRIBUTION The exposure concentration factor proposed for the PRA is the distribution of arsenic in RRR soil (C s ). The distribution of arsenic concentrations in soil was tested for goodness-of-fit using the EPA statistical program ProUCL Version (ProUCL), and was determined to fit a normal distribution with a mean of milligrams per kilogram (mg/kg), standard deviation (SD) of mg/kg, minimum of 18.5 mg/kg, and maximum of 241 mg/kg (Appendix A). For the purposes of PRA, this normal distribution is proposed to represent arsenic concentrations in RRR soil that a person may contact (Table 2) DRA EXPOSURE POINT CONCENTRATION The exposure point concentration (EPC) is an estimate of the average concentration of a chemical in the exposure unit that a receptor may contact on a long-term basis. The 90 percent upper confidence limit of the mean (90% UCL) will be used to represent the RME EPC of arsenic in RRR soil (DEQ, 2000). The 90% UCL is a value that has a 90 percent probability of being greater than the true population mean. The 90% UCL provides an estimate of the population mean that is biased on the high side to account for the uncertainty introduced by extrapolating from a sample to the population. For the CTE scenario, the EPC will be the sample mean, which represents the average concentration across sample locations. ProUCL was used to estimate the 90% UCL for arsenic in RRR soil used as the RME EPC. ProUCL estimates a UCL about the mean after evaluating which particular statistical method appears to be most appropriate for a given data set. The program evaluates distributional characteristics of a data set (e.g., normal, lognormal) to determine the preferred statistical method for estimating a UCL. Parametric statistical methods are generally preferred if data fit a normal or lognormal distribution. However, a number of nonparametric methods can be used if data do not meet the assumptions of parametric statistics. ProUCL recommends the best approach for estimating a 95% UCL about the mean, but not a 90% UCL. This is because the USEPA typically uses the 95% UCL about the mean as an EPC (Singh et al., 2010), while the DEQ uses the 90% UCL about the mean (DEQ, 2000). It was assumed that the preferred method for estimating a 95% UCL is also the preferred method for estimating a 90% UCL. The 90% UCL estimates from ProUCL, including the estimate selected as the proposed arsenic EPC for the DRA, are included in Appendix A. 19

28 A total of 58 samples were used to estimate the UCL about the mean concentration of arsenic in RRR soil (Table 4, Appendix A). As mentioned previously, arsenic concentrations in soil of the RRR appeared to fit a normal distribution (Appendix A). The 90% Student s-t UCL of mg/kg recommended by ProUCL is proposed as the arsenic RME EPC for soil from RRR (Appendix A). The mean concentration of mg/kg is proposed as the arsenic CTE EPC for soil from RRR (Appendix A) VARIABILITY AND UNCERTAINTY The exposure concentration distribution primarily reflects variability of arsenic levels in RRR soil. Given the relatively similar concentrations of arsenic over different portions of RRR, it appears that the material from the Bonanza mine used to develop the RRR had a relatively uniform composition. Presumably, the ore body that was mined was relatively homogenous. Scenario uncertainty in the arsenic concentration distribution used for the PRA arises because a sample from limited portions of RRR is used to estimate concentrations over the entire RRR. Fitting concentration measurements to a normal distribution involves model uncertainty. Parameter uncertainty results from potential errors in sampling, analysis, and reporting. Relative to other exposure factors, overall uncertainty in the concentration distribution is considered low. Also, the above uncertainties appear to be unbiased (i.e., they can either over- or underestimate actual exposure concentrations). The DRA RME EPC addresses natural variability through bias. The RME EPC is biased high so that the true mean concentration of arsenic in soil is unlikely to be underestimated by the sample mean. Sources of uncertainty in the DRA point estimates are the same as those discussed above for the concentration distribution. Again, these types of errors are expected to be small and unbiased, and the associated uncertainty is considered low relative to other types of uncertainty in risk estimates. 4.3 BODY WEIGHT Body weight (BW) tends to change over a person s life due to growth in early life stages and other factors in later stages. Also, males of the same age generally tend to have higher body weights than females. Therefore, body weight tends to be associated with both the age and gender of an individual PRA DISTRIBUTIONS Proposed age- and gender-specific body weight distributions are from the DEQ s (1998) PRA guidance (Table 5). The DEQ developed these distributions using data collected between 1976 and 1980 and reported in the Second National Health and Nutrition Survey (NHANES II) conducted by the National Center for Health Statistics. The NHANES II study had a relatively large sample size of over 20,000 individuals from a wide geographic area, and was intended to represent the entire US population based on age, sex, and race. Statistical analyses of the NHANES II data found that the natural logarithm of body weight tended to fit a normal distribution (lognormal) for both males and females (see DEQ, 1998). 20

29 Assuming a lognormal distribution, DEQ (1998) used the mean and standard deviation values reported by Burmaster and Crouch (1997) to create age- and gender-specific body weight distributions (Tables 2 and 5). Body weight tends to increase significantly each year prior to about the age of 18, followed by less pronounced changes during adulthood. Therefore, DEQ (1998) provides separate genderspecific distributions for each age from 0 to 17, and then for age classes that include multiple ages beginning at age 18 (Tables 2 and 5). As mentioned previously, each PRA simulation estimates exposure (and risk) for a hypothetical individual from the focal population, and there are typically 10,000 simulations. In some cases, the exposure duration for the hypothetical individual will be sufficiently long to include multiple age classes. Because some exposure factors vary with age, average annual exposures can change over the full exposure duration. To account for changes in body weight as a person ages, a constant weight is added to an individual s weight each time they advance to a different age in the subadult life stage, and when a person advances from the subadult to the adult age class. The constant is the gender specific difference between the mean body weights of the initial and subsequent ages. For example, assume that the randomly selected gender for an individual is male, the randomly selected initial age is 15, and the randomly selected agespecific exposure duration is 20 years. For this hypothetical individual, the first 3 years of the exposure duration would be spent in the subadult life stage, with the remaining 17 years in the adult stage. To account for changes in exposure over time, an average annual exposure for the 3 years in the subadult stage (ages 15 through 17) would be calculated along with an average exposure over the 17 years spent in the adult life stage. To estimate the body weight for age 16, a constant representing the difference between the average weight of a 15 year old male and a 16 year old male is added to the initial randomly selected body weight of a 15 year old male. A similar process is used to estimate the weight of the individual when they become 17, and when they become an adult (age 18 and over) DRA POINT ESTIMATE The EPA recommends that the RME point estimate for body weight be the average over the exposure period for a receptor (EPA, 1989b). DEQ s default RME for adult body weight (BW a ) is 70 kg, and child body weight (BW c ) is 15 kg. Both of these body weights will be used for adult and child residents, respectively (Table 2). The adult body weight estimate is reported in EPA s Risk Assessment Guidance for Superfund, Volume 1: Human Health Evaluation Manual, Part A, Interim Final (EPA, 1989a), and appears to represent the mean for all age groups of males and females as reported in the EPA s 1989 Exposure Factors Handbook (EPA, 1989b). The child body weight appears to represent the mean for male and female children under age 7 years (EPA, 1991a). However, it is unclear if the 15 kg estimate represents the grand mean based on the means of several age classes, or the mean of all individuals under age 7 years that were studied. Presumably both of the above adult and child body weight estimates were based on national studies and are not regional estimates. CTE body weight estimates are the same as RME estimates (DEQ, 2010). Typical body weights of people in the US appear to have increased in the recent past. The 2011 Exposure Factors Handbook (EPA, 2011) reports that the average adult body weight based on 21

30 data collected as part of NHANES from 1999 to 2006 is 80 kg, no longer 70 kg. For the purposes of this HHRA, the DEQ default adult body weight of 70 kg is proposed along with the child body weight of 15 kg to better maintain consistency with previous assessments. It should also be noted that EPA often estimates the toxicity of a chemical using an adult body weight of 70 kg, and this is another reason for using DEQ s default adult body weight. The Resident Worker scenario assumes that people perform regular outdoor work that could lead to contact with soil starting at age 12. This starting work age is greater than the age range of children assumed to have high soil ingestion rates. Subadults tend to have lower body weights than adults. For example, the average body weight of males and females in the age class given in the DEQ (1998) body weight distributions is estimated at 65.6 kg (assumed to be 70 kg in the DRA), and the average body weight of 12 year old males and females is approximately 40.5 kg. To be conservative, the body weight of people between age 12 and 18 years is assumed to be 41 kg. As discussed in Section 4.7.2, an age-adjusted soil ingestion rate that integrates soil ingestion rate, body weight, and exposure duration over multiple life stages is used to estimate exposure to carcinogens over the lifetime of a resident (DEQ, 2010). The age-adjusted soil ingestion rate for a Resident Worker was estimated assuming that the body weight of people age 12 through 18 years is 41 kg, and 70 kg for adults older than 18 years. For noncarcingenic effects, the Resident Worker is assumed to be a subadult (age 12 through 18 years) with a body weight of 41 kg VARIABILITY AND UNCERTAINTY The age- and gender-specific body weight distributions primarily reflect measured variability in body weight within the US. Use of the DEQ body weight distributions introduces several sources of uncertainty into the PRA. Scenario uncertainty is introduced because data from a national sample are used to estimate body weight characteristics of a local population that may differ from the national group. It appears that the DEQ body weight distributions may be biased low because they are based on older data, and typical body weights of people in the US are now higher than before. Use of these potentially low body weight distributions is conservative because lower body weights are associated with higher potential exposure and risk estimates. Errors in measurement, age classifications, and statistical extrapolations can introduce model and parameter uncertainty that is likely to be low and unbiased. Overall, relative to other exposure factors, uncertainty in body weight is considered low. The DRA body weight estimates share many of the same uncertainties discussed above for body weight distributions. However, the point estimates involve greater scenario uncertainty. For example, the point estimate for children is based on the average body weight of children between birth and age 6 years. This subgroup of children was selected because children under age 7 were reported to have relatively high soil ingestion rates (EPA, 1991a). Defining children as the subgroup of people under age 7 years introduces bias that is likely to overestimate risks to residents. The Resident Worker subadult body weight estimate was selected using professional judgment, which introduces uncertainty. It is lower than the adult body weight and likely results in overly conservative exposure estimates for the Resident Worker. 22

31 4.4 EXPOSURE DURATION To estimate the total time in years that a person may be exposed to soil at a site, the DEQ uses an estimate of residential occupancy period (DEQ, 1998). This is an estimate of the total time a person spends living in a particular residence (e.g., house or apartment). This concept assumes that a resident may be exposed to a nearby contaminated site as long as they reside in a single residence, but exposure would discontinue after they move from the residence. Residential exposure duration (ED) varies with age due to general changes in behavior that occur as one ages. For example, many people are relatively mobile in early adulthood because they may move away from their parent s home, rent and not own homes, etc. Later in adulthood, people tend to move less often PRA DISTRIBUTION DEQ-estimated age-specific exposure duration distributions are given in Table 6. DEQ based these distributions on a model of residential occupancy period by Johnson and Capel (1992). The Johnson and Capel (1992) study is also used by EPA in the 2011 Exposure Factors Handbook to define residential exposure duration, suggesting that few alternative data and/or approaches are available to estimate resident exposure duration. Therefore, DEQ (1998) exposure duration distributions for several age classes are proposed in the PRA (Tables 2 and 6). As mentioned previously, the randomly selected exposure duration for a hypothetical subadult could potentially extend into the adult age class. In these situations, it is necessary to define at least two exposure intervals to account for changes in body weight and other potential exposure factors as the individual advances in age. For example, the subadult and adult exposure intervals are defined as the portions of the total exposure duration that occur within each life stage (i.e., before and after [including] age 18). If the randomly selected starting age of a hypothetical individual was 12 and the randomly selected age-specific exposure duration was 9 years, the subadult exposure interval would be 6 years (age 12 17), and the adult exposure interval would be 3 years (age 18 20) DRA POINT ESTIMATE The DEQ (2010) recommends a RME point estimate of exposure duration for a resident (ED r ) of 30 years. This total exposure duration is divided into a child component (ED c ) of 6 years, and an adult component (ED a ) of 24 years. These exposure duration estimates will be used for child and adult residents, respectively (Table 2). It appears that the total resident exposure duration of 30 years was first reported in EPA s Risk Assessment Guidance for Superfund, Volume 1: Human Health Evaluation Manual, Part A, Interim Final (EPA, 1989a), and represents a 90 th percentile from a national study. The child exposure duration of 6 years (birth through age 6) was selected as the RME value to represent the time period when incidental soil ingestion is typically highest (EPA, 1991a). The Resident Worker exposure scenario does not include children, but does include subadults at or above age 12. As with the Residence On Road and Residence Near Road scenarios, the Resident Worker RME exposure duration was set at 30 years. The subadult component of the 23

32 RME exposure duration was set to 6 years (age 12 through 18 years), and the adult component was set to 24 years (Section 4.7.2). The DEQ (2010) CTE exposure duration for an adult is 3 years, and again 6 years for a child. The rationale and data that support these recommended CTE estimates are not given by DEQ. These estimates will be used for the Residence On Road and Residence Near Road CTE scenarios. The Resident Worker CTE scenario will use an adult exposure duration of 3 years, and a subadult value of 6 years (Table 2). When evaluating carcinogenic effects, the exposure duration term is integrated into the ageadjusted soil ingestion rate (Section 4.7.2). Age-adjusted soil ingestion rates for residents were estimated using the above-mentioned RME and CTE exposure durations for the child, subadult (only Resident Worker), and adult life stages (Table 2) VARIABILITY AND UNCERTAINTY The age-specific exposure duration distributions primarily reflect variability in the time that individuals of different ages spend at a particular residence, based on national data. Scenario uncertainty is introduced because data from a national sample are used to estimate total time spent at a residence near the RRR, and local mobility patterns may differ from national patterns. Also, some of the mobility data used to develop distributions are decades old, and it is possible that patterns in mobility have changed in the recent past. Models were used to develop the agespecific distributions, and there is uncertainty associated with model extrapolations. Parameter uncertainty includes potential errors in measurement, age classifications, etc. Relative to several other exposure factors, uncertainty in exposure duration distributions is considered low and unbiased. The DRA RME exposure duration estimates share the same uncertainties associated with the age-specific distributions, but add additional scenario uncertainty. Default point estimates involve greater scenario uncertainty because they are biased. For example, the total exposure duration for a resident was set at the 90 th percentile, and the child component of this exposure duration was set at 6 years to represent the time frame when incidental soil ingestion is highest. The biased point estimates of exposure duration are likely to overestimate exposure. DRA CTE exposure duration estimates are uncertain because the data and rationale for these estimates are not clearly described in DEQ (2010) guidance. 4.5 EXPOSURE FREQUENCY Exposure frequency (EF) is the number of days per year that a resident is likely to contact RRR soil. DEQ PRA guidance presents two potentially relevant concepts for estimating exposure frequency: the number of days per year a person spends time at their residence, and the number of days per year a resident is likely to contact soil (DEQ, 1998). DEQ (1998) does not provide empirical data for either concept of exposure frequency, and both are estimated using professional judgment. The average exposure frequency based on the number of days per year a person spends time in their residence is greater than the average number of days per year a resident is likely to contact soil. The more conservative concept is proposed for RRR for all residential scenarios (Table 2). 24

33 4.5.1 PRA DISTRIBUTION The DEQ (1998) exposure frequency distribution for days per year spent in a residence is a uniform distribution with the EPA s DRA RME estimate of 350 days per year (EPA, 1991a, DEQ, 2010) as the minimum, and the maximum possible (365 days per year) as the maximum (Table 2). As a result, this distribution based on professional judgment will result in more conservative estimates of exposure frequency than the RME point estimate. The distribution assumes that people spend no more than about 2-weeks per year away from their home DRA POINT ESTIMATE The DEQ (2010) RME and CTE estimates for exposure frequency are both 350 days per year (Table 2). The rationale for the DEQ (2010) CTE exposure frequency is not reported. Given that both RME and CTE estimates are simply based on professional judgment, it is unclear why they are identical. Presumably insufficient information is available regarding days per year spent in a residence, and potential high end (RME) estimates could not be reliably differentiated from typical (CTE) estimates VARIABILITY AND UNCERTAINTY The exposure frequency distribution for residents does not reflect measured variability, and instead represents a potential conception of an uncertain parameter. The distribution assumes that no resident spends more than 15 days away from the home, and this assumption is likely incorrect. Given the lack of data, an assumed uniform distribution is reasonable, but the true unknown distribution may be modal (or possibly multi-modal). Scenario uncertainty is relatively significant with exposure frequency. As mentioned previously, exposure frequency is estimated as the number of days per year that a person spends time in the home. It is likely that many residents will not contact soil each day they are at a residence. This concept of exposure frequency appears to be well-suited to estimate inhalation exposures for the Resident On Road scenario where dust particles from RRR are assumed to be pervasive in the home, but results in bias for direct soil contact exposures. The DEQ (2010) exposure frequency based on days per year that a person contacts soil is conceptually more appropriate for the direct soil contact exposure estimates. Both the point estimates of exposure frequency and the distribution are based on professional judgment. The DEQ (1998) exposure frequency distribution is associated with additional uncertainty because the distribution assumes that exposure frequency cannot be less than the RME estimate, while the actual, unknown distribution likely contains values lower than this reasonable maximum estimate. Relative to other exposure factor estimates, overall uncertainty associated with exposure frequency estimates are considered moderate to high. For direct contact exposures, exposure frequency appears to be biased high resulting in overestimates of potential exposure. 25

34 4.6 EXPOSURE TIME When a contaminated environmental medium (e.g., soil, air) represents only a fraction of the medium that a person may routinely contact, the exposure time (ET) concept may be used to account for exposure to contaminants. ET is defined as the proportion of the daily exposure activities in hours per day (hr/d) that take place in contaminated locations. DEQ (1998) also defines exposure time as exposure frequency (EF hd ) in units of hr/d, but the acronym ET is used in this Work Plan. The DEQ (1998) describes the process for estimating ET for soil ingestion as follows: For incidental soil ingestion, exposure to a contaminant of concentration C s in soil is assumed to occur at a rate of X mg per 24 hour day (IRS), EF dy days per year. The worst case assumption is that soil contaminated at concentration C s is ubiquitous in an individual s environment; i.e., all the soil that they contact on a daily basis is contaminated at a level of C s, in which case EF hd = 24. Alternatively, it is possible to consider that an individual has an opportunity to incidentally ingest soil contaminated at concentration C s only at certain times during a 24 hour day; for example, while gardening or playing outdoors. In these instances, EF hd < 24, but EF dy may remain the same. DEQ (2010) uses ET to estimate inhalation risks when only a fraction of inhaled air is contaminated. A conceptually similar approach is used by DEQ to evaluate exposure to contaminants in foods (e.g., fish, home-grown vegetables, home-raised meat, etc.) when only a proportion of the foods consumed by a person come from a contaminated site (DEQ, 1998, 2010). For example, DEQ adjusts the daily vegetable ingestion rate using the fraction of vegetables from a contaminated site (F v ) to estimate exposure to contaminants that have accumulated in vegetables (DEQ, 1998; DEQ, 2010). The proposed HHRA will use ET to estimate exposure to both soil and airborne dust from RRR for some exposure scenarios. It will be assumed that daily soil and airborne dust exposure from RRR is correlated with the time spent at contaminated locations. A similar approach was used by DEQ to estimate contaminated soil ingestion on a portion of the RRR that underwent a previous remedial action (DEQ, 2009). Exposure scenarios for RRR were defined, in part, based on the different exposure activities of each receptor type. For example, Resident Workers include residents that are exposed to RRR soil when performing outdoor agricultural work away from the home. The Residence On Road and Residence Near Road scenarios include residents exposed to RRR soil while working or recreating in and near the home. DEQ (1998) provides ET distributions for several scenarios, including time (hr/d) spent indoors, outdoors, or at work. DEQ (1998) information regarding the time that residents spend outdoors each day is proposed to estimate the proportion of total soil ingested due to RRR (Table 2). Time spent outdoors is used as a proxy for time spent on RRR, and it is assumed that the fraction of daily soil ingestion due to RRR is proportional to the fraction of the day spent on RRR. To estimate soil ingestion associated with activities that take place for a fraction of a day, DEQ has assumed that ingestion (and inhalation) rates are uniform throughout the day (DEQ, 1998). There are very few data accurately characterizing daily soil ingestion by adults (EPA, 2011), and there appear to be no good data on soil ingestion rates at finer temporal scales. The assumption 26

35 that soil ingestion rates and air inhalation rates are constant throughout the day involves moderate uncertainty PRA DISTRIBUTION DEQ (1998) provides distributions for time that residents spend outdoors for two age classes: ages 0 to 12 years, and greater than 12 years. As with body weight, potential changes in time spent outdoors as a person ages are accounted for by selecting from the appropriate distribution for each year spent within the two DEQ age classes. These DEQ distributions were based primarily on interpretations of activity pattern data given in EPA s 1997 Exposure Factors Handbook (EPA, 1997). EPA (1997) had recommended activity pattern data from the National Human Activity Pattern Survey (NHAPS). The NHAPS data were collected from 9,386 randomly selected respondents in the contiguous 48 states between 1992 and 1994 using 24-hour diaries. For both age classes, DEQ (1998) recommends triangular distributions for time spent outdoors, where the mode is the most likely time spent outdoors estimated based on NHAPS data. ET is used differently for the various residential exposure scenarios. For example, the Residence On Road scenario assumes that these residents are exposed to only RRR soil and dust at all times, and exposure was not adjusted based on ET (ET was set to 24 hr/d). For the Residence Near Road and Resident Worker scenarios, it was assumed that exposure to RRR soil and dust occurs when a person is outdoors, and that all time spent by a resident outdoors is in a location where a person may contact RRR soil and inhale RRR dust. The Residence Near Road scenario assumes that exposure to RRR soil can occur through outdoor play in childhood, and through outdoor work and recreation in adulthood. Therefore, both age class distributions are used to estimate exposure for the Residence Near Road scenario. The Resident Worker scenario assumes that soil exposure occurs primarily as a result of outdoor work starting at age 12, and only the distribution for age greater than 12 years is used for this scenario DRA POINT ESTIMATE DEQ DRA guidance includes the ET factor for estimating inhalation exposures, but not for soil ingestion (DEQ, 2010). Both the default RME and CTE values for ET used to estimate inhalation exposures for a resident are 24 hr/d (DEQ, 2010). For the Residence On Road scenario, the RME and CTE ETs for soil ingestion and dust inhalation were set at 24 hr/d (Table 2), consistent with DEQ DRA guidance (DEQ, 2010). The Residence Near Road and Resident Worker scenarios assume that exposure to RRR soil occurs when a person is outside of the home and near RRR. Based on the DEQ distribution of time that residents spend outdoors, professional judgment was used to estimate point values of ET for these two scenarios (Table 2). For both scenarios, the RME was set at the maximum of the DEQ distribution, and the CTE was set at the mode. For the Resident Worker scenario, the values for the >12 year age group (RME of 4 hr and CTE of 2 hr) will be used. For the Residence Near Road scenario, the RME (6 hr) and CTE (4 hr) values for the 0-12 year age group will be used. This is conservative since ET values for children will be used to calculate combined child/adult cancer risk estimates incorporating age-adjusted soil ingestion rates 27

36 (Section 4.7.2). As mentioned previously, the ET for soil ingestion and inhalation assumes that rates are constant throughout the day VARIABILITY AND UNCERTAINTY DEQ (1998) ET distributions are largely driven by reported variability in the time that residents spend outdoors. However, professional judgment was used in assigning triangular distributions and boundary conditions (minimum and maximum) for the distributions. Therefore, the distributions include uncertain assumptions. The proposed ET distributions include important scenario uncertainty. It is assumed that all time spent outdoors involves contact with RRR soil. In fact, a substantial amount of outdoor activity may occur at locations where residents are unlikely to contact RRR soil. It is likely that use of time spent outdoors to estimate time contacting RRR soil overestimates soil contact. Similarly, soil ingestion rates are only available for daily exposures and there appear to be no good data on soil ingestion rates at finer temporal scales. Parameter uncertainty includes potential errors in time budget estimates, age classifications, etc. Relative to other exposure factors, uncertainty in ET is considered low. 4.7 SOIL INGESTION RATE Soil ingestion is defined as the incidental ingestion of soil particles due, in part, from hand-tomouth activities such as eating food with adhered soil. Soil ingestion may also include the swallowing of small particles that are inhaled and captured by linings of upper respiratory and alimentary surfaces (these particles do not enter the lungs). Three general types of material are assumed to make up the soil that is incidentally ingested by people (EPA, 2011): Soil the mineral and organic substrate on the ground surface of the earth (typically outdoors). Indoor settled dust particles that have settled onto indoor surfaces and objects. Outdoor settled dust particles that have settled onto outdoor surfaces. Because it may be impossible to distinguish between outdoor settled dust and soil, these two categories of material types are typically lumped. Soil ingestion rate estimates given below include all three of the above materials. Several studies of soil ingestion have been performed using a variety of methods (DEQ, 1998). Measuring soil ingestion rate is difficult, and all studies have important limitations (EPA, 2011). Soil ingestion estimates vary somewhat across studies and methods, but in general, different studies have resulted in roughly similar estimates (EPA, 2011). However, it should be noted that uncertainty in soil ingestion estimates is high, and confidence in estimates is low PRA DISTRIBUTION The soil ingestion rate (IRS) distributions in milligrams per day (mg/d) estimated by DEQ (1998) are proposed for the RRR site. Because several studies suggest that soil ingestion rates of 28

37 young children are higher than those for adults (EPA, 2011), likely due to mouthing behaviors of young children, DEQ (1998) developed two soil ingestion rate distributions: one for people aged <1 to 6 years, and another for people aged 7 years or greater (Table 2). The Residence On Road and Residence Near Road scenarios include young children and will involve use of both soil ingestion rate distributions. The Resident Worker scenario includes people age 12 and older, and will only involve use of the DEQ soil ingestion rate distribution for age 7 and older. Results of several studies suggest that soil ingestion estimates tend to fit a lognormal distribution, and DEQ (1998) used this type of distribution for both adult and child soil ingestion rates. The parameters of these distributions were based on a combination of data from several separate studies. As with body weight, exposure duration, and exposure time, soil ingestion rates vary as a function of age. To account for changes in soil ingestion rate for hypothetical individuals with a starting age <7 years that advance to the next age class, ingestion rates will be selected from the appropriate distribution for each year spent within the corresponding age class. Because intake relative to body size is a function of both soil ingestion rate and body weight, an ageadjusted soil ingestion rate (IRS adj ) will be calculated that incorporates changes in these exposure factors across all years of the total exposure duration. The IRS adj combines agespecific soil ingestion rate and body weight with the portion of the total exposure duration spent within each age range. For example, assume that the randomly selected initial age is 5, and the randomly selected age-specific exposure duration is 20 years. In this case, the first 2 years of the ED are spent within the <1 to 6 year age class, and the remaining 18 years are spent in the age 7 or older class; these are the two age classes corresponding to different distributions for soil ingestion rate. Because body weight for children and subadults changes annually, the portion of the total exposure duration spent at each body weight will be one year for each weight up to age 18 (i.e., the first 13 years), with the remaining 7 years spent at an adult body weight. So, the component of the IRS adj representing the first two years of exposure would be equal to [(1 year x IRS for >1 to 6 age class)/bw age 5] + [(1 year x IRS for >1 to 6 age class)/bw age 6]. The component representing the next 11 years would be [(1 year x IRS for 7+ age class)/bw age 7] + [ ] + [(1 year x IRS for 7+ age class)/bw age 17] incorporating each age from 7-17 years. For the remaining 7 years, it would be [(7 years x IRS for 7+ age class)/bw adult]. The resulting IRS adj is similar to that described below for the DRA (Section 4.7.2), but incorporates changes in both soil ingestion rates and body weights with age over the total exposure duration DRA POINT ESTIMATE DEQ s default RME soil ingestion rates for adult (IRS a ) and child (IRS c ) residents are 100 mg/d and 200 mg/d, respectively (DEQ, 2010). The child life stage is defined as extending from birth through age 6 years. Both IRS estimates are reported to be upper percentile values from adult and child soil ingestion rate distributions (EPA, 1989), but the specific upper percentile is not reported (DEQ, 2010). CTE soil ingestion rates are 100 mg/d for resident children, and 50 mg/d for adults. The particular central tendency estimate (e.g., mean, median, mode) upon which these default values are based is not reported (DEQ, 2010). For residents exposed to a carcinogen such as arsenic, DEQ (2010) recommends use of an age-adjusted soil ingestion rate (IRS adj ). Age-adjusted soil ingestion rates integrate exposure 29

38 from birth until age 30 by combining the soil ingestion rate and body weight for both the child and adult components of the exposure duration. For the Residence On Road and Residence Near Road scenarios, the child exposure spans 6 years (birth to age 6 yr) and the adult exposure spans the remaining 24 years of the exposure duration. The equation for estimating age-adjusted soil ingestion rate for a resident is as follows (DEQ, 2010): Using the above equation and the exposure factor values previously discussed, the RME ageadjusted soil ingestion factor for a resident is 114 mg-yr/kg-d, and the CTE factor is 57 mgyr/kg-d (Table 2). For the Resident Worker, exposure begins as a subadult (age 12 years). The subadult exposure duration is assumed to span 6 years (age 12 through 18 years) and the adult exposure spans the remaining 24 years of the exposure duration. The soil ingestion rate for the subadult stage is the same as the adult stage. The equation for estimating age-adjusted soil ingestion rate for a Resident Worker is as follows (DEQ, 2010): Where the undefined parameters are as follows: ED sa = Exposure duration of subadults (age 12 through 18 years) of 6 yr = Body weight of subadults of 41 kg. BW sa Using the above equation and the exposure factor values previously discussed, the RME ageadjusted soil ingestion factor for a Resident Worker is approximately 49 mg-yr/kg-d, and the CTE factor is approximately 24 mg-yr/kg-d (Table 2) VARIABILITY AND UNCERTAINTY Soil ingestion rate distributions reflect measured variability, but the distributions appear to be significantly influenced by uncertainty. Parameter uncertainty is likely relatively high given the difficulty in measuring soil ingestion (EPA, 2011). For example, one common method to estimate soil ingestion is to measure tracer elements such as aluminum in feces, foods that people eat, soil that people may contact, and other materials people may ingest. Using information on the composition of the element in various materials that a person may ingest, and attempt is made to tease out the portion of the element in feces due to soil ingestion versus other ingested sources. Typically, a number of assumptions must be made to derive the soil ingestion estimate. Other methods for estimating soil ingestion involve similar measurement uncertainties (EPA, 2011). Limited data are available characterizing soil ingestion rates, especially for adults (DEQ, 1998). Because data sets are small, in all likelihood only a limited number of potential soil exposure activities are reflected in available estimates. Soil exposure activities by residents near RRR may be poorly represented in available data. The process of fitting a distribution to limited soil 30

39 ingestion data and then extrapolating to estimate soil ingestion for residents near RRR involves important model and scenario uncertainty. Relative to other exposure factors, uncertainty in soil ingestion rate distributions is considered moderate. In general, soil ingestion rate distributions appear to be relatively unbiased. All of the above-mentioned uncertainties also apply to DRA point estimates. However, point estimates consider both variability and uncertainty through bias. The RME point estimates are reported to be biased high, but the degree to which they are biased high is unclear. Similarly, CTE estimates presumably represent typical exposures, but because these estimates are poorly defined, it is difficult to evaluate whether they represent accurate central tendency values. 4.8 GASTROINTESTINAL ABSORPTION FACTOR The exposure factors discussed previously are used to estimate chemical intake. Intake is defined as the amount of a substance that contacts an exchange boundary (e.g., gastrointestinal tract) in a person, and may be used to estimate the administered dose (EPA, 1989a). With some metals such as arsenic, only a fraction of the substance that contacts an exchange boundary crosses the barrier and is absorbed by the body. For example, some arsenic in soil may be present in a relatively insoluble complex with other minerals, and instead of being absorbed by the body, the arsenic passes through the body and is excreted unchanged. With metals such as arsenic, the unabsorbed fraction is unlikely to cause toxicity. The gastrointestinal absorption factor (ABS gi ) is used to estimate the portion of the chemical intake via ingestion that is absorbed by the gut. Using a weight-of-evidence evaluation, a point value of 0.1 was estimated as the reasonable upper bound for the gastrointestinal absorption factor for arsenic in RRR soil (Table 2). This estimate was based on site-specific data regarding the form of arsenic in RRR soil, and the bioaccessibility of arsenic in RRR soil based on in vitro extraction tests that simulated gastrointestinal conditions. The arsenic speciation study found that the predominant form of arsenic in RRR soil is an iron oxide phase that tends to have a low oral bioavailability. Also, the RRR soil arsenic bioaccessibility study suggested that bioavailability is low (GSI, 2011a). As discussed previously, TERA s independent review of the site-specific gastrointestinal absorption factor estimate confirmed that 0.1 is a reasonable estimate of the upper bound gastrointestinal absorption factor for arsenic in RRR soil. Insufficient information is available to develop a potential distribution of values for the arsenic gastrointestinal absorption factor for RRR soil. Therefore, a point estimate of 0.1 is proposed for the gastrointestinal absorption factor. This point estimate will also be used to evaluate both RME and CTE scenarios under the DRA. The arsenic gastrointestinal absorption factor includes several types of uncertainty. Potential absorption was estimated using bioaccessibility results from test systems, and uncertainty is created when these test results are extrapolated to estimate absorption across the human gut. Model and parameter uncertainty can result from errors in test systems, measurement errors, and reporting errors. Professional judgment was used in interpretation of bioccessibility and other data, resulting in potential uncertainty. The selected point value for the gastrointestinal absorption factor was above the range of measured bioaccessibility, potentially resulting in a somewhat biased (conservative) estimate of absorption. Relative to other exposure factors, uncertainty in the gastrointestinal absorption point estimate is considered moderate. 31

40 4.9 PARTICULATE EMISSION FACTOR The particulate emission factor (PEF) is an estimate of the amount of airborne dust (particles under 10-microns) that may be present above an area of contaminated soil due to wind erosion. In the environment, entrainment of dust in air due to wind effects is a complex process shaped by many factors, including surface soil characteristics (e.g., particle size, moisture content), wind speed, vegetation cover, and others. DEQ s default PEF is based on a model (EPA, 1996) where several conservative assumptions were used to estimate dust entrainment in air (DEQ, 2003a). Given that a variety of assumptions are used to model PEFs, and there appear to have been few, if any, attempts to evaluate the predictive performance of modeled PEFs in the field, developing a meaningful distribution of PEFs is difficult. Therefore, DEQ s default point estimate of PEF is proposed for both the PRA and DRA (Table 2). The same value is proposed for both RME and CTE scenarios. As mentioned above, the DEQ PEF is based on a model that uses a number of conservative assumptions, and is likely to overestimate airborne dust levels at most sites in Oregon. Furthermore, because little to no information is available regarding the correspondence between measured and modeled estimates of airborne contaminant concentrations due to wind erosion at contaminated sites, relative uncertainty in the PEF is considered high. 32

41 5. TOXICITY ASSESSMENT Potential toxic effects of chemicals are generally classified as carcinogenic (i.e., cancercausing), or noncarcinogenic (i.e., noncancer health effects). These endpoints are separately quantified in HHRAs as cancer risks and noncancer health effects, respectively. Toxicity values numerically express the magnitude of potential toxic effects of chemicals. Oral reference doses (RfDs) are used to quantify noncancer health effects associated with ingestion exposures, and inhalation reference concentrations (RfCs) are used for inhalation exposures. Similarly, oral slope factors (SFs) are used to quantify potential cancer risks associated with oral exposures, and inhalation unit risk factors (IURs) are used for inhalation exposures. Both cancer and noncancer endpoints may be evaluated for carcinogenic chemicals depending on the chemicals toxic effects and availability of noncancer toxicity values. The hierarchy of sources used to select toxicity values proposed for the DRA and PRA evaluations was to first use values from EPA s Integrated Risk Information System (IRIS). IRIS is an online database of toxicity values that have gone through a peer review and EPA consensus review process (EPA, 2014a). If no toxicity value was available in IRIS, the EPA Regional Screening Levels table was reviewed (EPA, 2014b). As discussed below, IRIS was used to identify the oral RfD, oral SF, and IUR. The inhalation RfC was taken from the EPA Regional Screening Levels table. Arsenic toxicity values are estimated using several professional judgments, and are typically based on a variety of studies that may have investigated different types of effects in different groups of people or laboratory animals (EPA, 2014a). As a result, creating a distribution for a toxicity value is difficult. Therefore, both the DRA and PRA will use the toxicity point values discussed below. The RME and CTE scenarios use the same toxicity point values. 5.1 ORAL REFERENCE DOSE Expressed in units of mg/kg-day, the RfD is an estimate of the daily exposure dose to humans (including sensitive populations) that is likely to be without an appreciable risk of deleterious effects during a lifetime. The RfD represents an estimate of a threshold dose for adverse noncancer effects with uncertainty spanning perhaps an order of magnitude. The oral RfD for inorganic arsenic is 3 x 10-4 mg/kg-d (EPA, 2014a). This RfD is based largely on studies of people (primarily in Taiwan) that ingest relatively high levels of natural inorganic arsenic in well water. The primary noncancer effect upon which the RfD is based is skin lesions. 5.2 INHALATION REFERENCE CONCENTRATION The inhalation RfC (expressed in units of mg/m 3 ) is an estimate of the concentration in air that is a threshold for adverse noncancer effects in people with continuous long-term inhalation exposure. The RfC differs from the RfD in that it is a concentration, not a dose. IRIS does not provide a RfC for arsenic. The EPA Regional Screening Levels table lists the arsenic RfC as 1.5 x 10-5 mg/m 3 (EPA, 2014b). 33

42 5.3 ORAL SLOPE FACTOR A carcinogenicity assessment is a weight-of-evidence evaluation of the likelihood that a chemical is a human carcinogen and the conditions that may influence the expression of a carcinogenic effect. The oral SF represents a plausible upper bound on the estimate of risk per mg/kg-day of oral long-term exposure. SFs are typically estimated by assuming there is a linear relationship between the long-term exposure dose and cancer risk. Using this linear relationship, cancer risks associated with high exposures are used to estimate potential low dose effects. IRIS gives an oral SF for arsenic of 1.5 (mg/kg-day) -1 (EPA, 2014a). Again, much of the information upon which the SF is based is from studies of people in Taiwan reported to have higher cancer rates due to ingesting well water with high levels of inorganic arsenic. 5.4 INHALATION UNIT RISK FACTOR The inhalation IUR is a plausible upper bound estimate of the cancer risk based on μg of substance per m 3 of air breathed (EPA, 2014a). Like the SF, the IUR is based on a weight-ofevidence evaluation. The IUR is a concentration, and the toxicity value assumes people have long-term continuous exposure to the concentration in air. IRIS gives an IUR for arsenic of 4.3 x 10-3 (µg/m 3 ) -1. The IUR is reported to be based primarily on lung cancer effects. 34

43 6. RISK CHARACTERIZATION As mentioned previously, the scenarios that will be evaluated with DRA and PRA include Residence On Road, Residence Near Road, and Resident Worker. Potential cancer and noncancer health risks are estimated separately using slightly different methods (DEQ, 2010). Noncancer health effects associated with potential exposure to arsenic in soil are estimated with a Hazard Quotient (HQ). HQs are estimated by comparing the noncancer exposure dose to the corresponding noncancer reference dose (i.e., ratio of dose to RfD). For residents, HQs are only estimated for children. To estimate noncancer risks associated with exposure to multiple noncarcinogens, a hazard index is often calculated as the sum of chemical-specific HQs. The term hazard index may also be used to represent the sum of HQs over multiple exposure routes for the same chemical. Because only a single hazardous substance is evaluated (i.e., arsenic), this Work Plan uses the term total HQ to represent the sum of HQs over the soil ingestion and inhalation exposure routes. This convention is proposed to avoid potential confusion associated with the hazard index term because only a single chemical is being evaluated. Cancer risks are estimated as a theoretical lifetime excess cancer risk (LECR). The term theoretical lifetime excess risk is used to distinguish risk estimates associated with exposure to RRR soil from cancer risks in the general population from numerous sources. For example, the chance of being diagnosed with cancer at any time in a person s life is about 1 in 2 for US males, and about 1 in 3 for US females (American Cancer Society, 2014). LECR is an estimate of the cancer risk in excess of the general lifetime risk. LECR is estimated as the product of the exposure dose and cancer slope factor. LECR estimates include exposures that occur in both childhood and adulthood. Both HQs and LECRs are estimated for each exposure pathway (e.g., ingestion, inhalation), and then are summed across all pathways. DEQ (2010) recommends that HQs be expressed in two significant units, although this convention likely overstates precision in the risk estimate. LECRs are expressed as one significant unit. DEQ acceptable risk levels are used to evaluate the significance of HQ and LECR estimates. The DEQ chemical-specific acceptable HQ is one (Oregon Administrative Rules [OAR] (4)(a)), and the acceptable LECR is 1 x 10-6 (OAR (2)(a)). For PRA, the acceptable cancer risk is 1 x 10-6 at the 90 th percentile of the LECR distribution, and 1 x 10-5 at the 95 th percentile of the same distribution (OAR (2)(b)). 6.1 DETERMINISTIC RISK ASSESSMENT The process for estimating both cancer and noncancer risks can be broken down into three steps: 1) estimate average daily doses (ADDs) for each exposure pathway, 2) estimate pathway-specific risk estimates, and 3) sum pathway-specific risk estimates to get the total risk estimate for a particular exposure scenario. Quantitative risk estimates are proposed for two exposure pathways: soil ingestion and particulate inhalation. Given the low potential for arsenic in soil to be absorbed by the skin, potential dermal risks will be evaluated separately in the 35

44 uncertainty evaluation. General equations for estimating HQs and LECRs in the DRA are described below HAZARD QUOTIENTS Equations used in the three step process for estimating HQs are given below. Again, HQs are estimated only for the child life stage. Ingestion ADD: 1 2 Where: ADDsi EPC EF EDc ET IRSc ABSgi CF1 CF2 BWc ATnc = Average daily dose from soil ingestion (mg/[kg d]) = Exposure point concentration (mg/kg) = Exposure frequency (d/yr) = Exposure duration for a child (yr) = Exposure time (hr/d) = Soil ingestion rate for a child (mg/d) = Gastrointestinal absorption factor (unitless) = Unit conversion factor (1 d/24 hr) = Unit conversion factor (10-6 kg/mg) = Body weight for a child (kg) = Averaging time for a noncarcinogen (d); AT nc = ED x 365 d/yr Inhalation ADC: 1 Where the undefined parameters are as follows: ADC ap = Average daily concentration from inhalation of particulates (mg/m 3 ) ET = Exposure time (hr/d) PEF = Particulate emission factor (m 3 /kg) Ingestion HQ: Where the undefined parameters are as follows: HQsi = Hazard quotient from soil ingestion (unitless) RfD = Oral reference dose (mg/[kg d]) 36

45 Inhalation HQ: Where the undefined parameters are as follows: HQap = Hazard quotient from inhalation of particulates (unitless) RfC = Inhalation reference concentration (mg/m 3 ) Total HQ: Where: HQ = Hazard quotient for both ingestion and inhalation (unitless) LIFETIME EXCESS CANCER RISK Equations used in the three step process for estimating LECRs are given below. LECRs are estimated assuming exposure may occur in both childhood and adulthood. Ingestion ADD: 1 2 Where the undefined parameters are as follows: IRS adj = Age-adjusted soil ingestion rate ([mg yr]/[kg d]) ATc = Averaging time for a carcinogen (d) Inhalation ADC: Ingestion LECR: 1 Where the undefined parameters are as follows: LECRsi = Lifetime excess cancer risk from soil ingestion (unitless) SF = Oral cancer slope factor (mg/[kg d]) -1 Inhalation LECR: 3 37

46 Where the undefined parameters are as follows: LECRap = Lifetime excess cancer risk from particulate inhalation (unitless) IUR = Inhalation unit risk factor (µg/m 3 ) -1 CF3 = Unit conversion factor (1,000 µg/mg) Total LECR: Where: LECR = Lifetime excess cancer risk from soil ingestion and particulate inhalation (unitless) 6.2 PROBABILISTIC RISK ASSESSMENT The basic equations that can be used in PRA are similar to those described above for DRA. The primary difference is that distributions are used for some exposure factors instead of point values. Because distributions capturing the range of known variability are used in PRA, biased risk estimates such as those based on RME values are not made. Instead, conservatism is used when evaluating which portion of the distribution of risk estimates represents unacceptable risk. PRA estimates risk to hypothetical individuals of the population. For each individual, the gender is randomly selected from the gender distribution, and the starting age is selected from the age distribution. Both of these distributions were discussed previously. After hypothetical individuals are selected, the general equations given below can be used to estimate an HQ and LECR for each individual. The PRA output is a distribution of HQs and LECRs for the population HAZARD QUOTIENTS Equations used in a three step process for estimating HQs are given below. Ingestion ADD: 1 2 Where: ADDsi C s EF ED ET IRS ABSgi CF1 CF2 = Distribution of average daily dose from soil ingestion (mg/[kg d]) = Distribution of concentrations in soil (mg/kg) = Distribution of exposure frequency (d/yr) = Distribution of exposure duration (yr) = Distribution of exposure time (hr/d) = Distribution of soil ingestion rate (mg/d) = Point value of gastrointestinal absorption factor (unitless) = Unit conversion factor for (1 d/24 hr) = Unit conversion factor (10-6 kg/mg) 38

47 BW ATnc = Distribution of body weight (kg) = Averaging time for a noncarcinogen (d); AT nc = ED x 365 d/yr Inhalation ADC: 1 Where the undefined parameters are as follows: ADC ap = Distribution of average daily concentration from inhalation of particulates (mg/m 3 ) PEF = Point estimate of particulate emission factor (m 3 /kg) Ingestion HQ: Where the undefined parameters are as follows: HQsi = Distribution of hazard quotients from soil ingestion (unitless) RfD = Oral reference dose (mg/[kg d]) Inhalation HQ: Where the undefined parameters are as follows: HQap = Distribution of hazard quotients from inhalation of particulates (unitless) RfC = Inhalation reference concentration (mg/m 3 ) Total HQ: Where the undefined parameter is as follows: HQ =Distribution of total hazard quotients (unitless) LIFETIME EXCESS CANCER RISK Equations used in the three step process for estimating LECRs are given below. Ingestion ADD:

48 Where the undefined parameter is as follows: ATc = Point estimate of averaging time for a carcinogen (d) Inhalation ADC: Ingestion LECR: 1 Where the undefined parameter is as follows: LECRsi = Distribution of lifetime excess cancer risks from soil ingestion (unitless) Inhalation LECR: 3 Where the undefined parameter is as follows: LECRap = Distribution of lifetime excess cancer risks from particulate inhalation (unitless) Total LECR: Where: LECR = Distribution of lifetime excess cancer risks from soil ingestion and particulate inhalation (unitless) 6.3 SENSITIVITY ANALYSES Sensitivity analysis can evaluate the contribution that each exposure factor plays in determining the magnitude and variance in risk estimates. In general, sensitivity analyses can identify the variables in the risk model that are driving risk estimates. Results of the sensitivity analysis can be used to better evaluate the potential roles of variability and uncertainty in shaping risk. For example, if exposure frequency plays a relatively important role in determining risk, one can infer that the professional judgment used to estimate the uncertain exposure frequency variable (the exposure frequency distribution was not based on measured variability) represented an important assumption. In this case, depending on the needs of decision makers, alternative assumptions about exposure frequency may warrant exploration. It should be noted that the above models for calculating risks are linear equations. With these types of models, sensitivity analyses essentially identify exposure factors that determine variance in risk estimates. When linear equations are used to calculate risk estimates, each variable in the model has a roughly equal contribution to risk. In these particular applications, sensitivity analyses will not identify factors that drive risk estimates; rather, they will identify 40

49 factors that drive variability in risk estimates. The proposed sensitivity analysis can only evaluate the effects of exposure factors with variability (i.e., a distribution). Point estimates such as toxicity values will have an important role in determining risk estimates, but because these values do not vary, the sensitivity analysis will not identify these variables as important in structuring risk estimates. Therefore, in addition to sensitivity analyses, other more qualitative evaluations of the roles of uncertainty and variability in determining risk estimates will be performed. The sensitivity analyses will be performed using Crystal Ball. This software package computes sensitivity by calculating the rank correlation coefficients between input variables and risk estimates during risk simulations. Correlation coefficients reflect the degree to which variables and risk estimates change together. For example, if an exposure factor and risk estimate have a high correlation coefficient, the exposure factor likely plays an important role in determining variability in the risk estimate. 41

50 7. REFERENCES American Cancer Society Cancer Facts & Figures Atlanta: American Cancer Society. ASA Interim Cleanup Action Implementation Report Red Rock Road ECSI No Sutherlin, Oregon. October 21, Prepared for Oregon DEQ. ATSDR Toxicological Profile for Arsenic. Agency for Toxic Substances and Disease Registry (ATSDR), August. Burmaster, D.E. and E.A Crouch Lognormal distributions of body weight as a function of age for males and females in the United States, Risk Analysis 17, CH2M Hill Investigation Data Report and Human Health Risk Assessment, Red Rock Road Site, Sutherlin, Oregon. September CH2M HILL. 2006a. Focused Feasibility Study Red Rock Road Site Sutherlin, Oregon. February Prepared for Weyerhaeuser. CH2M HILL. 2006b. Focused Feasibility Study Addendum Red Rock Road Site Sutherlin, Oregon. February Prepared for Weyerhaeuser. CH2M Hill and DEQ Inventory of Current Conditions and Land Uses, Red Rock Road, Douglas County. June CH2M HILL Red Rock Road Arsenic Speciation and Bioaccessibility Laboratory Results. July Prepared for Weyerhaeuser. DEQ Guidance for Use of Probabilistic Analysis in Human Health Risk Assessments. Oregon Department of Environmental Quality. January. DEQ Guidance for Conduct of Deterministic Human Health Risk Assessments. Oregon Department of Environmental Quality. Updated in May. DEQ, 2003a. Risk-Based Decision Making for Remediation of Petroleum-Contaminated Sites. Oregon Department of Environmental Quality (DEQ). September 22. DEQ, 2003b. Risks from Home-Grown Vegetables Grown in Soils with Arsenic. Memorandum from Angie Oberie, Western Region Toxicologist. Oregon Department of Environmental Quality. September 22. DEQ Supplemental Risk Analysis for Additional Red Rock Road Properties. Memorandum. Oregon Department of Environmental Quality. July 15. DEQ Human Health Risk Assessment Guidance. Oregon Department of Environmental Quality. October. 42

51 DEQ Definitions for Various Risk Scenarios in Support of Identifying Remedial Alternatives. Memorandum. Oregon Department of Environmental Quality. October 10. Drexler, J.W Final Laboratory Report. Prepared for CH2M HILL by John W. Drexler, Laboratory for Environmental and Geological Studies. University of Colorado, Boulder. E & E Red Rock Road Site Preliminary Assessment Sutherlin, Oregon. May Prepared for EPA Region 10 by Ecology and Environment, Inc. (E & E). E & E Red Rock Road-Sutherlin Site Inspection Report. Prepared by Ecology and Environment, Inc. October EPA. 1989a. Risk Assessment Guidance for Superfund Volume I Human Health Evaluation Manual (Part A). Office of Emergency and Remedial Response, U.S. Environmental Protection Agency (EPA). EPA/540/1-89/002, December. EPA. 1989b. Exposure Factors Handbook. Office of Health and Environmental Assessment, U.S. Environmental Protection Agency. EPA/600/8-89/043. EPA. 1991a. Human Health Evaluation Manual, Supplemental Guidance: Standard Default Exposure Factors. Publication Office of Emergency and Remedial Response, U.S. Environmental Protection Agency, Washington, DC. NTIS PB EPA Technical Background document for Soil Screening Guidance, Office of Solid Waste and Emergence Response, U.S. Environmental Protection Agency. EPA/540/R-95/128, May. EPA Exposure Factors Handbook. Office of Research and Development, U.S. Environmental Protection Agency. August. EPA Risk Assessment Guidance for Superfund: Volume III - Part A, Process for Conducting Probabilistic Risk Assessment. Office of Emergency and Remedial Response, U.S. Environmental Protection Agency. EPA 540-R , December. EPA Risk Assessment Guidance for Superfund: Volume I Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment). Office of Superfund Remediation and Technology Innovation, U.S. Environmental Protection Agency. EPA/540/R/99/005, July. EPA Exposure Factors Handbook: 2011 Edition. Office of Research and Development, U.S. Environmental Protection Agency. EPA/600/R-09/052F, September. EPA. 2014a. Integrated Risk Information System. U.S. Environmental Protection Agency. EPA. 2014b. Regional Screening Levels for Chemical Contaminants at Superfund Sites. U.S. Environmental Protection Agency. 43

52 GSI. 2011a. Red Rock Road Data Report and Updated Risk Assessment. Prepared for Weyerhaeuser by GSI Water Solutions (GSI). March. GSI. 2011b. Red Rock Road Level I Ecological Scoping Assessment. October Prepared for Weyerhaeuser. GSI. 2014a. Land Use and Risk Exposure Scenario Summary, Red Rock Road, Sutherlin, Oregon. Technical Memorandum, May 21, Prepared for Weyerhaeuser. GSI. 2014b. Project Work Plan for Red Rock Road, Sutherlin, Oregon Third Revision. Technical Memorandum. August 1, GSI. 2014c. Dust Study Summary for Red Rock Road, Sutherlin, Oregon. Technical Memorandum DRAFT November Prepared for Weyerhaeuser. Johnson, J. and J. Capel Monte Carlo Approach to Simulating Residential Occupancy Periods and Its Application to the General U.S. Population. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. EPA 450/ Lowney, Y.W., R.C. Wester, R.A. Schoof, C.A. Cushing, M. Edwards, and M.V. Ruby Dermal absorption of arsenic from soils as measured in the Rhesus monkey. Toxicol. Sci. 100(2): Post, G Dermal absorption of inorganic arsenic from water. Environmental Assessment and Risk Analysis Element, White Paper Summary. New Jersey Department of Environmental Protection Division of Science, Research and Technology. Singh, A., R. Maichle, and N. Armbya ProUCL Version User Guide (Draft). Office of Research and Development, U.S. Environmental Protection Agency, Washington, D.C. EPA/600/R-07/041, May. TERA from Michael Dourson/TERA to Susan Turnblom/DEQ. Subject: RE: Your StateHELP Request Red Rock Road Arsenic Bioaccessibility. April 28, U.S. Census Bureau Census. Wester, R.C., H.I. Maibach, L. Sedik, J. Melendres, and M. Wade In vivo and in vitro percutaneous absorption and skin decontamination of arsenic from water and soil. Fundam. Appl. Toxicol. 20,

53 LIMITATIONS The services described in this work product were performed in accordance with generally accepted professional consulting principles and practices. No other representations or warranties, expressed or implied, are made. These services were performed consistent with our agreement with our client. This work product is intended solely for the use and information of our client unless otherwise noted. Any reliance on this work product by a third party is at such party's sole risk. Opinions and recommendations contained in this work product are based on conditions that existed at the time the services were performed and are intended only for the client, purposes, locations, time frames, and project parameters indicated. The data reported and the findings, observations, and conclusions expressed are limited by the scope of work. We are not responsible for the impacts of any changes in environmental standards, practices, or regulations subsequent to performance of services. We do not warrant the accuracy of information supplied by others, or the use of segregated portions of this work product. The purpose of an environmental assessment is to reasonably evaluate the potential for, or actual impact of, past practices on a given site area. In performing an environmental assessment, it is understood that a balance must be struck between a reasonable inquiry into the environmental issues and an appropriate level of analysis for each conceivable issue of potential concern. The following paragraphs discuss the assumptions and parameters under which such an opinion is rendered. No investigation can be thorough enough to exclude the presence of hazardous materials at a given site. If hazardous conditions have not been identified during the assessment, such a finding should not therefore be construed as a guarantee of the absence of such materials on the site, but rather as the result of the services performed within the scope, practical limitations, and cost of the work performed. Environmental conditions that are not apparent may exist at the site. Our professional opinions are based in part on interpretation of data from a limited number of discrete sampling locations and therefore may not be representative of the actual overall site environmental conditions. The passage of time, manifestation of latent conditions, or occurrence of future events may require further study at the site, analysis of the data, and/or reevaluation of the findings, observations, and conclusions in the work product. This work product presents professional opinions and findings of a scientific and technical nature. The work product shall not be construed to offer legal opinion or representations as to the requirements of, nor the compliance with, environmental laws rules, regulations, or policies of federal, state or local governmental agencies. 45

54 FIGURES Figure 1 Figure 2 Red Rock Road Overview Map Human Health Conceptual Site Model Figure 3 Zip Code 97479

55 FIGURE 1 i ll n Cr Douglas County, Oregon Work Plan C oo M 5 ee Cr k e ek Red Rock Road Overview Map B oy dc re e k sett C reek k r ee 17 eek k re e thy C H a ne y C o Ti m G os O ld g C r ee k ha z za r Bu r ee k Ho LEGEND 16 r mc Red Rock Road Red Rock Road Milepost d Roost Creek Gravel Cap Placed in 2009 e lo rc All Other Features ch 15 Ba Je f City Limit Parcel that Intersects Red Rock Road fe rs Cr Road ee k bi n Waterbody Sa l t L ic k kc ree e Cr e Watercourse 14 k Ca 13 OAKLAND 12 re ek C n kl e C reek 10 Lo n g Val ey l Hi l d Cree k Sl 2 ec id re k e Platt I Reservoir M 0 ar k am C r s e ek Cr ri 1 Fo ste r C re ek ee k N or SUTHERLIN F ie 4 F ra Cooper Creek Reservoir se r Denley Reservoir yon C an 0 3,000 Feet 5 MAP NOTES: Date: December 10, 2013 Data Sources: DEQ, ESRI, Douglas Co, Aerial Photo Taken 2010 by Bing Maps File Path: P:\Portland\171 - Weyerhaeuser\5- Red Rock Road\Project_GIS\Project_mxds\2013\Work_Plan\Figure1_Overview.mxd, Date: December 10, :45:58 AM 6,000 9,000

56 Figure 2 Human Health Conceptual Site Model Red Rock Road Sutherlin, Oregon Primary Source Primary Release Mechanism Secondary Sources Exposure Route Primary Resident Secondary Resident Occasional Workers / Recreationists Dust generation Air Red Rock Road Soil Ingestion Dermal Contact Inhalation (dust) I I I Solubilization/ Leaching Groundwater Ingestion Dermal Contact Inhalation (vapor) ᴓ ᴓ ᴓ ᴓ ᴓ ᴓ ᴓ ᴓ ᴓ Notes: Primary Pathway Insignificant Pathway Potentially Complete Exposure Route Insignificant Exposure Route Incomplete Exposure Route I ᴓ C:\Users\lmorrill\Documents\Projects\RedRockRoad\HH CSM.xls

57 Figure 3 Zip Code Red Rock Road Sutherlin, Oregon

58 TABLES Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Exposure Scenarios Exposure Parameters Age Distribution Arsenic Concentrations in Red Rock Road Soil Body Weight Distributions and Constants by Gender and Age Exposure Duration Distributions by Age Range

59 Table 1 Exposure Scenarios Exposure Scenarios Exposure Activities Age Classes Residence on Road Recreation and work in and near the home Child and Adult (age 0 to 60) Residence Near Road Recreation and work near the home Child and Adult (age 0 to 60) Resident Worker Outdoor work on the property Youth and Adult (age 12 to 60) Timberlands Forestry related outdoor work Adult (age 18+) Recreational Periodic recreation Youth (approximately age 10) Construction/Utility Worker Construction related outdoor work Adult (age 18+)

60 Table 2 Exposure Parameters DRA Point Estimates PRA Distributions Resident Worker Residence On/Near Road DRA Resident Worker Residence on Road Residence Near Road PRA Parameter Units RME CTE RME CTE Source Distribution Parameters Distribution Parameters Distribution Parameters Source Soil Arsenic Concentration mg/kg Site Data Normal Mean = 147.2, SD = 50.18, min = 18.5, max = 241 Normal Same as Resident Worker Normal Same as Resident Worker Site Data Age yr 12 to 42 Same as RME Child: 1 6 Adult: 7 30 Same as RME Resident Worker: Professional judgment Residence On/Near Road: DEQ 2010 Custom Percentages of Sutherlin, OR population within each age range (in years), incorporating ages Custom Percentages of Sutherlin, OR population within each age range (in years), incorporating ages <1 60 Custom Same as Residence On Road U.S Census Gender NA NA NA NA NA NA Custom Percentages of males and females in Sutherlin, OR population; 0 = males (48%), 1 = females (52%) Custom Same as Resident Worker Custom Same as Resident Worker U.S Census Body Weight (BW) kg Subadult: 41 Adult: 70 Same as RME Child: 15 Adult: 70 Same as RME Professional Judgment Custom Percentiles for males and females of specific ages (yearly from 12 to 17) or age ranges (several from 18 to 60) Custom Percentiles for males and females of specific ages (yearly from <1 to 17) or age ranges (several from 18 to 60) Custom Same as Residence On Road DEQ 1998 Exposure Duration (ED) yr Subadult: 6 Adult: 24 Subadult: 6 Adult: 3 Child: 6 Adult: 24 Child: 6 Adult: 3 Resident Worker: Professional judgment Residence On/Near Road: DEQ 2010 Custom Percentiles for several age ranges (incorporating ages 12 to 60) Custom Percentiles for several age ranges (incorporating ages 0 to 60) Custom Same as Residence On Road DEQ 1998 Exposure Frequency (EF) d/yr 350 Same as RME Same as Resident Worker Same as RME DEQ 2010 Uniform Min = 350, Max = 365 (upper and lower truncations same) Uniform Same as Resident Worker Uniform Same as Resident Worker DEQ 1998 Soil Ingestion Rate (IRS) mg/d Child: 200 Adult: 100 Child: 100 Adult: 50 DEQ 2010 Lognormal For ages 7 and greater; Logmean = 4.0, LogSD = 0.31, LB = 0, UB = 480, Location = 0 Lognormal For ages <1 6; Logmean = 3.61, LogSD = 1.15, LB = 0, UB = 400, Location = 0 For ages 7 and greater; Logmean = 4.0, LogSD = 0.31, LB = 0, UB = 480, Location = 0 Lognormal Same as Residence On Road DEQ 1998 Age adjusted Soil Ingestion Rate (IRS adj ) mg yr/kg d Resident Worker: Professional judgment Residence On/Near Road: DEQ 2010 Account for changes in soil ingestion rate and body weight as person ages. Account for changes in soil ingestion rate and body weight as person ages. Account for changes in soil ingestion rate and body weight as person ages. Gastrointestinal Abs Factor (ABS gi ) unitless 0.1 Same as RME Same as Resident Worker Same as RME Site Data Point Estimate 2009 RRR As bioavailability data upper bound estimate based on weight of evidence; 10% (0.1) Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker Site Data Particulate Emission Factor (PEF) m 3 /kg 1.32E+09 Same as RME Same as Resident Worker Same as RME DEQ 2003 Point Estimate 1.32E+09 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker DEQ 2003 Exposure Time (ET) hr/d h/d 4 2 Residence On Road: 24 Residence Near Road: 6 Residence on Road: 24 Residence Near Road: 4 Based on time spent outdoors distribution where RME = Max and CTE = Mode Triangular Time spent outdoors for ages >12; Min = 1, Mod = 2, Max = 4 (LB&UB Point Estimate 24 (Assumes constant exposure) Triangular same as min&max). Time spent outdoors for ages 0 12; Min = 2, Mod = 4, Max = 6 (LB&UB same asmin&max) Time spent outdoors for ages >12; Min = 1, Mod = 2, Max = 4 (LB&UB same as min&max). DEQ 1998 Page 1 of 2

61 Table 2 Exposure Parameters DRA Point Estimates PRA Distributions Resident Worker Residence On/Near Road DRA Resident Worker Residence on Road Residence Near Road PRA Parameter Units RME CTE RME CTE Source Distribution Parameters Distribution Parameters Distribution Parameters Source Averaging Time Noncarcinogen (AT nc ) Averaging Time Carcinogen (AT c ) d Subadult: 2,190 Same as RME Child: 2,190 Same as RME DEQ 2010 AT nc = ED Same as Resident Worker Same as Resident Worker d 25,550 Same as RME 25,550 Same as RME DEQ 2010 Point Estimate 25,550 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker DEQ 2010 Oral Reference dose (RfD) mg/kg d 3.0E 04 Same as RME 3.0E 04 Same as RME EPA 2014a Point Estimate 3.0E 04 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker EPA 2014a Oral Slope Factor (SF) (mg/kg d) Same as RME 1.5 Same as RME EPA 2014a Point Estimate 1.5 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker EPA 2014a Reference Concentration (RfC) mg/m 3 1.5E 05 Same as RME 1.5E 05 Same as RME EPA 2014b Point Estimate 1.5E 05 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker EPA 2014b Inhalation Unit Risk (IUR) (µg/m 3 ) 1 4.3E 03 Same as RME 4.3E 03 Same as RME EPA 2014a Point Estimate 4.3E 03 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker EPA 2014a Conversion Factor 1 (CF1) d/hr 1/24 Same as RME 1/24 Same as RME Point Estimate 1/24 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker Conversion Factor 2 (CF2) kg/mg 1.00E 06 Same as RME 1.00E 06 Same as RME Point Estimate 1.00E 06 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker Conversion Factor 3 (CF3) µg/mg 1,000 Same as RME 1,000 Same as RME Point Estimate 1,000 Point Estimate Same as Resident Worker Point Estimate Same as Resident Worker Notes: Custom distributions are created by entering data directly rather than assigning a distribution paired with descriptive statistics (e.g., mean, standard deviation). Percentiles were entered for some parameters. Normal distributions were defined using the mean and standard deviation, with the minimum and maximum observed values identified as the upper and lower truncation points. DRA = deterministic risk assessment PRA = probabilistic risk assessment RME = reasonable maximum exposure CTE = central tendency exposure RRR = Red Rock Road LB&UB = Lower & Upper Bounds References: Oregon Department of Environmental Quality (DEQ) Guidance for Use of Probabilistic Analysis in Human Health Risk Assessments. January. DEQ Human Health Risk Assessment Guidance. October. U.S. Census Bureau Census. U.S. Environmental Protection Agency (EPA). 2014a. Integrated Risk Information System. EPA. 2014b. Regional Screening Levels for Chemical Contaminants at Superfund Sites. concentration_table/ Page 2 of 2

62 Table 3 Age Distribution a Age Range (Years) Percentage Min Max of Population Footnote: a Distribution from U.S. Census Bureau, 2010 Census data for zip code encompassing Sutherlin, OR. Age distribution was modified to be continuous by adding 0.99 to the upper end of each age range. For practical reasons, the age distribution of local residents was capped at 60. Reference: U.S. Census Bureau Census.

63 Table 4 Arsenic Concentrations in Red Rock Road Soil Final Arsenic Sample ID a Sample Date Arsenic Concentration (mg/kg) Concentration b (mg/kg) RR01SS May RR03SS May RR04SS May RR05SS May RR06SS May RR07SS May RR08SS May RR09SS May RR10SS May RR11SS May SUR Aug SUR Aug SUR Aug SUR Aug SUR Aug SUR Aug SUR 09D Aug SUR Aug SUR 10D Aug SUR Aug SUR Aug SUR Aug SUR 1 Jan SUR 1D Jan SUR 2 Jan SUR 3 Jan SUR 1 Jan SUR 2 Jan SUR 3 Jan SUR 1 Jan SUR 2 Jan SUR 3 Jan SUR 3D Jan SUR 1 Jan SUR 2 Jan SUR 3 Jan SUR 1 Jan SUR 2 Jan SUR 3 Jan SUR 1 Jan SUR 2 Jan SUR 3 Jan SUR 1 Jan SUR 2 Jan SUR 3 Jan SUB 1 Jan SUB 2 Jan SUB 3 Jan Golden SUR 1 Jan Golden SUR 2 Jan Golden SUR 2D Jan Golden SUR 3 Jan Hainey SUB 1 Jan Hainey SUB 2 Jan Hainey SUB 3 Jan SUR 1 Jan SUR 2 Jan SUR 3 Jan SUR G03 Jan SUR G08 Jan SUR G13 Jan SUR G18 Jan SUR G23 Jan Notes: = not applicable (see footnote b) a Arsenic results from GSI 2011 b Final result includes averaging of primary and duplicate samples.

64 Table 5 Body Weight Distributions and Constants by Gender and Age a Males Age or Age Range < Percentile Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max 5% % % % % % % % % % % % % % % % % % % % Females Age or Age Range < Percentile Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max 5% % % % % % % % % % % % % % % % % % % % Page 1 of 3

65 Table 5 Body Weight Distributions and Constants by Gender and Age a Age or Age Range Percentile 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Age or Age Range Percentile 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Males Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Females Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Page 2 of 3

66 Table 5 Body Weight Distributions and Constants by Gender and Age a Weight Gain Constants (kg/yr) During Youth Stage b Age Range Males Females 0 1 c NA NA NA NA NA Upon Reaching Adult Stage d Age Males Females Footnotes: a Body weights are expressed in kilograms (kg), and ages are expressed in years (yr). Distributions from DEQ (1998), Tables 3 47 and 3 48 (males) and 3 49 and 3 50 (females). Distributions were modified to represent continuous ranges for each percentile. The value provided for each percentile was used as the maximum value for that percentile. The minimum value from each distribution was used as the minimum for the 5th percentile, and the minimum of each higher percentile was set as the previous percentile value plus The maximum value from each distribution was used as the maximum of the 100th percentile. b Constants are applied for each year of the exposure duration spent in an age range past the first year (i.e., age 1 starting weight becomes starting weight range constant at age 2, starting weight + [2*1 6 range constant] at age 3, etc.). The constants represent the average annual weight gain within an age range. Age ranges are defined differently for males and females due to differences in developmental timing, and are based on ranges within which average weight gain is similar from year to year. c The difference in average and percentile weights between ages <1 and 1 is negligible for both males and females. d Constants are applied once the receptor reaches adulthood (e.g., for a starting age of 1, body weight during adulthood is the starting weight plus the adult constant for age 1). The constants represent the difference between average weight at the starting age and average adult weight. Reference: Oregon Department of Environmental Quality (DEQ) Guidance for Use of Probabilistic Analysis in Human Health Risk Assessments. Page 3 of 3

67 Table 6 Exposure Duration Distributions by Age Range a Age Range Percentile Min Max Min Max Min Max Min Max Min Max Footnote: a Exposure durations and age ranges are expressed in years. Distributions from DEQ (1998), Table Distributions were modified to represent continuous ranges for each percentile. The value provided for each percentile was used as the maximum value for that percentile. The minimum value from the DEQ (1998) simulated distribution (Table 3 38) was used as the minimum for the fifth percentile. The minimum of each higher percentile was set as the previous percentile value plus The 99 th percentile was used to represent the 100 th percentile to allow the Crystal lbll Ball software to properly read the distribution. ib ti Reference: Oregon Department of Environmental Quality (DEQ) Guidance for Use of Probabilistic Analysis in Human Health Risk Assessments. January.

68 APPENDIX A PROUCL OUTPUT FOR ARSENIC CONCENTRATIONS IN SOIL

69 UCL Statistics for Uncensored Full Data Sets User Selected Options Date/Time of Computation 9/4/2014 1:39:24 PM From File Updated As stats_b.xls Full Precision OFF Confidence Coefficient 90% Number of Bootstrap Operations 2000 Arsenic General Statistics Total Number of Observations 58 Number of Distinct Observations 57 Number of Missing Observations 0 Minimum 18.5 Mean Maximum 241 Median SD Std. Error of Mean Coefficient of Variation Skewness Normal GOF Test Shapiro Wilk Test Statistic Shapiro Wilk GOF Test 5% Shapiro Wilk P Value Data appear Normal at 5% Significance Level Lilliefors Test Statistic Lilliefors GOF Test 5% Lilliefors Critical Value Data appear Normal at 5% Significance Level Data appear Normal at 5% Significance Level Assuming Normal Distribution 90% Normal UCL 90% UCLs (Adjusted for Skewness) 90% Student's-t UCL % Adjusted-CLT UCL (Chen-1995) % Modified-t UCL (Johnson-1978) Gamma GOF Test A-D Test Statistic Anderson-Darling Gamma GOF Test 5% A-D Critical Value Data Not Gamma Distributed at 5% Significance Level K-S Test Statistic Kolmogrov-Smirnoff Gamma GOF Test 5% K-S Critical Value Data Not Gamma Distributed at 5% Significance Level Data Not Gamma Distributed at 5% Significance Level Gamma Statistics k hat (MLE) k star (bias corrected MLE) Theta hat (MLE) Theta star (bias corrected MLE) nu hat (MLE) 632 nu star (bias corrected) MLE Mean (bias corrected) MLE Sd (bias corrected) 64.7 Approximate Chi Square Value (0.1) Adjusted Level of Significance Adjusted Chi Square Value Assuming Gamma Distribution 90% Approximate Gamma UCL (use when n>=50)) % Adjusted Gamma UCL (use when n<50) Lognormal GOF Test

70 Shapiro Wilk Test Statistic Shapiro Wilk Lognormal GOF Test 5% Shapiro Wilk P Value 1.550E-12 Data Not Lognormal at 5% Significance Level Lilliefors Test Statistic Lilliefors Lognormal GOF Test 5% Lilliefors Critical Value Data Not Lognormal at 5% Significance Level Data Not Lognormal at 5% Significance Level Lognormal Statistics Minimum of Logged Data Mean of logged Data Maximum of Logged Data SD of logged Data Assuming Lognormal Distribution 90% H-UCL % Chebyshev (MVUE) UCL % Chebyshev (MVUE) UCL % Chebyshev (MVUE) UCL % Chebyshev (MVUE) UCL Nonparametric Distribution Free UCL Statistics Data appear to follow a Discernible Distribution at 5% Significance Level Nonparametric Distribution Free UCLs 90% CLT UCL % Jackknife UCL % Standard Bootstrap UCL % Bootstrap-t UCL % Hall's Bootstrap UCL % Percentile Bootstrap UCL % BCA Bootstrap UCL % Chebyshev(Mean, Sd) UCL % Chebyshev(Mean, Sd) UCL % Chebyshev(Mean, Sd) UCL % Chebyshev(Mean, Sd) UCL Suggested UCL to Use Recommendation Provided only for 95% Confidence Coeficient Note: For highly negatively-skewed data, confidence limits (e.g., Chen, Johnson, Lognormal, and Gamma) may not be reliable. Chen's and Johnson's methods provide adjustments for positvely skewed data sets.

71 UCL Statistics for Uncensored Full Data Sets User Selected Options Date/Time of Computation 9/4/2014 1:40:18 PM From File Updated As stats_b.xls Full Precision OFF Confidence Coefficient 95% Number of Bootstrap Operations 2000 Arsenic General Statistics Total Number of Observations 58 Number of Distinct Observations 57 Number of Missing Observations 0 Minimum 18.5 Mean Maximum 241 Median SD Std. Error of Mean Coefficient of Variation Skewness Normal GOF Test Shapiro Wilk Test Statistic Shapiro Wilk GOF Test 5% Shapiro Wilk P Value Data appear Normal at 5% Significance Level Lilliefors Test Statistic Lilliefors GOF Test 5% Lilliefors Critical Value Data appear Normal at 5% Significance Level Data appear Normal at 5% Significance Level Assuming Normal Distribution 95% Normal UCL 95% UCLs (Adjusted for Skewness) 95% Student's-t UCL % Adjusted-CLT UCL (Chen-1995) % Modified-t UCL (Johnson-1978) Gamma GOF Test A-D Test Statistic Anderson-Darling Gamma GOF Test 5% A-D Critical Value Data Not Gamma Distributed at 5% Significance Level K-S Test Statistic Kolmogrov-Smirnoff Gamma GOF Test 5% K-S Critical Value Data Not Gamma Distributed at 5% Significance Level Data Not Gamma Distributed at 5% Significance Level Gamma Statistics k hat (MLE) k star (bias corrected MLE) Theta hat (MLE) Theta star (bias corrected MLE) nu hat (MLE) 632 nu star (bias corrected) MLE Mean (bias corrected) MLE Sd (bias corrected) 64.7 Approximate Chi Square Value (0.05) Adjusted Level of Significance Adjusted Chi Square Value Assuming Gamma Distribution 95% Approximate Gamma UCL (use when n>=50)) % Adjusted Gamma UCL (use when n<50) Lognormal GOF Test

72 Shapiro Wilk Test Statistic Shapiro Wilk Lognormal GOF Test 5% Shapiro Wilk P Value 1.550E-12 Data Not Lognormal at 5% Significance Level Lilliefors Test Statistic Lilliefors Lognormal GOF Test 5% Lilliefors Critical Value Data Not Lognormal at 5% Significance Level Data Not Lognormal at 5% Significance Level Lognormal Statistics Minimum of Logged Data Mean of logged Data Maximum of Logged Data SD of logged Data Assuming Lognormal Distribution 95% H-UCL % Chebyshev (MVUE) UCL % Chebyshev (MVUE) UCL % Chebyshev (MVUE) UCL % Chebyshev (MVUE) UCL Nonparametric Distribution Free UCL Statistics Data appear to follow a Discernible Distribution at 5% Significance Level Nonparametric Distribution Free UCLs 95% CLT UCL % Jackknife UCL % Standard Bootstrap UCL % Bootstrap-t UCL % Hall's Bootstrap UCL % Percentile Bootstrap UCL % BCA Bootstrap UCL % Chebyshev(Mean, Sd) UCL % Chebyshev(Mean, Sd) UCL % Chebyshev(Mean, Sd) UCL % Chebyshev(Mean, Sd) UCL % Student's-t UCL Suggested UCL to Use Note: Suggestions regarding the selection of a 95% UCL are provided to help the user to select the most appropriate 95% UCL. These recommendations are based upon the results of the simulation studies summarized in Singh, Singh, and Iaci (2002) and Singh and Singh (2003). However, simulations results will not cover all Real World data sets. For additional insight the user may want to consult a statistician. Note: For highly negatively-skewed data, confidence limits (e.g., Chen, Johnson, Lognormal, and Gamma) may not be reliable. Chen's and Johnson's methods provide adjustments for positvely skewed data sets.

73

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