Final Status Survey Plan for the Luckey Formerly Utilized ...€¦ · 2. Site Overview – contains...

PLN-5508 Rev. 2

Final Status Survey Plan for the Luckey Formerly Utilized Sites Remedial Action Program Remediation Project

U.S. Army Corps of Engineers Buffalo District, Buffalo, New York

Applicability: Luckey FUSRAP Remediation

Effective Date: 07/31/17 Owner: Project Manager

Signature

FINAL STATUS SURVEY PLAN FOR THE LUCKEY FORMERLY UTILIZED SITES REMEDIAL ACTION

PROGRAM REMEDIATION PROJECT

Identifier: Revision: Page:

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History of Revisions

Revision Issue Date Action Description 0 04/26/2017 New document. Initial issue.

1 05/11/2017 Revise document. Edits and comments from USACE Buffalo District tech editor(s).

2 07/31/2017 Revise document. Response to stakeholder comments.




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Table of Contents

1. INTRODUCTION ................................................................................................................ 9

2. SITE OVERVIEW ............................................................................................................. 10

2.1 Site Description ...................................................................................................... 10

2.2 Site History ............................................................................................................. 12

2.3 Contamination Characterization ............................................................................. 15

2.3.1 Lagoons ................................................................................................. 18 2.3.2 Disposal Pits/Diked Disposal Area ....................................................... 18 2.3.3 Sewage Treatment Plant ........................................................................ 19 2.3.4 Material Handling/Storage Areas .......................................................... 19 2.3.5 Buildings ................................................................................................ 20 2.3.6 Underground Utilities ............................................................................ 20 2.3.7 Drainage Ditches ................................................................................... 21 2.3.8 Organic Contaminants ........................................................................... 21

2.4 Remediation Plan Summary ................................................................................... 22

2.5 Background Reference Area .................................................................................. 22

3. DATA QUALITY OBJECTIVES ..................................................................................... 26

3.1 Final Status Survey ............................................................................................... 26

3.1.1 Modified (Derated) Soil Cleanup Goal ................................................. 27

3.2 Identify Information Inputs .................................................................................... 30

3.3 Define the Boundaries of the Study ...................................................................... 30

3.4 Develop the Analytic Approach ........................................................................... 30

3.5 Specify Performance or Acceptance Criteria ......................................................... 31

3.6 Develop the Plan for Obtaining Data ..................................................................... 32

4. FINAL STATUS SURVEY DESIGN ............................................................................ 32

4.1 Survey Unit Design ................................................................................................ 32




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4.1.1 Classification and Identification of Survey Units ................................. 33 4.1.2 Survey Unit Design ............................................................................... 34

4.2 Sample Design ........................................................................................................ 35

4.2.1 Sample Size ........................................................................................... 35 4.2.2 Sample Locations .................................................................................. 39 4.2.3 Small Areas of Elevated Activity .......................................................... 41 4.2.4 Chemical Sampling ............................................................................... 45 4.2.5 ScanSortSM Soils .................................................................................... 45 4.2.6 Rolling FSS ........................................................................................... 48

4.3 Initial FSS Design .................................................................................................. 48

5. SURVEY METHODS AND INSTRUMENTATION ....................................................... 51

5.1 Soil/Sediment Sampling ......................................................................................... 51

5.1.1 Sample Labels ....................................................................................... 53 5.1.2 Chain-of-Custody Records .................................................................... 54 5.1.3 Sample Packaging and Shipping ........................................................... 55

5.2 Gamma Scanning Surveys ................................................................................... 55

5.3 Instrumentation ..................................................................................................... 56

5.3.1 Calibration ............................................................................................ 57 5.3.2 Minimum Detectable Concentration .................................................. 57 5.3.3 Reporting Results ................................................................................. 60

5.4 Soil Sample Analysis ............................................................................................ 60

5.5 ScanSortSM Analysis .............................................................................................. 60

6. QUALITY CONTROL AND DATA ASSESSMENT ...................................................... 60

6.1 Quality Control ..................................................................................................... 61

6.1.1 Data Management ................................................................................ 61 6.1.2 Sample Custody ................................................................................... 61 6.1.3 Quality Control Measurements ........................................................... 62

6.2 Measurement Uncertainty and Data Quality Indicators ......................................... 62

6.3 Data Quality Assessment ..................................................................................... 66

6.3.1 Data Verification .................................................................................. 66




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6.3.2 Statistical Evaluations .......................................................................... 68 6.3.3 Decision Rules ...................................................................................... 69

7. REPORT OF SURVEY FINDINGS .................................................................................. 72

8. REFERENCES ................................................................................................................... 74

Attachment 1. Development of DCGLEMC Values for the Luckey FUSRAP Site

Figures Figure 2-1. Site Location Map ..................................................................................................... 11 Figure 2-2. Site Features .............................................................................................................. 14 Figure 2-3. Modeled Depth of FUSRAP-Contaminated Soil, Fill, and Debris ........................... 16 Figure 2-4. Modeled Lateral Extent of FUSRAP-Contaminated Soil, Fill, and Debris .............. 17 Figure 2-5. Modeled Lateral Extent of FUSRAP Contamination and Example Remediation Work Areas .................................................................................................................................. 23 Figure 2-6. Background Reference Area ..................................................................................... 25 Figure 4-1. Sample Approach for ScanSortSM Soils Below-Criteria Material ............................ 47 Figure 4-2. Planned FSS Areas .................................................................................................... 50

Tables Table 2-1. Luckey Soil Contaminant Concentrations .................................................................. 15 Table 2-2. RI Background Soil Concentrations ........................................................................... 24 Table 3-1. COCs for Impacted Soils at the Luckey Site .............................................................. 27 Table 3-2. Correlations of Ra-226 to the Other ROCs ................................................................ 28 Table 3-3. Relative Contributions of Individual Radionuclide COCs to the SOR ...................... 29 Table 4-1. Survey Unit Classification Guidelines ....................................................................... 33 Table 4-2. Class 1 Survey Unit Sample Size ............................................................................... 38 Table 4-3. Class 2 Survey Unit Sample Size ............................................................................... 38 Table 4-4. Class 3 Survey Unit Sample Size ............................................................................... 39 Table 4-5. Recommended Scan Coverage ................................................................................... 42 Table 4-6. Area Factors for DCGLEMC ........................................................................................ 43 Table 4-7. Final Status Survey Investigation Levels ................................................................... 44 Table 4-8. Planned FSS Survey Units.......................................................................................... 49 Table 4-9. Planned FSS Volumetric Samples .............................................................................. 51 Table 5-1. MDCRSURVEYOR Values .............................................................................................. 59 Table 6-1. Target Data Quality Indicators ................................................................................... 64 Table 6-2. Data Verification Parameters ..................................................................................... 67 Table 6-3. Summary of Decision Rules ....................................................................................... 71




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ACRONYMS AND ABBREVIATIONS

μg/m3 micrograms per cubic meter

µR/h microroentgens per hour

AEC Atomic Energy Commission

ALARA as low as reasonably achievable

ARAR applicable or relevant and appropriate requirement

BBC Brush Beryllium Company

bgs below ground surface

CG concentration guideline

cm centimeter(s)

COC constituent of concern

CFR Code of Federal Regulations

CY cubic yard(s)

DCGL derived concentration guideline level

DCGLEMC derived concentration guideline level, elevated measurement comparison

DCGLW derived concentration guideline level, survey unit average (median) concentration

DoD U.S. Department of Defense

DOE U.S. Department of Energy

DQA data quality assessment

DQO data quality objective

EM Engineer Manual

EPA U.S. Environmental Protection Agency

FS feasibility study

FUSRAP Formerly Utilized Sites Remedial Action Program

FSS final status survey




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FSSP final status survey plan

ft foot (feet)

g gram(s)

GPS Global Positioning System

Ho null hypothesis

ID identification

in. inch(es)

IPR Industrial Properties Recovery

kg kilogram(s)

LBGR lower bound of gray region

m meter(s)

m2 square meter(s)

m3 cubic meter(s)

MARSSIM Multi-Agency Radiation Survey and Site Investigation Manual

MDC minimum detectable concentration

mg milligram

mrem/yr millirem(s) per year

NaI sodium iodide

NRC U.S. Nuclear Regulatory Commission

NUREG U.S. Nuclear Regulatory Commission Report

ODH Ohio Department of Health

PARCC precision, accuracy, representativeness, comparability, and completeness

Pb lead

PCB polychlorinated biphenyl

RESRAD RESidual RADioactivity

RG remediation goal




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pCi picocurie(s)

QA quality assurance

QAPP quality assurance project plan

QC quality control

Ra radium

RI remedial investigation

ROC radionuclide of concern

ROD record of decision

SOP standard operating procedure

SOR sum of ratios

SOW scope of work

SSOP soil sorting operations plan

SVOC semivolatile organic compound

Th thorium

TPP technical project planning

TRPH total recoverable petroleum hydrocarbons

U uranium

UCL upper confidence limit

USACE U.S. Army Corps of Engineers

VSP visual sample plan

VOC volatile organic compound

WRS Wilcoxon Rank Sum




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1. INTRODUCTION

The United States Army Corps of Engineers (USACE) Buffalo District has selected Portage, Inc., under Contract Number W912P4-15-D-0006, to remediate the Luckey Site located in Luckey, Ohio. Portage is completing this remediation under the USACE’s Formerly Utilized Sites Remedial Action Program (FUSRAP), which was established to identify, investigate, and clean up or control sites previously used by the Atomic Energy Commission (AEC) and its predecessor, the Manhattan Engineer District. This site has been identified as having materials contaminated with FUSRAP-related constituents of concern (COCs), which include beryllium, lead (Pb), radium-226 (Ra-226), thorium-230 (Th-230), uranium-234 (U-234), and uranium-238 (U-238).

The primary objective of the remediation project is the timely and effective cleanup of the site in accordance with the Luckey Site Record of Decision for Soils Operable Unit, Final (ROD) (USACE 2006). The selected remedial alternative provides for the excavation of impacted soils, including on-site and off-site contiguous soils, where contamination has migrated through natural means, to achieve cleanup goals for unrestricted use by the critical group for the site, the subsistence farmer. Portage will place clean backfill and acceptable place-back soils in excavated areas. Portage will ship excavated soils exceeding cleanup goals off-site for disposal at a licensed/permitted disposal facility. This alternative meets the evaluation criteria while protecting human health and the environment, and will comply with applicable or relevant and appropriate requirements (ARARs). Portage will conduct remediation so that it provides a level of protection to the public and remediation workers consistent with applicable radiation exposure guidelines and with the objective of maintaining chemical and radiological exposure levels as low as reasonably achievable (ALARA).

This plan provides a framework for conducting a final status survey (FSS) of soils at the Luckey remediation project. The purpose of this final status survey plan (FSSP) is to provide the basis for conducting FSS of soil and for conducting surveys to determine if soil meets the remediation goal (RG) for reuse as backfill. Excavated soils may be processed by automated soil segregation as means of FSS. The guidance found in the following sources will be used to demonstrate compliance with the ROD: the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM); (EPA et al. 2000); the USACE Technical Project Planning (TPP) Process Engineer Manual (EM) 200-1-2 (USACE 1998); and data quality objective (DQO) process guidance (EPA 2006). The objective of sampling and/or survey activities is to obtain data of sufficient quantity and quality to evaluate the suitability of material for clearance or on-site reuse as FUSRAP backfill. Soil sorting will be used for assessment of excavated soils by the Orion ScanSortSM system. For the chemical contaminants, the use of the MARSSIM process should ensure a sufficient number and density of samples are collected to also determine if chemical cleanup goals are met.




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This document is organized into the following sections:

1. Introduction ‒ briefly describes this document’s content and purpose. 2. Site Overview – contains a physical description of the site and site contaminants. 3. Data Quality Objectives – outlines a systematic procedure for defining the site criteria

by which the data collection design is satisfied. 4. Final Status Survey Design – describes planning survey units and calculates the number

of samples required. 5. Survey Methods and Instrumentation – specifies the measurement techniques and the

equipment and tools to conduct field activities. 6. Quality Control and Data Assessment – discusses data quality and data assessment

issues. 7. Report of Survey Findings – provides an overview of the basic information to be

provided in the final status sampling survey report. 8. References – lists documents cited in the plan.

This plan is based on information available at the time of its preparation. Sources of information used in the plan primarily include the Luckey Site, Luckey, Ohio, Final Remedial Investigation Report (RI) (USACE 2000); Feasibility Study Report, Luckey Site (FS) (USACE 2003a); Luckey Site, Luckey, Ohio, Final Proposed Plan Report (USACE 2003b); and the Luckey Site ROD for the Soils Operable Unit (USACE 2006). The conditions and findings encountered during and/or upon completion of the remedial action and at the time of the FSS implementation may trigger modifications to this plan. If modifications are deemed necessary, they will be justified and documented, including appropriate project approvals.

2. SITE OVERVIEW

2.1 Site Description

The Luckey Site is located at the corner of Gilbert and Luckey Roads at 21200 Luckey Road, northwest of the Village of Luckey in Wood County, Ohio. The village of Luckey is 22 miles southwest of Toledo, Ohio (Figure 2-1). The site encompasses approximately 40 acres and consists of a large production building and warehouse, two abandoned railroad spurs, and several smaller process and support buildings. The area surrounding the site to the west, north, and east is primarily residential farmland; a quarry is to the south. From 1949 to the early 1960s, the Brush Beryllium Company, as a contractor to the AEC, used the Luckey Site for beryllium processing to support the national defense program. Beryllium production activities brought different types of source media or potential contaminants to the site. Primary source media at the Luckey Site included materials delivered for processing or reprocessing: beryl ore from Africa and South America, scrap beryllium, and radiologically contaminated scrap steel.




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Figure 2-1. Site Location Map




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2.2 Site History

In 1942, a magnesium processing facility was built at the site on U.S. Government land. National Lead operated the facility for the U.S. Government during World War II until 1945. In 1949, the AEC built a beryllium production facility at the site that was operated by Brush Beryllium Company (BBC), which later became Brush Wellman. The facility produced beryllium oxide, beryllium hydroxide, and beryllium pebbles that were shipped to other facilities for further processing. The site facilities were owned by the AEC and operated by BBC from 1949 to 1958. During nonpeak use of the facilities, BBC leased portions of the plant for commercial uses.

In late 1951 and early 1952, the AEC sent approximately 1,000 tons of radiologically contaminated scrap metal to the site in anticipation of resuming magnesium processing. The scrap metal, which contained radioactivity levels within guidelines at the time, was stored at the site and never used for its intended purpose. Records indicate the possibility that radiologically contaminated beryllium scrap from other AEC operations was also sent to the site. Brush Beryllium Company operated the facility until 1958 when beryllium production ceased. Sintering and powder blending operations, established at the Luckey facility in 1957, continued until 1960.

In 1961, the General Services Administration sold the site to the privately owned Aluminum and Magnesium, Inc., with the government retaining access rights in order to remove any remaining beryllium ore. In 1962, Luckey Industries, Inc., purchased the facility, hoping to reclaim magnesium from World War II incendiary bombs. The reclamation process was unsuccessful, and the property reverted back to Aluminum and Magnesium, Inc. The facility was then used to recover zinc from byproducts of the steel industry. In 1967, Aluminum and Magnesium, Inc., transferred the property to its parent company, the Vulcan Materials Company.

In 1968, the Goodyear Tire and Rubber Company purchased the site and began producing automotive foam seating and other urethane products. In 1983, the Motor Wheel Company leased the property from Goodyear, later purchasing it in 1988. Motor Wheel used the site to coat steel automotive steering wheels with polyurethane foam and to manufacture other automotive products. Hayes Lemmerz International, Inc., is the successor company to Motor Wheel. From 1995 to 2004, Hayes Lemmerz leased about 23 acres of the site to Uretech International, Inc., which manufactured urethane parts for the automotive, sporting goods, and health-care industries.

In 2006, Hayes Lemmerz sold the property to Industrial Properties Recovery, LLC (IPR), an industrial scrapping business. Shortly after purchase, IPR began demolishing several ancillary buildings, including the former production annex building. In December 2006, the Ohio Department of Health (ODH) issued an Emergency Adjudication Order to IPR, to cease demolition of buildings and handling of any radioactive material.




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The Wood County Combined General Health District deemed the site a public health and safety concern, and the Wood County Court issued an injunction against IPR in June 2009. The injunction required IPR to either demolish or make necessary repairs to site structures and salvage or properly dispose of all debris, rubbish, and garbage. Industrial Properties Recovery resumed demolition and salvage activities in late 2013. In December 2013, the ODH issued another Emergency Adjudication Order against IPR to halt demolition activities again.

There are several large buildings and smaller structures at the site that were built to house or support magnesium production activities and subsequently used during beryllium processing and sintering activities (see Figure 2-2).

Large buildings at the site:

• Production building. • Production annex (now demolished). • Melting, alloying, and shipping building. • Laboratory building. • Maintenance office building. • Main office. • Employee activity building.

Small structures at the site:

• Pump house (for fire protection water). • East and west well houses. • Guard house. • Shack. • Sewage treatment plant.

The large buildings were constructed with brick and concrete with some metal sheathing over steel supports. The building roofs are steel trusses that are generally triangular shaped. The small structures (guard shacks and pump houses) are a mix of concrete block, brick, and wood framing. Elevation, section, and miscellaneous details of the production building are provided in historical documents and facility drawings. The production annex building has been demolished and only the building floor slab and demolition debris remain. As stated previously, the current site owner, IPR, resumed demolition and salvage activities during late 2013.

The sewage treatment plant consisted of a wet well and pump room, dosing chamber, aeration tank, settling tank, septic tank, and sand filters. Two abandoned railroad spurs and two water supply wells are also on-site.




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Figure 2-2. Site Features




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2.3 Contamination Characterization

The USACE identified six AEC-related COCs posing unacceptable risks to human health at the Luckey Site: beryllium, lead, Ra-226, Th-230, U-234, and U-238. Table 2-1 summarizes levels of FUSRAP soil contaminants on-site based on the RI report (USACE 2000).

Table 2-1. Luckey Soil Contaminant Concentrations Parameter Maximum Value Average Value ROD Background Units

Metals Beryllium 13300 227.96 1.13 mg/kg

Lead 28900 198.88 23.2 mg/kg Radionuclides

Radium-226 4000 17.62 2.97 pCi/g Thorium-230 88.5 4.27 3.2 pCi/g Uranium-234 52.3 2.9 2.61 pCi/g Uranium-238 280 6.85 2.63 pCi/g

The modeled depth and lateral extent of FUSRAP soil contaminants on-site are reflected in Figures 2-3 and 2-4.

The FUSRAP-contaminated media, which includes soils, sediments, fill, and debris, is defined as media located in areas of the site that correspond to previous disposal activities, material handling activities, and sediment transport by site drainage features, and exceed the cleanup goals. Documented material handling and disposal areas at the site, as reflected on Figure 2-2, include:

• Lagoons that received beryllium processing sludges.

• Waste disposal pits in the eastern section of the site.

• A diked disposal area in the northeast section of the site.

• A sewage treatment plant.

• Material handling areas adjacent to railroad sidings and production buildings.

• Scrap metal storage areas.

• Spoils areas near the sewage treatment plant.

• A settling basin for solids produced from softening of extracted groundwater.

These areas are discussed below.




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Figure 2-3. Modeled Depth of FUSRAP-Contaminated Soil, Fill, and Debris




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Figure 2-4. Modeled Lateral Extent of FUSRAP-Contaminated Soil, Fill, and Debris




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2.3.1 Lagoons

Four lagoons (labeled A, B, C, and D) were used at the site, as shown in Figure 2-2. The lagoons were formed by embankments constructed with scraped soil and lined with compacted clay. Lagoon A was approximately 3 to 4 feet (ft) deep. Lagoon B was constructed in two stages: the first stage was built in 1949 and was approximately 3 to 4 ft deep, and the second stage was built in 1950 and was approximately 5 to 6 ft deep. Lagoon C was 1 1/2 ft deep and also constructed in two stages.

Lagoons A, B, and C received beryllium process waters and were used to precipitate solids. The supernatant liquid was discharged to the main drainage ditch. This activity was conducted under a permit from the ODH. Lagoon D was used for stormwater detention but did not receive any process water. This was supported by characterization data in the area (USACE 2000). No details of the Lagoon D construction are available.

Most lagoon sludges were removed at plant closing (1959) and placed in a diked disposal area in the northeast corner of the site. After closure, the lagoon dikes and embankments were used to fill the lagoons. A 2-ft-thick clay cap was installed on Lagoons A and B in 1988.

The FUSRAP contamination in the former lagoons predominantly occurs to a depth of 5 ft below ground surface (bgs) and in isolated areas to 10 ft bgs.

2.3.2 Disposal Pits/Diked Disposal Area

The RI report (USACE 2000) and the ROD (USACE 2006) identified up to seven trenches at the site. The sludge from Lagoons A, B, and C was dredged every summer and placed into disposal trenches in the northeast corner of the facility (Trenches 1 through 4). Also, a 2-acre 2-ft-deep clay bottom diked disposal area was built in the northeast corner of the site at plant closing for remaining sludge. Scrap metal, building debris, and graphite crucibles with soluble beryllium fluoride, and possibly sludge from Lagoons A, B, and C, were placed into excavated trenches (Trenches 5, 6, and 7).

Three disposal pits (potentially Trenches 2, 5, and 7) have been confirmed by direct sampling. Trench 2 was located in the northeast corner of the site (approximately 250 × 90 ft), adjacent to the east fence. Descriptive logs of soil borings drilled in Trench 2 identify a black or gray sludge to a depth of approximately 8 ft bgs. Buried fill has been confirmed in Trench 5 northeast of the sand filters. Descriptive logs of soil borings drilled in this area identify fill between approximately 3 and 6.5 ft bgs that consists of metal debris, wood, ash, brick, woven fabric, black sand, and glass fragments. Trench 7 (approximately 150 × 12 ft) was located between two railroad spurs in the east-central portion of the site. Descriptive logs of soil borings and an exploratory trench indicate fill material consisting of steel and fiberboard drums, metal, wood, brick, black sand, and glass fragments to a depth of approximately 12 ft bgs. Based on




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physical data and geophysical surveys, the estimated boundaries for all seven trenches are shown in Figure 2-2.

Sampling from potential trench areas indicates that FUSRAP-contaminated soils and fill materials occur to 13 ft bgs in the vicinity of Trench 7; 10‒12 ft bgs at Trenches 5 and 6; and more than 18 ft bgs across Trenches 1‒4 and “disposal area.” Sampling, historic records, and worker interviews indicate that multiple disposal activities occurred around Trenches 1‒4 and the “disposal area,” resulting in a nearly contiguous region of FUSRAP-contaminated soil and fill; whereas Trenches 5, 6, and 7 are anticipated to be distinct pits bordered by native materials.

2.3.3 Sewage Treatment Plant

A sewage treatment plant was located north of the production building. The plant received only sanitary wastes (not process wastes) and contained the structures listed below.

• Septic tank.

• Pump room and wet well.

• Aeration tank and settling tank.

• 2,000-gallon dosing tank.

• Sand filters.

Some FUSRAP-contaminated soils and fill are present to depths of 10 and 15 ft bgs. These FUSRAP-contaminated soils and fill are also present south, east, and west of the sewage treatment plant in areas associated with previous material handling/storage activities. These areas are described in the following sections.

2.3.4 Material Handling/Storage Areas

Beryl ore arrived at the site in bags and drums and was stored on both sides of railroad sidings, on runways adjacent to the production building, and in the vicinity of the sewage treatment plant sand filter beds. The FUSRAP-contaminated soils and fill within these areas occur primarily from ground surface to 5 ft bgs, occasionally reaching depths of as much as 10 ft bgs.

Debris piles that are remnants of ore staging and disposal activities are located south and east of the sewage treatment plant sand filters. The FUSRAP-contaminated soils and fill in these areas are present primarily from the ground surface to 5 ft bgs. The FUSRAP contamination extends to 10 ft bgs in areas east and southeast of the sand filters.

West of the sewage treatment plant is a bare spot/stressed vegetation area that contains FUSRAP-contaminated soils from ground surface to between 5 ft and 10 ft bgs.




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There are two water supply wells (east well and west well) north of the production buildings. Both are open bedrock wells that penetrate the Lockport Dolomite. Steel casing was used in the well construction to seal off the unconsolidated overburden. Solids from water softening processes associated with the pumping of these wells were stored in the northwest corner of the site. A single, isolated exceedance of lead has been documented in this area.

2.3.5 Buildings

Previous surveys and analyses conducted to investigate the potential for FUSRAP contaminants within the on-site buildings included a radiological survey, beryllium and radionuclide swipe surveys, and analysis of bulk dust samples. Results of these surveys and analyses are summarized below.

• Radiation surveys identified several areas within the production annex (now demolished) and two isolated areas in the production building that contained activity above Nuclear Regulatory Commission surface contamination guidelines for release to the public. The majority of these areas were in the building’s structural components (i.e., beams).

• Beryllium swipe samples identified removable contamination in the production annex (now demolished), production building, laboratory, and maintenance office building. Lower concentrations of beryllium were also identified in the former melting, alloying and shipping building; shack; east/west extraction well buildings; fire pump house; guard house; production annex (now demolished); sewage treatment plant pump house; and employee activity building.

• Bulk dust samples contained beryllium at concentrations that were interpreted to indicate that there is a potential to exceed the USACE occupational exposure limit of 0.2 micrograms per cubic meter (μg/m3) and action level of 0.1 μg/m3 if dust is resuspended during disruptive activities.

• Buildings that contain significant concentrations of beryllium within the construction materials (paint, brick, and concrete) include the former laboratory, maintenance building, production building, and production annex (now demolished).

Elevated levels of beryllium have also been detected in subsurface soil samples collected adjacent to and beneath the production annex (now demolished). The extent of contaminated soil beneath the buildings has not been fully delineated.

2.3.6 Underground Utilities

Some FUSRAP contamination has been identified in manholes and in soil surrounding underground utility lines.




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• Sediment within a manhole (MH09) west of the melting, alloying, and shipping building contained beryllium above the site cleanup goal.

• Sediment within manholes (MH28 and MH11) east of the sewage treatment plant contained beryllium above the site cleanup goal.

Soil surrounding an underground utility line near Lagoons B and C contained beryllium and radionuclides above the site cleanup goals.

2.3.7 Drainage Ditches

Some FUSRAP-contaminated sediment is present within and adjacent to the main drainage ditch on the site and north of the property boundary. The FUSRAP contamination occurs within the upper 1 ft of sediment/soil within the ditch and on the bank immediately east of the ditch. The contaminated soil/sediment east of the ditch may represent materials previously excavated from the ditch.

2.3.8 Organic Contaminants

Organic contaminants have been detected in site soils during the RI. The concentrations, locations, and depths of these compounds in soil are identified in the RI report (USACE 2000) and are summarized below.

• Three volatile organic compounds (VOCs) – dichloromethane, toluene, and xylenes (total) – were widely detected in site soils during the RI.

• Various semivolatile organic compounds (SVOCs) were detected near a former underground storage tank and sewage treatment plant filter beds.

• Total recoverable petroleum hydrocarbons (TRPH) were detected in soil samples collected from a former oil pump house.

• SVOCs, TRPH, and polychlorinated biphenyls (PCBs) were detected in soil samples from a former transformer room.

• PCBs were detected in soil samples from a former electrical substation.

These VOCs, SVOCs, TRPH, and PCBs are not FUSRAP COCs, and the extent of this soil remedial action is not determined by these contaminants or by other contaminants that may be present at the site.




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2.4 Remediation Plan Summary

The site remediation currently planned and designed involves removal of contaminated soils as shown in the designated areas of Figure 2-5. Figure 2-5 was developed as part of the Contaminated Soil Volume Estimate Summary contained in Attachment 3 of the Final Scope of Work, Remediation of Soils Operable Unit, Luckey Site, Luckey, Ohio (SOW) (USACE 2014), and reflects the modeled lateral extent of FUSRAP contamination, as applied to remediation work areas. These work areas reflect specific material handling and disposal areas, as discussed in Section 2.3, and represent the actual remediation work areas to be established. Buildings and contaminants associated with the buildings are not part of the SOW.

2.5 Background Reference Area

Since the FUSRAP COCs that may be present at the site include uranium, thorium, radium, lead, and beryllium, which are also naturally occurring, a background reference area will be needed for the land areas. Background with similar properties to Luckey Site soils were sampled and analyzed as part of the RI. A total of 69 soil samples were collected and used to determine background concentrations in the RI. A summary of the RI background results is provided in Table 2-2 and locations shown in Figure 2-6. Given the potential for differences in results due to laboratory methods and the importance of the background reference area when the COCs are present in background, Portage will collect and analyze a new background reference set of samples. The new set of background samples will be analyzed by the same laboratory as the FSS samples to minimize any potential bias to the FSS. Portage will collect a total of 36 background samples of surface soils (0 to 6 in.) and subsurface soils (6 to 18 in.) to ensure a sufficient population for FSS. The background reference sample data will be used for the statistical tests, as described later.

Portage will collect the FSS background reference soil samples from one of the same three areas identified during the RI – OFFBKG 1/1A, OFFBKG 2, or OFFBKG 3, as indicated on Figure 2-6. Portage will collect 36 surface/subsurface soil samples with minimum spacing of 1 meter, surface soil sample from the top with subsurface sample directly beneath. During sample collection, the soil must be confirmed as Hoytville clay since this is the soil type at the Luckey Site.




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Figure 2-5. Modeled Lateral Extent of FUSRAP Contamination and Example Remediation Work Areas




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Table 2-2. RI Background Soil Concentrations

Parameter Minimum Value

Maximum Value

ROD Background

Value

Average Background

Value Units

Metals

Beryllium 0.61 1.20 1.13 0.90 mg/kg

Lead 10.10 23.20 23.20 15.10 mg/kg

Radionuclides

Radium-226 0.65 3.50 2.97 1.75 pCi/g

Thorium-230 1.35 3.89 3.20 2.11 pCi/g

Uranium-234 0.82 2.61 2.36 1.64 pCi/g

Uranium-238 0.85 3.04 2.63 1.63 pCi/g




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Figure 2-6. Background Reference Area




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3. DATA QUALITY OBJECTIVES

3.1 Final Status Survey

Portage will use this FSSP to determine whether residual radionuclide and chemical concentrations in soils at the Luckey Site comply with cleanup criteria as defined in the ROD (USACE 2006). Compliance with the ROD will be demonstrated by using guidance found in MARSSIM (EPA et al. 2000).

For excavated soils considered for reuse (i.e., place-back soils), Portage will perform FSS data collection to satisfy the Backfill and Restoration Plan for the Luckey Formerly Utilized Sites Remedial Action Program Remediation Project (USACE 2016a) and the Uniform Federal Policy Quality Assurance Project Plan for the Luckey Formerly Utilized Sites Remedial Action Program Site Remediation, Luckey, Ohio, Sampling and Analysis Plan (USACE 2016b). The FSS data collection will be necessary to demonstrate that place-back soils meet the ROD criteria.

This plan assumes that upon the completion of the selected remedy – the excavation and off-site disposal of contaminated soil – residual concentrations of the radionuclides of concern (ROCs) will meet the criteria associated with the ROD. In addition, it will be used to determine the disposition of excavated materials as either reuse soil or waste material for off-site disposal. The intent of this plan is to use FSS data to determine whether site contaminants are present at activity concentrations above or below cleanup levels in the ROD.

The USACE identified six AEC-related COCs in impacted soils posing unacceptable risk to human health: beryllium, lead, Ra-226, Th-230, U-234, and U-238. All six COCs pose unacceptable risks under a subsistence farmer scenario (i.e., a human health receptor who resides on the site and is self-sufficient from food grown or produced on-site). Given the mixture of radionuclides present, a sum of ratios (SOR) will be a necessary part of compliance determination. In this case, two SORs will be needed, one for statistical assessment of survey units and the other for dose assessment with the final concentrations. Table 3-1 shows the DCGLW values for the Luckey Site, as documented in the ROD (USACE 2006).




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Table 3-1. COCs for Impacted Soils at the Luckey Site

Contaminant Cleanup Goala ROD Background Average Background

Beryllium 131 mg/kg 1.13 mg/kg 0.90 mg/kg Lead 400 mg/kg 23.2 mg/kg 15.10 mg/kg Uranium-238 26 pCi/gb 2.63 pCi/g 1.63 pCi/g Uranium-234 26 pCi/gb 2.61 pCi/g 1.64 pCi/g Thorium-230 5.8 pCi/gb 3.2 pCi/g 2.11 pCi/g Radium-226 2 pCi/gb 2.97 pCi/g 1.75 pCi/g

a SESOIL® modeling results indicate that risk-based and/or ARAR-based cleanup goals selected for soils are protective of groundwater. b Soil cleanup goals for radionuclides represent activity levels above site background corresponding to 25 mrem/yr (10 CFR 20 Subpart E, and OAC 3701:1-38-22). If a mixture of radionuclides is present, then the sum of ratios applies per MARSSIM, and the ratio should not exceed unity. For example, use the 25 mrem/yr cleanup goals for unrestricted use by the critical group, which is the subsistence farmer for the Luckey Site, for soil to get the following SOR equation:

𝑆𝑆𝑆𝑆𝑆𝑆 =𝑆𝑆𝑅𝑅226

2.0 pCi/g+

𝑇𝑇ℎ2305.80 pCi/g

+𝑈𝑈234

26 pCi/g+

𝑈𝑈23826 pCi/g

Where: SOR = sum of ratios result (ROD dose assessment) Ra226 = net Ra-226 soil concentrations Th230 = net Th-230 soil concentrations U234 = net U-234 soil concentrations U238 = net U-238 soil concentrations

The soil concentration for each of the chemical COCs will be compared directly with the cleanup goal concentration. For the ROCs, a comparison will be made to determine if compliance with radiological cleanup goals has been met by using the Wilcoxon Rank Sum (WRS) test as compared to the background reference area. For beryllium and lead, the 95 percent upper confidence limit of the mean (residual) concentration will conservatively be used to compare to cleanup goals independently.

3.1.1 Modified (Derated) Soil Cleanup Goal

The ROCs Th-230, U-234, and U-238 are relatively difficult to detect under field variable conditions and at concentrations near their respective concentration guidelines (CGs). It is desirable to identify an appropriate concentration of Ra-226 that will serve as a surrogate for the SOR compliance metric when performing real-time gamma emission measurements. The remedial action process for the Luckey Site incorporates the advanced radiological material survey and sorting technology – Orion ScanSortSM – provided by Amec Foster Wheeler.




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The ScanSortSM system will be outfitted and configured to measure the sentinel gamma emissions from Ra-226, an “easy-to-detect” radionuclide. Ra-226 will also be the sentinel ROC for gamma scanning surveys, providing a consistent FSS process for soils.

3.1.1.1 Assessment of Corollary Relationships between COCs

An assessment of the available historical data collected from the Luckey Site was performed to evaluate the relationships between the concentrations of the ROCs and their relative contributions to the SOR compliance metric. It was determined that there is a suitably strong corollary relationship between the concentrations of ROCs and that the Ra-226 consistently consumed the largest fraction of its CG.

Correlations between Ra-226 and the other FUSRAP-related ROCs present at the site are presented in Table 3-2.

Table 3-2. Correlations of Ra-226 to the Other ROCs Radionuclides

Compared Pearson Correlation

Coefficient (r) Strength of

Relationship Ra-226 and Th-230 0.8227 Strong Ra-226 and U-234 0.8729 Strong Ra-226 and U-238 0.8292 Strong

Note: Correlations computed using the natural logarithm of each radionuclide.

These findings support the conclusion that the concentration of Ra-226 in soil is a suitable surrogate for the ROCs Th-230, U-234, and U-238. Consequently, the Ra-226 CG is modified (or derated) to account for the mean contributions of the difficult-to-detect radionuclides.

3.1.1.2 Derivation of the Modified Ra-226 Cleanup Goal

Having established that suitable corollary relationships exist among the ROCs, calculations were performed to determine the degree (magnitude) that each ROC contributes to the SOR. The mean1 background-corrected concentrations for each of the ROCs were compared with the radionuclide-specific CG as published in the ROD. The sequence of equations that follows describes the process mathematically. The results are compiled in Table 3-3.

1. When establishing relationships between collocated ROCs, it is statistically important that best estimates of

central tendency concentrations are used (e.g., mean, median) rather than metrics that are designed to express confidence in a compliance decision (e.g., mean UCL95, or UTL95%, 95). The mean background-corrected concentrations used are background corrected using the mean ROC-specific background (as opposed to the UTL95%, 95 background published in the ROD and intended for use in the compliance test).




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𝑆𝑆𝑆𝑆𝑆𝑆 = 𝑋𝑋�𝑁𝑁𝑁𝑁𝑁𝑁 (𝑅𝑅𝑅𝑅-226)

𝐶𝐶𝐶𝐶(𝑅𝑅𝑅𝑅-226)+𝑋𝑋�𝑁𝑁𝑁𝑁𝑁𝑁 (𝑁𝑁ℎ-230)

𝐶𝐶𝐶𝐶(𝑁𝑁ℎ-230)+𝑋𝑋�𝑁𝑁𝑁𝑁𝑁𝑁 (𝑈𝑈-234)

𝐶𝐶𝐶𝐶(𝑈𝑈-234)+𝑋𝑋�𝑁𝑁𝑁𝑁𝑁𝑁 (𝑈𝑈-238)

𝐶𝐶𝐶𝐶(𝑈𝑈-238)

𝑆𝑆𝑆𝑆𝑆𝑆 = 15.87 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔

2.0 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔+

2.16 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔5.8 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔

+1.42 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔26 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔

+5.22 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔26 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔

= 8.56

The SOR and the individual radionuclides’ contribution to the SOR were used to calculate the radionuclide-specific percentage of contribution to the SOR.

𝑆𝑆𝑆𝑆𝑆𝑆𝑅𝑅𝑅𝑅226𝑆𝑆𝑆𝑆𝑆𝑆𝑁𝑁𝑇𝑇𝑇𝑇𝑅𝑅𝑇𝑇

= 7.948.56

= 0.9267 (92.67%)

Table 3-3. Relative Contributions of Individual Radionuclide COCs to the SOR

ROC

Concentration On-site (pCi/g) CG

(pCi/g)

ROC Contribution to the SOR

Contribution to SOR

Mean in Impacted

Areas

Mean Background

Background-Corrected

Mean Ra-226 17.62 1.75 15.87 2.0 7.935 92.67% Th-230 4.27 2.11 2.16 5.8 0.372 4.35% U-234 2.90 1.48 1.42 26 0.055 0.64% U-238 6.85 1.63 5.22 26 0.201 2.34%

Total (SOR): 8.563 100%

From Table 3-3, it is evident that Ra-226 consumes, on average, 92.67 percent of the SOR for ROCs. That Ra-226 consumes such a large fraction of the permissible limit (SOR) supports the conclusion that it is an ideal choice for use as a sentinel for evaluating the radioactive content of the soils at the Luckey Site. When making contaminant concentration measurements of the soil, the sorting threshold settings will reflect the fact that Ra-226 consumes, on average, 92.67 percent of the SOR by derating the CG for Ra-226 by 7.4 percent.

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑁𝑁𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝑅𝑅𝑅𝑅226 = 2.0𝑝𝑝𝐶𝐶𝑝𝑝 𝑔𝑔⁄ ∗ 92.6% = 1.85 𝑝𝑝𝐶𝐶𝑝𝑝/𝑔𝑔

3.1.1.3 Adjusted Ra-226 Soil Cleanup Goal

Based on these derivations, it is appropriate to use an adjusted Ra-226 soils CG of 1.85 pCi/g as the target net concentration threshold for remedial action decisions corresponding to soil sorting and gamma scanning surveys of soils for the Luckey Site.




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3.2 Identify Information Inputs

Inputs to the decision include the type, quality, and quantity of data that will be sufficient to make decisions. These inputs include identifying and classifying survey units, and identifying appropriate measurement techniques. These inputs provide the framework for measuring the residual radioactivity in each survey unit. Inputs required to make decisions involve developing estimates of the average (median) residual concentrations, average residual concentrations in locally elevated areas, and maximum residual concentrations.

3.3 Define the Boundaries of the Study

The definition of the site physical, temporal, and spatial boundaries for all media, including background reference areas, will be covered by the decision process. Survey units are the smallest subsets of the site for which decisions will be made. The size of the survey unit and the measurement frequency within a survey unit are based on the other steps of the DQO process. The study area boundary consists of trenches, lagoons, and the surrounding soils within the Luckey Site and contiguous property off-site, as shown in Figure 2-5. The buildings and support structures are not considered part of this FSSP.

3.4 Develop the Analytic Approach

The purpose of this step is to define the parameter of interest (measurement), specify the action level (DCGL), and integrate previous DQO steps into a logical basis for choosing among alternative actions. The decision rule to consider is if the data collected in the FSS provides sufficient evidence that each survey unit has residual radioactivity below the applicable DCGLs, and then conclude that the site meets the criteria for release from radiological controls without restriction. In addition to ROC, sufficient data must also be collected to demonstrate that beryllium and lead are below the RGs so that the site meets release criteria for these COCs.

The approach to soil survey units will be to perform gamma scans over the area after remediation is complete. If any scan survey results exceed the investigation level (Table 4-7), then additional data (e.g., hotspot soil sample(s) and/or scan surveys) will be collected. Based on the results of the data, the area may be reclassified, or additional remediation may be needed. Excavated soils will be processed by soil sorting, which will provide 100 percent assay of these soils, and these results will be used for FSS of soils determined to be below criteria by this process. Beryllium and lead data will be collected only from soil samples.

In summary, if the DCGLs are met within a survey unit or below criteria pile from soil sorting (difference between survey unit measurement and background reference area for WRS test), then the survey unit passes, and the soils meet the ROD criteria. In addition, beryllium and lead must meet RG. If the DCGLs or RG are not met, then the survey unit fails, and additional




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excavation will be required. A detailed discussion of testing for DCGL and RG compliance is presented in Section 6 of this plan.

3.5 Specify Performance or Acceptance Criteria

This step is to specify the limits for Type I and Type II decision errors in support of the null hypothesis. Statistical sampling designs in accordance with MARSSIM attempt to control design error by defining the types of errors and incorporating them into the statistical sampling design process.

There are two types of fundamental decision errors. The Type I (alpha) decision error to be used in data testing is 0.05, or 5 percent. The Type I error rate determines the minimum number of sample analyses required for each survey unit for establishing compliance with the DCGLW, The Type II (beta) decision error may range between 0.01 (or 1 percent) and 0.25 (or 25 percent). The initial Type II decision error to be used is 0.10 (or 10 percent). The acceptable probability of a Type II error is used to determine additional sample numbers necessary for controlling Type II errors during a DCGLW evaluation.

Portage has established the following data quality indicators for precision, accuracy, representativeness, comparability, and completeness (PARCC).

• Precision will be determined by a comparison of replicate values from field measurements and from a sample analysis; the objective will be a relative percent difference of 50 percent or less for samples with concentrations at a minimum of 50 percent of the DCGL values.

• Accuracy is the degree of agreement with the true or known; the objective for this parameter will be ±30 percent for samples with concentrations at a minimum 50 percent of the DCGL values.

• Representativeness and comparability are ensured through the selection and proper implementation of systematic sampling and measurement techniques.

• Completeness refers to the portion of the data that meets acceptance criteria and is therefore usable for statistical testing. The objective is 90 percent for this project.

The generic PARCC criteria that focus on activity concentration results and analytical performance around the DCGL requirements may not be meaningful if no contamination is encountered, which will likely be the case during FSS work; thus, other factors should be taken into account when evaluating the quality and usability of the produced data sets. For example, if a sample media measurement and a replicate of that measurement using a similar type but different instrument are taken, and both indicate less than detection levels for the method or




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radionuclide, Portage will use naturally occurring radionuclides detected to document precision, such as K-40, Bi-214, Pb-212, and Ac-228.

3.6 Develop the Plan for Obtaining Data

Field screening techniques, soil sampling, soil sample analysis, gamma measurements, and the data quality assessment (DQA) process will be used, as appropriate, throughout the final status sampling survey to focus efforts and minimize cost. As data are collected and analyzed from initial survey units, the assumptions in this plan will be reviewed for accuracy.

4. FINAL STATUS SURVEY DESIGN

The survey unit represents the fundamental element for compliance demonstration using statistical tests. Numerous factors influence the delineation of a survey unit and the design of the survey within the unit. This section of the plan will focus on the parameters and decisions that affect the delineation of survey units, the classification of survey units, and the number and location of measurement and sampling points within survey units. The number of samples necessary to statistically demonstrate compliance with DCGLW requirements can be calculated using MARSSIM guidance. Section 4.1 lists the steps and describes the process for creating survey units. The data used for the preliminary calculations are based on data from the Luckey Site, and the number of samples per survey unit is calculated in Section 4.2.

Soil samples collected as part of FSS may also be used to demonstrate compliance with the chemical criteria. Additional soil samples may need to be collected to meet the chemical requirements. Soil samples collected that are not from the FSS design will be considered biased and may be used for statistical evaluations only if it can be shown that they are likely from the same population as samples placed using random distribution methods.

4.1 Survey Unit Design

Different areas of the site will not have the same potential for residual radioactivity and, accordingly, will not need the same level of survey coverage to achieve the required confidence that the release criteria have been satisfied. The FSS process will be more efficient if the survey is designed so that areas with higher potential for contamination will receive a higher degree of survey effort. The first part of the survey unit design process is to establish the potential for residual radioactivity across the site and then to classify the areas based on MARSSIM guidance. This portion of the FSS design section will provide the process to delineate impacted areas and to further subdivide them into various FSS units.




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4.1.1 Classification and Identification of Survey Units

The MARSSIM defines impacted areas as those with some potential for contamination. Impacted areas are subdivided into three classes:

• Class 1 units have, or had prior to remediation, radionuclide contamination that exceeded the DCGLW.

• Class 2 units have a potential for radioactive contamination or known contamination, but levels are not expected to exceed the DCGLW.

• Class 3 units are expected to contain no residual radioactivity or to contain levels of residual radioactivity concentrations at only a small fraction of the DCGLW.

By definition, any area requiring excavation will be encompassed by Class 1 units (excluding the bench/side slopes). For soils, MARSSIM suggests that a Class I unit be limited to a maximum area of 2,000 m2. The Class 2 units will include the remaining unexcavated areas surrounding the excavation and any stockpiled soils accumulated from the ScanSortSM processing (below criteria). Other areas may be Class 3 units, and there is no limitation to the size of Class 3 units, as shown in Table 4-1.

Table 4-1. Survey Unit Classification Guidelines

Classification Basis Suggested Area (Land)

Class 1 Areas expected to exceed the DCGLW Up to 2,000 m2

Class 2 Areas not expected to exceed the DCGLW

2,000 to 10,000 m2

Class 3 Areas having a small fraction of the DCGLW No limit

If confirmatory surveys and data collected during the remedial action activities warrant a need to reclassify other parts of the site, the MARSSIM classification areas will be updated as required.

The layout of the actual survey areas may deviate from this initial design depending on the final footprint of remediation.

Each survey unit will be identified by a unique alphanumeric code. Survey unit identification throughout the course of the FSS shall be labeled by:

• FSS designation (FSS).




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• Two-digit FSS area number (XX). • Two-digit survey unit number (YY).

The format of the survey unit identification will be as follows: FSS-XX-YY.

Survey units (YY) shall be numbered sequentially within an FSS area. The FSS areas and survey unit designators (XX) are described below.

FSS Survey Unit Designation (XX).

10 Buffer/General Areas 20 S1 Area 30 Trench Area 40 Lagoon Area 50 Drainage Ditch 60 ScanSortSM segregated soil

For example, the third survey unit in the trench area would be labeled FSS-30-03.

Initial classification of the survey units is based on historical information and characterization data. Survey data from routine operations or remediation may be used to change the initial classification of a survey unit. Once FSS of a survey unit begins, the basis for any reclassification will be documented. If reclassification of a survey unit will change the number or location of samples, Portage may terminate the survey without completion and design a new survey.

4.1.2 Survey Unit Design

The majority of the impacted area is open land. To facilitate survey design in open land areas, survey units will comprise land areas with a common history or other characteristics, or which are naturally distinguishable from other portions of the site. The MARSSIM guidance summarized in Table 4-1 will be used to create the survey units. In general, lead and beryllium are commingled with radionuclides, so the radionuclides will be the primary basis for design of survey units. If there are areas where lead and/or beryllium are more significant than radionuclides, then these areas may be classified at a higher level.

At excavations, the same basic process will be followed. If there are any indications of elevated residual radioactivity, then additional evaluations will be performed. Areas where soil discoloration, odor, etc., indicate leakage may have occurred will be marked for further evaluation. After the impacted materials are removed, scanning of the excavation will be performed to identify areas of residual radioactivity where greater than 50 percent of the




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DCGLs exist. These areas will have further sampling and analysis to determine appropriate actions.

To meet remediation goals and ensure that safety measures are met for excavations (sloping), trenches will be over-excavated to ensure safe access without the use of ladders and confined space entry requirements. Therefore, impacted sloped sidewalls (buffer areas) will require smaller sample densities as Class 2 areas, since the soil removed in those areas is not likely to exceed the DCGL but because of proximity to the remediation areas is impacted. The area of the sidewall will be used for survey unit design. The sidewalls will be sloped so as to allow personnel safe access for gamma walkover surveys and soil sampling. For excavations, the bottom area will be treated as Class 1 and the sidewalls as Class 2 survey units. This will focus the FSS in excavations on the area with the greatest potential for residual radioactivity (bottom) since the sidewalls have been over-excavated to slope the excavation.

A preliminary design of FSS survey units for the Luckey Site is provided in Section 4.3.

4.2 Sample Design

The FSS sampling design entails determining the how many samples will achieve the desired level of statistical confidence and power, locating the samples so they are representative of the survey unit, and evaluating the survey units for the presence of areas of elevated residual radioactivity. This section presents the equations and methods used to estimate the number of samples required for each survey unit to determine whether the unit may be released without radiological restrictions in accordance with MARSSIM guidance for radionuclides. Visual sample plan (VSP) (PNNL 2014) software (or equivalent) will be used as appropriate instead of hand-calculating the various parameters. The VSP is a software tool for selecting the right number and location of samples, so results of statistical tests performed on the data collected via the sampling plan have the required confidence for decision making. Sample numbers provided here may be modified on the basis of additional information.

4.2.1 Sample Size

The MARSSIM methodology for evaluating whether a survey unit meets its applicable release criteria using fixed measurements plus scans is based on using nonparametric statistical tests for data assessment. Specifically, the WRS test will be the nonparametric test. The number of samples or measurements in a survey unit will be determined by the acceptable decision error rates, the estimated variability of the residual radioactivity concentration in the survey unit, the DCGLW, and the lower bound of the gray region (LBGR). Selection of the required minimum number of data points depends on which statistical test is going to be used to evaluate the data, and thus depends on what type of measurements are to be made (gross measurement, net




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measurement, or radionuclide specific) and if the radionuclide(s) of interest appear(s) in background. The following process will be used to calculate the sample size. A decision error is the probability of making an error in the decision on a survey unit by failing a survey unit that should pass or by passing a survey unit that should fail. When using the statistical tests, larger decision errors may be unavoidable when encountering difficult or adverse measuring conditions. This is particularly true when trying to measure residual radioactivity concentrations close to the variability in the concentration of those materials in natural background. The probability of making decision errors can be controlled by adopting an approach called hypothesis testing. The “Ho” is treated like a baseline condition and is defined as follows:

Ho = residual radioactivity in the survey unit exceeds the release criteria.

This means that survey units will be assumed to be contaminated above criteria until proven otherwise. A Type I decision error (α) occurs when an area is determined to be below the criteria when it is really above the criteria (survey unit is incorrectly released). A Type II decision error (β) occurs when an area is determined to be above the criteria when it is really below the criteria (survey unit is incorrectly not released). The Type I decision error rate for the Luckey Site is set at 0.05 (5 percent), and the Type II decision error rate is set at 0.10 (10 percent).

The estimated variability of the residual radioactivity (σ) will be determined from survey results (characterization), or will be estimated based on professional judgment. The standard deviation of the characterization data should provide a reasonable estimate of σ. The LBGR should be set at the mean concentration of residual radioactivity that is estimated to be present in the survey unit based on the characterization data. The number of samples needed will depend on a ratio involving the concentration to be measured relative to the variability in the concentration. The ratio to be used is called the relative shift, ∆/σ. The relative shift is defined as shown in Equation 4-1.

σ

σ LBGRDCGLW −=∆ / (4-1)

Where: DCGLW = derived concentration guideline levels LBGR = concentration at the lower bound of the gray region σ = an estimate of the standard deviation of the concentration of residual

radioactivity in the survey unit or the standard deviation established for the corresponding reference area if the survey data are to be evaluated against a reference area(s)




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When a comparison of measurements from a background reference area and the survey unit is required, the WRS test will be used. For the WRS test, the following number of samples can be determined by Equation 4-2.

( )( )2

211

5.0321

−

+×= −−

rPZZ

N βα (4-2)

Where: N = the minimum number of measurements required for each survey area or reference

area Pr = the probability that a random measurement from the survey unit exceeds a random

measurement from the background reference area by less than the DCGLW when the survey unit median is equal to the LBGR above background

The value of N computed for the WRS test applies for both the survey unit and the background reference area (i.e., at least N measurements should be performed in both areas).To ensure against lost or unusable data, the value of N will be increased by at least a factor of 1.2 when assigning the number of measurements to be made. Additionally, it should be noted that the number of survey area samples may not always equal the number of background reference area measurements, since a given set of background reference area measurements may be used for more than one survey unit.

Alternatively, the number of required samples (N/2) for the WRS test can be obtained directly from Table 5-3 of MARSSIM (EPA et al. 2000).

While the number of samples for each MARSSIM survey unit class is determined as part of this FSSP, the Class 1 survey unit number of samples will be applied to all survey units as a conservative approach. Additional samples may be necessary to demonstrate there are no small areas of elevated activity. This process is detailed in Section 4.2.3.

4.2.1.1 Class 1

Class 1 survey units have the potential for residual radioactivity at a large fraction of the DCGLs or even greater than the DCGLs. With multiple ROCs, for the WRS test the SOR will be used. The LBGR was conservatively selected to be 70 percent of the SOR. The standard deviation was also conservatively approximated high (25 percent of SOR) as a safety margin to reduce the chance of failing the decision criteria. The 25 percent assumption for the coefficient of variation value is reasonable compared with the large variations of values from ROC data. The survey design parameters used to calculate the minimum required sample size for Class 1 survey units for the WRS test are shown in Table 4-2.




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Table 4-2. Class 1 Survey Unit Sample Size Parameter SOR

α decision error 0.05 β decision error 0.10 DCGLW (pCi/g) 1.0 LBGR (maximum estimated mean/median) (pCi/g) 0.7 Standard Deviation (σ) (pCi/g) 0.25 Relative Shift (∆/σ) 1.2 Sample Size (N/2) WRS Test 20

4.2.1.2 Class 2

Class 2 survey units have the potential for residual radioactivity but are not expected to exceed the DCGLs. With multiple ROCs, for the WRS test the SOR will be used. The LBGR was selected to be 50 percent of the SOR. The standard deviation was conservatively assumed to be 25 percent SOR (as described previously) for Class 2 areas to provide a margin of safety for minimizing the chance of failing the decision rule. The survey design parameters used to calculate the minimum required sample size for Class 2 survey units for the WRS test are shown in Table 4-3. Table 4-3. Class 2 Survey Unit Sample Size

Parameter SOR α decision error 0.05 β decision error 0.10 DCGLW (pCi/g) 1.0 LBGR (maximum estimated mean/median) (pCi/g) 0.5 Standard Deviation (σ) (pCi/g) 0.25 Relative Shift (∆/σ) 2.0 Sample Size (N/2) WRS Test 11

4.2.1.3 Class 3

Since Class 3 survey units are not expected to have measurable residual radioactivity in excess of background or are expected to have only a small fraction of the DCGLs. With multiple ROCs, for the WRS test the SOR will be used. The LBGR was selected to be 30 percent of the SOR. The same standard deviation was used for Class 3 areas as it was for the Class 1 and Class 2 areas, which should also provide a margin of safety for minimizing the chance of failing




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the decision rule. The survey design parameters used to calculate the minimum required sample size for Class 3 survey units for the WRS test are shown in Table 4-4.

Table 4-4. Class 3 Survey Unit Sample Size Parameter SOR

α decision error 0.05 β decision error 0.10 DCGLW (pCi/g) 1.0 LBGR (maximum estimated mean/median) (pCi/g) 0.3 Standard Deviation (σ) (pCi/g) 0.25 Relative Shift (∆/σ) 2.8 Sample Size (N/2) WRS Test 9

4.2.2 Sample Locations

Sample locations within a survey unit will be distributed randomly (Class 3) or on a systematic grid (Class 1/2). Judgmental or biased samples may be used to address areas of suspicion that might not have been included in characterization work. These results will need to be specifically evaluated for inclusion in the statistical analysis of the survey unit for demonstration of compliance with the release criteria (DCGLW or (DCGLEMC). Random measurement patterns are planned for Class 3 survey units to provide independent results. Systematic triangular grids are planned for Class 2 and Class 1 survey units since there is an increased probability of small areas of elevated activity. The systematic grid will allow for limitations on the size of an unsampled area with the potential for elevated activity based on the area in between measurement locations. The systematic grids will have a random starting point to provide an unbiased set of measurement locations.

Random sample locations (Class 3) will be determined by generating sets of random numbers by calculator, computer, or mathematical tables. Each set of random numbers will be multiplied by the appropriate survey unit dimension to provide coordinates. This process will be repeated until enough locations have been created to meet the required sample size. Another option is to use specialized computer software designed for applications such as VSP.

The grid spacing will be estimated in one of two ways, depending on the shape of the grid. Another option is to use specialized computer software designed for applications such as VSP. If a triangular grid is used (preferred), the grid spacing will be estimated as follows:




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n

AL866.0

= (4-3)

Where: L = grid spacing A = area of the survey unit n = number of survey locations

If a square grid is used, the spacing will be estimated as follows:

nAL = (4-4)

In the event that a portion of the study area is long and narrow, the sample grid will extend linearly and not in a square or triangular grid. For these areas, the width of the study area is less than the distance between grid nodes. Under this condition, the spacing between samples will be calculated as follows:

lengthtotalwidth

A= (4-5)

)(1#

samplesbetweenlengthLsamples

lengthtotal=

+ (4-6)

The “+ 1” term in Equation 4-6 is added to the denominator so that sample locations do not overlap when long and narrow units lie end to end. Systematic grids will always make use of a randomly selected initial starting point.

4.2.2.1 Reference Coordinate System

A reference coordinate system will be established for each survey unit to provide a mechanism for referencing a measurement to a specific location, so that the same survey point can be relocated. For land area surveys, sample locations will be marked in some visually discernible manner (pin flags, stakes, marking paint, etc.). Sample locations will be located using differential Global Positioning System (GPS) surveying. A Trimble GeoXH submeter GPS survey system (or equivalent) will be used for locating sample locations, locating pertinent site features, and navigating to previous sample locations.




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In general, land area survey unit reference coordinates will be based on compass directions N (north), S (south), E (east), and W (west), and distance. For survey units that include subsurface samples, depth of the sample will be used to identify the vertical dimension. The GPS survey system will use North American Datum 1983, Ohio State Plane, for FSS.

4.2.2.2 Relocating a Sample

If a sample location is placed in an inaccessible location or in a location deemed unsafe to access, the sample location will be moved to the nearest accessible location within the same survey unit while conforming to the overall spatial coverage theme. Generally, the nearest available location that complements the goal of even spatial coverage would be the most defensible choice. Alternate measurement locations will be documented as a revised location selection, indicating the reason for relocation.

4.2.3 Small Areas of Elevated Activity

The sample design will evaluate whether or not the residual contamination in an area exceeds the DCGL for contamination conditions that are approximately uniform across the survey unit. These tests may not correctly assess compliance with RGs when small areas of elevated activity are present. Scanning is used to obtain adequate assurance that small areas of elevated activity are identified. If such areas are found, the DCGLEMC may be used to evaluate their impact. If a scanning technique’s minimum detectable concentration (MDC) is inadequate, the systematic survey grid spacing may need to be reduced to increase the probability of detecting the small areas of elevated activity. One approach to investigating small areas of elevated residual radioactivity is to perform scan surveys. In general, scan surveys will be performed along transects of the survey unit along with select areas based on professional judgment (e.g., specific areas known to have been involved with radioactive material activities). Based on the widespread nature of the residual radioactivity in soils, the scan survey coverage recommended is presented below.




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Table 4-5. Recommended Scan Coverage Survey Unit Classification Scanning Coverage

Class 1 100% Class 2 100% Class 3 100%

4.2.3.1 Scan Surveys

Since land area scan surveys evaluate small areas, the DCGLW needs to be modified in order to have an equivalent measure. This can be achieved by using a correction factor that accounts for the difference in area and the resulting change in dose. The area factor is the magnitude by which the concentration within a small area of elevated activity can exceed the DCGLW and still comply with the release criterion. The area factors were derived by using the same RESidual RADioactivity (RESRAD) computer code case used for derivation of the site-specific DCGLs. In RESRAD, the area of the contaminated area was sequentially reduced from 11,400 m2 (RESRAD area – essentially infinite) to 1 m2. Specific details and the RESRAD results for this process are provided in Attachment 1.

The area factors were calculated so that multiplying the DCGLW by the area factor results in the concentration in the smaller area that will deliver the same calculated dose (DCGLEMC). The calculation of the area factors is shown in Equation 4-7, and the calculation of the DCGLEMC is shown in Equation 4-8. The area factors calculated for the FSS are presented in Table 4-6.

i

am D

DA = (4-7)

Where: Am = area factor Da = dose from DCGLW Di = dose from small area i

WmEMC DCGLADCGL ∗= (4-8) Where:

DCGLEMC = derived concentration guideline level for small areas of elevated activity DCGLW = derived concentration guideline level for average concentrations Am = area factor




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Table 4-6. Area Factors for DCGLEMC

Parameter Area (m2)

1 2 5 10 100 500 1000 U-238 Area Factor 61 35 21 15 5.7 3 2.3

DCGLEMC U-238 pCi/g 1,585 908 534 384 149 78 59

U-234 Area Factor 101 59 34 24 8.3 3.5 2.2

DCGLEMC U-234 pCi/g 2,634 1,527 891 660 217 91 58

Ra-226 Area Factor 24 14 7.8 5.4 2.3 1.5 1.3

DCGLEMC Ra-226 pCi/g 49 29 16 11 4.5 3.0 2.6

Th-230 Area Factor 24 14 7.7 5.3 2.2 1.5 1.3

DCGLEMC Th-230 pCi/g 138 81 45 31 13 8.7 7.5

Table 4-6 provides a range of potential DCGLEMC values that may be needed during FSS. The calculation of additional DCGLEMC values (different sized areas) can also be performed utilizing the equations provided in Attachment 1.

The potential for localized areas of elevated residual radioactivity will vary across the site. Given the uncertainty associated with gamma walkover scans of soils, some additional evaluations may be performed to determine if the sampling density is sufficient for the survey unit. The characterization/remediation data for Class 1 or Class 2 survey units will be evaluated for the potential of elevated residual radioactivity.

This process begins with a statistical evaluation of the data to determine the concentration that represents the 95th percentile of the data. That concentration is compared to the DCGLEMC values in Table 4-6. The corresponding area associated with the DCGLEMC value is then compared to the sample density in the survey unit. If the sample density is greater than the DCGLEMC area representing the 95th percentile of the data, then the number of samples needs to be increased. The number of samples and grid size will be adjusted so that the sample density will be at least equal to the DCGLEMC area representing the 95th percentile of the data.

The adjustments to the sample grid can be estimated by dividing the area of the survey unit by the DCGLEMC area representing the 95th percentile of the data. The grid spacing is calculated by




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using this value for n in Equation 4-3. However, the shape of the survey unit or inaccessible areas within the survey unit may allow a larger area to go unsampled. Another option is to use the VSP software to determine the sample design that will have at least a 95 percent probability of placing at least one sample in an area corresponding to the DCGLEMC area. The VSP software computes the probability of success in locating hot spots based on the assumed size, shape, and orientation of the hot spots, and on the specified grid spacing.

As an example, consider the following survey unit. Statistical sampling design determined that 14 samples (including an extra 20 percent) will be needed to satisfy the DCGLW statistical test, and the area of the survey unit is 2,000 m2. The systematic triangular grid has a spacing of 12.8 m, and a sample density of one per 142.9 m2. Evaluation of the characterization data finds that the maximum concentration is 2.3 pCi/g Ra-226. This concentration corresponds to a DCGLEMC area of 100 m2 from Table 4-6. Since this area is less than the sample density for the statistical-based design, the grid size needs to be reduced. The “locate a hotspot” sampling goal within VSP software was used to determine the fewest samples needed to achieve a 95 percent probability of detection. For a hot spot with an area of 100 m2, the VSP software determined that 21 samples on a triangular grid would be required. This sample pattern provides a triangular grid spacing of 10.59 m and a sample density of one per 95.2 m2.

4.2.3.2 Investigation Levels

Another aspect of FSS is evaluating the preliminary results to investigation levels. Investigation levels are used to indicate when additional investigations may be necessary. When an investigation level is exceeded, the original measurement may be confirmed by reanalysis or collecting additional samples. Depending on the results of the investigation, the survey unit may require reclassification, remediation, and/or resurvey. The investigation levels are presented in Table 4-7.

Table 4-7. Final Status Survey Investigation Levels Survey Unit Classification

Sample Measurement Investigation Level

Scanning Measurement Investigation Level

Class 1 > DCGLW > DCGLEMC Class 2 > DCGLW > DCGLW Class 3 > 80% DCGLW > DCGLW

Any additional samples collected as part of an investigation or as judgmental (biased) samples will be presented in the FSS report and may help compare elevated measurements. However, these samples may not be included as part of the statistical evaluation unless it is obvious that they will not impart any bias to the randomly collected samples.




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If it is determined that additional samples need to be collected to define the area of the locally elevated area, then the following process is planned. If scan survey data provide an indication of the boundaries of the locally elevated area, then the samples will be distributed within that area. The number of samples will be dependent on the actual size of the locally elevated area. If scan survey data do not provide any clear indication of the boundaries, then samples will be placed around the location of elevated activity as an initial assessment. The results of these samples may trigger additional samples in order to define the extent of the locally elevated area.

4.2.4 Chemical Sampling

Chemical sampling will also be performed in conjunction with MARSSIM sampling. Verification of compliance with soil cleanup goals and criteria will be demonstrated using surveys developed in accordance with the MARSSIM for radionuclides with additional sample material collected at each location for beryllium and lead analysis. This approach will provide sufficient samples for calculation of statistically significant 95 percent upper confidence limit of the mean concentration as needed for comparison to the cleanup goal, as stated in the ROD.

By following the MARSSIM approach for in situ soils, the sampling densities for beryllium and lead will be no greater than (maximum area for Class 1 and Class 2; estimated maximum area for Class 3):

• Class 1 survey unit 1 sample per 100 m2 (20 samples in 2,000 m2).

• Class 2 survey unit 1 sample per 500 m2 (20 samples in 10,000 m2).

• Class 3 survey unit 1 sample per 2,500 m2 (20 samples in 50,000 m2).

In addition, since there are no practical means for scan surveys of beryllium or lead (compared to gamma scan surveys), additional biased samples, identified by staining or significantly elevated concentrations of lead or beryllium found during remediation, may be collected. Chemical data from a survey unit will be evaluated for comparison to RG.

4.2.5 ScanSortSM Soils

The below-criteria soil from the Orion ScanSortSM system will be subject to the FSS process and verified as meeting the criteria for use as place-back in accordance with the Waste Management, Transportation, and Disposal Plan for the Luckey Formerly Utilized Sites Remedial Action Program Remediation Project (USACE 2016d) and Backfill and Restoration Plan. This will include samples to verify radiological and chemical constituents.




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For materials that have been processed through the ScanSortSM system and segregated to the below-criteria stockpiles, samples will be collected as QA of the soil sorting, to verify surrogates, and to demonstrate the material meets the cleanup goals (lead and beryllium) and criteria for use as place-back material (Backfill and Restoration Plan). If these materials do not pass FSS or do not meet place-back criteria, then the soils will be disposed of.

The below-criteria soil from the ScanSortSM system will be categorized as Class 2 area. The below-criteria segregated material will be placed into 100-cubic-yard (CY) stockpiles (place-back criteria requirement). This will facilitate demonstration with both cleanup goals and place-back requirements and provide specific data for each stockpile for the decision-making process. A composite sample will be collected to provide the amount of material needed for laboratory analysis for each 100-CY stockpile. As described in the Soil Sorting Operations Plan for the Luckey Formerly Utilized Sites Remedial Action Program Remediation Project (SSOP) (USACE 2016c), 15 representative samples will be collected during each batch representing a 100-CY stockpile. This approach improves representativeness where a considerable degree of variability is present. These 15 samples will be composited and blended to create five samples for laboratory analyses (ROCs and COCs), as shown in Figure 4-1. For ROD compliance, four stockpiles will be combined, which will provide 20 samples for the FSS statistical assessment (Section 6.3.2). Each stockpile will be evaluated individually for compliance with the place-back criteria. In addition, ScanSortSM system processing will provide 100 percent gamma scan and assay of the soils processed.




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Figure 4-1. Sample Approach for ScanSortSM Soils Below-Criteria Material




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4.2.6 Rolling FSS

Some areas may need to have FSS performed prior to completion of remediation in an area (e.g., a portion of Lagoon C excavation). Performance of rolling FSS or partial FSS of a survey unit is acceptable provided sufficient isolation controls are used to protect the FSS area from impact during remaining remedial operations. This process will be performed in areas that will need to have backfill/restoration performed with minimal delays due to safety, water management, or other concerns that warrant the additional actions to implement rolling FSS.

For example, if remediation on a portion of an excavation area is complete but excavation is ongoing directly adjacent to this area and there is a need for backfill/restoration, then a rolling FSS can be performed. As described in Section 4.1.2, the bottom of the excavation will be a Class 1 area and the sidewall a Class 2 area. A portion of each Class 1/Class 2 survey unit can be surveyed after isolation controls are established to ensure there is an appropriate buffer. The portions of the Class 1 and Class 2 survey units that are ready can be FSS (partial area), and the results, both gamma scans and samples, must be acceptable (pass tests) on their own (i.e., without the rest of the survey unit data). This will allow for a measure of confidence that the entire survey unit will not warrant resurvey if the remaining FSS data obtained for the Class 1 and Class 2 survey units (when available for FSS) pass the statistical tests after the adjacent area remediation is completed. The rolling FSS assessment will be used only to authorize backfill of an area; final compliance will be determined with the complete survey unit FSS data.

4.3 Initial FSS Design

The survey unit represents the fundamental element for compliance demonstration during FSS results evaluation. There are numerous factors that influence the delineation of a survey unit and the design of the survey within the unit. Individual survey units have been identified based on the likelihood of soils containing residual radioactivity following the approach in Section 4.1. Historical process knowledge and data gathered during characterization surveys were used to estimate the extents of the survey units. The Class 1 areas were identified to ensure that remediated areas were adequately surveyed. The Class 2 areas were based on characterization sample data which demonstrated that the residual soil/sediment activity levels did not exceed the established DCGLs or areas that will potentially be impacted during remedial activities. The Class 3 areas were based on areas with very little residual soil/sediment activity levels or areas with minimal impact during remedial activities. During initial mobilization to the site, the areas accessed for setup and infrastructure will have FSS performed to ensure that there are no issues or concerns in those areas. This is primarily the northwest corner of the site, where the office trailers, laboratory, access control point, and Orion ScanSortSM system buildings will be located. A summary of the survey units for FSS areas is presented in Table 4-8 and depicted in Figure 4-2. The hatched areas in Figure 4-2 represent the modeled extent of contamination and the solid lines demarcate individual areas as defined in Table 4-8.




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Table 4-8. Planned FSS Survey Units

Area Survey Unit Class Area

(m2)

Number of Survey

Units Description

General Areas FSS-10-YY 3 17,300 2 Estimate for areas in center of site with no remediation activities.

Administrative FSS-10-YY 3 19,115 2 Administrative area, routine traffic areas.

S1 FSS-20-YY 1 8,980 5 Remediation, 1/3 of area.

S1 FSS-20-YY 2 17,962 2

Buffer around remediation and routine handling of contaminated material in this area, 2/3 of area.

Trench FSS-30-YY 1 36,232 20 Remediation, 3/4 of area.

Trench FSS-30-YY 2 12,078 2 Buffer around remediation, 1/4 of area.

Lagoon FSS-40-YY 1 44,000 22 Remediation, 3/4 of area.

Lagoon FSS-40-YY 2 14,529 2 Buffer around remediation, 1/4 of area.

Ditch FSS-50-YY 1 12,000 6 Remediation, 1/2 of area.

Ditch FSS-50-YY 2 11,226 2 Buffer around remediation, 1/2 of area.

Excavated soils ‒

ScanSortSM system

FSS-60-YY 2

180

80% of 90,000 CY excavated soil as below criteria in 100-CY piles (720), 4 piles combined.




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Figure 4-2. Planned FSS Areas




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As indicated in Section 4.2.1, there will be approximately 20 samples for each survey unit (Class 1 survey unit). The actual number of survey units/samples may be adjusted during performance of the remedial activities based on whether more or less material is excavated, additional elevated areas are identified, or other factors. A summary of the estimated survey units and samples to be collected during FSS is provided in Table 4-9. Table 4-9. Planned FSS Volumetric Samples

Area Survey Unit Class Number of

Survey Units

Samples per

Survey Unit

Total Number of

Samples Planned

General Areas FSS-10-YY 3 2 20 40 Administrative FSS-10-YY 3 2 20 40

S1 FSS-20-YY 1 5 20 100 S1 FSS-20-YY 2 2 20 40

Trench FSS-30-YY 1 20 20 400 Trench FSS-30-YY 2 2 20 40 Lagoon FSS-40-YY 1 22 20 440 Lagoon FSS-40-YY 2 2 20 40 Ditch FSS-50-YY 1 6 20 120 Ditch FSS-50-YY 2 2 20 40

Excavated soils ‒ ScanSortSM FSS-60-YY 2 180 20 3,600

Total 245 4,900

5. SURVEY METHODS AND INSTRUMENTATION

The principal field activities to be conducted as part of the Luckey Site FSS include surficial gamma scans of the overburden, bench/side-slope soils, and floors and walls of excavated areas, and a collection of soil samples and their analyses. In addition, excavated soils will be processed by Orion ScanSortSM system for soil segregation, which provides FSS data for those materials. The remainder of this section briefly describes each of these activities.

5.1 Soil/Sediment Sampling

Most volumetric soil samples collected will be surface soil using hand collection techniques, but some samples may need to be collected at depth. Surface soil samples will be collected from the top 6 in. A number of techniques have been developed to obtain samples from various




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depths bgs. The techniques that have been selected provide a practical and efficient means of obtaining samples in a manner consistent with safety protocols. Additionally, they employ equipment that is normally available for use. Sampling procedures for obtaining subsurface soil samples include direct push and soil boring.

Volumetric samples collected during site FSS activities will be assigned unique sample identification numbers. These numbers are necessary to identify and track each for analysis during completion of the project. In addition, the sample identification numbers will be used to identify and retrieve the analytical results received from the laboratory, as well as other data related to the sample.

Each sample shall be identified by a unique alphanumeric code. To maintain consistency and comparability of sample location identification throughout the course of the FSS, samples shall be labeled by:

• A two-letter sample type designation (AA).

• An FSS designation (FSS).

• A two-digit FSS area number (BB).

• A three-digit exploration location designation (CCC).

• A two-digit depth layer interval number (##).

The format of the sample identification will be as follows: AAFSSBBCCC##.

The sample type designator (AA) identifies the specific sample type, such as sediment or soil. The FSS area number (BB) identifies the specific FSS survey area, such as the trench area, lagoons, or other FSS areas. The exploration location designation number (CCC) is a sequential number within a specific FSS area. The depth layer interval number (##) is a number that represents the incremental depth layer from where the sample was collected. For surface volumetric sample locations where there is a significant thickness of material to be sampled, and multiple samples will be collected from the same location, the samples will be identified starting with “01” as the first layer sample.

Sample type designators and FSS area numbers are described below:

Sample Type Designation (AA).

SS surface soil SB soil boring SD sediment SC soil beneath concrete/asphalt




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EX excavated soil SX surface soil from trench or excavation

FSS Area Numbers (BB).

10 Buffer/General Areas 20 S1 Area 30 Trench Area 40 Lagoon Area 50 Drainage Ditch 60 ScanSortSM segregated soil

For soil samples, the depth layer (sample interval layer) identifier, as measured from the ground surface, shall be included with the sample identification. For example, the soil sample collected from the survey unit at the second depth layer interval (e.g., the 1- to 2-ft depth level) in a soil boring within the lagoon FSS area would be labeled SBFSS4000102.

To permit proper evaluation of the sample analysis results, it is important that the actual location of the samples is properly documented. Sample locations will be identified in the field with pin flags, stakes, or other markers. A GPS reading at each sample location will record its position.

Split or duplicate samples shall be provided as requested to USACE, the State, and/or their authorized representatives of samples collected. Similarly, split or duplicate samples may be taken by USACE, the State, and/or their authorized representatives. Identical procedures shall be used to collect all samples unless otherwise specified by USACE or the State.

Sample tracking and custody procedures will be followed for samples collected as part of the FSS. This is to assure that each sample is accounted for at all times. To maintain this level of sample monitoring, sample container labels and chain-of-custody records will be used to track samples collected for analysis.

5.1.1 Sample Labels

Sample labels will be affixed to all containers during sampling activities at the time of sample collection and securely affixed to the container prior to shipment. The sample identification number may also be provided as a bar code on the sample label to reduce errors and facilitate use of a sample tracking system in the laboratory. Sample label information will include, but not be limited to:

• Sample identification number.




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• Site name and area.

• Sample interval.

• Type of sample (discreet, grab, or composite).

• Type of chemical preservative present in container.

• Date and time of sample collection.

• Sampler’s name and signature or initials.

5.1.2 Chain-of-Custody Records

Chain-of-custody procedures implemented for the project will provide documentation of the handling of each sample from the time of collection until completion of laboratory analysis. The chain-of-custody form serves as a legal record of possession of the samples. A sample is considered to be under custody if one or more of the following criteria are met:

• The sample is in the sampler’s possession.

• The sample is in the sampler’s view after being in possession.

• The sample was in the sampler’s possession and then was placed into a locked area to prevent tampering.

• The sample is in a designated secure area.

Custody will be documented throughout the field sampling activities by a chain-of-custody form initiated each day during which samples are collected. The chain-of-custody form will accompany the samples from the site to the laboratory and will be returned to the laboratory coordinator with the final analytical report. Personnel with sample custody responsibilities will be required to sign, date, and note the time on a chain-of-custody form when relinquishing samples from their immediate custody (except in the case where samples are placed into designated secure areas for temporary storage prior to shipment).

Chain-of-custody forms will be used to document the integrity of all samples collected. To maintain a record of sample collection, transfer between personnel, shipment, and receipt by the laboratory, chain-of-custody forms will be filled out for sample sets as determined appropriate during the course of fieldwork.

The individual responsible for shipping the samples from the field to the laboratory will be responsible for completing the chain-of-custody form(s) and noting the date and time of shipment. This individual will also inspect the form for completeness and accuracy. After the form has been inspected and determined to be satisfactorily completed, the responsible




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individual will sign, date, and note the time of transfer on the form. The chain-of-custody form will be placed in a sealable plastic bag and placed inside the cooler used for sample transport after the field copy of the form has been detached. The field copy of the form will be appropriately filed and kept at the site for the duration of the site activities.

Chain-of-custody seals will also be placed on each cooler used for sample transport. These seals will consist of a tamper-proof adhesive material placed across the lid and body of the coolers. The chain-of-custody seals will be used to ensure that no sample tampering occurs between the time the samples are placed into the coolers and the time the coolers are opened for analysis at the laboratory. Cooler custody seals will be signed and dated by the individual responsible for completing the chain-of-custody form(s) contained within the cooler.

5.1.3 Sample Packaging and Shipping

Sample containers destined for off-site laboratory analysis will be packaged in thermally insulated rigid-body coolers and stored in a secure area between collection and shipment to the off-site subcontract laboratory. These samples will be packaged, classified, labeled, stored, shipped, and tracked in accordance with current U.S. Department of Transportation regulations (e.g., 49 CFR 173 et. seq.).

5.2 Gamma Scanning Surveys

Gamma walkover surveys, Orion ScanPlotSM, or other appropriate land area survey methods are specified for this FSS. Gamma scan surveys will be for Ra-226 utilizing the derated DCGL (Section 3.1.1.3) to account for the other ROCs. The relationship of the ROCs relative to Ra-226 will be verified during FSS, as described in Section 6.3.2.2. Gamma walkover surveys will be performed by holding the detector close to the ground surface and moving it in a pendulum (back-and-forth) motion while walking at a speed that allows the investigator to detect the desired investigation level. Discernible increases in the count rate (meter or audible) to the investigation level or greater will trigger a more focused survey of the area. This may include allowing the survey meter response to stabilize at the location or a time-integrated direct reading. When such an area is identified, a pause in the scan process will occur to verify increased count rate. If the pause confirms an elevated reading, then a static 1-minute count will be performed. If the 1-minute static measurement confirms that the predetermined gross scan investigation levels have been exceeded (suggesting the presence of an elevated area), a biased soil sample will be collected at that location. Locations that exceed the investigation level will be marked (flag or stake) for additional investigation as an elevated area. Land area surveys using multiple detector array system methodology, along with GPS and data-logging capabilities, may be used to perform this survey so long as radiation emission type and scan MDCs are suitable for the detector system.




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Since land area scan surveys will be used as part of the investigation of areas with locally elevated concentrations, the MDCSCAN values that are developed using the guidance provided in Minimum Detectable Concentrations with Typical Radiation Survey Instruments for Various Contaminants and Field Conditions, NUREG-1507 (NRC 1998) and MARSSIM (EPA et al. 2000) should be evaluated with the investigation levels (DCGLs). Table 6.4 of NUREG-1507 for 2 × 2-in. NaI Detector systems makes it clear that the MDCSCAN values are greater than the Luckey Site DCGLs listed in Table 3-1; therefore, land area scan sensitivity may be unacceptable for this FSS without consideration of surrogate gamma-emitting radionuclides, slower scanning speeds, and/or detector side collimation to lower background levels. It should be noted that if the actual scan sensitivity is not adequate (i.e., the available scan sensitivity is not sufficient to detect small layers of elevated activity), then it will be necessary to increase the sample size, as discussed in MARSSIM (EPA et al. 2000). Depending on the site field conditions encountered, the investigation levels may have to be adjusted based on a correlation of the field instrument response to elevated area sample results.

Gamma scanning surveys will be evaluated for instrument response to actual soil concentrations by mapping the survey (using GPS coordinates) with an overlay of soil sample results. This will provide a means to spatially interpret data and will provide an assessment of the sensitivity of the gamma scanning surveys since numerous factors can influence the results, and very few can be reliably controlled during the surveys, especially given the nature/distribution of contamination. If elevated areas are not being readily identified by gamma scanning surveys, then adjustments will be made to the scan process, or additional samples will be required, as indicated above.

5.3 Instrumentation

Since a variety of instruments may be used during final status survey, this section will focus on pertinent issues generically. Instruments that may be used as part of FSS will be calibrated and have the MDC evaluated prior to actual use. The example for this process is a 2 × 2 NaI detector, but other detectors, such as FIDLER, 3 × 3, or larger, may be utilized as field portable or as part of ScanPlotSM system for conducting gamma scanning surveys.

Land area scan survey instrumentation may consist of a survey meter and sodium iodide (NaI) probe or a sophisticated drive-over multiple detector array system with GPS and data-logging capabilities. While a wide variety of portable instruments and detectors are readily available, the drive-over systems are usually one-of-a-kind instrument systems that provide a greater amount of survey data in a shorter timeframe. Although drive-over systems are currently not as common as the portable instruments, the drive-over multiple detector array systems are being used with a greater frequency for FSS.




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The rest of this section regarding portable instruments will use NaI probes and associated instruments as an example of how calibration, minimum detectable concentration, and reporting results will be performed. Similar processes will be utilized for any other portable survey instruments and detectors used during FSS.

5.3.1 Calibration

Calibration of portable instruments will conform to the manufacturer’s recommendations as well as established standards (ANSI 1997). In general, this will entail verifying that the electronics of the meter are working properly, performing a voltage plateau, and establishing the efficiency of the probe. Instrumentation used for land area scans does not require instrument efficiency due to the nature of the qualitative information collected with that type of instrument.

5.3.2 Minimum Detectable Concentration

For any of the survey instruments, the detection sensitivity will be affected not only by the factors influencing detector efficiency but also by the detector’s residence time over a given area and the uncertainty introduced by the human factors involved in moving the detector and interpreting the instrument response. Another factor is that surveys will be performed on soils, and the residual radioactivity will be part of the soil matrix, as compared to surface contamination evaluations for buildings. The combination of multiple source terms, the energy-dependent response rate of the NaI detector, and the residual radioactivity being part of a matrix, creates a very complex scenario to determine MDCs. The process follows that established in NUREG-1507 and the MARSSIM. The established 2 × 2 NaI MDCs in NUREG-1507 are 2.8 pCi/g for Ra-226 and 80 pCi/g for natural uranium.

Derivation of the MDCSCAN for soil is a four-step process. First, the relationship between the NaI detector counting rate to exposure rate (cpm per µR/h) as a function of gamma energy is determined. Second, the relationship between radionuclide concentration in soil and exposure (pCi/g per µR/h) is established. Next, the MDCRSURVEYOR is calculated, and finally all three parameters are used to calculate the MDCSCAN. This is an a priori determination of MDCSCAN that can be used for planning FSS. The actual MDCSCAN will be determined with the operating parameters (background, efficiency) of the actual instruments used in the FSS.

Several factors need to be determined to establish the relationship between the detector’s count rate to exposure rate. The response of the NaI detector is relative to the gamma energy interacting with the detector. Therefore, the cpm produced by the detector will be a function of the probability of interaction for a gamma of particular energy. This parameter is determined by taking a known detector response (calibration) and applying it to the relative response of the detector at different gamma energies. For this a priori determination of MDCSCAN, the manufacturer-provided value of 900 cpm per µR/h (Ludlum) for Cs-137 will be used in lieu of




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actual calibration efficiency. The relative response of the detector is calculated by multiplying the probability of interaction by the relative fluence rate for a given gamma energy. The probability of interaction is determined from the mass attenuation coefficients (µ/ρ) for NaI, and the fluence rate is determined from the mass energy-absorption coefficients (µen/ρ) for air following the NUREG-1507 process and values established.

The second phase of this process was to determine the relationship between the radionuclide concentration in the soil and exposure rate. This was accomplished by modeling the soil with a code such as Microshield in order to determine the exposure rate. The geometry used for this modeling was input as a cylindrical volume with a radius of 28.2 cm (area of 0.25 m2) and a thickness of 7.5 cm, as described in NUREG-1507. The dose point was located 10 cm directly above the center of the cylinder to represent the typical height above the surface during scanning.

The first step in determining the MDCSCAN is to calculate the minimum detectable count rate for the surveyor (MDCRSURVEYOR). The MDCRSURVEYOR is a function of the background count rate, the length of the counting interval, surveyor efficiency, and the index of sensitivity (statistical), as shown in Equation 5-1. Background for a 2 × 2 NaI detector is approximately 10,000 cpm, and the index of sensitivity (d′) will be based on a 95 percent true positive rate and a rate of 60 percent false positive, which yields a value of 1.38. The surveyor efficiency has a value of 0.5 and the length of the counting interval will be 1 second. The results of this evaluation are shown in Table 5-1 and indicate that 1,513 cpm above background (11,513 cpm with background) is the minimum value for 95 percent true positive detection.

p

ibdMDCR i

surveyor

)/60(∗∗′= (5-1)

Where:

surveyorMDCR = surveyor minimum detectable count rate (above background) d′ = the index of sensitivity (the number of standard deviations between the means

of background and radioactivity above background) bi = the number of background counts in the counting interval, i i = the length of the counting interval in seconds p = surveyor efficiency




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Table 5-1. MDCRSURVEYOR Values Parameter Value

i The length of the counting interval (seconds) 1

d’ Index of sensitivity 1.38 Cb Background count rate (cpm) 10,000

bi Number of background counts in counting interval i 167

si Minimum detectable net counts in counting interval i 17.8

MDCR Minimum detectable count rate (cpm) 1,070 p Surveyor efficiency 0.5

MDCRsurveyor Surveyor minimum detectable count rate (cpm) 1,513

The minimum detectable exposure rate in µR/h is calculated by dividing the MDCRSURVEYOR by the detector efficiency in cpm per µR/h. Multiplying the minimum detectable exposure rate by the soil concentration exposure rate factor in pCi/g per µR/h will yield the MDCSCAN, as shown in Equation 5-2.

ct

SCAN SMDC ∗=ε

surveyorMDCR (5-2)

Where:

MDCSCAN = the minimum radioactivity concentration in soil above background radioactivity (in pCi/g) that can be reliably detected

surveyorMDCR = surveyor minimum detectable count rate (above background) εt = counting system efficiency in cpm per µR/h Sc = soil concentration exposure rate factor in pCi/g per µR/h

It should be noted that the Scan MDC (MDCSCAN) to be used for uranium (U-238/U-234), 80 pCi/g, and Ra-226, 2.8 pCi/g, were taken from the 2 × 2 NaI Detector column from Table 6.4 of NUREG-1507, Minimum Detectable Concentrations with Typical Radiation Survey Instruments for Various Contaminants and Field Conditions.

Since land area scan surveys will be used as part of the investigation of areas with locally elevated concentrations, the MDCSCAN values should be evaluated with the investigation levels (DCGLs). If the actual scan sensitivity is not adequate (i.e., the available scan sensitivity is not




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sufficient to detect small layers of elevated activity), then it will be necessary to increase sample size as discussed in MARSSIM (EPA et al. 2000).

5.3.3 Reporting Results

The results from surveys will be reported as raw results on the survey data sheets. This will allow each step of the conversion to the concentration to be reviewed and will allow for any changes that might be necessary in the background or efficiency of the detector. In addition, the uncertainty associated with results and associated MDC will also be reported with the final results. In addition, GPS coordinates will be logged to provide a dataset for display and analysis.

5.4 Soil Sample Analysis

Samples will be transferred to a laboratory for analyses in accordance with the Uniform Federal Policy Quality Assurance Project Plan for the Luckey Formerly Utilized Sites Remedial Action Program Site Remediation, Luckey, Ohio, Sampling and Analysis Plan (USACE 2016b). In accordance with MARSSIM, analytical techniques will provide a minimum detection level of 25 percent of the individual radionuclide cleanup goals for all primary contaminants, with a preferred target minimum detection level of 10 percent of these individual radionuclide cleanup goals. For radionuclides present in background, the laboratory-specified MDCs for each individual nuclide shall be limited to 0.5 pCi/g or less, which applies to all ROCs for the project. The results of laboratory analyses will be reported by radionuclide as the actual concentration (pCi/g), the uncertainty associated with that result, and the MDC. The actual result may be less than the MDC or even a negative number. Statistical evaluations of the data will be performed on the actual results.

5.5 ScanSortSM Analysis

Soil sorting will be performed in accordance with the SSOP (USACE 2016c). Results of soil segregation processing by the Orion ScanSortSM system will be reported for each below-criteria stockpile as part of FSS. The report will include the stockpile ID, number of measurements, amount of material (tons), mean, median, maximum, minimum, and 2-Sigma population variance.

6. QUALITY CONTROL AND DATA ASSESSMENT

A data quality assessment is a scientific and statistical evaluation that determines if the data are of the right type, quality, and quantity to support their intended use. Detailed guidance for the DQA process is provided in the MARSSIM and Guidance for Data Quality Assessment (EPA 2000). The effort expended during the DQA should be consistent with the graded approach used in designing the survey.




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6.1 Quality Control

The goal of QC is to identify and implement sampling and analytical methodologies that limit the introduction of error into analytical data. This plan serves to provide the necessary control for providing sufficient data of adequate quality and usability for the purpose of confirming that the project’s release levels have been met. It also serves to ensure that such data are authentic, appropriately documented, and technically defensible. Quality control will be achieved through three primary approaches: data management, sample custody, and QC measurements.

6.1.1 Data Management

Sample collection, field surveys and direct measurement, and laboratory analytical result data will, to the extent practicable, be recorded both electronically and on paper. Records of field-generated data will be reviewed by supervisory personnel knowledgeable in the measurement method for completeness, consistency, and accuracy. Electronic copies of original electronic data sets will be preserved on a retrievable data storage device. No data reduction, filtering, or manipulation will be performed on the original electronic versions of data sets.

Record copies of surveys, sampling, and analytical data (and their supporting data) will be located appropriately in project record files.

6.1.2 Sample Custody

Sample quality could be impacted by sample collection methods and by preservation of sample quality after the sample is collected and through the analytical process.

Sample quality related to sample collection will be controlled through the use of trained sample personnel implementing approved standard operating procedures (SOPs) referenced throughout this FSSP. Methods employed in SOPs will take into account the need to prevent sample contamination through the use of techniques such as disposable sampling apparatus and materials, decontamination of equipment between sample collection, and isolation of samples in discrete sample containers.

Once the sample has been collected and isolated, sample quality related to the processing and subsequent handling and analysis of the sample will be controlled by maintaining appropriate sample custody.

Sample custody and control will be affected by:

• Assigning unique sample identification numbers to samples collected expressly for FSS in accordance with this plan.




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• Recording the date, time, sample type, and location and linking that information with the sample identification number and the required analysis.

• Requiring that trained and qualified personnel be permitted to possess samples while they are on-site.

• Requiring that a chain-of-sample-custody protocol be employed for volumetric sample materials.

6.1.3 Quality Control Measurements

The final status survey will rely on in situ measurements using conventional health physics techniques and methods and on media samples measured with either gamma or alpha spectroscopy. Both will require additional steps to ensure the accuracy of the sampling techniques and analysis methodologies.

Quality control activities performed during field survey and sampling will consist of field replicate (split) volumetric sampling (i.e., a single sample that is collected, homogenized, and split into equivalent fractions in the field) and replicate (duplicate) direct measurements for direct surveys. Split samples are designed to assess the consistency and precision of the overall sampling and analytical system while duplicate direct measurements are designed to provide an estimate of overall precision of the field survey measurement system.

Split samples will be collected at a frequency of 5 percent (1:20), with at least one for each survey unit. Split samples will be collected from the same sampling location, depth, or interval as the original field sample after field homogenization.

Quality control activities performed during radiological scan surveys will consist of survey instrument checks and biased replicate measurements. The survey instruments used for performing scan surveys will have source response checks and background checks performed at least twice per day. These data will be used to generate control charts to demonstrate that the instruments have been performing accurately and consistently.

6.2 Measurement Uncertainty and Data Quality Indicators

Measurement uncertainty in the techniques prescribed in the FSS arises from two principal sources: field sampling variation and instrument measurement variation. Of the two sources, field sampling variation will likely be the greatest contributor to overall uncertainty because of the inherent logistics of sample collection activities. To the extent practicable, field operations will be governed by procedures, and survey personnel will be trained. Additionally, individuals well versed in the overall survey approach and its data quality objectives will be available to guide and referee unclear situations that may arise. The measurement methods, on the other




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hand, employ standard instruments and laboratory procedures whose aspects and nuances are well understood through many years of application. Procedures and their associated rigor will also govern instrument calibrations, source response checks, and operations.

A major activity in determining the usability of the sampling and survey data is assessing the effectiveness of the sampling program. Data quality indicators (DQIs) listed in Table 6-1 will be used in the field and in the DQA process to provide quantitative and qualitative measures of overall data quality and usability. Key points evidenced by Table 6-1 include:

• Completeness ‒ The project is striving for a 90 percent completeness objective. Attaining or not attaining the objective does not necessarily authenticate or compromise the study. However, a 90 percent completion goal is a desirable performance metric that indicates that nearly all of the specified data have been acquired. Completeness is a measure of the amount of valid data obtained from a measurement system compared to the amount that was expected under correct, normal conditions.

• Comparability ‒ Comparability expresses the confidence with which one data set can be compared to another. When comparing data, it is important to compare data collected under the same set of conditions. Seasonal trends, depth of sample collection, analytical protocol, method detection limits, and any other sampling/analytical variables must be taken into account when comparing data sets. Comparability of data has been “designed in” to the random spatial sampling approach. The same instrument types and measurement techniques will be used in comparable areas subject to FSS. As a result, interarea comparability should be assured for randomly selected (unbiased) locations. The nature of an FSS is to collect not only data from randomly selected sample locations, but also to augment the data set with data collected based upon professional judgment and knowledge of the history and processes that occurred in specific areas of the site. Data from judgment-based sampling will naturally have some selection bias and may have unique characteristics that will not permit comparability with data collected from randomly selected locations. Still, if data from one strata or sample allocation type can be shown to be sufficiently comparable to data from a related strata or sample allocation type, the data sets may be pooled to extend the capability to make decisions about broader areas or media types and to improve the statistical power for making a decision.




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Table 6-1. Target Data Quality Indicators DQI Significance Action/Remark

Completeness Less than complete data set could decrease confidence in supporting information.

Objective of 90 percent completeness.

Comparability Affects ability to combine analytical results.

Data collected from randomly selected locations within a survey area are unbiased and comparable by design and can be combined. Combining of other data sets will be subject to appropriate two-sample statistical test methods designed to detect significant differences between samples or populations.

Representativeness Nonrepresentativeness increases or decreases Type I error depending on the bias.

Sample allocation will include a minimum number of unbiased, randomly distributed sample locations based on survey design.

Precision Measurement variability, due to techniques and/or technology, may increase uncertainty.

Field sampling and instrument operation will be governed by procedures. Replicate measurements, background measurements, and source response check measurements will be used to gauge reproducibility. Relative percent difference of 50 percent or less with a minimum concentration of 50 percent of the DCGL.

Accuracy Sampling and data handling can introduce bias and affect Type I and Type II errors.

Field measurement will be governed by procedures. Instruments will be calibrated with NIST traceable sources. Objective of + 30 percent with a minimum concentration of 50 percent of the DCGL.

• Representativeness ‒ Representativeness is a measure of the degree to which the measured results accurately reflect the medium being sampled and the overall situation at the site. It is a qualitative parameter that is addressed through the proper design of the sampling program in terms of sample location, number of samples, and actual material collected as a sample of the whole. The random sample design with its unbiased allocation and preference for spatial distribution within the survey unit was intended to ensure representativeness to the extent practicable. Deviations from the unbiased allocation (e.g., selecting locations




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based on prior knowledge as might be gained from scanning for elevated concentrations of residual radioactivity) will indicate bias and compromise the ability to defend representativeness as a DQI. For this reason, data acquired through judgment sampling will be representative only of the immediate locality from which it was collected. Thus, a posteriori data analysis will not include aggregation of randomly acquired data with data acquired through judgment sampling without a suitable treatment to correct the bias.

• Precision ‒ Precision refers to the level of agreement among repeated measurements of the same parameter. The overall precision of a piece of data is a mixture of its parts (e.g., sampling and analytical factors for volumetric samples and instrument operation and measurement technique for measurements). The analytical precision and instrument operation is much easier to control and quantify because the laboratory is a controlled and therefore measurable environment and field instrumentation is calibrated and checked daily. Sampling precision is unique to each site, making it much harder to control and quantify, while the measurement technique varies between instrument type and technician performing the measurement. In general, sampling and measurement techniques specified for obtaining FSS can be controlled so that data have a high degree of reproducibility. Consequently, good precision between replicate measurements of a specific activity radioactive check source is usually achieved. However, it is expected that the FSS will encounter a significant proportion of measurements that are representative of background. Thus, in cases where a replicate measurement is made at a location that does not have an appreciable concentration of residual radioactivity (clearly elevated above background), the instrument response will express (1) variation stemming from the instrument’s inherent detection and counting capability, and (2) the variation from background. Experience indicates that, frequently, the later component, background variability, obscures the ability to gauge instrument precision between replicate measurements when replicate measurements are made at locations where significant detectable radioactivity is not present. For samples with elevated concentrations of COCs, a relative percent difference of 50 percent or less with a minimum concentration of 50 percent of the DCGL will apply.

• Accuracy ‒ Accuracy refers to the difference between a measured value for a parameter and the true value for the parameter. It is an indicator of the bias in the measurement system. Field instrument accuracy will be evaluated by implementing instrument source response checks and the charting of the results (control charts). Laboratory analysis accuracy will be evaluated by laboratory QC methods. Samples may be sent to an off-site laboratory for comparison as well. Generally, the accuracy of analyses must be within 30 percent with a minimum concentration of 50 percent of the DCGL.




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6.3 Data Quality Assessment

Assessment of environmental and analytical data will be used to evaluate whether the data meet the objectives of the sampling event, and whether the data are sufficient to determine compliance with release levels. The assessment phase of the data life cycle consists of two phases: data verification and statistical evaluations.

6.3.1 Data Verification

Data verification compares the collected data to the DQOs documented in this plan. Data verification will ensure the FSSP and SOP requirements have been implemented as prescribed. The data and documentation used for release will be verified (100 percent). Data verification will include:

• Assessment of activities performed during implementation by means such as inspections, QC checks, or surveillance.

• Documentation of deficiencies or problems encountered during implementation.

• Review of corrective actions to ensure adequacy and appropriateness.

Data verification activities will ensure that the results of data collection activities and analytical results support the objectives of the sampling event as documented in this plan, or support a determination that these objectives should be modified. The data verification parameters for this FSSP are summarized in Table 6-2.

The data analysis framework will incorporate DQA components discussed in MARSSIM to assess the overall usability of the data for its intended use. The data evaluation process will be validated, and statistical analysis methods will be used, to assess whether variability and bias in the data are small enough to allow the data to support release of the site from radiological controls with acceptable confidence.

Missing data may reduce the precision of estimates or introduce bias, thus lowering the confidence level of the conclusions. The importance of lost or suspect data will be evaluated in terms of the sample location, analytical parameter, nature of the problem, decision to be made, and the consequence of an erroneous decision. Critical locations or parameters for which data are determined to be inadequate may be resampled.




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Table 6-2. Data Verification Parameters Data

Descriptor Consideration Impact if Not Met Corrective Action

Reports to decision maker

Sample design with measurement locations. Analytical method and detection limit. Background radiation data. Field reports.

Unable to perform a quantitative survey or analysis.

Request missing information.

Documentation Chain-of-custody records. SOPs. Field and analytical records.

Unable to have adequate assurance of results. Unable to verify survey results or sample results.

Resurveying or resampling. Correct deficiencies.

Data sources Data used meet DQOs. Inadequate sample design. Lower confidence of data quality.

Resurveying, resampling, or reanalysis for unsuitable or questionable measurements.

Analytical method and detection limit

Routine methods used to analyze contaminants of concern.

Unquantified precision and accuracy. Potential for Type I and Type II decision errors.

Reanalysis, resurveying, or resampling. Documented statements of limitation.

Data review Defined level of data review.

Potential for Type I and Type II decision errors. Increased variability and bias due to analytical process, calculation errors, or transcription errors.

Perform data review.

Data quality indicators

Completeness of survey and sampling. Representativeness of sampling locations. Precision of analytical methods. Accuracy of sampling and surveying.

Insufficient power to defend decision. Increases or decreases Type I decision error depending on the bias. Increase in data variability.

Conduct additional surveying or sampling. Use proper analytical instrumentation and approved protocol. Follow FSSP.




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6.3.2 Statistical Evaluations

After data validation and verification is complete, the next step in DQA is to interpret the data. This will be accomplished by performing a data review, testing the data, and drawing conclusions from the data.

6.3.2.1 Data Review

The data review entails calculating various statistical parameters (mean, median, standard deviation, etc.) in order to evaluate the data. In addition, data review will include qualitative visual analysis (histograms, scatter diagrams, box and whisker plots, etc.). Some additional analytical methods (e.g., spatial correlation) as well as spatial analysis (e.g., probability plots) not required to support the decision rule are not explicitly planned for but could be employed on an ad-hoc basis to gain insight. The intent of the data review is to evaluate the data for skewness or spatial trends that might be indicators of levels of residual radioactivity that exceed the release criterion. 6.3.2.2 Surrogate Radionuclides

Surrogate radionuclides will be used for soil sorting and gamma scanning surveys. The basis is provided in Section 3.1.1 and SORsurrogate will be used to verify. Calculate SORsurrogate for each sample (see equation below) using the gross results for the samples vs. DCGLs plus background reference area. Trend the collected surrogate data to evaluate the mean and 50th percentile values.. First, calculate SORsurrogate for each sample (Equation 6-1) using the gross results for the samples vs. DCGLs plus background reference area. Next, calculate mean SOR value and 50th percentile. If the mean SOR value and 50th percentile are less than 1.0, then QA check passes.

𝑆𝑆𝑆𝑆𝑆𝑆𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑇𝑇𝑠𝑠𝑅𝑅𝑇𝑇𝑠𝑠 = 𝑅𝑅𝑅𝑅226

2.0pCig +𝐵𝐵𝑅𝑅𝑅𝑅226+ 𝑁𝑁ℎ230

5.80pCig +B𝑇𝑇ℎ230+ 𝑈𝑈234

26pCig +B𝑈𝑈234+ 𝑈𝑈238

26pCig +B𝑈𝑈238 (6-1)

Where: SORsurrogate = sum of ratios (surrogate assessment) Ra226 = gross Ra-226 soil concentrations Th230 = gross Th-230 soil concentrations U234 = gross U-234 soil concentrations U238 = gross U-238 soil concentrations BRa226 = background Ra-226 soil concentrations BTh230 = background Th-230 soil concentrations BU234 = background U-234 soil concentrations BU238 = background U-238 soil concentrations




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6.3.2.3 Statistical Tests

Statistical tests will be used to evaluate compliance with DCGLW. The WRS test is a two-sample, nonparametric test that is recommended when the contaminant(s) under evaluation is present at significant levels in the background. The WRS test compares the sample results to the background reference area sample population with the DCGL as the difference as part of the test. Given multiple ROCs, the WRS test will use the weighted sums of the reference background and survey unit data sets. The weighted sums are determined for each sample data set (location) by dividing the soil ROC concentrations by each DCGLW (Equation 6-2). The reference background data set has the DCGLW (i.e., 1.0) added to the weighted sum for each location. The WRS test is performed on the weighted sums (sample data set and adjusted reference background data set).

Weighted Sum = 𝑅𝑅𝑅𝑅2262.0 pCi/g

+ 𝑁𝑁ℎ2305.80 pCi/g

+ 𝑈𝑈23426 pCi/g


(6-2)

Where: Ra226 = gross Ra-226 soil concentrations (sample or reference background) Th230 = gross Th-230 soil concentrations (sample or reference background) U234 = gross U-234 soil concentrations (sample or reference background) U238 = gross U-238 soil concentrations (sample or reference background)

6.3.3 Decision Rules

The combination of sample data and scan data will be used to demonstrate compliance with the release criterion. In addition to the WRS test, some additional comparisons are needed, including SOR and the elevated measurement comparison (EMC). The unity rule is to ensure that the total dose due to the sum of discrete source terms does not exceed the release criteria. There are two related but different versions of the unity rule (SOR) for this FSS, SORMARSSIM and SORROD. The SORMARSSIM will be used to assess individual survey unit data with respect to the WRS test (Equation 6-3). The SORROD will be used to verify the final dose meets the ROD criteria (Equation 6-4). The decision to release the survey unit will be based on the comparisons in Table 6-3.




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𝑆𝑆𝑆𝑆𝑆𝑆𝑀𝑀𝑀𝑀𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 = 𝑅𝑅𝑅𝑅226

2.0pCig +𝐵𝐵𝑅𝑅𝑅𝑅226+ 𝑁𝑁ℎ230

5.80pCig +B𝑇𝑇ℎ230+ 𝑈𝑈234

26pCig +B𝑈𝑈234+ 𝑈𝑈238

26pCig +B𝑈𝑈238≤ 1 (6-3)

Where: SORMARSSIM = sum of ratios result Ra226 = gross Ra-226 soil concentrations Th230 = gross Th-230 soil concentrations U234 = gross U-234 soil concentrations U238 = gross U-238 soil concentrations BRa226 = background Ra-226 soil concentrations BTh230 = background Th-230 soil concentrations BU234 = background U-234 soil concentrations BU238 = background U-238 soil concentrations

𝑆𝑆𝑆𝑆𝑆𝑆𝑅𝑅𝑅𝑅𝑅𝑅 = 𝑅𝑅𝑅𝑅2262.0 pCi/g

+ 𝑁𝑁ℎ2305.80 pCi/g



≤ 1 (6-4)

Where: SORROD = sum of ratios result Ra226 = net Ra-226 soil concentrations Th230 = net Th-230 soil concentrations U234 = net U-234 soil concentrations U238 = net U-238 soil concentrations




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Table 6-3. Summary of Decision Rules Survey Result Conclusion

Difference between largest survey unit measurement and smallest background reference area measurement is less than DCGLW.

Survey unit meets release criteria.

Difference between survey unit average and background reference area average is greater than DCGLW.

Survey unit does not meet release criteria.

Difference between any survey unit measurement and any background reference area measurement is greater than derated Ra-226 DCGLW, and the difference between survey unit average and background reference area average is less than DCGLW.

Conduct WRS test and elevated measurement comparison.

Beryllium 95 percent upper confidence limit (UCL) of the mean is greater than CG.


Lead 95 percent UCL of the mean is greater than CG.


Another factor in the decision rule is the EMC. Each measurement in the survey unit (systematic and scan) is compared to the investigation levels. Any measurement that is equal to or greater than the investigation level should be investigated. The EMC is intended to flag potential failures in the remediation process, not to demonstrate compliance with the release criterion. The DCGL for the EMC is shown in Equation 6-5.

WmEMC DCGLADCGL ∗= (6-5) Where:

EMCDCGL = derived concentration guideline level for small areas of elevated activity

mA = area factor for the area of the systematic grid (a priori) or actual area of elevated concentration (a posteriori)

WDCGL = derived concentration guideline level for average concentrations If an isolated area or elevated residual radioactivity is found, a variation of the unity rule will be used to ensure that the total dose (uniformly distributed and elevated) is within the release criterion. This variation is shown in Equation 6-6.

1230226238

238238

234

234234

230

230

226

226

238

238

234

234 230230226226 ≤∗

−+

∗

−+

∗−

+∗

−++++

ThmRamUm

UU

Um

UU

Th

Th

Ra

Ra

U

U

U

U

DCGLADCGLADCGLADCGLADCGLDCGLDCGLDCGLThThRaRa

δχδχδχδχδδδδ (6-6)




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Where: 234Uδ = estimate of average uranium-234 (U-234) residual radioactivity in the survey unit

238Uδ = estimate of average uranium-238 (U-238) residual radioactivity in the survey unit

226Raδ = estimate of average radium (Ra-226) residual radioactivity in the survey unit

230Thδ = estimate of average thorium (Th-230) residual radioactivity in the survey unit

234Uχ = average U-234 concentration in elevated area

238Uχ = average U-238 concentration in elevated area

226Raχ = average radium concentration in elevated area

230Thχ = average thorium concentration in elevated area

mA = area factor for the actual area of elevated concentration

234UDCGL = derived concentration guideline level for U-234

238UDCGL = derived concentration guideline level for U-238

226RaDCGL = derived concentration guideline level for Ra-226

230ThDCGL = derived concentration guideline level for Th-230. If there is more than one area of elevated residual radioactivity in a survey unit, then additional terms can be added to Equation 6-6. An alternative is to use the actual results as input into RESRAD and calculate the dose for each area of elevated residual radioactivity to show that the total dose is within the release criterion.

7. REPORT OF SURVEY FINDINGS

Survey procedures and sampling results will be documented in an FSS report, following the general guidance for FSS reports in NUREG-1757, Vol. 2, Rev. 1 (NRC 2006) and MARSSIM (EPA et al. 2000). This FSS report will become an integral part of the site radiological assessment report. This FSS report will contain, at a minimum, the following information:

• A facility map that shows scan data, locations of elevated direct radiation levels, and sampling locations from each survey unit.

• Tables of radionuclide concentrations in each sample from each survey unit, including, but not limited to, the results in picocuries per gram, measurement errors, detection limits, and sample depths.

• Summary statistics for analytical data, surface scan data, and gamma logging data from each survey unit.

• A graphical display of individual sample concentrations in the form of posting plots and/or histograms for each survey unit and visual identification of trends.




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• Results of SOR.

• Results of any EMC comparisons.

• Results of the statistical test.

As each survey unit is completed, technical data packages will be prepared to provide the USACE and stakeholders information, data, statistics, and calculations to demonstrate that the ROD criteria and FSSP requirements have been met. Each technical data package will contain the necessary documentation of results and data evaluation for FSS and will be included as the survey unit appendix in the FSS report. Each technical data package (in situ soils) will include:

• Basic description of survey unit area, size (area), FSS class.

• Figure of the survey unit, sample locations, and sample identification.

• Figure of the survey unit with sample results.

• Summary data table by sample, including SOR.

• GPS coordinates of sample locations.

• Descriptive statistics report.

• Probability plots and histograms.

• WRS test report (gross results vs. background reference area).

• Figure of gamma scan survey results.

• Gamma scan survey data.

• Sample chain of custody.

• Sample laboratory results.

Each technical data package (soil sorting) will include:

• ScanSortSM system summary report (four below-criteria piles).

• Summary data table by sample, including SOR.

• Descriptive statistics report.

• Probability plots and histograms.

• WRS test report (gross results vs. background reference area).

• Sample chain of custody.

• Sample laboratory results.




PLN-5508 2 74 of 75

8. REFERENCES

American National Standards Institute (ANSI), 1997, Radiation Protection Instrumentation Test and Calibration, N323A.

EPA, 2006, Guidance on Systematic Planning Using the Data Quality Objectives Process, EPA QA/G-4, EPA/240/B-06/001, Office of Environmental Information, Washington, D.C., February 2006.

EPA, 2000, Guidance for Data Quality Assessment, EPA QA/G-9, EPA/600/R-96/084, Office of Environmental Information, Washington, D.C., July 2000.

EPA, NRC, DoD, and DOE, 2000, Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM), EPA 402-R-97-016, Rev. 1, U.S. Environmental Protection Agency, U.S. Nuclear Regulatory Commission, U.S. Department of Defense, and U.S. Department of Energy, August 2000.

NRC, 2006, Consolidated Decommissioning Guidance, NUREG-1757, Rev. 1, Vol. 2, September 2006.

NRC, 1998, Minimum Detectable Concentrations with Typical Radiation Survey Instruments for Various Contaminants and Field Conditions, NUREG-1507, June 1998.

Pacific Northwest National Laboratory (PNNL), 2014, Visual Sample Plan, Version 7.5, http://vsp.pnnl.gov.

USACE (U.S. Army Corps of Engineers), 2016a, Backfill and Restoration Plan for the Luckey Formerly Utilized Sites Remedial Action Program Remediation Project, PLN-5510, U.S. Army Corps of Engineers, Buffalo District, Buffalo, New York.

USACE, 2016b, Uniform Federal Policy Quality Assurance Project Plan for the Luckey Formerly Utilized Sites Remedial Action Program Site Remediation, Luckey, Ohio, Sampling and Analysis Plan, PLN-5503, U.S. Army Corps of Engineers, Buffalo District, Buffalo, New York.

USACE, 2016c, Soil Sorting Operations Plan for the Luckey Formerly Utilized Sites Remedial Action Program Remediation Project, PLN-5505, U.S. Army Corps of Engineers, Buffalo District, Buffalo, New York.

USACE, 2016d, Waste Management, Transportation, and Disposal Plan for the Luckey Formerly Utilized Sites Remedial Action Program Remediation Project, PLN-5507, U.S. Army Corps of Engineers, Buffalo District, Buffalo, New York.

USACE, 2014, Final Scope of Work, Remediation of Soils Operable Unit, Luckey Site, Luckey, Ohio, U.S. Army Corps of Engineers, Buffalo District, May 2014.

http://vsp.pnnl.gov/




PLN-5508 2 75 of 75

USACE, 2006, Luckey Site, Luckey, Ohio, Record of Decision for Soils Operable Unit, Final, prepared for U.S. Army Corps of Engineers, Buffalo District, prepared by Science Applications International Corporation, Twinsburg, Ohio, June 2006.

USACE, 2003a, Luckey Site, Luckey, Ohio, Final Feasibility Study Report, Luckey Site, prepared for U.S. Army Corps of Engineers, Buffalo District, prepared by Science Applications International Corporation, Dublin, Ohio, May 2003.

USACE, 2003b, Luckey Site, Luckey, Ohio, Final Proposed Plan Report, Luckey Site, prepared for U.S. Army Corps of Engineers, Buffalo District, prepared by Science Applications International Corporation, Dublin, Ohio, June 2003.

USACE, 2000, Luckey Site, Luckey, Ohio, Final Remedial Investigation Report, Luckey Site, prepared for U.S. Army Corps of Engineers, Buffalo District, prepared by Science Applications International Corporation, Dublin, Ohio, September 2000.

USACE, 1998, Technical Project Planning (TPP) Process, Engineer Manual (EM) 200-1-2, Department of the Army, Washington, D.C., August 1998.

Attachment 1

Development of DCGLEMC Values for the Luckey FUSRAP Site

1

Development of DCGLEMC Values for the Luckey FUSRAP Site

1. Introduction

The Luckey Site, Luckey, Ohio, Final Remedial Investigation Report (RI; USACE 2000) and Feasibility Study Report, Luckey Site (FS; USACE 2003) include baseline risk calculations for unrestricted future use of the site for a number of potential receptors. The RI baseline risk assessment addressed a range of exposure scenarios, including a resident farmer. Subsequent U.S. Army Corps of Engineers (USACE) discussions with site planners and stakeholders resulted in the introduction of a more conservative scenario—a subsistence farmer—as the basis for determining cleanup guidelines for the site. This scenario, which was addressed in the FS report, was determined to be a reasonable future land use of the site by the USACE, based on the current surrounding rural property use. Hence, cleanup guidelines for the site were derived from an evaluation of this conservative scenario. The approved cleanup guidelines are given in the Luckey Site, Luckey, Ohio, Record of Decision for Soils Operable Unit, Final (USACE 2006).

The subsistence farmer scenario was used to develop cleanup guidelines for both radioactive and chemical contaminants at the site. The detailed evaluation of the risk assessment for this scenario is given in Appendix 3A, Subsistence Farmer and Revised Industrial Worker Exposure Scenarios, of the FS report. The radionuclides of concern at the site were determined to be those associated with the uranium-238 (U-238) decay series, specifically U-238, uranium-234 (U-234), thorium-230 (Th-230), and radium-226 (Ra-226). The RESidual RADioactivity (RESRAD) computer code, Version 6.1, was used to calculate the annual radiation doses from these four radionuclides in site soils. The contributions of the short-lived radioactive decay products of these radionuclides (including lead-210 [Pb-210] ingrowth for Ra-226) were considered in these calculations.

The results of these RESRAD simulations were used along with the annual radiation dose standard of 25 mrem/year, given in Subpart E of 10 CFR 20, to derive “wide-area” cleanup guidelines for the site. As noted above, a subsistence farmer was considered to be the average member of the critical group (as identified in 10 CFR 20) in this derivation. These derived concentration guideline levels (DCGLs), shown in Table 1, are provided in and stipulated by the ROD.

Table 1. DCGLw values for the radionuclides of concern at the Luckey site. Isotope DCGLW (pCi/g)a

Radium-226 2.0 Thorium-230 5.8 Uranium-234 26 Uranium-238 26

DCGLw = derived concentration guideline level, survey unit average (median) concentration a. Guideline values are provided to two significant figures.

2

These values are the DCGLW values1 (or wide-area average values of the DCGLs) as defined in the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM), issued by the EPA, U.S. Department of Defense (DoD), U.S. Nuclear Regulatory Commission, and U.S. Department of Energy (EPA et al. 2000). The ROD cleanup criteria are specified in terms of the DCGLW in acknowledgement of the USACE’s intention to use the MARSSIM methods as the principal tool for evaluating compliance.

The nature of environmental contamination is that it is rarely deposited in a uniform manner and is, therefore, rarely observed in the field as having a regular distribution. Rather, contaminants are likely to exist in spotty, unevenly distributed areas or “pockets.” The focus of this report is to present the cleanup guidelines to be used for smaller areas of site contamination that are encountered while implementing the remediation and compliance assessment programs. MARSSIM relies on the application of a localized area cleanup guideline metric—the DCGLEMC—to address such areas. The term “DCGLEMC” is defined as the concentration guideline level for elevated measurement comparison. The DCGLEMC values are the residual concentrations that can be applied to relatively small areas of contamination without resulting in an annual radiation dose in excess of 25 mrem/yr. Thus, they are as protective of dose to an exposed individual as the DCGLW.2

The DCGLEMC functions presented below will be used to assist in calculating areal estimates of contaminated soil and aid in the design and implementation of a MARSSIM-based compliance and closure strategy for the site that will address radiological contaminants at the site both over wide areas of the site and in smaller localized areas.

2. DCGLEMC Values for the Radiological Contaminants of Concern

The dose modeling code RESRAD, Version 6.1 (Yu et al. 2001), was used to calculate the DCGLW values for the Luckey site. RESRAD, Version 6.3 (Yu et al. 2005), was used to calculate the DCGLEMC values, since Version 6.1 was no longer available from the code developer.

As a first step in developing the DCGLEMC values for the Luckey site, RESRAD 6.3 was benchmarked with Version 6.1 using the same input parameter set used to derive the surface soil DCGLW values as reported in the FS. While some minor differences exist between the two versions of the code, the resulting doses projected by the two versions of RESRAD are nearly identical, with differences less than 0.11 mrem per year for each of the radionuclides evaluated. More importantly, the method for deriving DCGLEMC values relies on comparative dose modeling to establish the relative variance in dose between the baseline case (Wide Area = 11,400 m2) and each of the other 21 smaller localized source areas considered, averting the need for perfect agreement among the model versions used. Using RESRAD, Version 6.3, also

1. The wide-area cleanup guideline was derived to be protective of the annual radiation dose to an average

member of the critical exposure group (subsistence farmer) exposed at the site, assuming that residual radioactivity might be uniformly distributed over very large portions of the site. While conservative for the purposes of derivation of cleanup goals, this assumption is unlikely to be encountered when remediating the site.

2. More detailed information on the development of the DCGLW values can be obtained by reviewing the RI/FS (specifically, Appendix 3A of the FS report).

3

provides assurance that a more up-to-date set of algorithms, factors, and features are used to perform the comparative dose modeling and derive area factors.

The DCGLEMC values for the four radionuclides of concern at the Luckey site were developed using the same scenario used to develop the DCGLw values. That is, the DCGLEMC values were based on a subsistence farmer living at the site following completion of remedial actions. The DCGLEMC values were based on limiting the dose to this individual to 25 mrem/year in the year of maximum exposure for each individual radionuclide over a time period of 1,000 years.

For calculating the DCGLEMC values, the parameter area of contaminated zone in RESRAD was reduced from the RESRAD baseline value used to derive the DCGLW (area = 11,400 m2) to progressively smaller areal sizes. A series of 22 (one baseline case + 21 smaller potential contaminated areas ranging from 1 m2 to 10,000 m2) RESRAD model runs was evaluated. All parameters in RESRAD were held constant except for the size of the contaminated area and the following size-dependent parameters:

1. The length parallel to the aquifer flow was assumed to scale with the square root of the contaminated area.

2. The fraction of contaminated plant food was assumed to scale linearly from zero to 0.3 for an area of zero to 600 m2, and remained at 0.3 for all larger areas.

A comprehensive listing of parameter values used in the RESRAD code for an area of 100 m2 is provided in Appendix A. The results of these evaluations are summarized in Table 2 below for a contaminated area of 11,400 m2 (the DCGLw), as well as for areas of 1,000 m2 and 100 m2.

Table 2. Radiological DCGLs for contaminated areas of 100 m2, 1,000 m2, and 11,400 m2.

Radionuclide Contaminated Area

(m2) DCGL (pCi/g)a

Year of Maximum Dose

Ra-226 11,400 2.0 1 Th-230 11,400 5.9 1000 U-234 11,400 25 1000 U-238 11,400 26 1000

Ra-226 1,000 2.2 1 Th-230 1,000 6.3 1000 U-234 1,000 62 333 U-238 1,000 58 334

Ra-226 100 4.9 0 Th-230 100 14 1000 U-234 100 200 129 U-238 100 150 129

a. Guideline values are provided to two significant figures.

4

The guideline values given in Table 2 were developed on an individual radionuclide basis. The year of maximum dose given in this table represents the year in which the dose from that specific radionuclide is maximum as determined by the RESRAD computer code. In the case of Ra-226, the year of maximum dose is determined using the dose produced from Ra-226 and its short-lived progeny, namely Pb-210, combined. The year of maximum dose may not be the year in which the total dose from all residual radionuclides remaining at the site would peak. These guideline values represent the concentration in soil that will result in an annual dose of 25 mrem/year from each individual radionuclide remaining at the site for these different areas.

The maximum doses for Ra-226, U-234, and U-238 occur at earlier years as the size of the affected area decreases. When applying these guidelines at the site, the values are meant to be applicable for all future times. This is a conservative approach and will ensure that the annual dose of 25 mrem/year is met at all times during the evaluation period of 1,000 years.

For small contaminated areas (e.g., 100 m2), the major exposure pathway for residual Ra-226 and Th-230 contamination in soil is external gamma irradiation, with ingestion of contaminated produce grown onsite being the next most important pathway. The plant ingestion pathway is significant for Ra-226 largely due to the ingrowth of Pb-210 and its short-lived decay products. In addition, the Th-230 guidelines shown in Table 2 are largely attributable to Ra-226 ingrowth over a 1,000-year time period. For uranium contamination in soil, ingestion of contaminated groundwater is the largest exposure pathway with plant ingestion and external gamma irradiation being the two next most important exposure pathways.

Finally, while the uranium guidelines can be combined to develop one value for total uranium, this is not done in this report. The total uranium value will be somewhat smaller than the average of the values for U-234 and U-238 to account for the contribution of uranium-235. A total uranium guideline can be developed in the future if appropriate and desired.

3. Derivation of Area Factors

After performance of the isotope-specific dose modeling described above, a set of dose response curves were derived3 (Figure 1). Figure 1 shows how the maximum annual dose (as a percentage of the baseline) decreases with the size of the contaminated area for each isotope.

3 The dose response curves are derived from the dose-to-source ratios, as calculated by the RESRAD code.

5

Figure 1. Relative dose response curves as a function of contaminated area.

Using the derived relative dose-producing capability of each isotope for varying source sizes, an “area factor” can be calculated. The area factor is simply the ratio of the dose produced by the baseline case source term to the dose produced by a smaller contaminated area (Equation 1). The area factor can then be applied to the DCGLW to produce a DCGLEMC for that specific size of contaminated area using Equation 2.

Area

Baseline

DR

DRAF Equation 1 Derivation of the Area Factor

WEMC DCGLAFDCGL * Equation 2 DCGLEMC Calculation

For instance, the maximum annual dose produced by a source term composed of soil with Ra-226 activity uniformly deposited over 11,400 m2 is approximately a factor of 2 larger than the maximum annual dose produced by a source term with the same concentration of Ra-226 activity uniformly deposited over 150 m2; thus, the area factor for the Ra-226 source term in a 150-m2 area is 2.0. The resulting Ra-226 DCGLEMC for a 150-m2 contaminated area at the Luckey site is 2.0 × 2.0 pCi/g = 4.0 pCi/g.

In this manner, area factors were calculated for each of the four isotopes for 22 different contaminated areas ranging from 1 m2 to 11,400 m2.

6

As shown in Table 2, the guidelines for Ra-226 and Th-230 increase by a factor of about 2.4 when the size of the contaminated area decreases from 11,400 m2 to 100 m2. In contrast, the guidelines for U-234 and U-238 increase by more than a factor of 5 for this same reduction in the size of the contaminated area. This difference is due to the differences in the major (dominant) exposure pathways for these radionuclides. The area factors for each of the four isotopes for the 22 hypothetical contaminated areas are presented in Figures 2 through 5.

Figure 2. Area factors for Ra-226.

7

Figure 3. Area factors for Th-230.

Figure 4. Area factors for U-234.

8

Figure 5. Area factors for U-238.

While it is possible to model a discrete area factor value for any single specific source area under consideration, it is far more useful to derive the function of the curve that relates dose response and area to the area factor. In this way, any series of hypothetical contaminated areas can rapidly be assessed and area factors derived.

To derive the function of the area factor curves for each of the isotopes, the discrete area factor data were subjected to complex regression analysis (multivariate ratio fit analysis) using statistical analysis software (NCSS 2010). The best-fit model was selected and fit to the data, yielding a function that mathematically represents the area factor curve and from which an area factor can be readily calculated without additional dose modeling. The function of the curve that best fits the area factor data for each of the radionuclides are second-order polynomial ratios (quadratic ratios) and are given in Equations 3 through 6. The complete NCSS regression results are given in Appendix B.

9

2

2

226 *3751764.0*7974313.01*1732004.0*9984397.094329.48

AreaLNAreaLN

AreaLNAreaLNAFRa

Equation 3 Function of the Best-Fit Model for Ra-226 Area Factor

2

2

230*3616593.0*7799934.01

*5001847.0*431804.27285.137AreaLNAreaLN

AreaLNAreaLNAF Th

Equation 4 Function of the Best-Fit Model for Th-230 Area Factor

2

2

234*06890106.0*7204242.01

*12822.34*4828.494916.2633AreaLNAreaLN

AreaLNAreaLNAF U

Equation 5 Function of the Best-Fit Model for U-234 Area Factor

2

2

238 *05970472.0*7875461.01*855689.15*5099.308564.1584

AreaLNAreaLN

AreaLNAreaLNAFU

Equation 6 Function of the Best-Fit Model for U-238 Area Factor

The functions are used to produce the best-fit curves (Figures 6 through 9) and to calculate the area factor for any hypothesized contaminated area. The curve is plotted versus discretely calculated values derived from the RESRAD modeling. The process was repeated for each of the four isotopes identified. The regression coefficients for the best-fit functions for each of the four isotopes evaluated exceeds 0.998 (NCSS 2010).

10

Figure 6. Best-fit area factor curve––Ra-226.

Figure 7. Best-fit area factor curve––Th-230.

11

Figure 8. Best-fit area factor curve––U-234.

Figure 9. Best-fit area factor curve––U-238.

12

A tabular data set derived from the function of the best-fit model for each of the four isotopes considered over a reasonable range of potential contaminated areas is presented in Table 3. It should be recognized that this tabular data set represents just one of an infinite number of possible area-dependent solutions that can be derived using the best-fit functions.

Table 3. Area factors derived from best-fit model. Area Factorsa (Derived From Best-Fit Model)

Contaminated Area (m2)

Ra-226 Th-230 U-234 U-238

1000 1.3 1.3 2.2 2.3 600 1.5 1.4 3.1 2.8 500 1.5 1.5 3.5 3.0 400 1.6 1.6 4.0 3.3 300 1.7 1.7 4.7 3.7 200 1.9 1.9 5.9 4.3 150 2.0 2.0 6.8 4.9 100 2.3 2.2 8.3 5.7 75 2.5 2.4 9.6 6.4 50 2.8 2.8 12 7.5 30 3.4 3.3 15 9.3 20 4.0 3.9 18 11 10 5.4 5.3 24 15 5 7.8 7.7 34 21 2 14 14 59 35 1 24 24 100 61

a. Area factors are provided to two significant figures.

13

4. References

10 CFR 20, “Standards for Protection Against Radiation,” Subpart E, Radiological Criteria for License Termination, Code of Federal Regulations.

EPA, NRC, DoD, and DOE, 2000, Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM), EPA 402-R-97-016, Rev. 1, U.S. Environmental Protection Agency, U.S. Nuclear Regulatory Commission, U.S. Department of Defense, and U.S. Department of Energy, August 2000.

NCSS, 2001, NCSS, Inc., NCSS for Windows, Version 10, Statistical Analysis Software, May 2016.

USACE, 2006, Luckey Site, Luckey, Ohio, Record of Decision for Soils Operable Unit, Final, prepared for U.S. Army Corps of Engineers, Buffalo District, prepared by Science Applications International Corporation, Twinsburg, Ohio, June 2006.

USACE, 2003, Luckey Site, Luckey, Ohio, Final Feasibility Study Report, Luckey Site, prepared for U.S. Army Corps of Engineers, Buffalo District, prepared by Science Applications International Corporation, Dublin, Ohio, May 2003.

USACE, 2000, Luckey Site, Luckey, Ohio, Final Remedial Investigation Report, Luckey Site, prepared for U.S. Army Corps of Engineers, Buffalo District, prepared by Science Applications International Corporation, Dublin, Ohio, September 2000.

Yu, C., A. J. Zielen, J.-J. Cheng, D. J. LePoire, E. Gnanapragasam, S. Kamboj, J. Arnish, A. Wallo III, W. A. Williams, and H. Peterson, 2001. User’s Manual for RESRAD Version 6, ANL/EAD-4, Argonne National Laboratory, Environmental Assessment Division, Argonne, Illinois, July 2001.

Yu, C., A. J. Zielen, J.-J. Cheng, D. J. LePoire, E. Gnanapragasam, S. Kamboj, J. Arnish, A. Wallo III, W. A. Williams, and H. Peterson, 2005. RESRAD for Windows, Version 6.3, Computer Modeling Code, Developed by Argonne National Laboratory, Environmental Assessment Division, Argonne, Illinois, under joint sponsorship by the U.S. Department of Energy and the U.S. Nuclear Regulatory Commission, August 2005.

14

Appendix A

RESRAD Input Parameters

Amec Foster Wheeler November 2016

Luckey FUSRAP Site 1 DCGLemc Report Appendix A

15

Appendix B

NCSS Regression Reports

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Multivariate Ratio of Polynomials ReportDataset C:\... Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent Ra_226_DCGL_Max_Year

Minimization Phase Section

Itn Error Sum No. Lambda Lambda B0 B1 B2 B30 5.553845 4E-05 49.35516 -10.1434 0.5576738 0.88903791 3.870521 1.6E-05 49.135 -10.12907 0.6098989 0.8033332 3.170331 6.4E-06 49.08672 -9.459624 0.6155427 0.77785533 2.729853 2.56E-06 49.04124 -8.261493 0.5900893 0.76145744 2.434279 1.024E-06 49.00229 -6.449387 0.5272774 0.75559695 2.2681 4.096E-07 48.97359 -4.136847 0.428284 0.76154246 2.201637 1.6384E-07 48.95672 -1.866153 0.3196097 0.77489397 2.184037 6.5536E-08 48.94861 -0.2669212 0.2386478 0.78699468 2.180942 2.62144E-08 48.94521 0.5362204 0.1971275 0.79359319 2.180548 1.048576E-08 48.94392 0.8480362 0.1809722 0.796187510 2.180508 4.194304E-09 48.94348 0.9537179 0.1755082 0.797062511 2.180504 1.677722E-09 48.94333 0.9876969 0.1737533 0.797342812 2.180504 6.710886E-10 48.94329 0.9984301 0.1731992 0.797431313 2.180504 0.2684354 48.94329 0.9984344 0.1732004 0.797431314 2.180504 1.073742 48.94329 0.9984353 0.1732004 0.797431315 2.180504 0.4294967 48.94329 0.9984367 0.1732004 0.797431316 2.180504 0.1717987 48.94329 0.9984397 0.1732004 0.7974313Convergence criterion met.

Model Estimation Section

Parameter Parameter Asymptotic Lower UpperName Term Estimate Standard Error 95% C.L. 95% C.L.B0 Intercept 48.94329 0.3580704 48.18782 49.69875B1 U 0.9984397 6.763836 -13.27201 15.26889B2 U2 0.1732004 0.3865609 -0.6423718 0.9887726B3 u 0.7974313 0.08246379 0.6234479 0.9714146B4 u2 0.3751764 0.2212134 -0.09154297 0.8418958

R-Squared 0.999153Iterations 16

Symbolic ModelY = P1(U) / P2(U)P1(U) = B0+B1*U+B2*U2P2(U) = 1+B3*U+B4*U2whereY = Ra_226_DCGL_Max_YearU = LN(Area)

Estimated Model((48.94329)+(0.9984397)*(LN(Area))+(0.1732004)*(LN(Area))^2) / (1+(0.7974313)*(LN(Area))+(0.3751764)*(LN(Area))^2)

Analysis of Variance Table Sum of MeanSource DF Squares SquareMean 1 1291.997 1291.997Model 5 3865.583 773.1167Model (Adjusted) 4 2573.586 643.3966Error 17 2.180504 0.1282649Total (Adjusted) 21 2575.767


Luckey FUSRAP Site 1 DCGLemc Report Appendix B

Total 22 3867.764



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Multivariate Ratio of Polynomials ReportDataset C:\... \Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent Ra_226_DCGL_Max_Year

Asymptotic Correlation Matrix of Parameters

B0 B1 B2 B3 B4B0 1.000000 -0.049288 0.031669 0.188589 -0.058438B1 -0.049288 1.000000 -0.961087 0.790775 0.986070B2 0.031669 -0.961087 1.000000 -0.866984 -0.906907B3 0.188589 0.790775 -0.866984 1.000000 0.696976B4 -0.058438 0.986070 -0.906907 0.696976 1.000000

Plot Section



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Multivariate Ratio of Polynomials ReportDataset C:\... \Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent X_DCGL_Min_U_238_


Itn Error Sum No. Lambda Lambda B0 B1 B2 B30 617.3489 4E-05 1581.532 -313.6523 16.37936 0.77681111 598.8377 1.6E-05 1584.533 -308.3066 15.84424 0.78725112 598.8009 6.4E-06 1584.567 -308.6305 15.86732 0.78752723 598.7996 2.56E-06 1584.564 -308.5099 15.85689 0.78754614 598.7996 0.1024 1584.564 -308.5099 15.85689 0.78754615 598.7996 0.4096 1584.564 -308.5099 15.85689 0.78754616 598.7996 0.16384 1584.564 -308.5099 15.85689 0.78754617 598.7996 0.65536 1584.564 -308.5099 15.85689 0.78754618 598.7996 0.262144 1584.564 -308.5099 15.85689 0.7875461Convergence criterion met.


Parameter Parameter Asymptotic Lower UpperName Term Estimate Standard Error 95% C.L. 95% C.L.B0 Intercept 1584.564 5.897338 1572.122 1597.006B1 U -308.5099 23.23182 -357.5248 -259.4951B2 U2 15.85689 2.035626 11.5621 20.15169B3 u 0.7875461 0.01656556 0.7525958 0.8224964B4 u2 -0.05970472 0.02012812 -0.1021713 -0.01723811


Symbolic ModelY = P1(U) / P2(U)P1(U) = B0+B1*U+B2*U2P2(U) = 1+B3*U+B4*U2whereY = X_DCGL_Min_U_238_U = LN(Area)

Estimated Model((1584.564)-(308.5099)*(LN(Area))+(15.85689)*(LN(Area))^2) / (1+(0.7875461)*(LN(Area))-(0.05970472)*(LN(Area))^2)

Analysis of Variance Table Sum of MeanSource DF Squares SquareMean 1 1262045 1262045Model 5 4062444 812488.8Model (Adjusted) 4 2800399 700099.6Error 17 598.7996 35.22351Total (Adjusted) 21 2800997Total 22 4063043



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Multivariate Ratio of Polynomials ReportDataset C:\... Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent X_DCGL_Min_U_238_


B0 B1 B2 B3 B4B0 1.000000 -0.124015 0.094154 0.441776 -0.136293B1 -0.124015 1.000000 -0.995815 0.313555 0.968156B2 0.094154 -0.995815 1.000000 -0.366920 -0.946125B3 0.441776 0.313555 -0.366920 1.000000 0.118779B4 -0.136293 0.968156 -0.946125 0.118779 1.000000

Plot Section



10/30/2016 7:23:57 PM 1

Multivariate Ratio of Polynomials ReportDataset C:\... \Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent DCGL_Min_U_234


Itn Error Sum No. Lambda Lambda B0 B1 B2 B30 23115.07 4E-05 2597.517 -622.4743 37.48851 0.65424931 15725.98 1.6E-05 2632.496 -610.1202 35.88496 0.70930882 14944 6.4E-06 2633.195 -595.188 34.33642 0.71512463 14881.51 2.56E-06 2634.023 -596.4152 34.29576 0.72114884 14877.29 1.024E-06 2633.778 -592.8429 33.97915 0.71963465 14875.1 0.0004096 2633.922 -595.4095 34.20996 0.72103736 14873.37 0.00016384 2633.891 -593.8253 34.06985 0.72001927 14872.9 0.00065536 2633.901 -594.7681 34.15236 0.72068428 14872.53 0.000262144 2633.922 -594.3308 34.1152 0.72029049 14872.51 0.01048576 2633.878 -594.3734 34.11742 0.72042610 14872.51 0.004194304 2633.902 -594.4304 34.1228 0.720448111 14872.5 0.01677722 2633.908 -594.4413 34.12395 0.720442112 14872.5 0.006710886 2633.912 -594.4649 34.12628 0.720437213 14872.5 0.02684355 2633.914 -594.47 34.12682 0.72043414 14872.5 0.1073742 2633.914 -594.4713 34.12695 0.720433115 14872.5 0.04294967 2633.915 -594.4742 34.12726 0.720431316 14872.5 0.1717987 2633.915 -594.475 34.12733 0.720431317 14872.5 0.6871948 2633.915 -594.4752 34.12735 0.720431318 14872.5 0.2748779 2633.915 -594.4756 34.1274 0.720430719 14872.5 1.099512 2633.915 -594.4757 34.12741 0.720430720 14872.5 0.4398046 2633.915 -594.476 34.12744 0.720430721 14872.5 0.1759219 2633.915 -594.4767 34.12751 0.720429822 14872.5 0.7036874 2633.915 -594.4768 34.12753 0.720429823 14872.5 0.281475 2633.916 -594.4772 34.12758 0.720429824 14872.5 1.1259 2633.916 -594.4773 34.12759 0.720429825 14872.5 0.45036 2633.916 -594.4776 34.12762 0.720429826 14872.5 1.80144 2633.916 -594.4776 34.12763 0.720429827 14872.5 0.7205759 2633.916 -594.4777 34.12764 0.720429828 14872.5 2.882304 2633.916 -594.4778 34.12765 0.720429829 14872.5 1.152922 2633.916 -594.4778 34.12766 0.720429830 14872.5 0.4611686 2633.916 -594.4781 34.12768 0.720428831 14872.5 1.844674 2633.916 -594.4781 34.12769 0.720428832 14872.5 0.7378697 2633.916 -594.4783 34.1277 0.720428833 14872.5 2.951479 2633.916 -594.4783 34.12771 0.720428834 14872.5 1.180592 2633.916 -594.4784 34.12772 0.720428835 14872.5 0.4722367 2633.916 -594.4786 34.12774 0.720427936 14872.5 1.888947 2633.916 -594.4787 34.12775 0.720427937 14872.5 0.7555786 2633.916 -594.4788 34.12777 0.720427938 14872.5 3.022315 2633.916 -594.4789 34.12777 0.720427939 14872.5 1.208926 2633.916 -594.4789 34.12778 0.720427940 14872.5 0.4835703 2633.916 -594.4792 34.1278 0.720427941 14872.5 1.934281 2633.916 -594.4792 34.12781 0.720427942 14872.5 0.7737125 2633.916 -594.4794 34.12782 0.720427943 14872.5 3.09485 2633.916 -594.4794 34.12783 0.720427944 14872.5 1.23794 2633.916 -594.4794 34.12784 0.720427945 14872.5 0.495176 2633.916 -594.4797 34.12786 0.720426946 14872.5 1.980704 2633.916 -594.4797 34.12786 0.720426947 14872.5 0.7922816 2633.916 -594.4799 34.12788 0.720426948 14872.5 3.169127 2633.916 -594.4799 34.12788 0.720426949 14872.5 1.267651 2633.916 -594.4799 34.12789 0.720426950 14872.5 0.5070602 2633.916 -594.4802 34.12791 0.72042651 14872.5 2.028241 2633.916 -594.4802 34.12792 0.72042652 14872.5 0.8112964 2633.916 -594.4803 34.12793 0.72042653 14872.5 3.245186 2633.916 -594.4803 34.12794 0.720426



54 14872.5 1.298074 2633.916 -594.4804 34.12794 0.72042655 14872.5 0.5192297 2633.916 -594.4807 34.12796 0.72042656 14872.5 2.076919 2633.916 -594.4807 34.12797 0.72042657 14872.5 0.8307675 2633.916 -594.4808 34.12798 0.72042658 14872.5 3.32307 2633.916 -594.4808 34.12799 0.72042659 14872.5 1.329228 2633.916 -594.4808 34.12799 0.72042660 14872.5 0.5316912 2633.916 -594.4811 34.12801 0.720425161 14872.5 2.126765 2633.916 -594.4811 34.12802 0.720425162 14872.5 0.8507059 2633.916 -594.4812 34.12803 0.720425163 14872.5 3.402824 2633.916 -594.4812 34.12803 0.720425164 14872.5 1.36113 2633.916 -594.4813 34.12804 0.720425165 14872.5 0.5444518 2633.916 -594.4814 34.12806 0.720425166 14872.5 2.177807 2633.916 -594.4815 34.12806 0.720425167 14872.5 0.8711228 2633.916 -594.4816 34.12807 0.720425168 14872.5 3.484491 2633.916 -594.4816 34.12808 0.720425169 14872.5 1.393797 2633.916 -594.4817 34.12809 0.720425170 14872.5 5.575186 2633.916 -594.4817 34.12809 0.720425171 14872.5 2.230074 2633.916 -594.4817 34.12809 0.720425172 14872.5 0.8920298 2633.916 -594.4818 34.12811 0.720425173 14872.5 3.568119 2633.916 -594.4818 34.12811 0.720425174 14872.5 1.427248 2633.916 -594.4819 34.12811 0.720425175 14872.5 5.708991 2633.916 -594.4819 34.12812 0.720425176 14872.5 2.283596 2633.916 -594.4819 34.12812 0.720425177 14872.5 0.9134385 2633.916 -594.4821 34.12813 0.720425178 14872.5 3.653754 2633.916 -594.4821 34.12814 0.720425179 14872.5 1.461502 2633.916 -594.4821 34.12814 0.720425180 14872.5 5.846006 2633.916 -594.4821 34.12814 0.720425181 14872.5 2.338403 2633.916 -594.4821 34.12815 0.720425182 14872.5 0.935361 2633.916 -594.4822 34.12815 0.720424283 14872.5 3.741444 2633.916 -594.4822 34.12816 0.720424284 14872.5 1.496578 2633.916 -594.4823 34.12816 0.720424285 14872.5 0.5986311 2633.916 -594.4825 34.12818 0.720424286 14872.5 2.394524 2633.916 -594.4825 34.12818 0.720424287 14872.5 0.9578097 2633.916 -594.4826 34.12819 0.720424288 14872.5 3.831239 2633.916 -594.4826 34.12819 0.720424289 14872.5 1.532495 2633.916 -594.4827 34.1282 0.720424290 14872.5 6.129982 2633.916 -594.4827 34.1282 0.720424291 14872.5 2.451993 2633.916 -594.4827 34.12821 0.720424292 14872.5 0.9807972 2633.916 -594.4828 34.12822 0.720424293 14872.5 3.923189 2633.916 -594.4828 34.12822 0.720424294 14872.5 1.569275 2633.916 -594.4828 34.12822 0.7204242Convergence criterion met.



10/30/2016 7:23:58 PM 2

Multivariate Ratio of Polynomials ReportDataset C:\... \Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent DCGL_Min_U_234


Parameter Parameter Asymptotic Lower UpperName Term Estimate Standard Error 95% C.L. 95% C.L.B0 Intercept 2633.916 29.27359 2572.154 2695.677B1 U -594.4828 34.92219 -668.1622 -520.8033B2 U2 34.12822 3.38685 26.98259 41.27385B3 u 0.7204242 0.04394863 0.6277007 0.8131477B4 u2 -0.06890106 0.02330781 -0.1180762 -0.01972589


Symbolic ModelY = P1(U) / P2(U)P1(U) = B0+B1*U+B2*U2P2(U) = 1+B3*U+B4*U2whereY = DCGL_Min_U_234U = LN(Area)

Estimated Model((2633.916)-(594.4828)*(LN(Area))+(34.12822)*(LN(Area))^2) / (1+(0.7204242)*(LN(Area))-(0.06890106)*(LN(Area))^2)

Analysis of Variance Table Sum of MeanSource DF Squares SquareMean 1 3088660 3088660Model 5 1.111586E+07 2223172Model (Adjusted) 4 8027198 2006800Error 17 14872.5 874.8529Total (Adjusted) 21 8042071Total 22 1.113073E+07


B0 B1 B2 B3 B4B0 1.000000 -0.214111 0.122660 0.502879 -0.182148B1 -0.214111 1.000000 -0.986637 -0.036012 0.817744B2 0.122660 -0.986637 1.000000 -0.072621 -0.747948B3 0.502879 -0.036012 -0.072621 1.000000 -0.464448B4 -0.182148 0.817744 -0.747948 -0.464448 1.000000



10/30/2016 7:23:58 PM 3

Multivariate Ratio of Polynomials ReportDataset C:\.. \Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent DCGL_Min_U_234

Plot Section



10/30/2016 7:22:02 PM 1

Multivariate Ratio of Polynomials ReportDataset C:\... \Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent DCGL_Min_Th_230


Itn Error Sum No. Lambda Lambda B0 B1 B2 B30 44.94061 4E-05 138.9155 -28.54496 1.5661 0.86689411 31.43789 1.6E-05 138.289 -28.52236 1.709099 0.78453652 25.85253 6.4E-06 138.1514 -26.73467 1.726876 0.75980373 22.39844 2.56E-06 138.0224 -23.55449 1.662071 0.74389814 20.05532 1.024E-06 137.9105 -18.76243 1.500044 0.73777655 18.68174 4.096E-07 137.8254 -12.56608 1.239578 0.7428066 18.09443 1.6384E-07 137.7731 -6.2759 0.9424709 0.75556867 17.92499 6.5536E-08 137.7469 -1.612084 0.7084323 0.76802758 17.89213 2.62144E-08 137.7354 0.8791053 0.580304 0.77534769 17.88752 1.048576E-08 137.7309 1.904569 0.5273283 0.778421610 17.88699 4.194304E-09 137.7293 2.269134 0.5085228 0.779511611 17.88694 1.677722E-09 137.7287 2.391115 0.5022373 0.779875212 17.88694 6.710886E-10 137.7286 2.431066 0.5001795 0.779994113 17.88694 0.2684354 137.7286 2.431083 0.5001842 0.779993414 17.88694 1.073742 137.7285 2.431084 0.5001847 0.779993415 17.88694 4.294967 137.7285 2.431084 0.5001847 0.7799934Convergence criterion met.


Parameter Parameter Asymptotic Lower UpperName Term Estimate Standard Error 95% C.L. 95% C.L.B0 Intercept 137.7285 1.025532 135.5649 139.8922B1 U 2.431084 18.89408 -37.43193 42.29411B2 U2 0.5001847 1.077731 -1.773629 2.773999B3 u 0.7799934 0.08260463 0.6057129 0.954274B4 u2 0.3616593 0.2149825 -0.09191398 0.8152327


Symbolic ModelY = P1(U) / P2(U)P1(U) = B0+B1*U+B2*U2P2(U) = 1+B3*U+B4*U2whereY = DCGL_Min_Th_230U = LN(Area)

Estimated Model((137.7285)+(2.431084)*(LN(Area))+(0.5001847)*(LN(Area))^2) / (1+(0.7799934)*(LN(Area))+(0.3616593)*(LN(Area))^2)

Analysis of Variance Table Sum of MeanSource DF Squares SquareMean 1 10477.96 10477.96Model 5 30916.09 6183.217Model (Adjusted) 4 20438.12 5109.53Error 17 17.88694 1.052173Total (Adjusted) 21 20456.01Total 22 30933.97



10/30/2016 7:22:03 PM 2

Multivariate Ratio of Polynomials ReportDataset C:\... \Documents\NCSS 10\Data\Luckey EMCs.NCSSDependent DCGL_Min_Th_230


B0 B1 B2 B3 B4B0 1.000000 -0.051340 0.032928 0.188325 -0.061057B1 -0.051340 1.000000 -0.960906 0.795011 0.985855B2 0.032928 -0.960906 1.000000 -0.870147 -0.906072B3 0.188325 0.795011 -0.870147 1.000000 0.701161B4 -0.061057 0.985855 -0.906072 0.701161 1.000000

Plot Section



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