Data Analysis and Interpretation for Environmental ...

OCNF-9002111-

DE92 019009

Data Analysis and Interpretationfor Environmental Surveillance

Mark F. Tardiff, Chairman

Conference Proceedings

Lexington, KentuckyFebruary 5-7, 1990

Date Published—June 1992

Sponsored by theOffice of Environmental Compliance and DocumentationOak Ridge National Laboratory

Prepared by theOak Ridge National LaboratoryOak Ridge, Tennessee 37831managed byMartin Marietta Energy Systems, Inc.for theU.S. Department of Energyunder contract DE-AC05-84OR21400

;TFR

DISTRIBUTION OF THIS DOCUMENT IS UNLIMITED

CONTENTS

Invited SpeakerStatistical Issues in Testing for Compliance with Site-Specific Background Standards

Richard O. Gilbert 1

Program DesignSample Design Considerations for Environmental Monitoring Programs

John B. Murphy 3

Development of Data Quality Objectives in an Environmental Sampling ProgramKaren L. Daniels and Mark F. Tardiff 5

A Tracking System for Groundwater Sampling and Data Transfer SchedulesTheresa M. Mercier 11

SamTrack: A Sample Tracking System for Environmental MonitoringB. M. Horwedel 27

Environmental Sample Management SystemR. A. Evans and Kevin Newman 33

Data ManagementDevelopment of an Integrated Data Base for the Screening-Level Risk Assessment

for the Clinch River Resource Conservation and Recovery Act Facility InvestigationLeslie A. Hook, Merilyn J. Gentry, Jean A. Shaakir-Ali,and Mary Alice Faulkner 35

Management of Groundwater Hydrology Data at Oak Ridge National LaboratoryM. A. Faulkner and L. D. Voorhees 37

An Overview of the Portsmouth Gaseous Diffusion Plant's Integrated GeographicInformation System/Environmental Data Base Management System

Kevin K. Keller ana A. Keith B~acknell 39

Data Verification and Evaluation Techniques for Groundwater Monitoring ProgramsTheresa M. Mercier and Ralph R. Turner . 41

Development of a Consolidated Environmental Data BaseLarry D. Voorhees 59

Data Analysis and EvaluationData Evaluation Techniques Used for Groundwater QurUty Assessment

at the Feed Materials Production Center/ . E. Harmon and P. K. Longmire 69

Target Transformation Factor Analysis: A Data Reduction Techniqueto Identify Geochemical Processes and Data Deficiencies

Richard W. Arnseth 71

Co-Occurrence Patterns of Polycyclic Aromatic Hydrocarbons inSoils at Hazardous Waste Sites

William P. Eckel. Thomas A. Jacob, and Joan F. Fisk 81

IV

Total Error Components—Isolation of Laboratory VariationFrom Method Performance

David Bottrell, Ruth Bleyler, Joan Fisk, and Michael Matt 89

Data InterpretationDetermining the Effectiveness of Pollution Abatement: 7"he Need for

Integrated Monitoring and Statistical AnalysisKenneth A. Rose and Eric P. Smith 95

A Statistical Analysis for a Resource Conservation and Recovery ActGroundwater Quality Assessment

Dennis A. Wolf and Mark F. Tardiff 97

Analysis and Evaluation of Gross Radioactivity DataMark F. Tardiff and Dennis A. Wolf 129

The Clinch River Resource Conservation and Recovery Act FacilityInvestigation: Overview and Preliminary Scoping of the Off-SiteContamination of Surface Water Environments Downstream From theU.S. Department of Energy Oak Ridge Reservation

Bruce L. Kimmel and Curtis R. Olsen 143

Preliminary Screening Analysis of the Off-Site Environment Downstreamof the U.S. Department of Energy Oak Ridge Reservation

B. G. Blaylock, F. O. Hoffman, and M. L. Frank 145

Assessing Radiological Impacts of U.S. Department of Energy FacilitiesJ. P. Witherspoon and F. R. O'Donnell 147

Ecological and Environmental Risk Analysis: A Framework for DataAnalysis and Interpretation

Steven M. Bartell 149

FOREWORD

The Data Analysis and Interpretation for Environmental Surveillance Conference was held inLexington, Kentucky, February 5-7, 1990. The conference was sponsored by what is now theOffice of Environmental Compliance and Documentation, Oak Ridge National Laboratory.Participants included technical professionals from all Martin Marietta Energy Systems facilities,Westinghouse Materials Company of Ohio, Pacific Northwest Laboratory, and several technicalsupport contractors.

Presentations at the conference ranged the full spectrum of issues that effect the analysis andinterpretation of environmental data. Topics included tracking systems for samples and schedulesassociated with ongoing programs; coalescing data from a variety of sources and pedigrees intointegrated data bases; methods for evaluating the quality of environmental data through empiricalestimates of parameters such as charge balance, pH, and specific conductance; statisticalapplications to the interpretation of environmental information; and uses of environmentalinformation in risk and dose assessments. Hearing about and discussing this wide variety of topicsprovided an opportunity to capture the subtlety of each discipline and to appreciate the continuitythat is required among the disciplines in order to perform high-quality environmental informationanalysis.

The proceedings of this conference include either the manuscript provided by the author or theabstract of the presentation that was available at the time of the conference. I offer my sincereappreciation to all of the authors that followed through with a manuscript. The conference was aforum for information exchange; these proceedings are a vehicle through which this informationcan have an impact over time. The authors who were unable to write a paper about theirpresentation also made important contributions to the conference. Their abstracts and addresses arepublished to offer a synopsis of their ideas and a means of contacting them for more information.

Bringing an idea to fruition often requires attention to a daunting number of details andlogistics. This is indeed the case in organizing a conference. Three people deserve special mentionfor helping to transform this idea into a reality. Bonnie Reesor, Oak Ridge National LaboratoryConference Office, handled all the logistics of the conference setting. Pat Hileman, secretary forthe Information Integration and Analysis Group, accepted the responsibility of conference secretaryin addition to her full-time responsibilities. Wanda Jackson, Publications Division, providedtechnical editing. The conference and these proceedings would not have been possible withouttheir support. Finally, I am grateful to all the authors, session chairs, and attendees for making thisa positive experience and a meaningful technical forum.

Mark F. TardiffConference Chairman

STATISTICAL ISSUES IN TESTING FOR COMPLIANCEWITH SITE-SPECIFIC BACKGROUND STANDARDS

Richard O. GilbertExploratory Data Analysis Group

Computational Sciences DepartmentPacific Northwest Laboratory

P.O. Box 999Richland, Washington 99352

ABSTRACT

Once a site contaminated with radionuclides or chemicals has been remediated, adecision must be made regarding the need for additional remediation. One way tomake this decision is to use statistical methods to compare residual contaminationlevels at the site with a "limiting concentration" or "cleanup standard." This paperconsiders statistical issues that must be faced when the cleanup standard is determinedon the basis of site-specific background concentrations. Two of the important statisticalissues that will be discussed are (1) selecting the statistical hypotheses and the teststatistics and (2) determining the required number and location of samples. Theseissues will be discussed in the context of work currently being conducted by thePacific Northwest Laboratory for the U.S. Environmental Protection Agency, StatisticalPolicy Branch, to write a report that provides selected statistical methods forevaluating the attainment of site-specific background standards for soils and solidmedia at Superfund sites. The advantages and disadvantages of some of the statisticalmethods being considered for inclusion in the report will be presented.

SAMPLE DESIGN CONSIDERATIONS FOR ENVIRONMENTALMONITORING PROGRAMS

John B. MurphyOak Ridge National Laboratory*

P.O. Box 2008Oak Ridge, Tennessee 37831-6285

ABSTRACT

Three main topics will be presented:

1. sample considerations including discussions of types of samples, statistical designsfor sampling, and number of samples;

2. strategy for the characterization of an area versus a determination of the presenceof contaminants; and

3. a discussion of problems with "permit" requirements for sampling and analysis.

The purpose of topics 1 and 2 is to generate discussion, pro and con, of themethods used at Oak Ridge National Laboratory. The purpose of topic 3 is to point outto the audience the importance of "statistical input" in permit negotiations. At thepresent time, permits (e.g., from the National Pollutant Discharge Elimination System)are negotiated with little input from the statistician. Examples will be given illustratingfrequencies, etc., that are mandated through these permits but have little statisticalvalue.

'Managed by Martin Marietta Energy Systems, Inc., under contract DE-AC05-84OR21400 with the U.S. Departmentof Energy.

DEVELOPMENT OF DATA QUALITY OBJECTIVESIN AN ENVIRONMENTAL SAMPLING PROGRAM

Karen L. DanielsScience Applications International Corporation

301 Laboratory RoadOak Ridge, Tennessee 37830

Mark F. TardiffOak Ridge National Laboratory*


ABSTRACT

The development of data quality objectives is an integral part of the methodologyadopted by the U.S. Environmental Protection Agency (EPA) for use in hazardouswaste site (Superfund) investigations. We propose to adopt this approach and tailor itfor use in environmental sampling programs. Establishing data quality objectives willensure that data collected in an environmental sampling program is of known quality,that it is useful to the program and can answer specific questions, and that conclusionscan be drawn from the results. Data quality objectives can increase the efficiency andeffectiveness of environmental programs by minimizing wasteful use of resources.

As adapted to environmental sampling progiams, developing data qualityobjectives includes four phases: (1) identifying environmental program objectives,(2) identifying data users, (3) identifying data types, and (4) specifying data qualityand quantity goals. The development of data quality objectives for an environmentalsampling program is illustrated using Oak Ridge National Laboratory's (ORNL's)airborne radioactive emission sampling program.

Use of this approach directs the Sampling and Analysis Plan and provides input tothe Quality Assurance Plan. It provides a methodology for establishing quality goalsthat can be monitored. The drawback to this approach is that significant time isrequired prior to data collection, but we believe that it is time well spent to enablecollection of useful data of known quality.

INTRODUCTION

Data quality objectives (DQOs), as defined by the EPA, are qualitative and quantitativestatements that specify the quality of the data required to support decisions about remedial action

"Managed by Martin Marietta Energy systems, Inc., under contract DE-AC05-84OR214O0 with the U.S. Department ofEnergy.

programs (EPA 1987). The DQO development process was originated by EPA and has been usedin remedial response (Superfund) investigations.

DQOs are part of a program's planning process, and are thus established prior to datacollection. The results of DQO development are incorporated into the Sampling and Analysis Planand the Quality Assurance Plan. The result is a well-thought-out Sampling and Analysis Plan thatwill provide effective and efficient use of resources, including people, time, and dollars.Development of DQOs improves communication among program staff; improves efficiency byensuring that sufficient data are collected the first time; and provides cost savings by allowing forcollection of only the data necessary to meet program objectives. With established DQOs, qualityis measurable and trackable and can lead to improvements in work performance.

We propose that elements of the EPA DQO development process for remedial investigationsbe used in the early stages of environmental sampling programs. We propose to use only elementsof the process, rather than the entire process, because Superfund-type remedial investigations aredifferent from environmental sampling and analysis programs in scope and frequency ofoccurrence. A Superfund remedial investigation usually involves two intensive sampling andanalysis efforts at a waste site. Sampling analysis generally is comprehensive and covers a widevariety of analytes. Environmental monitoring, on the other hand, usually involves samplingemission sources or the ambient environment at fixed intervals over a period of years; this isuseful for determining changes over long periods of time and for trend analysis.

For use in Superfund remedial investigations, EPA divides the DQO development process intothree stages, with subparts.

Siage 1—Identify decision types:• identify and involve data users,• evaluate available information,• develop a conceptual design, and• specify objectives and decisions.

Stage 2—Identify data uses/needs:• identify data uses,• identify data types,• identify data quality needs,• identify data quantity needs,• evaluate sampling/analysis options, and• review precision accuracy representativeness completeness comparability (PARCQ

parameters.

Stage 3—Design data collection program:• assemble data collection components and• develop data collection documentation (e.g., Sampling and Analysis Plan) (EPA 1987).

EPA designed its DQO development process for use in hazardous waste site remedialinvestigations, which are usually large, comprehensive programs. The environmental samplingprograms for which we arc proposing the DQO development process are smaller in scope ofanalyses and occur frequently over a period of years. Because of these differences, the design ofDQOs for an environmental sampling program works best with some tailoring of EPA'sconfiguration. We find that breaking the process down into four phases, with pieces borrowed

from the EPA method, works best. Our DQO process for environmental sampling has evolved tothe following four phases:

1. identify objectives,2. identify data uses,3. identify types of data needed, and4. establish data quality and data quantity goals.

These activities were selected for the environmental sampling program DQO process becausethey work best with the frequent and long-term nature of these programs.

APPROACH

To illustrate the process of using DQOs in the development of an environmental samplingprogram, we will develop DQOs for ORNL's 7911 Stack.

Radioactive emissions are sampled continuously from six stacks at ORNL. The samplingsystems coasist of in-stack sampling probes, sample transport piping, a 47-mm-diameter particulatefilter, a 47-mm-diameter by 25-mm-thick activated-charcoal canister, a silica-gel trap, flow-measurement and totalizing instruments, a sampling pump, and return piping to the stack.Currently the sampling media are collected and evaluated weekly. The paniculate filters areanalyzed for gross alpha and gross beta activity 8 days after collection. This delay in analysisreduces the contribution of short-lived natural radionuclides. The weekly filter papers arecomposited monthly for analyses of uranium, thorium, pluionium, and total radioactive strontium.The silica-gel samples are analyzed for tritium, and the charcoal canisters are subjected to gammaspectroscopy. In addition to the continuous samplers, there are real-time monitors in Stack 7911that measure noble gas emissions. Nobie gases are chemically inert and cannot be trapped on achemical medium for analysis. Instead, after the monitoring system gas stream has passed throughthe particulate filter and charcoal trap, a part of the stream is pumped through a lead-shieldedchamber equipped with a beta-detecting monitor. The noble gas releases were assumed to be 83%133Xe and 17% 85mKr, a combination chosen to represent the spectrum of noble gas constituentsfrom a reactor.

Phase 1 of the environmental sampling DQO process is "identify objectives." Whenidentifying environmental sampling program objectives, it is necessary to determine who the datausers are and what their responsibilities are. For the radioactive emissions from ORNL, we haveidentified the following data users and their responsibilities:

• Exposure Assessment Staff—perform dose calculations using stack characteristics and emissions,rainfall data, and meteorological data.

• Compliance Staff—report annual emissions and determine compliance with the Clean Air Actusing emissions estimates and dose calculations.

• Environmental Staff—summarize data and review trends for significant changes.• Facility Operators—optimize the emission control systems.• Instrumentation Technicians—determine the effectiveness of the monitoring system.

By providing information on the types of data needed and how the data will be used, the usersprovide the information needed for Phase 2 of the environmental sampling DQO process, "identify

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data uses." Table 1 lists the objectives, uses, and types of data required to meet the needs of thedata users. For Stack 7911 at ORNL, the data users established three primary data uses:

• determine the types of contaminants,• determine the airborne emissions, and• determine if the population and maximally exposed individual doses are less than the National

Emission Standard for Hazardous Air Pollutants (NESHAP).

Table 1. Program objectives, data use, and data types

Objectives Data use Data type

Determine types ofcontaminants from Stack 7911

Determine airborne emissionsfrom Stack 7911

Characterization of emissionsEstablish baseline for comparisons

Dose calculationsTrend analysisSampler effectivenessFacility operationControl effectiveness

Determine if population andmaximum individual dose areless than NESHAP standard"

Establish level of protection forpublic health and safety

Isotopic analyses

Sample activityStack flowSample flowRainfallMeteorologyStack parameters (heights,

diameter, etc.)Sample conditionOperational changes

Average annual doseNESHAP standard

"NESHAP standard (National Emission Standard for Hazardous Air Pollutants), 10 mrem

Phase 3 of the environmental sampling DQO process is "identify types of data needed." Thisinformation is also provided by the data users. The specific types of data needed by the users were

• isoiopic analyses;• stack and sample flow;• rainfall and meteorology;• stack parameters (height, diameter, etc.);• sample condition;• operations changes;• annual average dose; and• NESHAP standard.

Data collected from Stack 7911 will be used to characterize the emissions and to establish abaseline for comparisons and trend analysis. The emissions will be used in models to estimate thedoses to the population and to the maximally exposed individual. Results of these analyses can beused to establish the level of protection needed for public health and safety. These data can beused to evaluate the effectiveness of the sampler, the facility operation, and the controls on thefacility that reduce emissions.

Up to this point we have looked at establishing data objectives, the usefulness of the data, andmeeting the data users' needs. Phase 4 in the environmental sampling DQO process is to

"establish data quality and quantity goals." The purpose of establishing DQOs is to ensure thatafter data collection and sample analysis, the results are useful and of known quality. By knowingthe quality of our data we can provide meaningful interpretations of our results. The real-worldresult of establishing quality and quantity goals is the identification of quality control (QQguidelines for the stack emission program.

Indicators of data quality include precision, accuracy, representativeness, completeness, andcomparability (PARCC parameters). Sometimes these goals cannot be quantified, but are onlyqualitatively expressed. Table 2 summarizes the following goals for the program.

Table 2. Quality and quantity goals

Parameter

PrecisionSystemAnalytical

AccuracyFlowAnalytical

Representativeness

Completeness

Comparability

Goal

±50%±30%

±10%±20%

±20

95%

Yes

How measured or achieved

Co-located samplesSpiit samples

StandardsStandards

Ratio of sampler to stack flow

Planned versus actual numbers

Standardized methods and procedures

Precision is a measure of the reproducibility of the data, and it indicates the variability of agroup of measurements compared to their average value. For the entire ORNL stack sampling andanalysis program, we suggest that precision be established at ±50%, which could be measured bycollecting and analyzing co-located samples. We suggest establishing the analytical precision at±30% for specific isotopes. This would be measured by splitting the samples once they had arrivedat the analytical laboratory and after dissolution.

Accuracy indicates how close the sample value is to the true value. It is a measure ofstatistical bias in the system (i.e., the difference between the expected value of the estimator andthe true value of the parameter). We recommend that both the sampler and the stack flow bemeasured to ±10%. This is a critical value that is used in calculating the emission from each stack.We recommend that the analytical precision be ±20%. Accuracy is measured through NationalInstitute of Standards and Testing (NIST) or NIST-traceable standards.

Representativeness is a measure of how well the sample depicts the population.Representativeness can be measured by comparing, over time, the ratio of the sampler to the stackflow. This ratio should not deviate by more than ±20%.

Completeness is a measure of how many valid measurements were obtained versus how manywere planned. For sampling, analysis, and monitoring, we set goals at 95%.

Comparability is an indicator of whether or not two or more sets of data can be compared.This goal is achieved by using standardized methods and procedures to collect, analyze, and reportresults.

Data quantity goals provide guidelines on the number and frequency of sample collection andthe number and frequency of analyses. In the case of the stack sampling program, the frequency ofsampling is affected by the half-life of the isotope, the analytical detection limit, the objectives of

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the program, and the levels of concern. Until we have sufficient da»a to determine the variability inthe operations at Stack 7911, we feel that continuous sampling is necessary. After sufficient dataare collected, we can determine if the process is stable. Because 131I has an 8-day half-life, it isnecessary to analyze samples weekly or biweekly. As a rule, no more than 4 half-lives (in this case32 days) should pass before analysis. For long-lived isotopes, weekly and monthly analyses maybe too frequent Each isotope should be evaluated as we evaluated m I .

SUMMARY

By establishing DQOs for Stack 7911, we determined that data from the weekly analysis ofgross alpha and gross beta activity were of no use. For determining doses and emissions, specificisotopes need to be identified.

The method we use to analyze m I is so sensitive and so far below our level of concern thatwe need to investigate less sensitive and costly methods. We can change our frequency of analysisfor I31I to biweekly, which will result in increased efficiency and lower costs.

Because the noble gas 133Xe was a primary contributor to the dose, we need to determine theisotopic distribution of the noble gas using another method. The current proportions are based onpredictions for reactors rather than sample analysis.

The analytical methods we currently use produce highly accurate and precise data. We need toexamine our AIRDOS model to determine which factors in the model are the most sensitive andresult in the greatest change in dose. This may enable us to use less sensitive and costly analyses.

CONCLUSIONS

The advantages of using the DQO approach for environmental sampling and analysis are thatthe rationale for sampling and analysis is defined and documented, communications among users isenhanced, and levels of concern are met by using the appropriate analytical methods. Additionally,by establishing quality and quantity goals, the numbers and types of samples, including QCsamples, are specified. Bringing the users into the process before the samples are collectedincreases the efficiency of the program because all needs can be met with a single approach.

The DQO approach for environmental sampling and analysis programs does require asignificant amount of time prior to sample collection for the development of the process. It alsorequires extensive coordination among the data users and the sample collectors. Existing data orresults of a pilot test and knowledge of the system are also needed to maximize the process.

Using the DQO process, however, we can develop site-specific objectives and sampling andanalysis plans. We can improve our existing sampling and analysis plans and possibly reduce costsassociated with sample collection and analysis. We can guarantee that data collected followingthese plans are useful and of known quality.

LITERATURE CITED

U.S. Environmental Protection Agency (EPA) 1987. Data Quality Objectives for RemedialResponse Activities, EPA 540/G-87/003.

A TRACKING SYSTEM FOR GROUNDWATER SAMPLINGAND DATA TRANSFER SCHEDULES

Theresa M. MercierH&R Technical Associates, Inc.

P.O. Box 215Oak Ridge, Tennessee 37830

ABSTRACT

Because ground water monitoring programs at the Oak Ridge Y-12 Plant* havebecome more complex and varied, and as the occasions to respond to internal andexternal reporting requirements have become more frequent and time constrained, theneed to track groundwater sampling activities and data transfer from the analyticallaboratories has become imperative. If backlogs can be caught early, resources can beadded or reallocated in the field and in the laboratory in a timely manner to ensure thatreporting deadlines are met.

The tracking system discussed in this paper starts with a clear definition of thegroundwater monitoring program at the facility. This information is input into basedata sets at the beginning of the sampling cycle.

As the sampling program progresses, information about well sampling dates anddata transfer dates is input into the base data sets. From the base program data and theupdate data, a status report is periodically generated by a computer program thatidentifies the type and nature of bottlenecks encountered during implementation of thegroundwater monitoring program.

INTRODUCTION

The groundwater monitoring program at the Y-12 Plant waste disposal facilities began in 1983with the characterization of 28 wells located in 2 waste disposal sites. Today, the program hasexpanded to include 221 wells located in 22 sites. Each site requires a sampling program designedto fulfill its unique regulatory requirements as well as additional research objectives. As theprogram increased in size and complexity, it became evident that a system for tracking samplingactivities and laboratory analyses was needed to ensure that backlogs were avoided and thatbottlenecks were identified and dealt with in a timely manner in order to meet internal andexternal reporting deadlines. This paper illustrates a computer-based tracking system written in

'Managed by Maitin Marietta Energy Syslems, Inc., under contract DE-AC05-84OR21400 with the U.S. Departmentof Energy.

11

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SAS™ language and using SAS products. The system defines the sampling programs andschedules, is updated to track program progress, and writes a report summarizing how well theprogram is meeting its objectives.

APPROACH

PLANNING

A Sampling and Analysis Plan is prepared each calendar year by the Y-12 Plant GroundwaterProtection Program (GWPP). Revisioas of the groundwater monitoring program are based oncurrent regulatory requirements, site assessment reports, and the need for additional information.Sampling considerations are addressed. Sites may be added or deleted from the program as a resultof new disposal activities or their current regulatory status. Wells may be added to or deleted fromthe program depending on new well installations and plugging and abandonment activities. Thewells are then assigned to sites based on functional and geographical relationships. In addition, thefrequency of sampling events is determined for each site (i.e., quarterly, semiannually, orannually). Sampling and analysis activities are tracked based on quarterly events.

Regulatory and research objectives are considered when establishing the current analyticalprogram for each site. Discussions are held between the GWPP managers and the project managerof the Analytical Chemistry Department (ACD) to determine the number of days within whichlaboratories will analyze and transfer data after receipt of the samples. Data are transmittedelectronically to the daia base manager from the ACD project manager as data on asite-/Iaboratory-specific basis. When all samples collected at a site and sent to a laboratory foranalysis have been completed and approved by the appropriate laboratory personnel, the data istransferred.

DATA SET STRUCTURE/SETUP

When the program has been finalized, three base data sets are set up (Fig. 1). The first dataset, named TRACKO, contains information on site-specific sampling schedules. The second dataset, named TRACK 1, contains an observation for each well to be sampled, identifying the site towhich it belongs and a comment variable to record any special sampling requirements for thatwell. The final data set, named TRACK2, contains an observation for each site-/laboratory-specificdata file expected to be transferred, including a comment variable stating specific parameters to bereported, if needed.

SAS is a registered trademark of SAS Institute, Cary, North Carolina.

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DATA SET: TRACKOONE OBSERVATION FOR EACH SAMPLING EVENT AT EACH SITE

VARIABLE

SITE

MONTH

EVENT

TYPE

CHARACTER

NUMERIC

CHARACTER

DESCRIPTION

Name of waste disposal site

Month during which samplingevent is scheduled

Indicates whether sampling eventis the "ANNUAL" event or aregular quarterly event Theannual event has differentsampling requirements

SOURCE

Sampling Plan

Sampling Plan

Sampling Plan

DATA SET: TRACK1ONE OBSERVATION FOR EACH MONITORING WELL

AT EACH WASTE DISPOSAL SITE

VARIABLE

WELL

SITE

COMMENTS

DATE

DUPE

COMMENT

TYPE

CHARACTER

CHARACTER

CHARACTER

NUMERIC

CHARACTER

CHARACTER

DESCRIPTION

Monitoring well name

Waste disposal site where wellis located

Special sampling requirementsfor well

Date well was sampled

="DUPE", if sample is a field=" ", duplicate if normalfield sample(Observation is added to basedata set if field duplicatesample is taken.)

Any unusual occurrencesencountered during the actualor attempted sampling event

SOURCE

Sampling Program

Sampling Program

Sampling Program

Sampling Event

Sampling Event

Sampling Event

Fig. 1. Base data sets for the tracking system.

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DATA SET: TRACK!

ONE OBSERVATION FOR EACH DATA FILE EXPECTEDFROM THE ANALYTICAL LABORATORY AND

ONE FROM EACH LABORATORY FOR EACH SITEFROM WHICH IT RECEIVED SAMPLES

VARIABLE

SITE

EVENT

LAB

PARMS

DATE_DUE

DATE_REC

TYPE

CHARACTER

CHARACTER

CHARACTER

CHARACTER

NUMERIC

NUMERIC

DESCRIPTION

Name of waste disposal site

Indicates whether samplingrequirements are for the"ANNUAL" event or a regularquarterly event. Corresponds toEVENT variable in TRACKO

Laboratory responsible foranalyzing and reporting data

Specific analytes to be reported bythe analytical laboratory

Date data file due to be transferredto data base manager

Date data file received by data basemanager

SOURCE

Sampling Program

Sampling Program

Sampling Program

Sampling Program

Calculated by TrackingReport Program

Data base Manager

Fig. 1 (continued)

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DATA SET ACTIVITY UPDATES

As a groundwater monitoring well is sampled, information on sampling activity is recorded ona field data sheet. This includes date and time of sampling, field measurements, and commentsrelating to problems encountered during sampling. For example, the well may have been dry orwell access problems may have been encountered. The Oak Ridge K-25 Site ACD sampling andanalysis project manager receives these sheets daily and sends copies to the data base manager viaFAX. The date on which a well was sampled is entered into the variable "DATE" in the data setTRACK1. Any additional information relating to attempts at sampling such as "NO ACCESS" or"DRY" is entered into the "COMMENT" variable. If a field duplicate sample is taken, theobservation is duplicated, and the variable "DUPE" is set to "DUPE". The customized screenand search capabilities of the SAS/FSP are used for this data input (Fig. 2).

Edit SAS data set: TRACK 1

Command —> Screen 1Obs 2

WHEN FIELD DATA SHEETS ARE RECEIVED FROM THE GROUNDWATER SAMPLINGTEAM, THIS DATASET IS UPDATED BY INPUTTING THE:

1. DATE SAMPLED2. ANY PERTINENT COMMENTS FROM THE SAMPLING TEAM.3. IF A FIELD DUPLICATE SAMPLE HAS BEEN TAKEN FOR A WELL, DUPLICATE

THE OBSERVATION AND ENTER "DUPE" ON THE FIELD DUPLICATE LINE

SITE: BURIAL GROUNDS - NORTHWELL: GW-040COMMENTS:DATE:COMMENT:DUPE:

Fig. 2. Customized screen for data input into data set TRACK 1.

When a laboratory has completed all analyses for samples received from a site, has enteredthese results into the AnaLIS system, and the results have been approved by the laboratory sectionhead, the K-25 Site ACD program manager transfers the data electronically to the data basemanager in a site-/laboratory-specific file for input into the SAS data base. The date on which asiteTlaboratory-analytical results file was received is entered into the variable "DATE_REC" inthe data set TRACK2 (Fig. 3). Again, the customized screens and data query capabilities ofSAS/FSP are used to facilitate data input.

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Edit SAS data set: TRACK2

Command —> Screen 1Obs 43

WHEN A SITE/LAB SPECIFIC FILE HAS BEEN TRANSFERRED FROM THE ANALYTICALCHEMISTRY DEPARTMENT TO THE DATABASE MANAGER, INPUT THE DATE THE FILEWAS RECEIVED

SITE: EAST CHESTNUT RIDGE WASTE PILE

LAB: WET CHEM

PARMS: ALK, SOLIDS, TUR, PH, COND, TOC

DATE_DUE: 11/03/89

DATE.REC:

Fig. 3. Customized screen for data input into data set TRACK2.

GENERATION OF THE TRACKING REPORT

Table 1 of the tracking report summarizes sampling activity to date. It is basically a printout ofTRACK 1—a listing by site of the wells sampled to date, the date each weh was sampled, and anyproblems encountered. In some cases, this information can be used to correct access problems intime to complete sampling at a site. Table 2 uses information in TRACKO to summarize thecurrent quarter's site sampling schedule, by month. Table 3 generates a sampling status reportfrom schedule information in TRACKO and activity information in TRACK 1. A site is considered"COMPLETE" when all wells within it are sampled or an attempt has been made to sample, "INPROGRESS" if some but not all wells have been sampled, and "PENDING" if no wells havebeen sampled. The report writing program compares the sampling schedule to the current status,and if a site has not been completed by the end of the month within which it was scheduled forsampling, it is flagged as "OVERDUE". The program manager can use this information to spotsampling backlogs in time to reallocate personnel, repair equipment, add vehicles to the fleet, ormake any other changes required to get sampling activities back on schedule. Table 4 reports thestatus of data file transfer, by site. As sites are completed, a data transfer schedule is establishedusing the report writing program. This program calculates "DATE_DUE" for each data file usingthe following information: the date the site was completed in data set TRACK 1, the laboratory thatwill be reporting data in data set TRACK2, and the data turnover schedules previously agreed toby the Y-12 Plant's project manager and the K-25 Site's ACD laboratory heads. For example,volatile organics are to be transferred 21 days after the date on which the last well at the site wassampled, field measurements and wet chemistry 30 days after, and all other laboratories 45 daysafter.

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Table 1. Third quarter 1989 groundwater monitoring programwells to be sampled and date sampled

SITE=BURIAL GROUNDS - NORTH

WELL

GW-040GW-042GW-082GW-162GW-242GW-249GW-250GW-342GW-370GW-371GW-372GW-373

DUPE DATE

09/26/8909/27/8910/02/8909/29/8909/26/8910/02/8909/29/8909/28/8909/25/8909/26/8909/25/8909/26/89

SITE=BURIAL GROUNDS -

WELL

GW-045GW-047GW-058GW-061GW-094GW-095GW-117GW-118GW-119GW-126GW-2 37GW-286GW-287GW-288GW-289GW-290GW-291GW-374GW-374GW-375

DUPE

DUPE

DATE

09/27/8909/27/8909/27/8910/09/8910/03/8909/27/8909/30/8909/30/8909/30/8909/29/8909/28/8909/25/8909/25/8909/28/8909/28/8910/04/8910/04/8909/26/8909/26/8909/27/89

COMMENT

NO ACCESS

SOUTH

COMMENT

NO SAMPLESNO SAMPLESNO SAMPLES

DRY

18

Table 2. Third quarter 1989 groundwater monitoring programsites to be sampled

MONTH=07/01/89

OBS SITE

1 CHESTNUT RIDGE SECURITY PIT2 INDUSTRIAL LANDFILL III3 NEW HOPE POND SITE4 UNITED NUCLEAR CORPORATION SITE5 9204-4 PIT

MONTH=08/01/89

OBS SITE

6 FLY ASH PIT7 KERR HOLLOW QUARRY8 LLWDDD PACKAGING SITE9 RUST SPOIL AREA10 S-3 PONDS SITE - NORTH11 S-3 PONDS SITE - SOUTH12 Si'OIL AREA I13 WASTE COOLANT AREA14 9754-2 FUEL FACILITY

MONTH=09/01/89

OBS SITE

15 BURIAL GROUNDS - NORTH16 BURIAL GROUNDS - SOUTH17 CHESTNUT RIDGE SEDIMENT DISPOSAL 3ASIN18 INDUSTRIAL LANDFILL IV19 LLWDDD LYSIMETER DEMONSTRATION SITE20 OIL LANDFARM - NORTH21 OIL LANDFARM - SOUTH22 OIL/SOLVENT DRUM STORAGE SITE

19

Table 3. Third quarter 1989 groundwater monitoring programstatus of sampling

STATUS=COMPLETE

SITE OVERDUE

BURIAL GROUNDS - NORTHBURIAL GROUNDS - SOUTHCHESTNUT RIDGE SECURITY PITCHESTNUT RIDGE SEDIMENT DISPOSAL BASINEAST CHESTNUT RIDGE WASTE PILEFLY ASH PITINDUSTRIAL LANDFILL IIIINDUSTRIAL LANDFILL IVKERR HOLLOW QUARRYLLWDDD LYSIMETER DEMONSTRATION SITELLWDDD PACKAGING SITENEW HOPE POND SITEOIL LANDFARM - NORTHOIL LANDFARM - SOUTHOIL/SOLVENT DRUM STORAGE SITERUST SPOIL AREAS-3 PONDS SITE - NORTHS-3 PONDS SITE - SOUTHSPOIL AREA IUNITED NUCLEAR CORPORATION SITEWASTE COOLANT AREA9754-2 FUEL FACILITY

20

Table 4. Third quarter 1989 groundwater monitoring programdata transfer schedule


LAB

AASFIELD MMTSICPRAD-K-25VOAWET CHEM

DATEDUE

11/16/8911/01/8911/16/8911/16/8910/23/8911/01/89

DATE RECEIVED

12/06/8911/10/8912/21/89

*11/10/8911/10/89

COMMENT

OVERDUE

SITE=BURIAL GROUNDS - SOUTH

LAB

AASFIELD MMTSICPRAD-K-25RAD-X-10VOAWET CHEM

DATEDUE

11/23/8911/08/8911/23/8911/23/8911/23/8910/30/8911/08/89

DATE RECEIVED

12/21/8912/21/8912/21/89

•

12/21/8912/21/89

COMMENT

OVERDUEOVERDUE

SITE=CHESTNUT RIDGE SECURITY PIT

COMMENTLAB

AASFIELD MMTSHGICPRAD-K-25VOAWET CHEM

DATEDUE

10/16/8910/02/8910/16/8910/16/8910/16/8909/21/8910/02/89

DATE RECEI\

11/01/8911/01/8911/01/8911/01/8912/21/8911/01/8911/01/89

21

If a data file has not been received on schedule, it is flagged as "OVERDUE". Again, thisinformation can be used to spot laboratory backlogs before they become critical. Basically, Table 5takes the same information presented in Table 4 and reformats it into a weekly data transferschedule. This can be used both by laboratory and data base managers to schedule personnel andresources for analysis and data base activities. Table 6 summarizes laboratory performance inmeeting data analysis and transfer schedules. The parameter of interest is the difference betweenthe date the data file transfer was scheduled to take place and the date on which the transfer tookplace. A negative number indicates that the transfer took place before it was scheduled. The tablesummarizes the number of files transferred to date, the best performance, the worst performance,and the mean performance for each laboratory. This serves as another tool to pinpoint bottlenecksin the laboratories. Depending on holding time requirements and laboratory turnover schedules,this information might also be a way to ensure that holding times are not being exceeded. After atracking report is issued, data sets TRACK 1 and TRACK2 are copied to data sets TRACK01 andTRACK02, respectively, and subsequent updates are made to the former. This allows the reportwriting program to compare the updated data sets to their previous versions in order to generateTables 7 and 8, which summarize sampling and data transfer activities since the last trackingreport was issued.

DISCUSSION

Informative tracking reports can be generated using (1) initial data sets created from well-designed sampling program information; (2) activity updates to these data sets using customizedscreens and data query capabilities; and (3) a program using SAS data steps and sort, print, andstatistical procedures. These reports have been used successfully during quarterly sampling eventsin the Y-12 Plant's groundwater monitoring program since 1989. They have served as acommunications link between the Y-12 Plant's GWPP manager, the K-25 Site's ACD, the database manager, and other end users of the groundwater sampling data. Potential problems andbacklogs were identified and solved when resources were available.

22

DATE SITEDUE/DAY

09/11/89MONDAY

Table 5. Third quarter 1989 groundwater monitoring programdaily data transfer schedule

LAB

RUST SPOIL AREA VOA

RECEIVED

09/12/89TUESDAY FLY ASH PIT

09/13/89WEDNESDAY UNITED NUCLEAR CORPORATION SITE

UNITED NUCLEAR CORPORATION SITEWASTE COOLANT AREA

VOA

FIELD MMTS XWET CHEM XVOA X

09/15/89FRIDAY 97 54-2 FUEL FACILITY VOA X

09/18/89MONDAY KERR HOLLOW QUARRY

KERR HOLLOW QUARRYFIELD MlrlTS XWET CHEM X

09/20/89WEDNESDAY CHESTNUT RIDGE SEDIMENT DISPOSAL BASIN

RUST SPOIL AREARUST SPOIL AREA

09/21/89THURSDAY CHESTNUT RIDGE SECURITY PIT

FLY ASH PITFLY ASH PITLLWDDD PACKAGING SITE

09/22/89FRIDAY

09/25/89MONDAY

09/28/89THURSDAY

NEW HOPE POND SITE

WASTE COOLANT AREAWASTE COOLANT AREA

9754-2 FUEL FACILITY9754-2 FUEL FACILITY

INDUSTRIAL LANDFILL IIIUNITED NUCLEAR CORPORATION SITEUNITED NUCLEAR CORPORATION SITEUNITED NUCLEAR CORPORATION SITEUNITED NUCLEAR CORPORATION SITE

VOAFIELD MMTSWET CHEM

VOAFIELD MMTSWET CHEMVOAVOA

FIELD MMTSWET CHEM

FIELD MMTSWET CHEM

VOAICPRAD-K-25RAD-X-10TOX

XXX

XXXXX

XX

XX

XXXXX

VARIABLE

LAG

LAG

LAG

LAG

LAG

LAG

LAG

LAG

LAG

LAG

LAG

23

Table 6. Third quarter 1989 groundwater monitoring program(lag = date data file received; — = date data file expected)

N MINIMUMVALUE

LAB=AAS —

17 -25

— LAB=BNA —

3 -37

LAB=FIELD MMTS

20 -23

LAB=HG

13 -37

. LAB=HPP —

3 -25

LAB=ICP —

16 -29

LAB=RAD-K-25 -

12 6

LAB=RAD-X-10 -

9 -5

. LAB=TOX

10 -15

LAB=VOA

17 -13

— — LAB=WET CHEM -

20 -4

MAXIMUMVALUE

37

52

37

37

66

44

23

59

52

MEAN

-11

-7

29

12

-2

17

16

24

OBS

Table 7. Fourth quartet-1989 groundwater monitoring programwells sampled since last update


WELL

12345678910111213

GW-014GW-040GW-042GW-082GW-162GW-242GW-249GW-250GW-342GW-370GW-371GW-372GW-373

DUPE DATE

12/20/8912/16/8912/20/8912/21/8912/18/8912/20/8912/21/8912/21/8912/21/8912/13/8912/18/6J12/19/8912/19/89

COMMENT

NO ACCESS

SITE=BURIAL GROUNDS - SOUTH

OBS WELL

14151617181920212223242526272829

GW-045GW-047GW-058GW-061GW-094GW-095GW-126GW-237GW-286GW-287GW-288GW-289GW-290GW-291GW-374GW-375

DUPE DATE

12/18/8912/20/8912/27/8912/19/8912/16/8912/16/8912/18/8912/21/8912/20/8912/20/8912/20/8912/20/8912/27/8912/27/8912/21/8912/18/89

COMMENT

DRY

25

Table 8. Data files received since last update

EVENT= SITE=INDUSTRIAL LANDFILL IV

OBS LAB DATE_REC

1 RAD-X-10 12/29/89

SAMTRACK: A SAMPLE TRACKING SYSTEMFOR ENVIRONMENTAL MONITORING

B. M. HorwedelOak Ridge National Laboratory*


ABSTRACT

The Environmental Monitoring and Compliance (EMC) section is responsible forthe development and implementation of an environmental program to (1) ensurecompliance with all federal, state, and U.S. Department of Energy (DOE) requirementsfor the prevention, control, and abatement of environmental pollution; (2) monitor theadequacy of containment and effluent controls; and (3) assess the impacts of releasesfrom Oak Ridge National Laboratory (ORNL) facilities on the environment. Eachmonth over 1000 samples of air, water, animals, and vegetation are collected andanalyzed for contamination. A sample tracking system, SamTrack, was developed toautomate the bookkeeping and to report generation procedures required by EMC.SamTrack makes it possible for EMC to monitor individual samples from the time ofsubmission to the Analytical Chemistry Division (ACD) through the electronic transferof analytical results. SamTrack dynamically maintains a sample status data base. Atanytime a variety of reports can be generated showing the status of samples beingprocessed. Three basic reports reflect the status of the entire sample data base: (1)samples that have been submitted for analysis; (2) samples that have completedanalysis; and (3) samples with incomplete analysis. This paper presents the needs for asample tracking system, how SamTrack addresses those needs, and futuremodifications that would improve the implementation of SamTrack.

INTRODUCTION

BACKGROUND

The EMC section is responsible for the development and implementation of an environmentalprogram to (1) ensure compliance with all federal, state, and DOE requirements for the prevention,control, and abatement of environmental pollution; (2) monitor the adequacy of containment andeffluent controls; and (3) assess the impacts of releases from ORNL facilities on the environment.Each month over 1000 samples of air, water, animals, and vegetation are collected and analyzedfor contamination.


27

28

Prior to the fall of 1986, when samples were submitted to ACD, a technician recorded in alogbook the request number, sampling date, analyses, date submitted to ACD, and date that a hardcopy of the results was received. Most samples were grouped by a project name or a series (e.g.,FISH). At the front of the logbook, there was an index of series and the page number(s) wheresample information could be found. The Analytical Request Sheets (Fig. 1) were stored in a filingcabinet. ACD returned a copy of the request sheet with the request number filled in, and thesewere also filed. The system was error prone; for example, if ACD did not return a copy of therequest sheet with the request number filled in, the request number could not be recorded in thelogbook. Specific data on samples were difficult to retrieve. There was no easy way to tabulate thenumber of samples or the number of analyses submitted o~ completed. Status of samples wasdifficult to ascertain. The method of data transfer was inconsistent. Sometimes EMC would receivehard copies of the results but not an electronic transfer, at other times, EMC would receive anelectronic transfer but no hard copy. Furthermore, if EMC needed information on a particularsample, for example, the expected completion date, the request number was not always known.

ACD's data base management system resided on the PDP-10 at ORNL. When results werecompleted, the ACD analyst would send an electronic mail message to the EMC analyst. TheEMC analyst would execute a procedure to transfer EMC results to an ASCII file in the EMCanalyst's PDP-10 area. Then the EMC analyst would execute a second procedure to transfer theASCII file from the PDP-10 to the DEM VAX. The DEM VAX is the EMC's VAX 11/750, aminicomputer operated by the Environmental Analysis Group. ACD results remained on-line for10 days. If the EMC analyst did not transfer the results within that 10 days, the appropriatelaboratory would have to be contacted to get the results back on-line. However, if too much timehad elasped, the results could not be retrieved.

When ACD obtained their VAX in the fall of 1986, AnaLIS, a data managment system forsample tracking and results reporting, was implemented. AnaLIS provided a more automatedprocedure for the electronic transfer of results from ACD to EMC. An ASCII file containingcompleted results could be created each day; there was essentially no time limit for the on-linestorage of the results. However, EMC was still using the logbook and the filing cabinet forrecording and tracking the sample information.

SamTrack

SamTrack was developed to eliminate logbooks, filing cabinets, and the need for humanintervention in the electronic transfer of results.

When a sample is submitted to ACD for analysis, the information on the Analytical RequestSheet is entered into ACD's data base. Samples on a request sheet are assigned a request numberand a three-letter acronym (representing the laboratory) followed by a number. AnaLIS assignseach sample a unique identifier composed of the date entered into the ACD data base followed bya unique number. At the end of each day, three files are created by AnaLIS:

1. samples that have been logged into AnaLIS for the day (Logged file);2. samples with incomplete analyses (Backlog); and3. sample results that have been completed that day (Results).

These three files are done automatically, with no human intervention, and they are used toprovide a complete status of samples that are logged, backlogged, or have completed analysis.

REQUEST

1*1 CHAIN OF CUSTODY

VESQ D|7| f

noi c

ROJE(

HARG

lie) FREOU

1 1

rr NO

I NO

EMCY

1 1

[1] SERIES

1

I6i a

1

FORSEE

UMPLE MA

|

1

1 1 1

ORNL ANALYTICAL SERVICESREVERSE SIDE FOR INSTRUCTIONS

mix

112) CU

no Hi

1

ST DEFT NO

ITERIAL DESCRI

1 1 1

"TION

(13) SAk

1

1 1CUSTOMC* NAMI

1 1HPUD FROM

1 1 1

1

1 1

ACD REQUEST NO.

PAGE OF

II) DEADLINE (MM/DD/YYI

1

1 I

1

B lHUHN 3 - MAIL STOP

1 1

1 1 1 1 II 1 1COMMENTS: NOTE Cuttonwi should p** up umpKa wrtfiln 10 dm of raportinf r«ulti

Additional chargat may raault » Acb l» nguirad lo diapoaa of tampta.

ACD NUMBER

111

CUSTOMER SAMPLE 10 (18)

' |AnaUS Fiald No] Fiald Nama (No Char)

To arranga E-trana of data aand E-maill>CN-l«B2S to STC NTJ or TEASLEYNAJR on OA VAX

171 SAMPLE 1

MM/DD/W

1ATE

TIME

.-

-

Compfitnca Samola? SCRA

If Orftar. pfaiaa apacrfy In

131

ANALYSIS (30}

RECEIVED «Y

METHOD ID (14)

NPOES OTHER1 1 1 1

comm«nts

DETECTIONLIMITS

SUBMITTED BY /PHONE

Fig. 1. Analytical Request Sheet

30

These files are transferred to the DEM VAX each morning. The Logged file contains thesample number, request number, laboratory number, customer sample identification (ID), frequencyof sampling, sampling date and time, customer series, charge number, and deadline date. TheBacklog file contains the request number, sample number, and analyses for samples that have notbeen completed. Each record in the Results file contains the request number, sample number,customer sample ID, name of person submitting the sample, charge number, matrix, materialdescription, customer series, frequency of sampling, sampling date and time, analysis, qualifier,result, units, method number, date completed, date received by ACD, and date of electronictransfer.

The procedure for updating sample results is shown in Fig. 2. The GET procedure transfers theresults from the ACD VAX to the DEM VAX each day. If the file transferred differs from theprevious file, the results on the DEM VAX are updated with the new information.

The procedure for creating a logbook entry is shown in Fig. 3. The logbook is the data setsthat contain the sample information found on the Analytical Request Sheet. The GET proceduretransfers the login information and the backlog from the ACD VAX to the DEM VAX. Theinformation in the logged file is merged with ihe backlog file to create each record in the logbookdata base. At this point, two variables are not defined: die date the analysis was completed and thedate of electronic transfer.

The procedure for updating the logbook data base is shown in Fig. 4. The data set created bythe procedure for updating the results data base is used to update the logbook data base. At thispoint, date completed and date transferred are filled in, and the sample is now consideredcompleted.

ACD results ACD Vax

DEM Vax GET

ResultsDatabase

Today'sUpdates

UpdatePrintouts

Fig. 2. Procedure for updating sample results.

31

ACD Vax

LOGBOOKDatabase

Log EntryPrintouts

Fig. 3. Procedure for creating a logbook entry.

The backlog is monitored each day. Today's backlog is compared to yesterday's backlog.Those request numbers in yesterday's backlog that do not show up in today's backlog are printed.This list is compared to the request numbers of results that transferred that day. If the requestnumber has not been transferred, that request number is reported in an exceptions report. TheEMC analyst must determined why the request number was not transferred.

CONCLUSION

SamTrack was developed to manage the information associated with the large volume ofsamples that EMC collects each month. The system is automated; human intervention is minimal.Sample and results information can be easily obtained. At any time reports can be generated toshow what samples have been submitted, when samples were submitted, when samples were

32

Today'sUpdates

DEM Vax

UPDATELog Entry

LogbookDatabase

LogbookUpdate

Printouts

Fig. 4. Procedure for updating the logbook.

completed, and when results were transferred. Adding procedures to track costs and schedulingrequirements will make SamTrack an even more useful and powerful product.

ACKNOWLEDGMENTS

This work was performed in support of the Environmental Analysis Group. I wish to thankKaren Daniels for her support and encouragement as this system evolved from the PDP-10 to thepresent. I also wish to thank Norm Teasley and Jeff Wade, members of the Analytical ChemistryDivision, for their cooperation in modifying AnaLIS to provide the information needed to makeSamTrack work.

ENVIRONMENTAL SAMPLE MANAGEMENT SYSTEM (ESMS)

R. A. EvansOak Ridge Y-12 Plant*


Kevin NewmanScience Application International Corporation


ABSTRACT

The Environmental Surveillance Section of the Oak Ridge Y-12 Plant'sEnvironmental Management Department is responsible for compliance sampling andenvironmental surveillance at the Y-12 Plant. The Environmental Sample ManagementSystem (ESMS) was developed to automate environmental sample label preparation,sample tracking, sampling supplies usage, and electronic data transfer capabilities. Thesystem has been implemented using a relational data base management system to logand track samples. This system also implements bar code technology to produceenvironmental sample labels.

INTRODUCTION

The Y-12 Plant Environmental Management Department's Environmental Surveillance Sectionis responsible for plant-wide environmental monitoring to ensure that environmental compliance ismaintained as established by various environmental regulations such as the National PollutantDischarge Elimination System, the Resource Conservation and Recovery Act, SuperfundAmendments, and the Reauthorization Act of 1986. In addition, the Environmental SurveillanceSection also performs monitoring activities for nonroutine sampling and special projects andresponds to emergency situations and spills as requested by the Plant Shift Superintendent and theSpill Response Coordinator. The Environmental Surveillance staff members collect and processapproximately 1500 requisitions each month, and this number is expected to increase asenvironmental compliance requirements continue to become more stringent. To reduce the amountof work in the sample collection process, such as preparation of sample bottle labels andrequisitions, the staff has initiated a project to develop a computerized system to automate samplebottle labels, sample tracking, sampling supplies inventory, and data transfer capabilities to existingand future computer systems.


33

34

METHODS AND MATERIALS

The ESMS consists of a Northgate 386/25 Personal Computer system with 640 kB of basememory and 16 MB of extended memory, which is utilized as a Ram disk. The Northgate systemis supported by a Bernoulli 44+44 disk cartridge subsystem for data storage and retrieval. It isconnected via asynchronous communications to an Intermac 8646 Thermal Printer and parallel to asystem line printer. The system software used is DOS 4.01, Clipper and dBASE IV, and Loftware.

APPROACH

The ESMS serves as a closed-loop system to maintain environmental sample information frominitiation of sample collection to submission of samples to the analytical laboratories and to recordsample collection data obtained from field sampling technicians.

The ESMS consists of five major components:

1. Generate Sample Labels—responsible for printing routine and nonroutine sample labelscomposed of text and bar code.

2. Sample Tracking—used to complete a sampling event, void a sample, or query active andcompleted samples.

3. Sample Bottle Usage—used to query sample bottle usage information, generate sample bottleusage reports, or initialize the sample bottle usage data base.

4. Sample File Transfer—generates ASCII data files of sample information that may beelectronically transferred to other environmental data management systems and AnalyticalLaboratory information management systems.

5. Data Base Administration—used for reference table maintenance of data base usage statistics,data base backup and retrieval, and archiving of completed sample information.

RESULTS

The system provides a means to prepare sample labels that will reduce data entry time, enablesingle entry of several data items pertaining to labels and requisitions, improve accuracy andintegrity of sample labels and requisition information, and assist the Y-12 Plant in meetingenvironmental permit and nonpermit requirements.

DEVELOPMENT OF AN INTEGRATED DATA BASEFOR THE SCREENING-LEVEL RISK ASSESSMENT

FOR THE CLINCH RIVER RESOURCECONSERVATION AND RECOVERY ACT

FACILITY INVESTIGATION

Leslie A. Hook and Merilyn J. GentryScience Applications International Corporation


Jean A. Shaakir-Ali and Mary Alice FaulknerP.O. Box 2008

Oak Ridge National Laboratory*Oak Ridge, Tennessee 37831-6038

ABSTRACT

The Oak Ridge Reservation (ORR) includes three U.S. Department of Energy(DOE) facilities where radioactive and hazardous substances have been handled since1942. Surface waters beyond the boundary of the ORR, including portions of theClinch and Tennessee rivers, have received contaminants as a result of past operationsand waste management practices. Thus, the U.S. Environmental Protection Agency(EPA), under the provisions of the Resource Conservation and Recovery Act (RCRA),requires that a Clinch River RCRA Facility Investigation (CRRFI) be conducted. TheCRRFI is being implemented in a phased approach to (1) define the nature and extentof off-site contamination, (2) quantify risk to human health and the environment, and(3) identify and evaluate the remedial action alternatives.

One of the first tasks was to perform screening-level risk analyses for human andaquatic organism exposure pathways, based on existing data. Thus, there was a need toidentify, compile, and integrate selected data into a single consistent data base for useby the investigators performing the risk assessments. The data required were recentmaximum and mean values of water quality parameters and of contaminantconcentrations in sediment, water, fish, and other biota (e.g., vegetation). The studyarea was divided into manageable units or reaches for the risk assessment, based uponproximity to known contaminant release points from DOE facilities and other potentialsources of pollution in the area. The major sources of data included Oak Ridge TaskForce results, DOE ORR Environmental Surveillance Reports, results of severalintensive sediment sampling activities, and Tennessee Valley Authority (TVA) dataretrieved from the EPA's STORET system. No one study or program had sufficientdata to meet the risk assessment requirements for any given reach. The structured


35

36

process by which data files were quality checked and manipulated into a common SASdata base structure will be discussed.

The necessity for this data collection activity and the numerous RCRA facilityinvestigations planned for the ORR underscore the need for development of aconsolidated environmental data base for the ORR and off-site areas, including non-DOE data (e.g., TVA, state of Tennessee, and U.S. Geological Survey) assupplemental information. Access to data with common units, nomenclature, and database format would facilitate future assessments; subsequent modifications inassessment methodology could be easily and quickly implemented. This type of database should be maintained and updated on a regular basis at all DOE facilities.

MANAGEMENT OF GROUNDWATER HYDROLOGY DATAAT OAK RIDGE NATIONAL LABORATORY*

M. A. Faulkner and L. D. VoorheesP.O. Box 2008

Oak Ridge National LaboratoryOak Ridge, Tennessee 37831-6036

ABSTRACT

More than 1300 wells have been drilled in the immediate vicinity of Oak RidgeNational Laboratory (ORNL) in the past 40 years. Because the wells were drilled atdifferent times and for different purposes, information concerning these wells wasrecorded in a wide variety of formats and in several different places. Initiation of theORNL Remedial Action Program (RAP) in 1985 made it necessary to compile thebasic information on the older wells in a common format and to develop a frameworkfor managing all data associated with installation and sampling of future groundwatermonitoring wells at ORNL. This task was accomplished by staff within ORNL'sEnvironmental Sciences Division (ESD) through the establishment of a computerizeddata base.

The data base principally uses SAS software installed on IBM and VAXmainframe computers. A geographic information system based on ARC/INFO softwareinstalled on the ESD VAX is used to analyze and display spatial aspects of the data.Information consisting of construction parameters (e.g., installation date, location,elevation, depth, diameter, and screen intervals) on the older wells was compiled byESD staff from various sources including ORNL and U.S. Geological Survey (USGS)publications, borehole geophysical logs, and personal communications. Constructiondata for all new wells installed by RAP are obtained according to establishedprocedures. Water-level measurements made by both ORNL and USGS are alsomaintained in the data base. Unique well identification codes allow all types of datafor a specific well to be merged.

Management of the data from the point of collection to validation, as well as someof the approaches used to satisfy requests for such information, is described. Examplesof the use of ARC/INFO to display data are also given. These descriptions andexamples illustrate the dynamic status of this data base.

Establishment of a computerized data base for groundwater data at ORNL hasoptimized the organization and distribution of this information. The data base has beenused frequently to provide selected information to RAP principal investigators,subcontractors, USGS, and the DOE Environmental Survey.


37

AN OVERVIEW OF THE PORTSMOUTH GASEOUS DIFFUSIONPLANT'S INTEGRATED GEOGRAPHIC INFORMATION

SYSTEM/ENVIRONMENTAL DATA BASEMANAGEMENT SYSTEM

Kevin K. KellerJohn Matties and Associates

Columbia, Illinois 62236

A. Keith BracknellPortsmouth Gaseous Diffusion Plant*

P.O. Box 628Piketon, Ohio 45661

ABSTRACT

One of the most important assets available to an environmental program at anyfacility is the data that has been generated to help characterize the systemic behaviorof that facility. Because of this, the application of an integrated GeographicInformation System/Environmental Data Base Management System (GIS/EDMS) hasbecome an essential component of the environmental program at the PortsmouthGaseous Diffusion Plant (PORTS). This integrated approach, taken to address thecomplex issues associated with site characterization and remediation, encompasses thevarious technologies and disciplines necessary for the comprehensive evaluation ofenvironmental data, which in most cases are unstructured, inconsistent, and in differentformats.

The objective is to consolidate all useful site data into a coherent, auditable, andaccessible data base and establish an ongoing data collection process to keep that database current and available for analysis and decision making. It is also important for thesite data base to be in a format that allows for its connectivity into whatever analysistools the site management needs to use (e.g., a groundwater model, a GIS, AutoCAD,a contouring package, a spreadsheet application, etc.).

This is accomplished at PORTS because of the GIS problem-solving capabilitiesrelating to spatially distributed data and the EDMS ability to organize and store thedata that have accumulated since environmental characterization of the site began. Tnisdiversification has proved that the PORTS GIS/EDMS is an effective technology,particularly in relation to hazardous waste site characterization and remediation.Flexibility of graphic and report output is a strength of the PORTS GIS/EDMS thatpermits displaying combinations of data being retrieved from the GIS/EDMS in theform of a structured query, which are meaningful to the PORTS site and existingenvironmental conditions. Thus, the GIS/EDMS technology being used at PORTS acts


39

40

as a foundation for the storage, analysis, and presentation of the diverse types of datacollected—data that are necessary for comprehensive site characterization.

DATA VERIFICATION AND EVALUATION TECHNIQUESFOR GROUNDWATER MONITORING PROGRAMS'

Theresa M. MerrierH&R Technical Associates, Inc.


Ralph R. TurnerOak Ridge National Laboratory*


ABSTRACT

To ensure that data resulting from groundwater monitoring programs are of thequality required to fulfill program objectives, it is suggested that a program of dataverification and evaluation be implemented. These procedures are meant to supplementand support the existing laboratory quality control/quality assurance programs byidentifying aberrant data that results from a variety of unforeseen circumstances suchas sampling problems, data transformations in the laboratory, data input at thelaboratory, data transfer, and end-user data input.

Using common-sense principles, pattern recognition techniques, andhydrogeological principles, a computer program was written to scan the data forsuspected abnormalities and to produce a text file staling sample identifiers, thesuspect data, and a statement of how the data has departed from the expected. This fileis then sent to the laboratory for question resolution. The evaluation procedures mayalso be used to identify outliers and to support the flagging in or deletion of this datafrom the data base. These techniques are especially useful given that sample sizerestrictions and data variability frequently preclude the valid use of statisticaltechniques. Most of the techniques considered in this paper check for the internalconsistency of results from samples collected at one well at one point in time. Thetechniques described in this paper have been developed to support the Oak Ridge Y-12Plant Groundwater Protection Program Management Plan.

"This project was supported by the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee, managed by Martin Marietta EnergySystems, Inc., for the U.S. Department of Energy under Contract No. DE-AC05-84OR21400.


41

42

INTRODUCTION

With the abundance of sampling and analytical quality control and quality assurance manualsand procedures, one might ask why data verification and evaluation procedures arc needed. Frommonitoring program design through sampling, analysis, data input, and data transfer, there is onecommon denominator—human beings accomplish these tasks, and mistakes can and do occur.Given that many of our decisions must be made based on limited sample size, it is important toidentify questionable data and to decide whether to correct, delete, or flag the data before it isanalyzed, used for regulatory reports, or released to outside users.

H&R Technical Associates has established and managed the groundwater data base resultingfrom the characterization and monitoring of the U.S. Department of Energy's (DOE's) Y-12 Plantwaste disposal sites for 7 years. The data base consists of thousands of observations and nearly300 variables. The current monitoring program generates approximately 400 observations of200 variables per quarterly sampling event. Given the volume of data generated on a regular basis,it became necessary to find techniques that would efficiently detect errors and departures fromestablished trends.

APPROACH

The following summary lists the data verification and evaluation methods that have evolvedover the 7 years during which H&R has managed the Y-12 Plant's groundwater data base.

Data Verification—Step 1

The SAS™ program checks data files transferred from the laboratory for

• correspondence of reported analyte and variable name,• correspondence of analyte and reported units,• invalid numeric data, and• unexpected character data.

Data Verification—Step 2

After data has been input into the SAS groundwater data base,

• SAS data set is tabulated, and• data is visually checked against laboratory report sheets and field sheets for completeness and

accuracy.

"SAS is a registered trademark of SAS Institute, Gary, North Carolina.

43

Data Evaluation

The SAS program run

• calculates charge balance,• calculates conductivity and compares to field and laboratory measurements of conductivity,• compares alkalinities and pH,• compares field duplicate measurements,• compares filtered and unfiltered elemental analyses,

compares inductively coupled plasma (ICP) and atomic absorption spectroscopy (AAS)elemental analyses,

• generates frequency distributions to check for extreme values and varying detection limits, andgenerates a summary of holding times for volatile organic compound (VOC) samples.

DATA VERIFICATION

The purpose of data verification is to ensure that the data in the data set matches the datareported on the backup documentation, usually the laboratory report and field report sheets. Thefirst verification step depends on how the data is transferred from the analytical laboratory.

If the data is transferred electronically from the laboratory data base to sequential data files,which are then input into the permanent data base, one can incorporate steps into the data inputprogram to check that the parameter names are correct, units associated with each parameter arecorrect, and the values reported for each parameter are within an expected range or valid numericvalues. The bulk of the groundwater data resulting from the Y-12 Plant's monitoring program istransferred in this manner. The data is input into the permanent SAS groundwater data set by aSAS program that performs the first verification tasks.

If data is transmitted via hardcopy of laboratory reports, it must be manually input. Doubleentry can be used to verify data that is input manually. Data is entered by one person into a dataset and by a second person into a second data set. A computer comparison procedure is then run tocheck for discrepancies. If manual data input is needed, H&R uses SAS facilities to aid andaccomplish these steps. First, customized screens are set up to look like the laboratory reportssheets, using SAS/FSP. Field attributes may be defined (e.g., maximum and minimum allowablevalues and required fields). An option that is available but not recommended is an initial valuescreen. When used judiciously, it can save time, but experience has shown that errant observationsor variables can be assigned the default values with the stroke of a key. These mistakes areextremely difficult to detect because by definition most of the data-screening procedures targetoutliers, not normal values. When the second input has been completed, the SAS procedureCOMPARE is run, the differences accounted for, the appropriate changes made to the data sets,and the process is repeated until there are no differences encountered.

The final data verification step is a visual check of the tabulated data set against the laboratoryand field report sheets as well as a visual check for data outliers. This step is very time consumingand was the motivation for attempting to create a computer-based system that could performsearches for completeness and outliers in a more consistent and efficient manner. At this point intime, the second step of data verification is still performed because data evaluation piocedureshave not been found to replace all its functions.

44

DATA EVALUATION

Data evaluation is a necessary but often overlooked data base activity. Stopping at dataverification is like running a spell-check procedure on a document without checking the context ofthe wonls. As previously mentioned, the data evaluation techniques generally check forconsistency of results of samples from a well at one point in time. These results are reported by upto eight different laboratories and are summarized in Table 1. One of the objectives of the dataevaluation program is to try cross checking results reported by one laboratory against thoserepotted by another.

Table 1. Laboratories analyzing samples received from theY-12 Plant Groundwater Monitoring Program

Laboratory

Inductively coupled plasmaArc emission spectrometryAtomic absorption spectrometry

Wet chemistry

Field measurements

Radiological

Gas chromatography/massspeclrometry

FilteredUnfilteredFilteredUnfilteredCationsAnions

Indicator parameters

Other

Volatile organicsSemivolatile organicsHerbicides, pesticides, and PCBs

Analytes

Elements

Elements

Ammonia-NSulfate, nitrate-N, chloride, fluoride,

alkalinity HCO,, and alkalinily-CO,Total organic carbon, pH,

conductivity, and total organicchloride

Bacteria, phenols, total and dissolvedsolids, and turbidity

pH, conductivity, redcx, temperature,and dissolved oxygen

Gross alpha, gross beta, and isotopicanalyses

Following is a discussion of each data evaluation procedure. Each discussion includes anexample of data that was targeted for further consideration by the laboratory. The examples areillustrated by the computer program report output sent to the laboratory. Unless otherwise stated, ameasurement is considered to be "different" from another if there is greater than an order ofmagnitude between the two. In the case of pH, the difference translates to 1 unit because pH ismeasured on a log scale. This rule-of-thumb is based on 7 years of observations, and one shouldnot assign any rigorous statistical or geochemical significance to this rule. In certain cases, thisrule needs to be a bit flexible.

CALCULATION OF CHARGE BALANCE

Calculation of charge balance is an excellent check on the internal consistency of the results ofanalyses conducted on samples collected from a well. Charge balance involves summing chargedue to anions, summing charge due to cations, and then expressing how well these summed

45

charges balance as a relative percentage. The calculation incorporates measurements reported bythe ICP Arc Emission Spectroscopy (AES) and Wet Chemistry laboratories. The major cationsconsidered in the Y-12 Plant groundwater program arc dissolved calcium, dissolved magnesium,dissolved potassium, dissolved sodium, ammonium-N, and hydrogen ion (pH). Dissolved (filteredsample) concentrations arc preferred for most cations because total analyses often include digestedcations that do not contribute charge. The major anions are sulfate, nitrate-N, chloride, fluoride,alkalinities, and hydroxyl ion (pH). If, due to contaminant load and/or natural groundwatercharacteristics, other cations (e.g., aluminum) or anions (e.g., boron) exist in significantconcentrations, these parameters must be considered in the equation. The appropriate conversionfactors can be found in a variety of reference books (American Public Health Association 1976).

These concentrations are converted to milliequivalents (meqs), and total cations and totalanions are calculated in Table 2 (American Public Health Association 1976; Stumm and Morgan1981). The charge balance is calculated as

total cations - total anions

total cations + total anions

Geochemists like to see charge balances between ±10%, but for purposes of data evaluation, it wasfound that ±20% was a more realistic goal.

Example:

GW-228 11/21/88

mg/L meq/L

CaMgKNaAmmonium-NPH

Total cations

FluorideSulfateNitrate-NChlorideAlk - HCO3

Alk - CO3

pHTotal anions

13041

5.816

5.8

5032737

301<1

5.8

6.48703.37270.14830.6960

0.001610.7056

10.47751.92791.04386.01510.00000.0000

19.4643

Charge balance = -29%.

On reexamination of sample results, it was determined that the sulfate result was atypographical error and should have been reported as 50 mg/L. The corresponding meq/L thenbecame 1.0415, and the charge balance was within 3%.

46

Table 2. Charge balance

Major cations(Elements from filtered sample)

Cation source Unit Conversion factor"Conversion to

meq/LResults

CalciumMagnesiumPotassiumSodiumAmmonia-N''PH

mg/Lmg/Lmg/Lmg/Lmg/L x 1.28785

lO"1"1 x 103

x 0.04990x 0.08226x 0.02557x 0.04350x 0.05544

= meq/L= meq/L= meq/L= meq/L= meq/L= meq/L

Total cations

Major anions

Anion source Unit Conversion factor"Conversion to

meq/LResult

FluorideSulfateNilrate-NChlorideAlk HCO, as CaCO,Alk CO, as CaCO,PH

mg/Lmg/Lmg/Lmg/Lmg/Lmg/L

x 4.42680

x 1.21926x 0.59956jQ(-UtpH) x

x 0.05264x 0.02083x 0.01613x 0.02821x 0.01639x 0.03333

= meq/L= meq/L= meq/L= meq/L= meq/L= meq/L= meq/L

Total anions

'Before proceeding with calculations, the following conversions must be made: pH must beconverted to H* for cations and to OH~ for anions.

Ammonia-N (ammonium-N) must be converted to ammonium:

14.0067 + (4 x 1.00797)14.0067

Nitrate-N must be converted to nitrate:

14.0067 + (3 x 15.9994)14.0067

Bicarbonate alkalinity must be converted to bicarbonate:

2 x 1.00797 + 12.01115 + (3 x 15.9994) _ {

40.08 + 12.01115 + (3 x 15.9994)

Carbonate alkalinity must be converted to carbonate:

12.01115 +(3 x 15.9994) = Q 59Q56

40.08 + 12.01115 + (3 x 15.9994) ~

Element Atomic weightH 1.00797C 12.01115N 14.0067O 15.9994Ca 40.08

6 Although reported as ammonia-N, the laboratory measurement is based on ammoniumchloride standards.

47

CALCULATION OF CONDUCTIVITY AND COMPARISONTO DIRECT MEASUREMENTS OF CONDUCTIVITY

The calculation of electrical conductivity incorporates measurements of the major cations andanions reported by the ICP AES and Wet Chemistry laboratories. This calculated conductivity canbe compared to conductivity measurements taken in the field at the time of sampling and alsomeasurements taken in the laboratory in replicate. Thus the comparison involves results from twolaboratories and field measurements. The major cations considered in the Y-12 Plant ground waterprogram are dissolved calcium, dissolved magnesium, dissolved potassium, dissolved sodium,ammonium-N, and hydrogen ion (pH). As for charge balance, dissolved (filtered sample)concentrations are preferred for most cations because total analyses often include digested cationsthat do not contribute equivalence. The major anions are sulfate, nitrate-N, chloride, alkalinities,and hydroxyl ion (pH). If, due to contaminant load and/or natural groundwater characteristics,other cations or anions exist in significant concentrations, these parameters must be considered inthe equation. The appropriate conversion factors can be found in a variety of reference books.These concentrations are converted to umhos in Table 3 (American Public Health Association1976; Stumm and Morgan 1981; Atkins 1978), and calculated conductivity is equal to thesummation of these results.

Calculated conductivity = total cations (umhos/cm) + total anions (umhos/cm).

Example:

GW-310 08/01/88

GW-313 08/01/88

GW-317 08/01/88

Field conductivityConductivity laboratory replicates

Calculated conductivity





127.5 umhos/cm12501270128012501430

66.8 umhos/cm636642645655683

37.2 umhos/cm372360359362372

48

Cation source

CalciumMagnesiumPotassiumSodiumAmmonia-N*pH

Anion source

SulfateNitrate-NChlorideAlk HCOj as CaCO,Alk CO, as CaCO,PH

Table 3. Calculated conductivityMajor cations

(Elements from filtered sample)

Unit

mg/Lmg/Lmg/Lmg/Lmg/L

Unit

mg/Lmg/Lmg/Lmg/Lmg/L

Conversion factor0

x 1.287851O1"1 x 10

Major anions

Conversion factor"

x 4.42680

x 1.21926x 0.59956jQ(-U>pH) x JQ3 ^ 2 5 ° C

Conversion topmhos/cm

x2.6X3.82x 1.84x2.13x4.07x 0.3498

Conversion topmhos/cm

x 1.54x 1.15x2.14x 0.715x2.82x 0.1976

Result

=pmhos/cm=umhos/cm=pmhos/cm=pmhos/cm=pmhos/cm=pmhos/cm

Total cations

Result

= pmhos/cm= pmhos/cm= pmhos/cm= pmhos/cm= pmhos/cm= pmhos/cm

Total anions

"Before proceeding with calculations, the following conversions must be made: pH must beconverted to H* for cations and to OH for anions.

Ammonia-N (ammonium-N) must be converted to ammonium:

14.0067 + (4 x 1.00797) _

14.0067

Nitrate-N must be converted to nitrate:

14.0067 + (3 x 15.9994)

14.0067

Bicarbonate alkalinity must be converted to bicarbonate:

12.01115 +(3 x 15.9994)

4.42680 .

40.08 + 12.01115 + (3 x 15:9994)

Carbonate alkalinity must be converted to carbonate:

0.59956 .

40.08 + 12.01115Element

HCNOCa

• " " ' -nswss+ (3 x 15.9994)Atomic weight

1.0079712.0111514.006715.999440.08

''Although reported as ammonia-N, the laboratory measurement is based on ammoniumchloride standards.

49

There was good agreement between calculated and laboratory measurements of conductivitybut an order of magnitude difference between these measurements and conductivity measurementsmade in the field. Because of the systematic nature of the difference and the fact that it occurredfor wells sampled on the same day and measured by the same person/instrument, it was assumedthat the instrument was set on the wrong range, and the field conductivities were deleted from thedata base.

If dissolved solids are present in the sample at a high concentration (>2000 mg/L), thecalculated conductivity will probably be greater than the measured because charged species (ions)will start forming uncharged ion pairs. This phenomenon has been observed in the data resultingfrom the Y-12 Plant Groundwater Monitoring Program. For this data, the mean value of the resultof subtracting calculated conductivity from measured conductivity is a negative number, and themagnitude of this mean value decreases (becomes more negative) as conductivity increases.

COMPARISON OF pH AND RELATIVE PRESENCEOF HCOj AND CO3 ALKALINITIES

Table 4 illustrates the relationship between the presence and absence and relativeconcentrations of carbonate and bicarbonate alkalinities as a function of pH. This relationship hasbeen translated into the following rules.

If the Laboratorymeasurement of

pH is

<4.5

>4.5 and <8.3

>8.3 and <10.3

>10.3 and <12.5

>12.5

The concentration ofbicarbonate alkalinity

should be

<10mg/L

>Omg/L

>10mg/L

>0mg/L

<10mg/L

And the concentrationof

carbonate alkalinityshould be

=0mg/L

<10mg/L

>0mg/Land<bicarbonate alkalinity

>10mg/Land>bicarbonate alkalinity

>10mg/L

50

The criteria translate as follows:

<10 mg/L, present at low concentration;>0 mg/L, present;>10 mg/L, present at concentrations above noise at detection limit; and= 0 mg/L, not present.

This comparison involves measurements of pH and alkalinities made in the Wet Chemistrylaboratory. The computer report printout also includes the field pH because it was discovered thatin some cases where field and laboratory measurements of pH differed significantly (see nextsection) and the relationship between laboratory pH and alkalinities was skewed, the relationshipbetween field pH and alkalinities was as predicted.

Example:GW-082 03/16/89

GW-095 03/15/89

GW-242 03/16/89

pH, Field measurementpH, Laboratory replicates

Alkalinity-COjAlkalinity-HCO3


Alkalinity-CO3

Alkalinity-HCO3


Alkalinity-CO3

Alkalinity-HCO,

7.36.56.56.56.4

144 mg/L<1 mg/L

8.88.78.78.78.7

225 mg/L25 mg/L

6.36.26.26.26.2

106 mg/L<1 mg/L

On review, it was discovered that for this batch of samples, the analyst transposed the carbonateand bicarbonate alkalinity values.

51

Table 4. Comparison of pH and alkalinities

(%dissolved CO2 at 1 atm pressureand 25°C) (Mem 1986)

PH

4.04.55.05.56.06.57.07.58.08.59.010.010.511.011.512.012.5

H2CO3 ;>

100999689724620841

HCCV

141128548092969896724320831

CO32

14285780929799

COMPARISON OF FIELD MEASUREMENTSAND LAB REPLICATE MEASUREMENTS

An attempt was made to compare pH measurements made in the field and those made in thelaboratory in replicate, but it was found to be unrealistic. Differences of one or more pH unitswere quite common. There are many reasons for this occurrence: holding time, sample handling,and sample sedimentation/precipitation. For example, if there is a high concentration of dissolvediron, the iron may precipitate out and thus the pH decrease in the time between measurement inthe Held and in the laboratory. If the initial pH is high, carbonate compounds may precipitate outand the pH decrease between field and laboratory measurements. Groundwaters with neutral pHthat are not in equilibrium with the atmosphere will show an increase in pH as this equilibrium isreached. Finally, groundwater often has a high level of carbon dioxide. If the carbon dioxidedegasses from the water, the pH will rise; that is, the laboratory pH will be greater than the fieldpH. These situations raise a question about the usefulness of the laboratory pH measurement if oneis really interested in the field conditions.

52

COMPARISON OF RESULTS FROM FIELD DUPLICATE SAMPLES

Independent samples taken from the same well and analyzed for an identical suite ofparameters during a sampling event are called field duplicate samples. These are considered qualityassurance samples and are taken to determine the consistency and reproducibility of field samplingprocedures. The Y-12 Plant Groundwater Monitoring Program specifies that at least one well ateach site has field duplicate samples taken or one for every ten wells sampled at that site. Becausefield duplicate measurements are made for all parameters on the sampling schedule for that well,comparisons may be made for results coming from all analytical laboratories. For the purposes ofsampling quality assurance, the laboratory uses the following decision rule:

~ ..ft, 1st result - 2nd result tnrt

%difference = x 100 .(1st result + 2nd result)/2

Ifabsolute value of % difference >25%

andboth results > (5 x reporting limit),

thendata are evaluated by program manager,

but ifabsolute value of % difference >25%,

andeither result < (5 x reporting limit),

thenno action is required.

For purposes of data evaluation, if any result differs from its corresponding duplicate result bymore than an order of magnitude, regardless of its relation to a reporting limit, the data is flaggedfor further consideration. An order of magnitude translates to a %difference = 163%.

Example:

GW-159 08/23/88

ICP boron, mg/LICP copper, mg/LICP silicon, mg/LICP titanium, mg/LICP vanadium, mg/L

It was determined that a l-to-10 dilution was done on the field duplicate sample but that themultiplier was not applied to the sample results.

Original sample

0.0840.0469.50.170.051

Field duplicate

<0.003<0.004

0.79<0.003<0.004

53

COMPARISON OF ELEMENTAL ANALYTICAL RESULTSFROM FILTERED AND UNFILTERED ANALYSES

Elemental analyses are run routinely in groundwater sampling programs to monitor heavymetals, drinking water metals, or other suspected contaminants. When an elemental analysis iscalled for in the Y-12 Plant sampling program, a filtered sample is also collected and analyzed.Theoretically, the filtered sample results should be less than or equal to the total or unfilteredresults.

The decision rule used to check for consistency between filtered and unfiltered results is: If afiltered result is greater than the corresponding unfiltered result by more than an order ofmagnitude, the data is flagged for questioning. This may happen if sample bottles have beenswitched, samples have been improperly prepared, samples have been contaminated duringfiltration, or if dilution/calibration factors have been incorrectly applied.

Example:

GW-274

GW-275

09/27/88Barium, by AAS mg/L

09/27/88Barium, by AAS mg/L

FilteredUnfiltered

FilteredUnfiltered

1370.44

1010.5

Upon further investigation of results from the ICP and AAS laboratories, the following informationcame to light.

Well

GW-274

GW-274-F

GW-098

GW-098-F

GW-275

GW-275-F

GW-073

GW-O73-F

Requisitionnumber

880929-059

880929-062

880926-059

880926-061

880929-058

880929-061

880926-058

880926-060

ICP notfiltered

111

0.33

115

0.38

ICPfiltered

AAS notfiltered

0.44

116

0.5

151

AASfiltered

137

0.43

101

0.56

It became evident from the above information that the AAS results for 880929-059 and880926-059 and the AAS results for 880929-058 and 880926-058 had been transposed or thebottles had been switched.

54

COMPARISON OF ELEMENTS ANALYZED BOTH BY ICP AND AAS METHODS

Elemental analyses may be conducted by a variety of methods. Two of the most common areICP and AAS. ICP is a scan for the presence of elements, and for most elements, it may result ingood estimates for the concentrations of those elements. AAS is a method used when more preciseestimates of the concentration of elements are needed. For elements such as antimony, arsenic,lead, and selenium, ICP yields results that may be biased and, therefore, inappropriate. For theseelements, AAS methods should be used. In the Y-12 Plant Groundwater Monitoring Program,sometimes an ICP scan is requested as well as AAS analysis for additional elements. If an elementhas been analyzed by both methods, and both methods are appropriate for the quantification of thatspecific element, the results are compared. The order of magnitude decision rule is applied to theresults, and the data is flagged for further consideration, if it violates the rule.

Example:

GW-290 08/03/90

Cadmium

Chromium

ICPAAS

ICPAAS

2.1 mg/L<0.002

2.1 mg/L<0.01

It was determined that the ICP sample had inadvertently been spiked with these elements.

GENERATION AND USE OF FREQUENCY DISTRIBUTIONS

The groundwater monitoring data base has been structured such that all numeric variables inthe data base may take on three types of values: positive, negative, and SAS special missingvalues (_. and A through Z). The positive numbers represent concentrations or measurements;negative numbers represent values for constituents that are not detected and reported as <#(where # is the detection limit and the value is stored as -#) or for parameters that may take onnegative values (redox for example); special missing values have been used for values that arereported but cannot be expressed as numbers, such as ">#," "interferences/* and "not detected."

One can use this data format information and frequency distribution to find potentiallyanomalous data. The first step is to change all positive results to a special missing value that is notalready assigned a meaning in the data base (e.g., "P") and all true negative results to a specialmissing value not already assigned a meaning in the data base (e.g.,"N")> This shouldtheoretically leave five types of frequency categories:

55

Frequency distribution . .. Meaningcategory **

" P " A positive numeric result above the

detection limit

*'N" A true negative numeric result

Other SAS special missing values Results for numeric variables unable to be

expressed as numbers

'*." Missing values

Negative numbers Numbers indicating results at or belowthe detection, # being the detection limit

After converting the data, frequency distributions for each numeric variable are generated andchecked for the following patterns.

1. If multiple detection limits (negative numbers) are listed, the least frequent ones arc noted andthe laboratory is asked for verification.

2. If most values of a variable are at or below the detection limit, those above the detection limitare checked against historical data for that well. Conversely, if most values of a variable areabove the detection limit, the values that are not detected are checked against the historicaldata for that well. In other words, a two-sided search is done to check for large and smalloutliers.

3. If most observations have a value for a parameter and a few do not, it might indicate that datahave not been transmitted from the laboratory. It is especially useful to run the frequencyprocedure by each waste disposal site to check that the sampling program assigned to the sitewas executed completely.

4. If a value of a variable has been stored as a special missing value, that value is checkedagainst the special missing value key associated with the data base to make sure the specialmissing value is included in the key and if its meaning is consistent with that in the key.

HOLDING TIMES FOR SAMPLES TO BE ANALYZEDFOR VOLATILE ORGANICS

Because the validity of volatile organic data is dependent on whether the samples met theirholding time requirement, holding time is calculated as the difference between the sampling dateand the analysis date. A frequency distribution is generated to summarize the success of thelaboratory in meeting this criteria.

THE FUTURE

We are now evolving from a sample-to-sample/univariate comparison process to apopulation/multivariate approach to data validation. This will entail categorizing wells intosubpopulations based on multivariate similarities reflecting the parent groundwater and

56

intersections with contaminant plumes. After this analysis, subpopulation means and variances willbe calculated for key parameters. Quarterly sampling results from wells will be compared to theircorresponding subpopulation means. Fn this way, we can use more specific information about thesubpopulation to decrease the noise in the comparison and to more efficiently detect invalid dataor breaks in precedent.

DISCUSSION

Given the amount of data generated by a groundwater monitoring program, the consequencesof the decisions based on the data resulting from this program, and the time constraints placed onthe decision making process, data verification and evaluation techniques need to become anintegral part of data base management. In order to make objective and reasonable decisions basedon sampling data, questionable data must be identified and dealt with in a consistent, objective,and efficient manner. The principles discussed in this paper have been incorporated into acomputer program that scans the monitoring data, flags the data according to the decision rules,and writes a report to be sent to the laboratory for question resolution. The process has been usedfor more than a year for the Y-12 Plant Groundwater Monitoring Program and has improved dataquality, communications with the analytical laboratory, and general understanding of the sourcesand consequences of laboratory and sampling variability.

ACKNOWLEDGMENTS

We would like to thank Robert Cook, Thomas Early, and Stephen Haase of ORNL and RoyHardwick of H&R Technical Associates, Inc., for sharing their knowledge and experience in thefield of geochemistry. Appreciation is also due to Mr. Wayne McMahon and Dr. CharlotteKimbrough, DOE's Y-12 Plant, who encouraged us to pursue this endeavor and supported theeffort. Finally, we would like to express our gratitude to Mr. Larry Burnett, Y-12 PlantGroundwater Program Manager, and all the laboratory supervisors at the K-25 Site ACD. Theyendured endless questions with patience and good humor. We would be remiss if we did not alsopoint out that the examples used in the paper represent an extremely small proportion (-0.05%) ofthe data received from the K-25 Site ACD.

LITERATURE CITED

American Public Health Association 1976. Standard Methods for the Examination of Water andWastewater, Washington, D.C.

Atkins, P. W. 1978. Physical Chemistry, W. H. Freeman Co., San Francisco.

Hem, J. D. 1986. Study and Interpretation of the Chemical Characteristics of Natural Water,3d ed., Water-Supply Paper 2254, United States Geol. Surv., Denver, Colo.

57

Stumm, W., and Morgan, J. J. 1981. Aquatic Chemistry, An Introduction to Emphasizing ChemicalEquilibria in Natural Waters, Wiley, New York.

BIBLIOGRAPHY

Mortimer, C. E. 1967. Chemistry, A Conceptual Approach, Von Nos. Reinhold, Florence, Ky.

Residuals Management Technology, Inc. 1985. Field Measurement Methods for HydrogeologicInvestigations: A Critical Review of the Literature, Electric Power Res. Institute, PaloAlto, Calif.

Tetra Tech, Inc. July 1985. Groundwater Data Analyses at Utility Waste Disposal Sites. EA-4165,Electric Power Res. Institute, Palo Alto, Calif.

DEVELOPMENT OF A CONSOLIDATEDENVIRONMENTAL DATA BASE

Larry D. VoorheesP.O. Box 2008


ABSTRACT

A great deal of time, resources, and money has been and continues to be spent ondesigning and implementing a variety of environmental monitoring programs at U.S.Department of Energy (DOE) facilities. Often data are collected for a single purposeand are either unsuitable or not readily available for other applications because ofinaccessibility, poor documentation, incompleteness, and lack of standardization withrespect to format and nomenclature. Increasing costs of data acquisition and limitedbudgets make it imperative that maximum use be made of historic and current DOEdata bases. A Federal Facility Agreement (FFA) between DOE, the U.S.Environmental Protection Agency (EPA), and the Tennessee Department of Health andEnvironment (TDHE) calls for the development of a consolidated environmental database for the DOE Oak Ridge Reservation (ORR) facilities managed by Martin MariettaEnergy Systems, Inc. (Energy Systems). This directive is related to environmentalrestoration of the ORR and, hence, pertains to a wide variety of data types.

The objectives of establishing the Energy Systems Environmental Data Base are to(1) ensure long-term retention and accessibility of data collected at all facilities,(2) support the development of a common format for reporting environmentalmonitoring data, and (3) facilitate the sharing of data in support of environmentalcompliance and restoration assessments. To achieve these objectives, a DataManagement Steering Committee will be established. This committee will consist of achairman, data management representatives from each facility, and outside expertise.The committee will (1) define the data management needs; (2) identify the types andsources of data to be maintained; (3) characterize the hardware and softwarecapabilities at each facility; (4) develop a standard data base scheme; and (5) setstandards for the contents, quality, and documentation of the data sets. These steps willculminate in the preparation of a Data Management Plan to be implemented at eachfacility. Major areas of concern include software standards, communications, datacollection procedures, data entry, variable naming conventions, chemical nomenclature,units of measurement, levels of precision, date and time formats, spatial coordinates,missing values, quality assurance elements, security, and data exchange formats andprocedures.


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The keys to the success of this task are a cooperative attitude by each datamanagement group involved and a commitment by management, in the form of staffand resources, to implement the standards developed by the Data Management SteeringCommittee. It is hoped that the Energy Systems Environmental Data Base and theexperiences gained in its development can serve as a model for consolidating similartypes of data at other DOE facilities.

INTRODUCTION

A great deal of time, resources, and money has been and continues to be spent on designingand implementing a variety of environmental monitoring programs at DOE facilities. Suchmonitoring is driven primarily by state and federal legislation requiring control over facilitydischarges and the cleanup of areas contaminated with low-level radioactive and/or hazardouschemical wastes. The most important of these regulations are the Resource Conservation andRecovery Act (RCRA), including the 1984 Hazardous and Solid Waste Amendments; theComprehensive Environmental Response, Compensation, and Liability Act (CERCLA), includingthe Superfund Amendments and Reauthorization Act of 1986; the Clean Water Act; the SafeDrinking Water Act; and the Clean Air Act. Although the data being collected meet the specificneeds of the regulations, many times they are either unsuitable or not readily available for otherapplications because of inaccessibility, poor documentation, incompleteness, and lack ofstandardization with respect to format and nomenclature.

On December 21, 1989, ORR was listed on the National Priorities List (NPL), promulgatedunder Section 105 of CERCLA. In anticipation of this action, an FFA between DOE, the EPA, andthe TDHE was drafted and is now in the final stages of approval by all parties. The Agreementcalls for the development of a consolidated environmental data base for the DOE ORR facilitiesmanaged by Energy Systems. The FFA, which is a legally binding compliance arrangement,requires the integration of all data and release characterization studies on the ORR with all datagenerated by the RCRA Facility Assessments and Investigations being conducted in accordancewith corrective action requirements contained in DOE's RCRA permit for the Oak Ridge facility.Specifically, "all data and studies produced under this Agreement shall be managed and presentedin accordance with the requirements contained in a Data Management Plan to be developed by theParties . . . after the effective date of the Agreement The DOE shall maintain one consolidateddata base for the Site which includes all data/studies generated pursuant to this Agreement andthose generated under Federal and State environmental permits." Although the Agreement goes onto state that "data may [emphasis added] be maintained in electronic form provided [sic] however,that hard copies of all data/studies and related documents are made available upon request," it isprudent to maintain all applicable numeric data in electronic format.

The purposes of establishing a consolidated data base are to (1) ensure long-term retention andaccessibility of data collected at all facilities, (2) support the development of a common format forreporting environmental monitoring data, and (3) facilitate the sharing of data in support ofenvironmental compliance and restoration assessments. The data base will be developed forenvironmental data; it will not contain elements for project management, cost accounting,milestone tracking, or health effects monitoring.

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DOE sites bound by the FFA are the Oak Ridge National Laboratory (ORNL), the Oak RidgeK-25 Site, the Oak Ridge Y-12 Plant (a weapons production facility), and any projects initiated todetermine if off-site contamination originating from the ORR poses a risk to human health or theenvironment (e.g., the Clinch River RCRA Facility Investigation or CRRFI). Because the gaseousdiffusion plants located at Paducah, Kentucky, and Portsmouth, Ohio, are part of Energy Systems,the data management system to be developed may have to accommodate data from these twofacilities as well.

CURRENT DATA MANAGEMENT PRACTICES

Development of the consolidated data base cannot be accomplished without regard for howdata are currently managed at these DOE sites. Many data management systems are already inplace and each is able to meet its current needs. The challenge is to be able to integrate all of thekey data management activities into a single system. This requires an appreciation for numerousareas of concern including hardware, software, communications, data collection procedures, dataentry, variable naming conventions, chemical nomenclature, units of measurement, levels ofprecision, date and time formats, spatial coordinates, missing values, quality assurance (QA)elements, security, and data exchange formats and procedures.

There are over 200 mainframe and minicomputer systems at Energy Systems in Oak Ridge.Most of these are dedicated to specific projects or types of work and are owned and operated byparticular divisions or groups. The remainder are general-purpose systems known as the CentralComputing Systems and include a Cray computer, several IBM and IBM-compatible mainframecomputers, and numerous Digital Equipment Corporation machines. The Central ComputingSystems are operated by Energy Systems' Computing and Telecommunication Division but areavailable to a broad spectrum of users through a variety of networks.

Current data management practices at the DOE sites managed by Energy Systems vary greatly.Even though a single ORR Environmental Report is published annually, each facility conducts itsown data management Furthermore, many data types are managed independently within each site.

The Remedial Action Program (RAP) at ORNL is supported by the ORNL RAP Data andInformation Management System (DIMS). DIMS consists of three components: (1) the NumericData Base, (2) the Bibliographic Data Base, and (3) the Records Control Data Base. The NumericData Base contains data generated from site characterization studies for ORNL RAP as well as awide variety of data pertinent to, but not funded by, RAP. Such data include those originatingfrom ORNL's National Pollutant Discharge Elimination System (NPDES) permit, surface waterradiological analyses, groundwater elevation and stream flow measurements made by the U.S.Geological Survey, waste inventory records, precipitation measurements, and data from variousproject-specific investigations. The data management system being developed for the CRRFIproject is modeled after the ORNL RAP DIMS. Data bases within the Y-12 Plant, as well aswithin the K-25 Site, have been developed independently of each other, as well as from the ORNLand CRRFI data management systems. The only common data management factor among facilitieson the ORR is that most of the sites use SAS software as their primary data management andanalysis tool. Data management systems at Paducah and Portsmouth have not been investigatedyet; Portsmouth will be describing its system at this conference, and preliminary indications arethat data management at Paducah is similar to that at the Y-12 Plant and the K-25 Site.

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APPROACH

A successful data base project must begin with a comprehensive study to identify all userrequirements, all data needs, and all available resources. By its very definition, a data base is notan isolated file on a computer; it is a shared resource. All users of the resource have to beidentified in advance to the best of one's ability; then, and only then, does the building of the database begin. The building process itself does not begin with programming but with planning anddesigning activities.

A Data Management Steering Committee, consisting of a chairman, data managementrepresentatives from each facility, and outside expertise, will be established. Use of outsideexpertise will provide an independent, unbiased perspective about how the system should bedeveloped. The committee will

• define the data management needs;• identify the types and sources of data to be maintained;• characterize the hardware and software capabilities at each facility;• develop a standard data base scheme; and• set standards for the contents, quality, and documentation of the data sets.

These steps will culminate in the preparation of a Data Management Plan to be implemented byeach facility. The plan will document the actions of the committee by describing the architectureof the consolidated data base and by setting the standards by which all data management mustoccur in order to achieve an internally consistent data base. The topics that must be addressed bythe committee are presented below. Although each topic could form the basis for a separate paper,discussions here have been obviously limited.

DATA MANAGEMENT NEEDS

The user and the user's needs should be Ihe most important part of the planning process ofmost data base systems. The primary concern should be defining the needs of the application, notmaking the application requirements fit a specific software package. It is vital that the users of thedata, as well as those who will be providing data to the system, be involved in the planning stagesof this project Such necessary interaction will be initiated February 14, 1990, in a meeting amongEPA, TDHE, the Agency for Toxic Substances and Disease Registry (ATSDR), DOE, and datamanagement representatives from ORNL, the Y-12 Plant, the K-25 Site, CRRFI, Paducah, andPortsmouth. The ATSDR is required by law to conduct health assessments on all sites listed on theNPL. The intent of the meeting is to initiate discussion on some of the key issues. Final resolutionon the regulatory data needs is expected to be established in follow-up meetings. Data needswithin the individual sites will be determined by the Steering Committee. Concentrating on thedata requirements will generally lead to a comprehensive data structure that is flexible enough tosatisfy all activities and reporting requirements.

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DATA TYPES AND SOURCES

The FFA very broadly indicates the kinds of data to be maintained. It states that "alldata/studies generated pursuant to this Agreement and those under Federal and Slate environmentalpermits'1 should be maintained in a consolidated data base. I believe the intent is to include datacollected for NPDES Permits, Air Permits, and those in support of documentation prepared to meetthe requirements of RCRA and CERCLA. Specific sources of these data at each site, as well as thedetailed data management procedures currently used, will be determined by the SteeringCommittee. Categories of data include well construction parameters, groundwater hydrology andquality, geology, soils/sediments, surface water hydrology and quality, meteorology, air quality,biological monitoring, waste inventory, and demographics.

HARDWARE AND SOFTWARE

A wide variety of software and hardware is available at each site for managing, analyzing,modeling, and displaying environmental data. The hardware/software configuration needed for thisproject depends on the structure of the data base, which, of course, depends on the user's needs. Inany event, maximum use should be made of existing computing facilities, but not to the detrimentof the project. Also, everyone would probably agree that powerful computer software systems arereadily available and should be used as the foundation of the data base because data baseprogramming is expensive and time-consuming. No single software product has been designed tosatisfy all requirements of complex projects such as this. Data will come from several sources inmany different file structures, and many different software packages may be needed for analysisand display of the data. Preliminary evaluations of Geographic Information Systems available atORR indicate that a combination of ARC/INFO and CADAM software packages will be used.ARC/INFO is currently used within the ORNL RAP DIMS.

DATA MANAGEMENT FORMAT

Data structure is the logical association of one item of data to another item or to multipleitems. Although data structure is based formally on graph theory, it is strongly influenced by userexperience and software tools. The structure of the data should be problem-oriented and influencedby the type of questions to be asked of the data. In addition, it should be kept in mind thatsimplicity and flexibility should always take precedence over complexity and rigidity.

CONTENTS OF THE DATA SETS

Many standards need to be set regarding the contents of the dala sets of a consolidated database. Variable naming conventions must be agreed upon to arrive at logical, easily rememberednames and to eliminate duplications. Standardization of nomenclature is also required. Forexample, some chemical compound names have several synonyms; without consistent names, allvalues for a particular chemical could not be retrieved easily. These issues must be identified anddealt with early in the data base project to avoid problems later. Chemical Abstract Service (CAS)numbers should probably be used as the common basis for chemical names, but codes must beagreed upon for those substances without CAS numbers. Like nomenclature, a standard set of

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reportable units of measurement must be agreed upon. Without standardization, the calculation of astatistic for values having different units of measure will produce a meaningless result. One mightargue that the user could make the necessary conversions at the time of use. Then, however, theuser must continually be aware of possible inconsistencies, and it will only be a matter of timebefore a mistake is made. Other areas of concern include levels of precision in the data, date andtime formats, spatial coordinates, and how missing values are handled. All of these factors affectthe contents of the data set and need to be resolved in order to develop a consolidated data base.Collectively, these factors are considered to be attributes of a data dictionary. A data dictionary issimply the catalog of all data elements together with their meanings and formats (Bestougeff1984). Some authors advocate an active data dictionary, integrating it directly into the data base sothat all data pass through the dictionary before being incorporated into the data base. A passivedata dictionary defines the data structures residing on the data base but is not used for validation.A data management system does not require a data dictionary, but its utility becomes obvious ifone tries to work without one, especially for larger data bases in which data originate from severalsources.

DATA ENTRY

Although data entry procedures are already established for the existing data bases, it will beimportant to review how this activity is currently handled at each site. A variety of computer-controlled routines can be used to reduce errors in the data. These include required fields to ensurethat key information is not omitted, type checking to catch alphanumerics in numeric fields or thereverse, consistency checking to compare the present value against others and flag any difference,value checking to impose a controlled vocabulary, and range checking to ensure that the entry lieswithin a predefined set of values. Once data have been entered into a computer, people tend not toquestion the information, especially if they have no basis for doing so. However, errors may stillexist. For example, the values of 19 mg/L and 91 mg/L may fall within an expected range ofresults for some particular chemical. If the actual value of 19 was inadvertently entered as a 91, noone would realize the mistake without checking the value against the original data collection form.Using double data entry, a process by which the data are keyed twice by two different people andthe files compared electronically, can significantly reduce the number of data entry errors. The costof performing double data entry is usually trivial compared to the expense of obtaining a sampleand determining its chemical composition. Furthermore, use of erroneous data may lead toincorrect conclusions, which could be extremely costly.

QUALITY ASSURANCE ELEMENTS

Computers can reduce the potential for simple mistakes that accompany human actions.However, computers also reduce the association between the user and how the data are developed.This requires a greater need for the use of data quality indicators. Data validation should takeplace as near as possible to the point of collection or analysis. If the users cannot examine data asclosely as needed, someone else should. The result of that examination should be carried alongwith the distribution of the data itself. This might consist of recording the analytical levels ofpublished data (EPA 1987) along with the data values. Quality assurance encompasses all aspectsof a project and, as such, is the responsibility of everyone involved. The Steering Committee, inassociation with QA organizations for Energy Systems, will define data management's QA

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responsibilities and the need for QA data bases. It should be the responsibility of the organizationcollecting the data to ensure that such requirements are being met.

SECURITY

Security refers to the protection of the. data in the data base against unauthorized disclosure,alteration, or destruction (Date 1981). A wide variety of security procedures already exists forEnergy Systems' computer facilities. Use of security functions on the mainframe computers makesit very easy to provide protection of the data. Procedures for protection of microcomputer systemsalso exist, but they require closer scrutiny by the user to ensure that they are followed. Some ofthe computers currently used to manage compliance monitoring data on the ORR are classified"sensitive;" this will have to be considered during the architectuial design of the consolidateddata base.

TELECOMMUNICATION

Incorporation of data from Paducah and Portsmouth into the consolidated data base willrequire a consideration of telecommunications. Possible options to be evaluated by the SteeringCommittee will depend upon whether the data base will be centralized or distributed.

DATA EXCHANGE FORMATS AND PROCEDURES

Although much could be said about data exchange formats and procedures regardingdevelopment of a consolidated data base, it is difficult to comment on this topic without knowingif the data base will be centralized or distributed. Whichever it may be, output data files will berequired for a variety of needs. The use of computer networking, floppy disks, and magnetic tapesmakes the actual file transfer rather painless. However, the internal format for data storage isanything but standard. Although there always seems to be a way to translate the file into acompatible format, much time and effort are sometimes required. Certain software that will beheavily used (e.g., statistical and graphic packages) should have interfaces as an integral part of thedata base system.

DEVELOPMENT TIME

Depending on its complexity, a data base management system is difficult to change. The easewith which a telephone directory for a few hundred people can be managed by computertechnology is not indicative of the ease with which analytical results from a multitude ofenvironmental sampling programs could be managed. Extrapolation in data base management isvery nonlinear, a point which cannot be made too strongly (Gault 1984). In most cases, data basesare designed to meet a specific need; the consolidated data base may have many different needs.The complexity of software, equipment, data, documentation, and procedures may make itextremely difficult to make quick changes in the data management systems used at EnergySystems' facilities. This is especially true when data reports must continue to be produced, insome cases, on a monthly basis. Development of the Data Base Management Plan for the Energy

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Systems Environmental Data Base is expected to take about one year. The plan will include thedefinition of the system architecture. The next step will be to purchase any necessary hardware andto develop application-specific software. The system will then be implemented in a test phase toverify that the system meets the specified requirements. Following successful testing,documentation will be prepared, and system managers and users wiil be trained. The final step willbe to fully implement the system. It is not unreasonable to expect this entire process to take aslong as 3 years.

DISCUSSION

Development of the consolidated environmental data base is in the early planning stages. Thetype of system to be developed will depend on many of the factors already discussed. Theresultant system could be either (1) a centralized data base containing data from all sites or (2) adistributed data base in which subsets of the data are located at several computer sites. Acentralized data base would serve as the host for all data management activities, and data wouldhave to be managed according to the standards set forth in the Data Management Plan. A variationof the centralized data base concept would be to expand the data base system developed for ORNLRAP. This would require obtaining input files from the different DOE facilities on a regular basis.Such files, which may be in various formats and have heterogenous data characteristics (e.g.,variable names, nomenclature, units, etc.), would be loaded into the data base as temporary files.The files would then be subjected to translation programs to achieve consistency among the datafrom the various sources. Translations can be simple programs, changing the way the da»a arestored for consistency, or they may include algorithms, changing the values themselves. Thisoption would probably result in the least amount of disruption to existing operations.

The concept of a distributed data base can be divided into two options: (1) the user can accessand manipulate the individual data bases from any site as if the data were in a central location or(2) the user can only access data through a request system. In the first case, the system would beoperated as if it were a central data base, but access to the various computers would be transparentto the user. In the second case, the response to a request would have to conform to the standardsof the Data Base Management Plan. This would require the data to be subjected to varioustranslations at the time of the request. The use of translations may be simple, but it does requireconsistency in the characteristics of the source data. If the characteristics change, then thetranslation programs would have to be modified accordingly. The overall goal of a distributed database system is to support the sharing of data and yet minimize the impact on existing operations.

What will the consolidated data base look like? The organization's political and economicenvironment, the current data processing activities at each facility, and the installation time frameare all real world stresses that must be factored into the evaluation and decision. Undoubtedly,development of the consolidated data base will take advantage of Energy Systems' CentralComputing Systems. This will simplify the backup and security requirements of the system.

Whatever the data base architecture may be, it will cause some changes in the way each siteperforms its work and the resources required for that work, whether the system requires newsoftware for file management on a personal computer or a mainframe data base managementsystem supporting on-line inquiry and updating. The organization must expect changes andunderstand the human and machine impacts of the resulting data base management system. It willbe important to consider how the data base will affect current data management operations. Will

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any of the current job tasks be changed? How will the staff interact with the system? What is theirresponsibility in contributing to or reviewing the data base? What can be improved from thecurrent system? These are just a few examples of the many questions that will have to beaddressed.

The keys to the success of this task are a cooperative attitude by each data management groupinvolved and a commitment by management, in the form of staff and resources, to implement thestandards developed by the Data Management Steering Committee. It is hoped that the EnergySystems Environmental Data Base and the experiences gained in its development can serve as amodel for consolidating similar types of data at other DOE facilities.

LITERATURE CITED

Bestougeff, H. 1984. "Designing the Database Management System," pp. 119-66 in DatabaseManagement in Science and Technology, ed. J. R. Rumble, Jr., and V. E. Hampel, ElsevierSci. Publishers B.V. (North Holland), Amsterdam.

Date, C. J. 1981. An Introduction to Database Systems, 3d ed., Addison Wesley, Reading, Mass.

Gault, F. D. 1984. "Database Management Systems for Science and Technology," pp. 39-73 inDatabase Management in Science and Technology, ed J. R. Rumble, Jr., and V. E. Hampel,Elsevier Sci. Publishers B.V. (North Holland), Amsterdam.

U.S. Environmental Protection Agency (EPA) 1987. Data Quality Objectives for RemedialResponse Activities, EPA 540/G-87/003.

DATA EVALUATION TECHNIQUES USED FOR GROUNDWATERQUALITY ASSESSMENT AT THE FEED MATERIALS

PRODUCTION CENTER

J. E. HarmonP. K. Longmire

Westinghouse Materials Company of Ohio*P.O. Box 398704

Cincinnati, Ohio 45239-8704

ABSTRACT

The Feed Materials Production Center (FMPC) is an industrial facility owned andoperated by the U.S. Department of Energy under a management contract withWestinghouse Materials Company of Ohio. The FMPC is located in a rural area ofsouthwestern Ohio approximately 20 miles northwest of Cincinnati and 8 milessouthwest of Hamilton. It has been the primary function of the FMPC to producemetallic uranium compounds for use in U.S. defense programs. In addition, smallamounts of thorium have been processed at this facility. As a result of these processes,the FMPC has generated both radioactive and nonradioactive wastes.

The FMPC has implemented a monitoring program, which includes over 300 wellsand piezometers, to assess the impact of its operations on groundwater. Large volumesof monitoring data are being collected in support of a Remedial Investigation andFeasibility Study, a Resource Conservation and Recovery Act groundwater qualityassessment program, and underground storage tank investigations. The data evaluationgoals of this program include establishing background or upgradient constituentconcentrations, identifying the presence and amount of contamination, determining therate and extent of migration of any contamination discovered, developing andcalibrating hydrologic and solute transport groundwater models, and tracking theprogress of cleanup activities.

This paper will address the methodologies employed for groundwater dataevaluation and the use of various techniques to handle data exhibiting features such asless than detection limit values and/or seasonal fluctuations. Methods used fordetermining whether the data fits a normal distribution and for normalizing data thatdoes not conform to normal distribution criteria will be presented. Approaches fordetermining statistical significance of concentration values in downgradient wellscompared to upgradient wells using maximum concentration limits will be addressed.Statistical techniques to be addressed include the student's t-test, analysis of variance,tolerance limits, prediction and confidence intervals, and test of proportions. Adiscussion will be provided on the decision-making process used for selecting theappropriate statistical procedures and also the progress made toward achieving the dataevaluation goals of the groundwater monitoring program.

'Operated for the U.S. Department of Energy under contract DE-AC05-86OR21600.

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TARGET TRANSFORMATION FACTOR ANALYSIS:A DATA REDUCTION TECHNIQUE TO IDENTIFY

GEOCHEMICAL PROCESSES AND DATA DEFICIENCIES

Richard W. AmsethScience Applications International Corporation


ABSTRACT

A true assessment of environmental impact requires an appreciation of the effectsof natural processes that may mitigate or magnify the impact. In the case ofgroundwater quality, natural interactions between contaminants and soil/aquifer solidscan restrict (e.g., sorption, exchange, and precipitation reactions) or enhance (e.g.,complex formation and colloid transport) the mobility of contaminants.

Target transformation factor analysis (TTFA) is an exploratory data analysistechnique that has been used to identify and apportion air pollutant sources atindividual monitoring points. TTFA may be applied to groundwater quality data basesto identify the number, chemical profile, and relative contribution of sources in amonitoring well network. However, TTFA is more likely to be useful in identifyingmonitoring points where simple, multisource mixing cannot explain the data.

TTFA was tested on groundwater quality data from a Resource Conservation andRecovery Act (RCRA) monitoring network with one contaminant source. In thissimple case, TTFA was unable to separate background and contaminant sources ateach monitoring well. Total sulfate data were especially difficult to reproduce.

The apparently anomalous behavior of sulfatc was ascribed to a combination ofspecific adsorption on iron oxide grain coatings and variable amounts of sediment inwater samples. Tests of this hypothesis were limited by deficiencies in RCRA-mandated analytes and sample-handling procedures. Major cations and anions are notreported in the filtered sample. Aluminum, silicon, calcium, magnesium, and alkalinity,as indicators of sediment content, are not reported in either analysis. In general, theresults in the data base are insufficient as input to any geochemical model. It seemsclear that regulations-driven water quality data bases are likely to be of limited valuefor geochemical process identification and, thereby, for environmental impactassessment.

INTRODUCTION

Regulatory mandates have generated a period of unprecedented growth in geochemical databases, especially those containing water composition data from groundwater monitoring wells.However, efforts to extract information on geochemical processes relevant to environmental impact

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assessment (e.g., transport phenomena) from these data have not kept pace with the growth of thedata bases. From the standpoint of a geochemist, the data are often incomplete, making it difficultto identify important processes. Furthermore, the size of the growing data bases is a formidableobstacle. Seeking to identify relationships by plotting one variable against another quicklygenerates more information than an analyst can display and digest. Despite this, a simple processsuch as plume mixing and subsequent source identification may be discerned through applicationsof appropriate variable reduction and pattern recognition techniques during initial rapid screeningof the data.

The intent of this research was to examine one screening technique, TTFA, which combinesthe variable reduction techniques of factor analysis with the ability to test simple geochemicalhypotheses.

Applications of factor analysis in studies of groundwater quality must assume, as a firstapproximation, that the chemical composition of each water sample is the result of linear mixingamong two or more discrete water types or sources. Geochemists have been concerned for sometime about processes occurring during the mixing of two or more natural waters. A number ofpossible mechanisms for mixing waters in surface and subsurface environments have beenreviewed (Runnels 1969). Recent identification of plume mixing and the possibility of facilitatedtransport of contaminants in groundwater via cosolvent or colloid processes has refocused attentionon mixing processes. Deviations from linear mixing behavior arise when solutes sorb or desorb,minerals either precipitate from or dissolve into the mixed water, or when dissolved gases areremoved from the mix. Despite this, the conservative behavior of many naturally occurring

. groundwater constituents makes the assumption of linear behavior of concentration data a goodfirst approximation and allows factor analysis to proceed. Detailed analysis of deviations fromlinear behavior can be addressed during construction and interpretation of source profiles whengeochemical insight is brought to bear.

In the simplest mixing case studied there are two sources: one source is the uncontaminatedgroundwater and the other is contaminated water (e.g., seeping from a retention ba«in). The task ofTTFA is to identify the number of discrete sources that best explain the variation in thegeochemical data and assign a source profile to each. Once the source profiles have been assigned,the relative contribution of each source to each sampling well can be calculated.


TTFA

In factor analysis, any data point, dik, can be represented by

where there are n sources (ignoring random errors) contributing to the value of d. The value of aparticular element in source j=\ may be rn with the relative contribution from source 1 being cn.Thus, every groundwater constituent is modeled as being the linear sum of contributions from end

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member sources. In addition, each source will have a distinctive elemental profile. Equation (1)can be expressed in matrix notation as

[D] = [R][C] , (2)

where D is the data matrix, and R and C are the matrices of the source profiles and weightings,respectively.

The objectives of factor analysis are (1) to determine of number of independent sources thatexplain the data, (2) to identify each of the source profiles, and (3) to determine the relativecontribution of each source to each sampling site. The process of factor analysis starts with thecalculation of a correlation or covariance matrix from the data matrix. Subsequent matrixmanipulation yields eigenvectors that are used individually and in combinations to reconstruct thedata matrix. Once the data matrix can be reconstructed within experimental error, thoseeigenvectors that account for the most variation in the data are retained as factors. This procedureis referred to as abstract factor analysis because the factors are mathematical constructions thatreproduce the data but do not necessarily have any physical meaning (Malinowski and Howery1980). In keeping with our objectives, the abstract factors and weightings must be transformed tophysically meaningful source profiles and relative contributions. This is represented in matrixnotation as

, (3)

where T is an appropriate transformation matrix that yields a physically significant solution:

Selection of an appropriate transformation matrix requires some understanding of the systembeyond that necessary to interpret abstract factors. Target transformation requires the sameknowledge but provides a mechanism for building and testing the transformation matrix.

The target transformation technique provides a method for building the appropriatetransformation matrix, [T], one eigenvector at a time. Using the notation in the targettransformation program FANTASIA (Hopke et al. 1983), X is the data matrix, A and F correspondto R and C, and Eq. (3) is rewritten as

[X] = [A][R][R-'][F] . (5)

Individual columns, r, in the transformation matrix, R, are obtained by a least-squares fit to apossible source elemental profile (test vector), b, using the equation

r = {([A'][W][A])1}[A'][W]& , (6)

where W is a diagonal matrix of weights that may be used in a least-squares fit.Target testing involves making an initial estimate about the elemental source profile, entering

it as b, calculating the rotation vector, r, and calculating a new test vector, b'. This iterativemethod continues while checking the error observed between successive pairs of b and b'. Eachiteration refines the initial estimate, and a transformation matrix can be built of vectors thatcorrespond to a physically meaningful model.

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FANTASIA

The TTFA software used in this study was FANTASIA (Factor ANalysis To ApportionSources In Aerosols), developed and described by Hopke and co-workers (Hopke et al. 1976;Hopke 1980; Alpert and Hopke 1981; Roscoe and Hopke 1981; Hopke et al. 1983).

GEOCHEMICAL DATA

The geochemical data come from quarterly sampling of shallow RCRA welis located in themain plant area (3500 area) of the Oak Ridge National Laboratory (ORNL) and the High FluxIsotope Reactor and Transuranium Processing Facility (TRU) area in Bethel Valley (7900 area).The wells surround flow stabilization and process waste receiving basins and include upgradientand downgradient wells with depths, measured from the top of the casing to the bottom of thewell, ranging from 3.85 m (12.6 ft) to 8.84 m (29 ft). The types of wastes typically dischargedinto the basins have been summarized (Taylor 1986).

Wells surrounding the basins were sampled quarterly to establish a baseline. Thereafter, thewells were sampled quarterly or annually. Several new wells were added after the second round ofsampling. Several of the sampling periods resulted in incomplete data, and only four sets ofconsistent and complete data were available for the wells.

The data used in this study are maintained as part of the Data and Information ManagementSystem (DIMS) that was developed for the Remedial Action Program at ORNL to provide acentralized storage location for data pertinent to long-term remediation of contaminated sites.Details of the data bases in DIMS, and their contents and organization have been published(Voorhees et al. 1988). The RCRA data sets are stored in a SAS library and are divided intoannual FIELD, DIS, and TOT data sets. The FIELD data sets include geochemical data taken atthe time of sampling (e.g., specific conductance, pH, and water temperature). The DIS data setscontain the analytical results on filtered groundwater samples, and the TOT data sets containsimilar results on unfiltered samples. The groundwater parameters stored in the data sets are listedin Table 1. The TOT data set was chosen to test TTFA because of the broader range of reportedspecies.

DATA PREPARATION

Data preparation or preprocessing involved compensating for the presence of below-detection-limit data and occasional missing values in the data sets.

Many of the trace metals and organics (e.g., Ag, Hg, Pb, endrin, and lindane) are alwaysreported as below-detection-limit, and those variables were eliminated from further processing. Forthose species where occasional measurable values were obtained (e.g., NO3), less than values werereplaced with values randomly selected from a normal distribution with a mean of one-half thedetection limit.

Nearly all species had a few missing values that, left untreated, resulted in the entire samplebeing ignored by the factor analysis procedure. Alternative means of handling missing values, forexample, replacing them with the mean of the non-missing values or replacing them with arandomly selected value from a distribution characteristic of the non-missing value distribution,were examined. Neither alternative affected the results of abstract factor analysis.

75

Table 1. Geochemical species and parameters in RCRA data sets

Total

AgAsBaCd

CT

FeHgMnNaPbSeTOC"

aFFecal Coli

NO,

PhenolsSO4

TOX"

Gross alpha

Gross betaTotal Ra

2,4,5-TP silvex

2,4-D

EndrinLindane

Methoxychlor

Toxaphene

Dissolved

AgAsBaCd

CT

FeHgMnNaPbSe

Field

Temperature

Specific condition

PH

Total organic carbon.'Total organic halides.

RESULTS

The results of principal factor analysis are shown in Table 2. Two principal factors werechosen for their ability to explain 87% of the data variance and their potential as physicallysignificant factors. After orthogonal varimax rotation, the first factor is dominated by Mn, Fe, andtotal organic carbon (TOC) whereas the second factor is composed of SO/", Beta, and NO3".

The second factor, with its emphasis on acid anions and radioactivity, is probably related towaste coming from some of the basins. The first factor is less easily interpreted but may representnatural groundwater concentrations of dissolved Fe and Mn.

76

Table 2. Abstract factor pattern after varimaxorthogonal rotation

MnFeTOCClNO,SO4

BetaAlpha% Var."

Factor 1

0.8150.8310.732

-0.336-0.200-0.1390.065

-0.06057.3

Factor 2

-0.177-0.1220.082

0.1450.5820.7760.5550.280

29.7

"Total organic carbon.'Percent of the data variance explained by the factor.

TARGET FACTORS

Two target factors were chosen to represent the simple mixing of two sources, backgroundgroundwater and waste seeping from the basins, contributing to each well sample analysis.

Table 3 illustrates the chemical data (Olsen et al. 1983) used to construct the target factors.Under conditions of normal operaUons, wastes leaking from the basins would have an elementalprofile characterized by acid anions and sodium. The waste profile is based on the composition ofORNL intermediate-level waste discharges to seepage pits near Solid Waste Storage Area 4. Thebackground groundwater profile (designated soil water in Table 3) is from a shallow well inweathered material overlying Conasauga Group inlcrbcddcd mudstones, shales, and limestones.Rocks underlying the Main Plant are a similar mix of interbedded limestones and siltstones of theChickamauga Group (Lee and Ketelle 1988).

FANTASIA RESULTS

A sample of the results of data reproduction with the target factors is shown in Table 4 andcompared with the original data from one well. The reproduction is poor, especially when inputvalues in the test vectors for a chemical are either zero or differ by several orders of magnitude.

Several attempts were made to improve the data reproduction by adding and removing speciesfrom the test vectors. In particular, the sodium value in the soil water test vector was reevaluated.Reexamination of the data used to construct the soil water test vector (Olsen et al. 1983) indicatedthat their background sample was probably contaminated by seepage from nearby trenches. Despitechanges in the test vector composition, data reproduction was never satisfactory. The difficultiesencountered suggest that either the original assumption of two sources was in error or thatuncontrolled or unknown errors in the data were sufficiently large to account for the problems.

77

Table 3. Target factors used as input with FANTASIA

„ . S ° U , . Waste factor*Species water factor

(ug/mL)

ciN O j

so4FeMnNa

ToeAlpha*Beta'

7.00.00.00.0500.028

200.00.00.00.0

0.04760.01280.0

0.50.0

4350.0

"Groundwater composition from Well T7-3, nearIntermediate Level Waste seepage trench 7 (Olsen et al. 1983,Table 8).

'Composition of ORNL intermediate-level wastedischarged to the seepage pits and trenches (Olsen et al. 1983,Table 1).

Total organic carbon.''Gross alpha and gross beta units are pCi/L.

Table 4. Original RCRA data andTTFA reproduction

ClNO,SO4

FeMnAlphaBetaTOC

Original

4.8002.473

110.00.540.300.191.601.60

Reproduction

4.80529.898.040.040.020.000.000.00

Total organic carbon.

78

DISCUSSION

Transport of dissolved constituents in groundwater and mixing of contaminant plumes arecomplicated by a host of possible geochemical processes. Reactions among dissolved constituentsand between dissolved species and aquifer solids can retard or facilitate transport of contaminants.Sorption of contaminants onto sediment grains can retard migration; however, sorption ofcontaminants onto colloidal solids formed in a plume can effectively facilitate their transport.

Despite these and other complications, some dissolved species behave in a conservativemanner in some subsurface environments and not in others. Anions like the halidcs and nitrate areuseful in environments where anion exchange capacity is negligible. Dissolved silica, which iselectrically neutral, may be a useful tracer of some processes in many environments. Given anydistinctive geochemical environment in an aquifer, other dissolved species may act as conservativetracers.

Even in the absence of conservative tracers, geochemical processes may be discerned ifappropriate dissolved chemical species arc reported (e.g., major cations and anions, alkalinity, Fe,Mn, Si, and Al) and the aquifer solids are described. The key to valuable data is the completenessof the dissolved species concentrations.

In this application of FANTASIA to data from RCRA wells, the program was unable toreproduce the original data by using two source terms. The most troubling aspect of this exercisewas the inability to reproduce the sulfate data. Whereas sulfate is a major anion and is not difficultto analyze, it is also selectively removed from soil solutions and sorbed onto iron oxide surfaces.Early in this study the TOT data set was chosen for examination because it contained the widestrange of analyzed chemical species. However, the samples for the TOT data set lump together twopossible source terms, truly dissolved species and species that were originally part of a solidparticle in the unfiltered sample and dissolved during a pre-analysis acid digestion.

The nature of trie local soils and the underlying geologic formations suggests that any acid-digested particulates would contribute dissolved Ca, Mg, Si, Fe, and Mn. In addition, the digestionstep would release any selectively sorbed SO4

2". Without information on the concentration ofsuspended solids or some other partick-derived constituent, it is difficult to separate the effect ofthis potential source from the analysis. It is possible that an unknown amount of variation in thedata is introduced by the inclusion of particulates contributing to the chemical analyses in eachsample. Furthermore, this variation may underlie the difficulties in data reproduction, especially forthose constituents strongly associated with both the hypothesized waste vector and the particulates,that is, SO4

2 .Thus identifying geochemical processes that are important in contaminant transport can be

difficult when the data are limited to those species mandated by regulation and data for majorcations and anions are not available. A true picture of groundwater transport processes andsubsequent environmental impact requires analysis of more complete geochemical data. Inaddition, if unfiltered samples are analyzed, the same suite of analyses should be performed onfiltered samples or, at a minimum, the amount of suspended sediment in the sample should bereported.

The failure of FANTASIA with this data base should not be construed as a failure of theprogram in groundwater applications. In its failure it may have identified the reason for failure(i.e., an association between SO4

2 and particulates in the samples). Another outgrowth of theapplication is a new appreciation for the level of chemical and geochemical iasight necessary toretrieve useful information from factor analysis in general and target transformation factor analysisin particular, especially when there are data limitations. Until more complete groundwaler data

79

bases are analyzed, the applicability of FANTASIA to chemical pattern recognition and sourceapportionment in groundwater problems remains an open question.

LITERATURE CITED

Alpert, D. J., and Hopke, P. K. 1981. "A Determination of the Sources of Airborne ParticlesCollected During the Regional Air Pollution Study," Atmos. Environ. 15(5), 675-87.

Hopke, P. K. 1980. "Source Identification and Resolution Through Application of Factor andOuster Analysis." Ann. N.Y. Acad. Sci. 383, 103-15.

Hopke, P. K., Alpert, D. J., and Roscoe, B. A. 1983. "FANTASIA—A Program for TargetTransformation Factor Analysis to Apportion Sources in Environmental Samples," Comput.Chem. 7(3), 149-55.

Hopke, P. K., et al. 1976. "The Use of Multivariate Analysis to Identify Sources of SelectedElements in the Boston Urban Aerosol," Atmos. Environ. 10, 1015-25.

Lee, R. R., and Ketelle, R. H. 1988. Subsurface Geology of the Chickamauga Group at Oak RidgeNation Laboratory. ORNL/TM-10749, Martin Marietta Energy Systems, Oak Ridge Nad. Lab.

Malinowski, E. R., and Howery, D. G. 1980. Factor Analysis in Chemistry, Wiley, New York.

Olsen, C. R., et al. 1983. Chemical, Geological, and Hydrological Factors GoverningRadionuclide Migration from a Formerly Used Seepage Trench: A Field Study,ORNL/TM-8839, Union Carbide Corp. Nuclear Div., Oak Ridge Natl. Lab.

Roscoe, B. A., and Hopke, P. K. 1981. "Comparison of Weighted and Unweighted TargetTransformation Rotations in Factor Analysis," Comput. Chem. 5, 1-7.

Runnels, D. D. 1969. "Diagenesis, Chemical Sediments and the Mixing of Natural Waters,"J. Sediment. Petrol. 39, 1188-1201.

Taylor, F. G. 1986. Inventory ofORNL Remedial Action Sites: 3. Process Ponds,ORNL/RAP/LTR-86/14, Martin Marietta Energy Systems, Oak Ridge Natl. Lab.

Voorhees, L. D., et al. 1988. Data Base Management Activities for the Remedial Action Programat ORNL: Calendar Year 1987, ORNL/TM-10694, Martin Marietta Energy Systems, OakRidge Natl. Lab.

CO-OCCURRENCE PATTERNS OF POLYCYCLIC AROMATICHYDROCARBONS IN SOILS AT HAZARDOUS WASTE SITES*

William P. Eckel and Thomas A. JacobViar and Company

300 North Lee StreetAlexandria, Virginia 22314

Joan F. FiskU.S. Environmental Protection Agency

401 M Street, S.W.Washington, D.C. 20460

ABSTRACT

This paper examines the co-occurrence of 18 Polycyclic Aromatic Hydrocarbons(PAHs) in the analytical results for 5928 environmental soil samples taken from theU.S. Environmental Protection Agency (EPA) Superfund Contract Laboratory Program(CLP) Analytical Results Data Base (CARD). The criterion for selection of soilsamples was the occurrence of at least 1 of the 18 PAHs currently on the CLP TargetCompound List (TCL). Occurrence rates for the 18 PAHs ranged from 14.2%(dibenz(a,h)anthracene) to 79.0% (pyrene). In over one-half of the 5928 samples, 6 ofthe 18 compounds occurred. The rates of co-occurrence of PAH pairs, calculated aspercentage of the total number of samples, ranged from 7.3% [dibenz(a,h)anlhracenewith acenaphthylene] to 70.7% (pyrene with fluoranthene). Combinations of the 6compounds whose occurrence exceeded 50% also exhibited co-occurrence rates over50%. Candidate "indicator compounds" for the contamination of soils by PAHs wereidentified by the use of a conditional probability matrix. Co-occurrences of compoundpairs were expressed as percentages of the number of occurrences of each compound.Three indicator compounds were identified as having high (>90%) conditionalprobabilities of occurring in samples containing any of the other 17 PAHs. Norelationship between co-occurrence and correlation coefficients for concentration datawas found. Factor analysis on the concentration data revealed that two principalcomponents accounted for 92% of the variance. Thirteen of the compounds werestrongly loaded on the first principal component and five compounds on the second.The second principal component represented compounds with three rings, and the firstprincipal component represented compoiinds with from two to four or more rings.

'This work was conducted under U.S. Environmental Protection Agency contract no. 68-D9-O135; however, it does notnecessarily reflect the views of the agency.

81

82

INTRODUCTION

PAHs are the most frequently detected compound class in soils and sediments at hazardouswaste sites (Department of Health and Human Services and Environmental Protection Agency1988). Many PAHs are known or suspected to be carcinogenic. However, the health effects of allPAHs are not fully understood (Agency for Toxic Substances and Disease Registry 1987a-1987e).Compounding this problem is the fact that PAHs tend to occur in environmental samples asmixtures, typically at such sites as city gas plants and creosoting facilities. Thus, when health risksare assessed at a PAH-contaminated site, the concentrations and relative toxiciaes of several PAHcompounds and possible synergistic or antagonistic effects must be considered to arrive at anoverall assessment of risk. (The Toxics Integration and Analytical Operations Branches of theSuperfund Program are currently engaged in a research project to determine the occurrence ofpotentially carcinogenic PAH compounds at Superfund sites. This project includes the analysis of17 PAHs currently not on the CLP's TCL.)

The purpose of this paper is to define the degree of co-occurrence among PAH compounds insoils at hazardous waste sites. Once the patterns of co-occurrence are known, it will be possible tobetter direct research on the toxicity of complex PAH mixtures and to select indicator compoundsfor PAH contamination in soils.

APPROACH

The data described in this paper were taken from the EPA Superfund CLP CARDpreproduction version. The CARD data base contains the analytical and quality control resultssubmitted to EPA by CLP laboratories on floppy diskettes. A program was written in SAS (SASInstitute 1985a) to extract the results for 5928 soil samples in which at least 1 of the 18 PAHscurrently on the CLP's TCL, including dibenzofuran, occurred (Table 1). To the maximum extentpossible, data for samples other than actual environmental soil samples were deleted from the dataset. (Before cleanup, the data set contained 8235 samples. The majority eliminated were matrixspike and matrix spike duplicate analyses). The concentration d?ta were then processed through theSAS procedure correlation (CORR) to produce an 18 x 18 matrix of Pearson product momentcorrelation coefficients and numbers of co-occurrences of compound pairs. Tables 2 and 3 werecomputed by hand from the CORR procedure output.

The concentration data were also processed through the SAS procedures factor (FACTOR) andprincipal components (PRINCOMP) analysis to detect any patterns that were not apparent from theCORR procedure output.

DISCUSSION

Table 1 gives the absolute and percent occurrences of the 18 PAH compounds in the data set,with descriptive statistics for the concentration data. The coding system of A through R is used forbrevity in all tables in this paper.

Percent occurrence ranged from 14.2% [Q, dibenz(a,h)anthracene] to 79.0% (J, pyrene). Thesehigh occurrence rates are due to the restriction of the data set to only samples containing PAHs.

83

Table 1. Occurrence and concentration of 18 PAHs in 5928 samplesfrom the CLP analytical results data base"

Code*

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

Compound

naphthalene

2-methylnaphthalene

acenaphthylene

acenaphthene

dibenzofuran

fluorene

phenanthrene

anthracene

fluoranthene

pyrene

benzo(a)anthracene

chrysene

benzo(b)fluoranthene

benzo(k)fluoranthene

benzo(a)pyrene

indeno( 123 -cd)pyrene

dibenz(a,h)anthracene

benzo(g,h,i)peiylene

2205

2089

928

1266

1272

1452

4185

2053

4410

4683

3115

3761

3160

2506

2907

1847

842

1797

Percentoccurrence

37.2

35.2

15.7

21.4

21.5

24.5

70.6

34.6

74.4

79.0

52.5

63.4

53.3

42.3

49.0

31.2

14.2

30.3

GM'

780

840

450

870

600

1000

750

710

770

720

730

740

830

800

730

650

500

690

Grr

17.7

16.8

12.0

21.8

18.3

20.4

12.4

15.3

11.3

10.9

9.6

9.0

8.4

8.9

7.8

6.5

5.8

6.4

"CARD preproduction version as of 1/11/90.^Compounds are listed in order of elution from a DB-5 GC column.'Number of occurrences.'Geometric mean, ppb, not corrected for nondetected samples.'Geometric deviation (unitless) not corrected for nondetected samples.

The geometric mean concentrations (of the samples reported as positive) were in the range ofseveral hundred parts per billion, with very wide ranges as indicated by the geometric deviations.

Table 2 is a matrix that represents the co-occurrence of compound pairs as a percentage of thetotal number of samples in the data set (5928). This may be considered the "absolute"co-occurrence. The co-occurrence is as high as 70.7% on this basis (compounds I and J,fluoranthene and pyrene). A number of compound pairs show absolute co-occurrence over 50%,including combinations of compounds G, I, J, K, L, and M [phenanthrene, fluoranthene, pyrene,benzo(a)anthracene, chrysene, and benzo(b)fluoranthene]. Naturally, these are all compoundswhose occurrence exceeded 50%.

Table 3 is a matrix of "conditional probabilities." It expresses the co-occurrence of compoundpairs as a percentage of the number of occurrences of the compound listed at the top of the matrix.For example, at the extreme top left of the matrix, compounds A and B occurred together in

Table 2. Co-occurrence of compound pairs as percent of total samples (N=5928); compound codes as in Table 1

A

B

C

D

E

F

G

H

I

J

K

L

M

N

0

P

Q

R

A

—

28.9

11.1

15.2

17.8

18.0

29.9

19.6

26.1

27.1

21.8

24.6

20.7

16.9

20.5

14.6

9.0

14.3

A

B

—

10.1

14.2

17.0

16.9

29.4

17.8

24.9

25.8

20.1

23.3

19.3

15.3

18.9

12.8

7.7

12.7

B

C

—

9.6

9.6

10.9

14.8

13.5

15.1

14.7

14.0

14.7

13.6

11.6

14.1

11.0

7.3

11.2

C

D

—

15.4

18.1

20.1

18.2

19.6

20.2

17.9

18.7

17.1

15.1

17.1

13.3

8.8

13.1

D

E

—

16.9

20.5

17.7

20.0

20.0

18.2

19.2

17.3

14.5

17.2

12.8

8.4

12.6

E

F

—

23.4

20.6

22.2

22.5

20.1

20.9

19.3

16.6

19.2

14.7

9.6

14.3

F

G

—

32.6

60.7

61.8

47.7

54.5

46.5

37.3

44.2

29.4

13.6

28.1

G

H

—

32.9

33.1

30.6

31.7

29.2

25.4

29.4

22.4

12.8

21.6

H

I

—

70.7

50.8

58.4

50.6

40.5

46.6

30.5

13.8

29.4

I

J

—

51.0

59.7

51.2

40.5

47.0

30.5

13.8

29.2

JJ

K

—

50.2

45.1

36.9

43.6

29.6

13.8

28.2

K

L

—

49.3

39.8

46.5

30.1

13.7

29.2

L

M

—

39.1

44.6

29.5

13.7

28.2

M

N

—

36.7

25.0

12.2

23.9

N

O P Q

—

30.3 —

14.0 13.7 —

29.0 27.5 13.5

O P Q

R

—

R

Table 3. Conditional probability matrix. Percentages of occurrences of compounds on abscissa (horizontal) representedby co-occurrence with compounds on ordinate (vertical)

D H I J M N O R

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

—

77.8

29.9

41.0

47.8

48.3

80.5

52.7

70.2

72.8

58.5

66.0

55.6

45.4

55.1

39.1

24.1

38.5

82.1

—

28.6

40.3

48.3

48.1

83.3

50.5

70.7

73.3

56.9

65.8

54.7

43.5

53.5

36.3

21.8

36.0

71.1

64.3

—

61.1

61.3

69.5

94.5

86.1

96.2

94.0

89.2

94.2

86.7

74.0

90.1

70.5

46.4

71.3

71.4

66.5

44.7

—

72.2

84.7

94.1

85.2

91.7

94.4

83.7

87.4

79.9

70.9

79.9

62.1

41.3

61.3

82.9

79.3

44.7

71.9

—

78.9

95.4

82.6

93.1

93.2

84.7

89.3

80.4

67.6

80.1

59.7

39.2

58.7

73.3

69.1

44.4

74.0

69.1

—

95.6

84.0

90.6

91.9

82.1

85.4

78.9

68.0

78.6

60.1

39.0

58.5

42.4

41.6

21.0

28.5

29.0

33.2

—

46.1

86.0

87.6

67.6

77.2

65.8

52.9

62.7

41.6

19.2

39.8

56.6

51.3

38.9

52.6

51.2

59.4

94.1

—

95.0

95.7

88.4

91.5

84.4

73.2

84.9

64.6

37.0

62.3

35.1 34.3 41.4

33.5 32.7 38.2

20.2 18.6 26.6

26.3 25.5 34.0

26.8 25.3 34.6

29.8 28.5 38.3

81.7 78.3 90.8

44.2 42.0 58.3

— 89.5 96.6

95.0 — 97.1

68.2 64.6 —

78.4 75.6 95.6

68.0 64.8 85.8

54.4 51.3 70.2

62.6 59.5 83.0

41.0 38.6 56.4

18.6 17.4 26.2

39.5 37.0 53.7

38.7

36.5

23.2

29.4

30.2

33.0

85.9

49.9

92.0

94.1

79.2

—

77.7

62.8

73.2

47.5

21.7

55.6

38.8

36.1

25.5

32.0

32.4

36.2

87.2

54.8

94.9

96.0

84.6

92.4

—

73.3

83.6

55.3

25.8

52.9

39.9

36.3

27.4

35.8

34.3

39.4

88.3

60.0

95.8

95.8

87.3

94.3

92.4

—

86.9

59.3

28.8

56.5

41.8

38.5

28.8

34.8

35.1

39.2

90.2

60.0

95.0

95.9

89.0

94.7

90.9

74.9

—

61.9

28.7

59.1

46.7

41.1

35.4

42.6

41.1

47.3

94.2

71.8

97.8

97.8

95.1

96.8

94.6

80.4

97.4

—

44.1

88.4

63.1

54.2

51.2

62.1

59.1

67.3

95.5

90.1

97.4

96.9

96.9

96.8

96.8

85.7

98.9

96.8

—

95.4

47.2

41.8

36.8

43.2

41.6

47.2

92.8

71.2

97.1

96.4

93.0

96.4

93.0

78.8

95.6

90.9

44.7

_

00

D H I J M N O

86

82.1% of the samples containing compound B and in 77.8% of the samples containingcompound A.

Table 3 may be read in two ways. The numbers listed vertically under a compound code letterindicate the percentage of occurrences of that compound in which the 17 other individualcompounds were also found. For example, in column H, we see that compound A occurred in56.6% of the samples containing compound H, and compound I occurred in 95.0% of the samplescontaining compound H. Reading Table 3 vertically enables us to answer the question, "Which ofthe 17 other compounds is most indicative of the presence of the compound of interest?" Thus,the compound most often found in all of the samples containing compound F (fluorene) wascompound G (phenanthrene), with a conditional probability of 95.6%. This means that thepresence of phenanthrene in a sample was most likely to also indicate the presence of fluorene.

Reading Table 3 horizontally enables us to answer the question "Which one of the18 compounds best indicates the presence of the other 17?" This is answered simply by observingwhich row has the highest conditional probabilities. Compound J (pyrene) has the highestconditional probabilities for 8 compounds (D, G, H, I, K, L, M, and O). Compound I(fluoranthene) has higher conditional probabilities for compounds C, Q, and R [acenaphthylene,dibenz(a,h)anthracene, and benzo(g,h,i)perylene]. Compounds N and P are equally well predictedby compounds 1 and J. Compound G (phenanthrene) has the highest conditional probabilities forfour compounds (A, B, E, and F), although for A and B, the values are only 80.5% and 83.3%.Compound O [benzo(a)pyrenej best predicts compound Q (dibenz(a,h)anthracenej at 98.9%. Otherthan for compounds A, B, G, and J, the highest conditional probabilities are at least 94%.

Table 4 gives Pearson correlation coefficients (r) for the concentration data for selectedcompound pairs with high, medium, and low absolute co-occurrence. Inspection of the tabledemonstrates that there is no apparent relationship between co-occurrence and concentration. Wedid not expect any such relationship a priori.

Table 4. Comparison of co-occurrence with Pearsoncorrelation coefficients (r) for concentrations (ppb)

Compound codes

IGGJIG

EDE

BD

GHAA

BB

JJILLL

FGD

FK

NMIB

Qc

% co-occurrence

70.761.860.759.758.454.5

16.920.115.4

16.917.9

37.329.226.128.9

7.710.1

r

0.960.230.310.900.840.23

0.99970.980.9999

0.090.07

0.170.390.430.79

0.840.37

87

Table 5 gives the results of principal component analysis on ihe concentration data using theSAS procedure FACTOR (SAS Institute 1985b) with "VARIMAX" rotation. The first twoprincipal components accounted for just over 92% of the variance in the concentration data(factor 1, 64.6%; factor 2, 27.7%). As can be seen in Table 5, factor 1 has high loadings forcompounds A and B and H through R (13 compounds) while factor 2 has high loadings forcompounds C through G (5 compounds). Compounds H (anthracene) and I (fluoranthene) havesomewhat lower loadings on factor 1 (-0.7) than the other 11 compounds (0.88-0.99) and mediumloadings on factor 2 (0.47-0.57). It appears that factor 2 represents the 3-ring compounds, exceptanthracene, while factor 1 represents the PAHs with two rings (A and B) and four or more rings.

The most frequently detected PAH compounds (G, I, and J) have the highest degree ofco-occurrence and the highest conditional probabilities for the occurrence of all but one of the18 PAHs. This is demonstrated in Tables 1, 2, and 3, respectively. Thus, it appears that these threecompounds—phenanthrene, fluoranthene, and pyrene—are good candidates to be indicatorcompounds for contamination of soils by PAH mixtures. Compounds K, L, and M, which occur inover 50% of the 5928 samples, might also be added to the core group of 3 indicator compounds.

Table 5. Principal components analysis: factor loadings

Compoundcode

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

Rings

2

2

3

3

3

3

3

3

4

4

4

4

5

5

5

6

5

6

Factor 1

0.91

0.92

0.26

0.005

0.001

0.016

0.057

0.75

0.72

0.97

0.97

0.97

0.88

0.95

0.99

0.98

0.94

0.97

Loadings

Factor 2

0.17

0.07

0.94

0.998

0.998

0.998

0.997

0.47

0.57

0.12

0.12

0.09

0.06

0.06

0.04

0.02

-O.004

0.003

88

The co-occurrence figures in Table 2 may be of use in directing studies of the toxicity of PAHmixtures by indicating which of these compounds are actually found together in the environment.

ACKNOWLEDGMENTS

The authors thank Gaynell Wheeler and Mimi Fallow for manuscript preparation.

LITERATURE CITED

Agency for Toxic Substances and Disease Registry (ATSDR) 1987a. Draft Toxicological Profilefor Dibenz(a,h)anthracene, Martin Marietta Energy Systems, Oak Ridge Natl. Lab.

Agency for Toxic Substances and Disease Registry (ATSDR) 1987b. Draft Toxicological Profilefor Benzo(a)pyrene, Martin Marietta Energy Systems, Oak Ridge Natl. Lab.

Agency for Toxic Substances and Disease Registry (ATSDR) 1987c. Draft Toxicological Profilefor Chrysene, Martin Marietta Energy Systems, Oak Ridge Natl. Lab.

Agency for Toxic Substances and Disease Registry (ATSDR) 1987d. Draft Toxicological Profilefor Benz(a)anthracene, Martin Marietta Energy Systems, Oak Ridge Nail. Lab.

Agency for Toxic Substances and Disease Registry (ATSDR) 1987e. Draft Toxicological Profilefor Benzo(b)fluoranthene, Martin Marietta Energy Systems, Oak Ridge Natl. Lab.

DeparOnem of Health and Human Services (DHHS) and Environmental Protection Agency (EPA)1988. "Hazardous Substances Priority List, Toxicological Profiles; Second List," Fed. Regis.53(203), 41280-85.

SAS Institute, Inc. 1985a. SAS User's Guide: Basics. Version 5 Edition, Cary, N.C.

SAS Institute, Inc. 1985b. SAS User's Guide: Statistics, Version 5 Edition, Cary, N.C.

TOTAL ERROR COMPONENTS—ISOLATION OF LABORATORYVARIATION FROM METHOD PERFORMANCE

David BottrellEM563, Trevion II

U.S. Department of EnergyWashington, D.C. 20585-0002

Ruth Bleyler'Viar and Company

300 North Lee StreetAlexandria, Virginia 22314

Joan FiskOffice of Emergency and Remedial Response

U.S. Environmental Protection Agency401 M Street S.W.

Washington, D.C. 20460

Michael HiattQuality Assurance and Methods Development Division

U.S. Environmental Protection AgencyEnvironmental Monitoring Systems Laboratory

P.O. Box 93478Las Vegas, Nevada 89193-3478

ABSTRACT

The consideration of total error across sampling and analytical components ofenvironmental measurements is relatively recent. The U.S. Environmental ProtectionAgency (EPA), through the Contract Laboratory Program (CLP), provides completeanalyses and documented reports on approximately 70,000 samples per year. Thequality assurance (QA) functions of the CLP procedures provide an ideal database—CLP Automated Results Data Base (CARD)—to evaluate program performancerelative to quality control (QC) criteria and to evaluate the analysis of blind samples.Repetitive analyses of blind samples within each participating laboratory provide amechanism to separate laboratory- and method performance. Isolation of error sourcesis necessary to identify effective options* to establish performance expectations, and toimprove procedures. In addition, optimized method performance is necessary toidentify significant effects that result from the selection among alternative proceduresin the data collection process (e.g., sampling device, storage container, mode of sampletransit, etc.). This information is necessary to evaluate data quality; to understandoverall quality; and to provide appropriate, cost-effective information required tosupport a specific decision.

'Currently with the U.S. Environmental Protection Agency, Office of Emergency and Remedial Response, ToxicsIntegration Branch.

89

90

INTRODUCTION

Current evaluation of the quality of environmental data is largely based on the assessment ofthe analytical determination (U.S. Environmental Protection Agency 1987). Data bases such asCARD can be used to evaluate and optimize method perfomiance and laboratory participation. Theimproved resolution of analytical results that can be gained is necessary to identify andcharacterize additional contributors to total error and to select among alternative procedures.

APPROACH

The orientation of this project is to identify requirements for the CLP (through the emphasison standardization across programs, criteria are extended, e.g., RCRA requirements) that controland monitor cost-effective data collection. This can be accomplished through the application ofcurrent data bases and utilization of this information to design investigations to meet needs foradditional data. Initial activities relating to the acceptance of modifications in gaschromatographic/mass spectrometric performance-monitoring criteria have been completed (Bottrellet al. 1989). The function of instrument performance criteria is maximized operation withinwindows of acceptable, identified performance characteristics.

The next step in program activity is the extension of efforts originally developed throughagreements with the U.S. Department of Energy and the U.S. Department of Defense to define andevaluate sample holding times (Maskarinec et al. 1989; Maskarinec and Moody 1988). This can bedone based on currently accepted definitions of holding times as the maximum period betweensampling and analysis before the occurrence of significant degradation (Taylor 1987; AmericanSociety for Testing and Materials 1985).

DISCUSSION AND RECOMMENDATIONS

Based on this definition, the American Society for Testing and Materials minimum of 85% ofthe mean initial concentration (99% confidence) is essentially never realized in overall programoperation (Table 1). Routine analytical performance and anticipated data quality are not consistent.Under typical laboratory conditions, holding times of samples preserved as described in EPAsampling/analysis requirements (Code of Federal Regulations 1979; U.S. Environmental ProtectionAgency 1989) cannot be isolated as a source of emor. Utilizing currently available methods (e.g.,EPA method 624 for the analysis of volatile organics in water), the cost of compliance withrequirements may not be justified by the contribution in overall quality of the resultinginformation. As demonstrated, significant losses for very few analytes were found after a 3 monthinterval (no preservation, 4°C storage conditions). Based on program performance for theseparticular analytes, they are effective estimates, regardless of holding times.

To isolate laboratory effects at the analyte level, specific target compounds can be evaluatedbased on QC requirements. The CLP quarterly blind monitoring samples are run as two additionalspiked samples (matrix spike and matrix spike duplicate). This provides triplicate analyses for eachof the "native" analytes that are not included in the spiking mixture for each individualparticipating laboratory. The evaluation of this data provides additional information relating to theapplicability of current holding time requirements to routine environmental investigations. A

91

Table 1. Method performance/analyte stability150 fig/L, initial concentration

Compound

Methylene chloride

Acetone

Carbon disulfide

!, 1 -Dichloroethene

1,1-Dichloroethane

1,2-Dichloroethene

ChJorofonn

1,2-Dichloroethane

2-Butanone*

1,1,1 -Trichloroethane

Carbon tetrachloride'

Vinyl acetate

Bromodichloromethane

1,2-Dichloropropane

Cis-1,3-Dichloropropenec

Trichloroethene

Dibroinochloromethane

1,1,2-Trichloroethane

Benzene

Bromoform

4 -Methyl -2-Pentanoner

2-Hexanone<:

Tetrachloroelhene

Toluene

1,1,2,2-Tetrachloroethane

Chlorobenzene

Ethyl benzene

Styrene

Xylene (total)

QB

Meanconcentration

(ug/mL)

NU*

131

155

140

150

140

145

145

135

150

139

NU"

150

160

119

145

160

145

140

160

126

101

127

140

140

145

129

114

140

3 Results"

Acceptancelimit

(% recovery)

—

41-130

67-140

67-120

80-120

67-120

80-110

70-110

47-130

80-120

65-120

—

80-120

93-120

45-110

80-110

87-130

80-110

80-110

80-130

55-110

35-110

63-110

80-110

73-110

80-110

64-110

51-100

73-110

QB

Meanconcentration

(ug/mL)

NU*

NU*

127

135

150

130

140

150

90

140

95

NU*

150

160

39

145

155

150

140

150

104

75

119

135

135

145

113

115

130

4 Results

Acceptancelimit

(% recovery)

—

—

55-110

66-110

80-120

66-110

73-110

80-120

6.7-110

67-120

33-93

—

80-120

87-130

13-38

73-120

80-130

80-120

73-110

73-130

32-110

6.7-93

58-100

73-110

67-110

80-110

50-100

46-110

67-110

"Results from third quarterly blind and fourth quarterly blind performance sample, FY 88, U.S. EPA ContractLaboratory Program.

*NU = Analyte not used in scoring; acceptance limit <CRQL.'Significant loss—three month period; no preservation.

92

representative summary of the laboratory-specific results is presented in Table 2. Reportedconcentrations were sorted based on statistical significance of change between two periods ofanalysis.

The reliability of environmental measurement can be considered from a variety of perspectives(e.g., analyte-, method-, and laboratory-specific). Some analytes may not be identifiable (or evenpresent) 3 days after spiking (e.g., vinyl acetate). Other analytes may be found, but reportedconcentrations up to 200 ppb may not be reliable. Sensitivity (i.e., false negatives) and falsepositives (i.e., contamination) are extremely frequent problems with common solvents. Lowconcentration environmental presence may be lost in contaminant noise. In addition, quantitiesequivalent to 5-25 ppb in field samples can be introduced during sample processing procedures.These variables, along with many others, affect the reported concentration in a sample. Laboratoryresults should not be considered absolute without qualification (Consumers Union of U.S.,Inc. 1990).

Table 2. Representative summary of single laboratoryperformance evaluation data

Laboratory ID

Analyte

Styrene

Cis-1,3-Dichloropropene

Xylenes

Carbon tetraehloride

1,1,2,2-Trichloroethane

2-Hexanone

1,1,1 -trichloroefhane

Carbon disulfide

1,2 -Dichloroethene

1,1,2-Trichloroethane

Acetone

Tetrachloroethene

Chloroform

Bromodichloromethar.e

1,2-Dichloropropane

2-Butanone

Trans-1,3-Dichloropropene

A

X

X

X

X

X

X

X"

X

X

B

X

X

X

X

X

X*

X*

c

X

X

X

X

X

X*

X"

X

X"

X"

X*

X

D

X*

X*

E

X

X

X

X

F G

X a

X

X

X

X

X

"Laboratory lacked precision and operating stability to identify a change in anyanalyte.

^Statistically different reported concentration was an increase.

93

The purpose of QA/QC is to control, monitor, and document the associated results to qualifythe final product based on anticipated performance and overall quality needs. Based on currentinformation, the ultimate assessment of the usability of data places too much emphasis on therelative contribution of holding time to total error. We suggest consideration of the extension ofholding times for preserved samples to practical limits over which essentially all analytesappropriate for current procedures are stable. An alternative holding time would addressconsistency with accepted definitions, EPA program data acquisition variation, and currentmonitoring requirements for data delivery.

Samples degrade over time, and mechanisms to ensure immediate analysis are appropriate ifthe sensitivity and accuracy to provide additional confidence in a measurement warrants theincrease in cost. The application of the data quality objective approach to these cases may suggestthe use of field measurements to facilitate minimization of holding time with confirmation ofselected samples to meet requirements for confidence in identification, to maintain datacomparability, and to ensure legal admissibility.

ACKNOWLEDGMENT

Dr. Thomas Jacob, Viar and Company, provided SAS programming and statistical analysis ofthe CARD data base to support this project.

LITERATURE CITED

American Society for Testing and Materials (ASTM) Aug. 30, 1985. Standard Practice forEstimation of Holding Time of Water Samples Containing Organic Constituents,No. D4515-85, Philadelphia, Pa.

Bottrell, D., et al. 1989. "Preanalytical Holding Time Study- -Volatiles in Water," presented atthe Fifth Annual Waste Testing and Quality Assurance Symposium, Washington, D.C., 1989.

Consumers Union of U.S., Inc. (CUU) January 1990. "Fit To Drink," p. 32 in Consumer Reports,Mount Vemon, N.Y.

U.S. Environmental Protection Agency (EPA) March 1987. Data Quality Objectives for RemedialResponse Activities, EPA/540/G-87/OO3.

U.S. Environmental Protection Agency (EPA) April 1989. Contract Laboratory Statement of Workfor Organics Analysis (Multi-Media, Multi-Concentration, rev. 2.

Code of Federal Regulations (CFR) Dec. 3, 1979. Title 40, Pt. 136.

94

Maskarinec, M. P., and R. L. Moody 1988. Storage ami Preservation of Environmental Samples,Principles of Environmental Sampling, ed. L. H. Keith, Martin Marietta Energy Systems, OakRidge Nati. Lab., pp. 145-55.

Maskarinec, M. P., et al. August 1989. Stability of Volatile Organics in Environmental WaterSamples: Storage and Preservation, ORNL/TM-11300, Martin Marietta Energy Systems, OakRidge Natl. Laboratory.

Taylor, J. K. 1987. Quality Assurance of Chemical Measurements, Lewis Publishers,Chelsea, Mich.

DETERMINING THE EFFECTIVENESS OF POLLUTION ABATEMENT:THE NEED FOR INTEGRATED MONITORING

AND STATISTICAL ANALYSIS

Kenneth A. RoseP.O. Box 2008

Oak Ridge National Laboratory'Oak Ridge, Tennessee 37831

Eric P. SmithDepartment of Statistics

Virginia Polytechnic Institute and State UniversityBlacksburg, Virginia 24061

ABSTRACT

Activities designed to reduce pollution discharge and to restore environmentalquality are presently underway at U.S. Department of Energy facilities and at manyother sites around the country. An important aspect of environmental restoration isperformance evaluation, the determination of the effectiveness of pollution abatementactivities. Performance evaluation involves pre- and post-treatment monitoring, and thesubsequent analysis of these data to quantify changes (improvements) in environmentalquality and attribute these changes to specific pollution abatement actions. The resultsfrom a performance evaluation also provide direct feedback on the usefulness and cost-effectiveness of alternative pollution abatement activities. Performance evaluationrequires environmental monitoring and quantitative techniques (both statistical andsimulation) capable of separating the system response attributable to pollutionabatement from the high underlying variability typically observed in naturalecosystems.

We advocate an integrated approach for the design and analysis of pre- and post-treatment monitoring based on (1) a priori specification of performance evaluationobjectives and <2) use of statistical techniques throughout all phases of monitoring,beginning with initial sampling design. We briefly discuss the general types ofstatistical techniques appropriate for both the design of pre- and post-treatmentmonitoring programs (e.g., power analysis and bootstrapping) and the subsequentstatistical analyses of the collected data (e.g., linear regression and nonparametricmethods). The importance of an integrated approach is illustrated wiih two long-term(~20-year) environmental data sets from Chesapeake Bay. Neither data set wascollected with the objective of long-term trend detection; trend analysis of both datasets results in contradictory conclusions concerning long-term trends in environmentalquality in Chesapeake Bay.

'Managed by Martin Marietta Energy Systems, Inc., under contract DE-AC0S-84OR21400 with the U.S. Departmentof Energy.

95

A STATISTICAL ANALYSIS FOR A RESOURCE CONSERVATION ANDRECOVERY ACT GROUNDWATER QUALITY ASSESSMENT

Dennis A. Wolf and Mark F. TardiffOak Ridge National Laboratory'

P.O. Box 2008Oak Ridge, Tennessee, 37831-6370

ABSTRACT

Beginning in July of 1988, four quarterly groundwater samples were collectedfrom monitoring wells on the perimeter of Solid Waste Storage Area 6 (SWSA 6), ashallow, land-burial site for low-level radioactive and nonradioactive wastes at the OakRidge National Laboratory (ORNL). The analytical measurements of these sampleswere used to establish baseline constituent levels and to investigate differencesbetween background as represented by groundwater up-gradient to the site andcompliance points as represented by groundwater down-gradient to the site.

Differences between up-gradient wells and down-gradient wells could be caused bynatural variations among groundwater quality regimes. A more precise backgroundversus compliance point comparison would therefore be possible if wells represent asimilar water chemistry. A statistical technique, called cluster analysis, was utilized togroup (cluster) wells with respect to three water quality parameters: pH, alkalinity, andspecific conductance. Three clusters of wells were identified and could also be relatedto the level of the water table at SWSA 6. The clusters were then used to account forgroundwater quality regime differences when testing for potential down-gradientcontamination.

A two-way analysis of variance and follow-up multiple comparisons wereperformed. The two factors were cluster (clusters 1, 2, and 3) and well type(up-gradient and down-gradient). For some constituents there was a clear violation ofassumptions important to the parametric analysis. A nonparametric analysis of varianceand follow-up comparisons were also performed. The results suggest that somedifferences among wells at SWSA 6 can be attributed to the variations in groundwaterquality regime alone; some can be attributed to well type; and some are morecomplicated. Differences between well types depend upon the groundwater qualityregime.

'Managed by Martin Marietta Energy Systems, Inc., under contract DE-AC05-84OR21400 for the U.S. Department ofEnergy.

97

98

INTRODUCTION

The U.S. Department of Energy's (DOE's) ORNL SWSA 6 is a shallow, land-burial site forlow-level radioactive and nonradioactive wastes. Wastes were disposed in unlined trenches andauger holes from 1969 until May 1986, at which time DOE closed the site because it wasdetermined that wastes regulated by the Resource Conservation and Recovery Act (RCRA) (e.g.,solids such as lead and liquids such as xylene and toluene) were being disposed there. The sitewas reopened in July 1986 following numerous changes in operations, including cessation ofRCRA waste disposal and isolation of wastes from the environment. SWSA 6 is currently the onlyactive low-level radioactive waste disposal area at ORNL.

Because RCRA-regulated wastes were disposed in SWSA 6, the facility is undergoing closureper Tennessee Department of Health and Environment (TDHE) regulations 1200-1-1 l-.O5(7) and(14) and U.S. Environmental Protection Agency (EPA) RCRA regulations 40 CFR, Part 265,Subpart G. Groundwater quality detection monitoring was initiated at SWSA 6 in the third quarterof 1988 in compliance with TDHE regulation 1200-1-11-.05(6)(a). Groundwater was sampledquarterly and analyzed for constituents required for detection monitoring and for other potentialcontaminants of concern such as volatile and semivolatile organic species. Monitoring for theadditional contaminants permitted evaluation of remediation needs and alternatives for SWSA 6.

The presence of contamination at the SWSA 6 boundary was confirmed by results from thefirst four quarters of sampling. Therefore, in accordance with TDHE regulation1200-1-1 l-.05(6)(a), which allows the owner or operator to install, maintain, and operate analternate groundwater monitoring system when the owner or operator assumes or knows thatgroundwater contamination exists, ORNL submitted a plan proposing an assessment monitoringprogram rather than proceeding with the detection monitoring program. The plan contained adescription of the original network of monitoring wells at SWSA 6 and provisions for monitoringparameters and constituents through groundwater sampling and analysis protocols. Reportssummarizing the assessment monitoring program results will be submitted annually to TDHE asrequired under TDHE I200-1-I l-.05(6)(e)2(ii).

The purpose of this report is to demonstrate the statistical analyses used to support thegroundwater quality assessment plan submitted to TDHE. The emphasis is on demonstrating themethods rather than on interpreting assessment results. Some guidelines on the statistical analysisof groundwater monitoring data have been published (U.S. Environmental ProtectionAgency 1989).

The law requires that constituent levels in compliance wells be compared to levels inbackground wells to determine whether contamination is present at the site perimeter. Analysis ofvariance and multiple comparisons are familiar techniques that can be used to do this, hi thesituation where multiple background and compliance wells exist, it may be necessary to determinewhich wells in these two classes should be contrasted. In this study, cluster analysis was used togroup (cluster) wells having similar water quality characteristics based upon alkalinity, pH, andspecific conductance (called conductivity in this report), parameters believed to be robust tocontamination and indicative of different groundwater quality regimes. (Lacking more specificinformation about groundwater flow paths, it seems likely that waterbome contaminants would betransported within these regimes rather than among them.)

Analytes can be grouped into three broad categories: analytes that were never detected,analytes that were detected in either background or compliance wells only, and analytes that weredetected in both background and compliance wells. General data summaries will be discussed in

99

this report. Graphical displays are emphasized because they take advantage of the rapid eye-braincommunication. General features of the data and specific details can be revealed simultaneously inplots.

Differences between background wells and compliance wells could be caused by a naturalvariation among groundwater quality regimes. A more precise background versus compliance pointcomparison would therefore be possible if wells were from the same regime. Three groundwaterquality regimes (clusters) were identified by cluster analysis. There appears to be a relationshipbetween clusters and depth of the water table at SWSA 6. Well type and cluster were used asfactors in a two-way analysis of variance. Follow-up comparisons of all pairs of well type bycluster means were performed to determine where the significant differences are. Nonparametricanalyses gave similar results lo the parametric results in most cases, except where noted.

The detection limit was assigned to any value below the detection limit when computationswere performed.


The DOE Oak Ridge Reservation (ORR) is located 40 km west of Knoxville, Tennessee, and240 km east of Nashville, Tennessee, in Roane and Anderson counties. ORNL is one of the threemajor DOE facilities on the ORR, and all are managed by Martin Marietta Energy Systems, Inc.

SWSA 6 is about 2.9 km southwest of the main ORNL complex. It covers 28 ha (68 acres), ofwhich about one-half is considered suitable for waste burial (Boegly 1984). The remainder of thesite has steep slopes or a shallow groundwater table. Prior to May 1986, wastes wero disposed in487 trenches and 582 auger holes, none of which arc lined, and 21 concrete-lined disposal casks(Davis et al. 1987). The trenches arc generally 15 m long, 3 m wide, and 4-6 m dejp, dependingupon the depth to highest known water table. The minimum distance between trenches is 1.5 m.When the waste level was within 0.9 m of ground surface, the trench was backfilled with soil,compacted with heavy equipment, and seeded to retard erosion.

Auger holes were used for disposal of low-level radioactive wastes; holes usually are 1 mdiam and al least 0.6 m above the highest known water table. Maximum depth is 5.5 m; minimumspacing is 0.9 m apart. Each hole was sealed with a concrete plug and covered to ground surfacewith 0.3-0.6 m of soil.

Trenches are classified according to the waste materials as high activity, low activity,biological, asbestos, baled, fissile, high-activity concrete-lined, or low-activity concrete-lined(Fig. 1). Auger holes are classified as high activity, solvent, or fissile (Davis and Solomon 1987).

Prior to 1986 the physical and chemical nature of the wastes were essentially undocumented.Packaging varied from bulk waste (no containerization) to stainless steel drums. The records fornonradioactive wastes are especially deficient. In May 1986 it was determined that about 25% ofthe landfill had received RCRA-regulated wastes, primarily xylene, toluene, and lead. The site wasclosed by DOE and reopened in July 1986 under revised operating procedures. NoRCR A-hazardous wastes have been disposed since then, and all radioactive waste disposal unitshave been designed to isolate the wastes from the environment.

The current monitoring well network at SWSA 6 was installed in 1987; development wascompleted in the second quarter of 1988. The network consists of 30 wells. See Fig. 2 for thelocations of wells, each identified by number. For compliance purposes, 22 of these wells(perimeter wells) are located on the boundary of SWSA 6, and the remaining 8 are sitecharacterization wells located within the site boundaries.

100

ORNL-DWG 86-17573

+ N18.000

+ N17.000

SUSPECTWASTE

LANDFILL

AUGER HOLES

LOW-ACTIVITY-LEVEL

HIGH-ACTIVITY LEVEL

ANIMAL

ASBESTOS

| | BALES

^ H EXPERIMENTAL

Fig. 1. Map of SWSA 6 indicating general locations of trenches and auger holes (1 ft = 0.3 m).

101ORNL-DWQ 66 I693SR

mtjaoo

N17.000-

NUXKW-

E24A00 E2SM0 E2iX)00

ORAINAGE' ' WAG BOUNDARY

y • LOCATION OF SWMUSNORTH A ADDITIONAL SWMUS

, /I • STREAM BOTTOM SEDIMENT"'-' r L SAMPLING SITE

•"•COM WELL LOCATIONAND WELL NUMBER

Fig. 2. Location of SWSA 6 water quality wells.

102

The results presented in this report correspond to data obtained on the 22 perimeter wells.These wells were divided into two groups, up-gradient (U) wells and down-gradient perimeterwells (P).

Well type Well identification number

Up-gradiem 831. 832, 846, 855, 856, 857, 858

Perimeter 745, 833, 835, 836, 837, 838, 839, 840,841, 842, 843, 844, 847, 859, 860

Because of their location, up-gradient wells are believed to be unaffected by contaminationfrom this solid waste management unit, whereas the other perimeter wells may be affected.Up-gradient wells were used as background for comparison with perimeter wells.

Calculations were performed using two statistical packages, S (Becker and Chambers 1984) forthe cluster analysis and plotting and SAS (SAS Institute Inc. 1985a and 1985b) for data basemanagement and additional statistical analyses.

CLUSTER ANALYSIS

Cluster analysis is a technique for grouping a sample of "objects," each measured on each ofp variables, into classes called "clusters," where the number of clusters is to be determined(Hveritt 1974). Objects are grouped according to "similarity" with respect to the p variables.

The similarity between two objects is defined as the Euclidean distance between them in thespace of the p variables (the data for each variable is standardized to have zero mean and unitvariance). Objects closer to each other in this space are more similar. In the case of p = 2variables, objects can be represented on a plot of one variable versus the other. Object 2 is mostsimilar to object 1 if the distance between object 2 and object 1 is shorter than the distancebetween object 1 and any other object. Similarity between two clusters (a single object can be acluster) can be defined as the average distance between objects in the two clusters. Ouster B ismost similar to cluster A if the average distance between objects in cluster B and objects in clusterA is shorter than the average distance between objects in cluster A and objects in any othercluster.

Clustering is used in this study to identiiy wells with sinvlar water quality characteristics basedupon pH, alkalinity, and conductivity. The results of cluster analysis are the number ofgroundwater quality regimes at SWSA 6 and groupings of wells representative of the regimes.

The results of cluster analysis are displayed in a diagram called a cluster dendrogram, whichcan be visualized as an inverted tree where the tips of the branches represent individual objectsand successive forks in the tree show linkage to other objects A group of objects linked at a lowerlevel in the tree are more similar than a group of objects linked at a higher level. The linking ofobjects can continue until all objects are connected. Determination of how many clusters there are,that is, where to stop linking objects, is usually guided by the interpretability of the resultingclusters and expert judgment.

103

ANALYSIS OF VARIANCE AND MULTIPLE COMPARISONS

Analysis of variance is a statistical tool for studying the relation between a response variable(e.g., pH) and one or more predictor variables, where the predictor variables usually indicate groupmembership (Neter and Wasserman 1974; Snedecor and Cochran 1980). Thus analysis of variancecould be described as a tool for comparing group means. A univariate, fixed-effects analysis ofvariance was used to test for simultaneous differences among well types (up-gradient andperimeter) and three aquifer regimes (three clusters) for each constituent. Testing is based upon thefollowing model assumptions: the mean response can be expressed as the sum of a constant, whichdepends upon group membership, and an error term. The errors are assumed to be independent andhave a statistical distribution with zero mean and constant variance (constant over all groups). Thenormal (Gaussian) distribution is assumed most frequently.

Jeveral tests for normality are available in existing statistical packages. For example, SASprocedure UNIVARIATE (SAS Institute Inc. 1985a) will calculate the Shapiro-Wilk test statistic(Shapiro and Wilk 1965) and will also produce a normal probability plot The analysis of varianceresiduals can be input to the procedure to assess the normality assumption.

The F-statistic used in a fixed-effects analysis of variance is fairly robust to departures fromnormality but can be affected substantially by unequal variances, especially when there are unequalnumbers of observations per group (Neter and Wasserman 1974). A modified test (Levene 1960) isrecommended for checking the homogeneity of variance assumption. A large number of tests havebeen studied (Conover et al. 1981) for homogeneity of variance to determine their characteristicsunder the null hypothesis of equal variance (for robustness) and under the alternative hypothesis ofunequal variances (for power). The results of this test and several other studies concluded that theclassical tests for variance homogeneity (including Bartlett's test, which is frequentlyrecommended) were very sensitive to the assumption of normality, or if robust, lacked power. Alarge Monte Carlo simulation study was conducted to evaluate the classical tests and ether morerecently developed tests. In particular, a modification (Brown and Forsythe 1974) of a test (Levene1960) fared well in this study. The test consists of doing an analysis of variance enZyJt = \Xljk - X~jk I where X~jk represents the median of the observations in group (combination ofwell type and aquifer regime) jk. Compare the F-statistic to the F-distribution to carry out the test.

Violations of the assumptions of homogeneity of variance and normality can sometimes becorrected by transformations of the data (e.g., taking logarithms of concentrations). The tests fornormality and homogeneity of variance can be used to assess a transformation.

Temporal and spatial effects may result in violation of the assumption of independence. In agroundwater study, there may be seasonal trends or site characteristics that should be incorporatedinto a model, but unless there is specific a priori information, insufficient data precludes empiricalmodeling of these effects. The basic data in this study consist of measurements obtained duringfour sampling quarters. The average of the measurements obtained over the sampling periods wasused in the analysis of variance.

Follow-up comparisons of all pairwise combinations of well type and cluster (denoted by welltype x cluster) means can be performed to investigate significant differences found by analysis ofvariance. The procedure used here was a modified Tukey's "honestly significant difference" testof pairwise differences. (The modification allows for unequal group sample sizes.) This procedureis based upon the studentized range and is designed to control the maximum experimentwiseType I error rate (rate of falsely rejecting equality of means) for the complete set of pairedcomparisons (Neter and Wasserman 1974; Miller 1981).

If the assumptions made for the parametric analysis of variance are not met, thennonparametric approaches to analysis of variance are available that are based upon the ranks of the

104

observations. The Kruskal-Wallis test is one such approach for a one-way analysis of variance(Conover 1980). The assumptions are that the observations are random samples from theirrespective populations. Either the populations have the same distribution functions or somepopulations tend to yield larger values than other populations. Generalization of Tukey's test ofpairwise differences to ranked data has been described (Miller 1981).

RESULTS

OVERALL SUMMARY OF CONTAMINATION LEVELS AND LABORATORYDETECTION LIMITS

Three different field measurements and 191 laboratory analyses were performed on eachgroundwater sample. For most analytes, a single measurement was taken on each groundwatersample. The exceptions were the field measurements: seven replicates of pH, conductivity, andtemperature and four replicates of total organic carbon and total organic halides. Tables 1-6contain the full list of parameters, the most conservative regulatory standard, the reference sourcecode, the number of times an analyte was detected, the total number of measurements, theminimum and maximum detection limit ranges, and the minimum and maximum observed values.Table 7 contains the list of reference codes used in Tables 1-6 and the corresponding referencedocument. Only regulatory standards in force are included. Of the 194 analyses, 70 resulted in atleast one detectable value in at least one sampling quarter and for at least one well water sample.Of these 70 analyses, 38 were retained for statistical evaluation.

Table 1 pertains to the water quality parameters used in a cluster analysis to group up-gradientand perimeter wells into three clusters for statistical comparisons. Table 2 contains a list ofanalytes detected in groundwater samples but attributed to either field and/or laboratorycontamination. Table 3 contains a list of analytes detected, but values were below quantifiablelimits of gas chromatographic/mass spectrometry. Estimated concentrations were used in statisticalanalyses.

The lists in Tables 2 and 3 contain 13 organic compounds: 6 are common analytical laboratorycontaminants; detected values are suspected to be artifacts of the analytical technique. These6 compounds, acetone, di-n-butylphthalate, diethyl phthalate, methylene chloride, toluene, andbis(2-ethylhexyl)phthalate, were excluded from statistical analysis. Six organics that were detectedbelow quantitation limits have been included in the data analysis because their presence due to asampling or analytical artifact is unlikely. They are benzene, ethylbenzene, naphthalene,1,1-dichloroethene, 1,1,1-trichloroethane, and 4-methyI-2-pentanone.

Dissolved mercury was detected in three up-gradient wells during the first quarter of sampling(Table 2). The values were at the detection limit, and mercury was not detected in any subsequentsamples. It is highly likely that mercury was present in those initial samples as an artifact oftechnique. Therefore, dissolved mercury was excluded from statistical analysis.

The herbicides detected in the fourth quarter coincided with herbicide spraying that wasconducted to contiol growth of vegetation near the wells prior to installation of the interim cap aspart of the interim corrective measures program for WAG 6. Because these analytes were found inthe field blanks and had not previously been detected, they probably were caused by contaminationof the sample at the well sites. Consequently, herbicides were excluded from the statistical analysis(Table 2).

Table 1. Water quality parameters. SWSA 6 groundwater

Parameter Standard" Jnits

std.

mg/L

ms/cm

Std.ref.

2

No. detected/No. of samples

585/585

83/83

585/585

Detection

Min

0.010

limit

Max

0.010

Observed

Min

4.3

4.0

0.010

value

Max

8.5

437

0.97

pH 6.5-8.5

Conductivity

Existing standards come from documents coded in the Standard Reference column. Reference codes can be found in Table 7.

Table 2. Detected analytes in SWSA 6 groundwater samples attributed to field and/or laboratory contaminationand not considered for statistical analysis

Parameter Standard" Units Std. No. detected/No. of samples

37/83

1/83

21/64

23/64

8/64

73/83

3/83

16/83

16/83

1/39

4/83

Detection

Min

0.010

0.0010

0.0055

0.0055

0.0055

0.0050

0.00010

0.0050

0.00020

0.00010

0.00010

lima

Max

0.010

0.0011

0.011

0.020

0.020

0.0050

0.00010

0.0050

0.00020

0.00010

0.00010

Observed

Min

0.0010

0.00060

0.00070

0.00040

0.00050

0.00060

0.00010

0.0010

0.00020

0.00010

0.00010

value

Max

4.7

0.00)1

0.019

0.019

0.048

0.0080

0.00040

0.0070

0.011

0.00030

0.00030

Acetone6

Aroclor-1254'

Bis(2-ethylhexyl)phthalate('

Di-n-butylphthalate*

Diethyl phthalate"

Methylene chloride*

Mercury, dissolved*

Toluene*

2.4-D"

2A5-T"

2,4.5-TP (Silvex)1'

0.0020

G.10

0.010

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

;ng/L

mg/L

mg/L

mg/L

mg/L

1,3

1.3

1,3

"Existing standards come from documents coded in the Standard Reference column. Reference codes can be found in Table 7.'"Suspected laboratory contaminants.'A detected PCB suspected to be an anomaly.''Sample contamination is attributed to localized application of herbicide in the area prior to installation of plastic caps on the wells.

Table 3. Analytes detected below the quantitation limits in SWSA 6 groundwater

Parameter

Benzene

Ethylbenzene

Naphthalene

1,1 -Dichloroethene

1,1,1 -Trichloroethane

4-Methyl-2-pentanone

Standard"

0.0050

0.0070

0.20

Units

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

Std.ref.

4

4

4

No. detected/ -No. of samples

4/83

13/83

3/64

2/83

1/83

1/83

Detection

Min

0.0050

0.0050

0.0055

0.0050

0.0050

0.010

limit

Max

0.0050

0.0050

0.020

0.0050

0.0050

0.010

Observed

Min

0.0020

0.0010

0.0020

0.00050

0.00040

0.0020

value

Max

0.0050

0.0050

0.011

0.0050

G.0050

0.010

.01

"Existing standards come from documents coded in the Standard Reference column. Reference codes can be found in Table 7.

Table 4. Detected total metals excluded from statistical analysis of SWSA 6 groundwater samples

Parameter

Aluminum, total

Beryllium, total

Cadmium, total

Calcium, total

Cobalt, total

Copper, total

Iron, total

Lead, total

Magnesium, total

Manganese, total

Mercury, total

Nickel, total

Selenium, total

Silicon, total

Silver, total

Sodium, total

Strontium, total

Titanium, total

Turbidity

Vanadium, total

Zinc, total

Standard"

0.010

1.0

0.30

0.050

0.050

0.0020

0.010

0.050

5.0

Units

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

NTU

mg/L

mg/L

Std.ref.

1,3

2

2

1,3

2

1,3

1.3

1.3

2


73/83

54/83

2/83

80/83

6/83

3/83

77/83

1/83

82/83

71/83

4/83

35/83

1/83

82/83

1/83

83/83

60/66

18/83

83/83

49/78

19/83

Detection

Min

0.036

0.00018

0.0020

0.060

0.0018

00060

0.050

0.020

0.010

0.010

0.00010

0.0036

0.0050

0.12

0.0050

0.020

0.0030

0.012

0.0024

0.0070

limit

Max

0.060

0.00040

0.0020

0.15

0.0030

0.010

0.050

0.020

0.010

0.010

0.00010

0.0060

0.0050

0.20

0.0050

0.020

0.050

0.020

0.0040

0.0080

Observed value

Min

0.050

0.00030

0.0020

0.10

0.0018

0.0060

0.050

0.020

0.010

0.010

0.00010

0.0036

0.0050

0.20

0.0050

0.69

0.0050

0.013

0.040

0.0040

0.0070

Max

0.89

0.025

0.0030

180

0.0048

0.030

4.8

0.060

54

0.24

0.00070

0.028

0.0050

14

0.0060

86

1.2

0.033

140

0.013

0.034

"Existing standards come from documents coded in the Standard Reference column. Reference codes can be found in Table 7.

Table 5. Detected analytes in SWSA 6 groundwater samples

Parameter

Carbon tetrachloride

Chloride

Chloroform

Chloromethane

"Co131Cs

Fecal coliform

Fluoride, total

Gross alpha

Gross beta

Iron, dissolved

Manganese, dissolved

Nitrate (as N)

Organic carbon, total

Organic halides, total

Phosphate

Radioactive strontium, total

Radium, total

Recoverable phenolics, total

Sodium, dissolved

Sulfate (as SO4)

Temperature

Tetrachloroethene

Trichloroethene

Tritium

Xylene, total

1,1 -Dichloroeihane

1,2-Dichloroe thane

1,2-Dichloroethene

Standard

0.0050

2500.10*

1.01.4-2.4

0.55

0.300.050

10

0.290.18

250

0.0050740

0.0050

Units

mg/Lmg/Lmg/Lmg/LBq/LBq/L

col/100 mLmg/LBq/LBq/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/LBq/LBq/Lmg/Lmg/Lmg/L

°Cmg/Lmg/LBq/Lmg/Lmg/Lmg/Lmg/L

Std.ref.

421

13

1.31,322

1,3

11,3

2

41

4


5/8383/8310/833/83

75/8377/83

5/8414/8383/8383/8317/8364/8314/83

207/33286/332

1/8380/8080/80

3/8383/8359/83

585/5857/83

11/8383/83

1/834/834/839/83

Detection limit

Min

0.00501.00.00500.0100.100.101.01.0

0.0500.0100.500.500.00505.0

0.00100.0205.0

0.00500.0050

0.00500.00500.00500.0050

Max

0.00501.00.00500.0100.200.101.01.0

0180.0100.500.500.00505.0

1.00.0205.0

0.00500.0050

0.00500.00500.00500.0050

Observed value

Min

0.000501.00.00100.0040

-0.90-0.70

1.01.0

-0.0096-0.0020

0.0500.0100.500.4005.0

-0.10-0.011

0.00100.685.09.50.00200.0010

-500.00500.00300.00500.0040

Max

0.092170.0970.015

130.78

2901.00.809.10.630.162.67.30.595.00.560.151.0

83330

170.0160.51

46,0000.0110.00900.0440.027

"Existing standards come from documents coded in the Standard Reference column. Reference codes can be found in Table 7.''Standard is for total trihalomethanes (chloroform, bromoform, bromodichloromethane, and dibromochloromethane).

110

Table 6. Undetected analytes in SWSA 6 wells

Alpha-BHC

Beta-BHC

Cis-1,3-DichloropropeneDelta-BHCGamma-BHC (Lindane)

Trans -1,3-DichloropropeneAcenaphtheneAcenaphthyleneAJ drill

Anthracene

Antimony, totalArsenic, dissolvedArsenic, total

Barium, dissolvedBarium, totalBenzo(a)anthraceneBenzo(a)pyrene

Benzo(b)iluorantheneBenzo(g,h,i)peryleneBenzo(k)fluorantheneBenzoic acid

Benzyl alcoholBenzyl butyl phthalate

Bis(2-chloroethoxy) methane

Bis(2-chloroethyl) ether

Bis(2-chloroisopropyl) etherBoron, total

BromodichloromelhaneBromoform

BromomethaneCadmium, dissolvedCarbon disulfide

ChlordaneChlorobenzeneChloroe thaneChromium, dissolved

Chromium, totalChryseneCyanide, total

Di-n-octylphthalateDibenzo(a, h)anthracene

DibenzofuranDibromochloromethaneDieldrin

Standard"

0.0500.050

1.01.0

0.10*

0.10*

0.010

0.050

0.050

0.10*

Units

mg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/Lmg/Lmg/Lmg/L

mg/Lmg/Lmg/Lmg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/Lmg/Lmg/Lmg/L

Stdref.

1,31,3

1,31,3

1

1

1,3

1,3

1,3

1


0/83

0/83

0/83

0/830/83

0/83

0/640/640/83

0/64

0/830/830/83

0/830/830/640/64

0/640/640/640/64

0/640/640/64

0/64

0/640/830/83

0/83

0/830/830/83

0/83

0/830/83

0/830/83

0/640/30/640/640/640/83

0/83

Detection limit

Min

0.000050

0.0000500.0050

0.0000500.0000500.0050

0.00550.00550.000050

0.0055

0.0300.0100.010

1.01.00.00550.0055

0.00550.00550.00550.027

0.0055

0.00550.00550.0055

0.00550.0480.0050

0.0050

0.0100.00200.0050

0.00050

0.00500.0100.020

0.020

0.00550.00200.0055

0.00550.00550.0050

0.00010

Max

0.000060

0.000060

0.0050

0.0000600.0000600.0050

0.0200.0200.000060

0.020

0.0500.0100.010

1.01.00.0200.020

0.020

0.0200.0200.10

0.020

0.0200.0200.020

0.0200.0800.0050

0.0050

0.0100.00200.0050

0.00060

0.00500.0100.050

0.20

0.0200.0020

0.0200.0200.0200.0050

0.00011

I l l

Table 6 (continued)

Dimethyl phthalateEndosulfan sulfate

Endosulfan I

Endosulfan IIEndrinEndrin ketone

FluorantheneFluoreneGallium, totalHeptachlor

heptachlor epoxide

HexachlorobenzeneHexachlurobutadieneHexachlurocyclopentadiene

HexachloroethaneIndenof 1,2V3 -cd)pyreneIsophorone

Lead, dissolved

Lithium, totalMethoxychlorMolybdenum, total

N-Nitrosodi-n-propylamineN-Nitrosodiphenylamine

NitrobenzenePeniachlorophenol

PhenanthrenePhenolPhosphorus, totalPyrene

PCB-1016

PCB-1221PCB-1232PCB-1242

PCB-1248PCB-1260Selenium, dissolvedSilver, dissolved

Styrene

SulfideTin, total

Toxaphene

Vinyl acetateVinyl chlorideZirconium, total

Standard"

0.00020

0.050

0.0100.050

0.0050

0.0020

Units

mg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/Lmg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/L

mg/L

mg/Lmg/Lmg/L

Std.Ttl.

1,3

1,3

1,3

1,3

1,3

4


0/640/83

0/83

0/830/830/83

0/64

0/640/660/83

0/83

0/640/64

0/64

0/640/640/64

0/83

0/830/830/83

0/640/640/640/64

0/64

0/640/830/64

0/83

0/830/830/83

0/830/830/830/83

0/830/3

0/83

0/83

0/830/830/83

Detection limit

Min

0.00550.00010

0.000050

0.000100.000100.00010

0.0055

0.00550.180.000050

0.000050

0.00550.0055

0.0055

0.00550.00550.0055

0.020

0.120.000500.024

0.00550.00550.00550.027

0.0055

0.00550.180.0055

0.00050

0.000500.000500.00050

0.000500.00100.00500.0050

0.00501.00.030

0.0010

0.0100.0100.012

Max

0.0200.00011

0.000060

0.000110.000110.00011

0.020

0.0200.300.000060

0.000060

0.0200.020

0.020

0.0200.0200.020

0.020

0.200.000600.040

0.0200.0200.0200.10

0.020

0.0200.300.020

0.000600.000600.000600.00060

0.000600.00110.00500.0050

0.00501.00.050

0.0011

0.0100.0100.020

112

Tible 6 (continued)

Stands**?1 T I '*Units

mg/L

mg/L

mg/Lmg/Lmg/Lmg/L

mg/Lmg/Ling/Lmg/L

mg/L

mg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/Lmg/L

mg/Lmg/Lmg/L

mg/L

mg/Lmg/L

Std.ref.

4


0/830/83

0/640/830/640/64

0/640/830/64

0/64

0/830/640/64

0/64

0/640/640/64

0/64

0/640/640/64

0/64

0/640/640/64

0/64

0/640/640/64

0/640/640/83

0/83

0/830/64

Detection limit

Min

0.00500.0050

0.00550.00500.00550.0055

0.0055

0.0100.0055

0.0055

0.0100.00550.0055

0.027

0.00550.00550.0055

0.027

0.00550.0270.0055

0.0055

0.0270.0110.0055

0.0055

0.00550.00550.0055

0.027

0.0270.00010

0.00010

0.000100.027

Max

0.00500.0050

0.020

0.00500.0200.020

0.0200.0100.020

omo0.0100.0200.020

0.10

0.0200.0200.020

0.10

0.0200.100.020

0.020

0.100.0400.020

0.020

0.0200.0200.020

0.100.100.00011

0.00011

0.000110.10

1,1,2-Trichloroelhane1,1,2,2-Tetrachloroethane

1,2-Dichlorobenzene1,2-Dichloropropane1,2,4-Trichlorobenzene1,3-DichIorobenzene1,4-Dichlorobenzene2-Butanone2-Chloronaphthalene

2-Chlorophenol

2-Hexanone

2-Methylnaphthalene

2-Methylpnenol2-Nitroaniline2-Nitrophenol2,4-Dichlorophenol2,4-Dimethylphenol2,4-Dinitrophenol2,4-Dinitro toluene2,4,5-Trichlorophenol2,4,6-Trichlorophenol2,6-Dinitro toluene3-Nitroaniiine3,3 '-Dichlorobenzidine4-Bromophenylphenyl ether4-Chloro-3-methylphenol4-Chloroaniline4-Chlorophenylphenyl ether4-Methylphenol4-Nitioaniline4-Nilrophenol4,4'-DDD

4,4'DDE4,4"-DDT4,6-Dinitro-2-methylphenol

0.0075

"Existing standards come from documents coded in the Standard Reference column. Reference codes can befound in Table 7.

''Standard is for total trihalomethanes (chloroform, bromoform, bromodichloromethane, anddibromochloromethane).

113

Table 7. References to regulatory standards coded in Tables 1-6

Standardreference Reference document

code

1 Safe Drinking Water Act-National Primary Drinking Water Regulations, 40 CFR 141, as amended

2 Safe Drinking Water Act-National Secondary Drinking Water Regulations, 40 CFR 143, as amended

3 State of Tennessee Hazardous Waste Regulations TN 1200-1-11-05, Appendix 05/B

4 National Primary Drinking Water Regulations: Synthetic Organic Chemicals, U.S. EPA, FederalRegister, July 8, 1987, pp. 25,690-25,717

Aroclor-1254 was detected in one sample below the quantitation limit and was not consideredfurther (Table 2). This is because the methodology for PCB analysis is designed to guard againstfalse negative determinations in waste samples. Consequently, results below the quantitation limitare suspect.

Table 4 contains the list of detected total metals. Total metals were excluded from dataanalysts because the purpose of this assessment is to evaluate groundwater contaminant levels atthe perimeter of SWSA 6 due to transport phenomena. The measurement of total metals may beconfounded by the presence of clays in the samples due to well construction. In contrast, thepotential for transport of metals in groundwater is greater in solution than in solid form, thusdissolved metals were considered rather than total metals.

The summaries for the analytes detected and undetected can be found in Tables 5 and 6,respectively. Although fluoride was detected in approximately 17% of the samples, the level wasnot different from the detection limit. Testing for differences is therefore not necessary.

Table 8 contains a list of analytes found in either the perimeter wells only or the up-gradientwells only. Some of these analytes were excluded from statistical analysis by arguments givenearlier. Of the 16 analytes remaining in the list, all but one were found in perimeter wells.

Plots of the data are invaluable for assessing data quality and suggesting relationships. Anexample of one display (Fig. 3) shows the relationship over time within a well and differencesamong the wells. Positions along the jc-axis correspond to SWSA 6 wells and are labeled by wellidentification numbers. The axis description is constructed to show whether the well is anup-gradient or perimeter well and to what cluster the well belongs. The v-axis is the responsemeasured, which is dissolved manganese concentration in this example. Two horizontal linesappear on the plot and denote the laboratory detection limit (dashed line) and a regulatory upperlimit on concentration (solid line). These limits and the percentage of the observations detectedappear at the top of the plot. The plotting symbol for an observed value is the sampling quarter(1 for the first sampling quarter, the third quarter of 1988, etc.). An asterisk denotes the wellmean, the average of the observed values during the year of sampling. From this plot, the viewercan quickly check for obviously discrepant observations that may need to be verified, contrastwells, observe time relationships, and compare values to the regulatory limit.

114

Table 8. Constituents detected in perimeter wellsonly (P) or in up-gradient wells only (U)

Parameter Well type

Aroclor-1254' P

Benzene P

Carbon tetrachloride P

Chloroform P

Chloromethane P

Cobalt, total* U

Fecal coliform P

Lead, total" U

Mercury, dissolved" V

Naphthalene P

Nitrate (as N) P

Phosphate P

Silver, total* P

TrichJoroethene P

Xylene, total P

1,1-Dichloroethane P

1,1-Dichloroethene P

1.1,1-Trichloroethane P

1,2-Dichloroelhane P

1,2-Dichloroethene P

2,4,5-T1 P

4-Methyl-2-pentanone U

"Excluded from statistical analysis. (See text forexplanation.)

ESTABLISHMENT OF BASIS FOR BACKGROUND AND COMPLIANCEWELL GROUPING

To determine whether there is a statistically significant difference between background andcompliance points, it is necessary to establish which compliance wells are to be compared withwhich background wells. To accomplish this, a cluster analysis was performed using water qualityparameters pH, alkalinity, and conductivity. These parameters were selected because they typicallydiffer among aquifer regimes and are generally robust to contaminants in the water. Clustering isused here in an exploratory manner to identify natural groupings of the up-gradient and perimeterwells with respect to the selected water quality parameters. The objects clustered were individualwell sampling events (denoted by well x sampling quarter). Clustering objects defined in thismanner permits a check on the consistency of well groupings over time. There is evidence forvalidity of a particular partitioning of wells if all sampling events for an individual well are placedinto the same cluster.

The cluster dendrogram displaying the similarity among objects is shown in Fig. 4. Objects arelabeled by well type (P or U), well identification number, and sampling quarter. Ticks on they-axis correspond to scaled Euclidean distances. There appears to be a natural grouping into three

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Fig. 3. Plot of groundwater concentration of dissolved manganese in SWSA 6 wells, regulatory and detection limits, and percent detected.

116

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117

clusters; the branches below the dashed line divide the tree into three sections. Notice that almostall sampling quarters for each well are grouped into the same cluster. For example, all foursampling quarters for up-gradient well 857, denoted by U857-1, U857-2, U857-3, and U587Awere placed into the leftmost cluster on the plot.

A summary of the cluster analysis is given in Table 9. Cluster characteristics might be looselysummarized as follows:

Cluster pH Alkalinity Conductivity

1 low low low2 neutral high high3 high low low

The wells are identified in the plot displayed in Fig. 5. Various elevations (y-axis) are plottedversus well identification number (x-axis) ordered so that fhe ground elevation of the wellincreases from left to right. The solid line increasing from left to right is the ground elevation ateach well site and the dashed line is the average groundwater elevation in the wells during the foursampling quarters. The vertical line segments show well screen ranges, the elevation range fromwhich water is drawn. Each line segment is labeled by the well identification number at the bottomand well type (U or P) and cluster number at the top. Cluster 1 wells (835-837, 857, and 859)tend to be more shallow; their screen ranges bracket the average water table elevation. Cluster 2wells (831, 832, 841-844. 846, and 847) tend to be deeper, and cluster 3 weUs (745, 833,838-840, 855, 856, 858, and 860) tend to be the deepest weUs.

The clustering results indicate three groundwater quality regimes for SWSA 6, based upon thedata collected to date. Water quality is quite homogeneous within each regime and appears to berelated to the depth of the screened interval relative to the depth of the water table. Furtherreference to clusters will imply clusters of wells (not individual well sampling events). Analysis ofcontaminant data was conducted within and between clusters, the rationale being that there is lessmixing of groundwater between clusters than within clusters. Therefore, transport of contaminantsbetween clusters is unlikely.

MODELING RELATIONS AMONG UP-GRADIENT AND PERIMETER WELLSIN THREE AQUIFER REGIMES

There are 15 constituents with detectable values in only perimeter wells and 1 constituent withdetectable values in only up-gradient wells. Therefore statistical analyses were not carried out onthese analytes to show differences between up-gradient and perimeter wells. For each of theremaining 21 constituents that were detected in both the up-gradient and perimeter wells, atwo-way analysis of variance with two factors was performed with cluster (factor levels cluster 1,cluster 2, and cluster 3 representing different aquifer regimes), well type (factor levels P and Urepresenting areas within SWSA 6 with and without potential contamination), and the interactionof cluster and well type (denoted by cluster x well type).

The populations of interest are SWSA 6 perimeter locations for each cluster and well typeduring the year the groundwater was sampled. Each well is assumed to be representative of itscorresponding population, that is, representative of locations classified according to cluster andwell type. The data for a well is the average constituent value for the four consecutive samplingquarters. Observed differences among the levels of the factors are declared significant if they are

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Fig. 5. SWSA 6 groundwater, well screen, and ground elevations.

119

Table 9. Summary of cluster analysis performed on well x sampling quarter combinationsshowing the up-gradient wells (U) and perimeter wells (P) m the same clusters.

[Well identification numbers and sampling quarters are listed («.g., samplingquarter 1 corresponds to the third quarter of 1988)]

u

p

Cluster 1

857

859°837'835836

1,2.3.4

...,3,4

.,.,3,41,2,3,41.2,3,4

Cluster 2

U 831"832846

P 844842841839^843847

1...3.41,2.3.41,2,3.4

1,2,3.41,2,3,4.,2,3,4?

1,2,3,41,2,3,4

Cluster 3

U 855856858

P 860859*839^838'83374584f/841

1,2.3,41.2.3.4,2.3,4

,2.3,4b

,'.3,4.2.3,4,2,3,4,2.3,4,2.3,4\

"Well 837 was dry in Ihe first sampling quarter; well 859 had insufficient water to do morethan the field measurements in the first sampling quarter; and wells 837, 859, and 831 were dry inthe second sampling quarter.

'Had an unusually high pH in the first sampling period.These wells are similar in that: (a) pH-like up-gradient wells in cluster 2, (b) conductivity

(more) like up-gradient wells in cluster 3, and (c) alkalinity between up-gradient wells in cluster 2and those in cluster 3.

''Had an unusually low conductivity in the first sampling period.

large relative to a pooled estimate of well-to-well variance. Foimally, the model entertained for anobservation vyt in well type / and cluster j is

yljk = u + WT, + CluSj + (WT + Clus)v + error,yi,

where u. WT,, and CluSj ate constants; i indexes well type (U or P); j indexes cluster (1, 2, or 3);and k indexes observation in the ij group. The errors are assumed to be random and independent,with mean 0 and constant variance. For formal tests of hypotheses, the further assumption is madethat the errors have a normal (Gaussian) distribution.

The results of the two-way analysis of variance are given in Table 10. Factors significant atthe 0.05 level are marked with asterisks. When possible, a general indication of the orderrelationship among factor levels is given. If the interaction well type x cluster is not significant,the differences between well means at levels of cluster are the same regardless of well type (U orP). For alkalinity, there is no significant well type x cluster interaction, but there is a significantcluster effect. The symbols 2 > 3 > 1 represent the significant ordering of the cluster means; thatis, cluster 2 mean is larger than cluster 3 mean, which is larger than cluster 1 mean, regardless ofwell type. (A significant interaction implies that the differences between cluster means dependupon which well type is considered.) The ordering of the well type and cluster means is shown inthe last column of the table. Homogeneous groups of means are underlined (as determined byTukey's "honestly significant difference" test of pairwise differences and the maximumexperimentwise error rate set at 0.05). For alkalinity, the cluster 2 means (U2 and P2 denotingup-gradient and perimeter wells in cluster 2) are statistically larger than the remaining means, the

120

Table 10. Summary of two-way analysis of variance results on constituents detected inboth the up-gradient and perimeter wells

Ordering of well type bycluster means (largest tosmallest). Homogeneousgroups are underlined^

Constituent Cluster" Well w

cluster'

Alkalinity, as CaCO,

Chloride

«Co'Conductivity'"CsIron, dissolved1*

Manganese, dissolved'Sodium, dissolved'Ethylbenzene*Gross alpha

Gross Beta'Organic halides, tolai'"

PHRecoverable phenolics, total'1

Sulfate (as SO4)TemperatureTetrachloroethene*

Organic carbon, total'

Radioactive strontium, totalRadium, totalTritium''

*2 > 3 > 1

*3 > 2 > 1 *P > U

•2 > 3,1

U2

P2

P2

U2PIV2

PIP2P3Ul

P2P21)3

U3

U2PIP2

PI

U2P2P2

P2

U2

UlP2U2P2

P2U2PIU2

Ul

PIP3

U2

P2UlPI

P2

U3U2P3

P3

PI

P3

P3P2P3

P3P3P2P2

U2

P3U2

PIU3U2P3

U2

P2P3PI

U3

P3

PIU3U3U3

U2U3UlP3

U3UlP2Ul

P3U3

Ul

U3

P3U3Ul

PI

Ul

U3UlP3Ul

U3PIU3U3

P3U3Ul

P2PIP3

U3

P3

UlPIU2

Ul

U3

U2

PIUlPI

UlUlU2PI

PIU2PI

P3

UlP2U2

Ul

PIUlU3

'An asterisk (*) indicates significance at the 0.0S level. When possible, a general indication of factorlevel order realtionship is given.

'Tukey's "honestly significant difference" test for pairwise differences applied to the well type xcluster means.

'Constituent level is clearly higher in well 842 (cluster 2, perimeter well) than in any other well.''Constituent level is clearly higher in well 832 (cluster 2, up-gradient well) than in any other well.'Constituent level is clearly higher in well 835 (cluster 1, perimeter well) than in any other well.'Significant differences among well type and cluster means were found using a nonparametric

(Kruskal-Wallis) analysis.fewer than 9% of the original measurements were above the detection limit.''About 16% of the original measurements were above the detection limit, and all were below the level

of quanlitation.

121

cluster 3 means are homogeneous, but the up-gradient well mean in cluster 3 is also a member ofa homogeneous group containing the cluster 1 means.

Graphical analysis and tests on residuals (observed value minus the model predicted value)were performed to check analysis of variance assumptions. The modified test (Levene 1960) forhomogeneity of variance identified only tritium with significant heterogeneity (p < 0.05). Tests fornormality lead to rejecting normality for 13 of the constituents. Plots of the data showed thereasons for this. In some cases, there was a single well with a high constituent level that wasobviously different from the other wells. In other cases, most values were at the detection limit,suggesting that a nonparametric analysis would be more appropriate (see Table 10 footnotes). Inthe remaining cases, where some of the assumptions may be violated (total radium and sulfate),there appeared to be greater variability in at least one of the groups.

A nonparametric analysis (Kruskal-Wallis) was performed and resulted in similar conclusions.Conflicting results can be attributed to a violation of assumptions made in the parametric approach.Significant differences were found for dissolved sodium and tritium. For dissolved sodium, cluster2 means are significantly larger than cluster 1 means. For tritium, the perimeter-cluster 2 mean issignificantly larger than any up-gradient well mean. These differences can be seen in Fig. 6.

Plots of the data for alkalinity, conductivity, pH, total organic carbon, dissolved iron, dissolvedsodium, sulfate, total organic halides, cobalt-60, gross a, gross p\ and tritium are shown in Fig. 6.This type of plot is useful for visualizing the comparisons tested in an analysis of variance. Tickmarks on the jr-axis correspond to factor combinations (e.g., U cluster 1 denotes up-gradient wellsin cluster 1). Tick marks on the y-axis correspond to constituent values. The sampling year averageva!ue for each well is plotted and labeled with the well number. A dashed vertical line segmentconnects the minimum and maximum values for all wells in a factor combination. The two solidlines on the plot are called profiles. The up-gradient well profile is the line connecting theup-gradient well means denoted by U's on the plot. Similarly the perimeter well profile is the lineconnecting the perimeter well means denoted by P's on the plot. A significant well type x clusterinteraction would be seen in the plots as nonparallel lines (profiles). For alkalinity, the up-gradientwell profile is nearly parallel (nonsignificant test for interaction in the two-way analysis ofvariance) to the perimeter well profile. This is in contrast to the nonparallel profiles displayed onthe plot of pH or gross a (significant test for interaction in the two-way analysis of variance).

These plots also demonstrate the need to supplement formal statistical analysis with graphicalanalysis. Several constituents in Table 10 were found at much higher levels in a single well thanany other well. Although significant differences were not declared in either the parametric ornonparametric analyses, it is clear that differences do exist and can be seen in Fig. 6.

Table 11 contains a summary of all parameters considered for statistical evaluation. The meanvalues are given for each well type and cluster. Means calculated from detection limits only areidentified as such. Parameters in Table 8 and parameters identified in Table 10 as havingsignificant differences among the well type and cluster means are not&J.

DISCUSSION

Cluster analysis was useful for establishing a basis for comparing the compliance wells andbackground wells. The three groundwater regimes as determined from the water quality parametersalkalinity, conductivity, and pH are related to the well depth. Lacking more specific informationabout groundwater flow paths, it appears more likely that waterbome contaminants would betransported within these regimes than among these regimes. As a result, comparisons of constituent

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124

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125

Table 11. Summary of mean values by cluster and well type (P for perimeter, U for up-gradient) for allparameters retained for statistical evaluation

Parameter

Alkalinity, as CaCO, (mg/L)"Benzene (mg/L)*Carbon tetrachloride (mg/L)*Chloride (mg/L)Chloroform (mg/L)*Chloromethane (rng/L)*MCo (Bq/L)Conductivity (ms/cm)"137Cs (Bq/L)Ethylbenzene (mg/L)Fecal coliform (col/100 mL)*Fluoride (mg/L)Gross alpha (Bq/L)*Gross beta (Bq/L)Iron, dissolved (mg/L)Manganese, dissolved (mg/L)Naphthalene (mg/L)6

Nitrate (as N) (mg/L)*Organic carbon, total (mg/L)Organic halides, total (mg/L)pH (std. units)*Phosphate (mg/L)6

Radioactive strontium, total (Bq/L)Radium, total (Bq/L)Recoverable phenolics, total (mg/L)Sodium, dissolved (mg/L)°Sulfate (as SO4) (mg/L)*Temperature (°C)Tetrachloroethene (mg/L)Trichloroethene (mg/L)Tritium (Bq/L)*Xylene. total (mg/L)''1,1-Dichloroethane (mg/L)*1,1-Dichloroethene (mg/L)''1.1,1-Trichloroethane (mg/Lf1,2-Dichloroethane (mg/L)*1,2-Dichloroethene (mg/L)*4-Me!hyl-2-pentanone (mg/L)*

Cluster 1

P

160.00410.0050*4.30.00510.010*0.0580.0650.110.00471.0*1.0*0.0540.0800.050*0.0400.00810.702.20.00695.15.0*0.0400.0240.0010*3.65.0*

140.00500.0047

4000.00550.0050*0.0050*0.0050*0.0050*0.0050*0.010*

U

6.80.0050*0.0050*1.00.0050*0.010*0.170.063

-0.0220.00421.0*1.0*0.260.580.050*0.0120.010*0.50*0320.0050*535.0*0.0400.0230.0010*0.745.0*

140.0050*0.0050*

140.0050*0.0050*0.0050*0.0050*0.0050*0.0050*0.010*

Cluster 2

P

3600.0050*0.0198.70.0200.0097250.520.0670.00431.0*1.00.111.70.0690.0300.010*0.871.00.107.05.0*0.0630.0350.0010*

1953140.00570.092

140000.0050*0.00530.00470.00470.0100.00830.0092

U

3700.0050*0.0050*450.0050*0.010*0.0330.730.0740.00411.0*1.00.130.320.170.0210.010*0.50*0.900.00367.05.0*0.120.0320.0010

13150140.00480.0050*

110.0050*0.0050*0.0050*0.0050*0.0050*0.0050*0.010*

Cluster 3

P

1800.0050*0.0050*4.00.0050*0.0100.100.220.0310.0048

211.0*0.0760.170.0680.0290.010*0J0*0.630.00517.05.00.0480.0280.00106.7

10140.0050*0.0048

3600.0050*0.0050*0.00480.0050*0.0050*0.0050*0.010*

u970.0050*0.0050*1.00.0050*0.010*0.0550.0960.0450.00421.0*1.0*0.0620.190.0510.0200.010*030*0.640.00428.05.0*0.0700.0260.084*4.1

11140.00490.0050*450.0050*0.0050*0.0050*0.0050*0.0050*0.0050*0.010*

'Statistically significant differences among the cluster by well type means were found. (See Table 10.)^Parameters found in perimeter wells only or in up-gradient wells only. (See Table 8.)'All values were at the detection limit

126

levels among compliance and background wells should be influenced less by fundamentalgroundwater differences that are unrelated to waste disposal.

The analyses show that some differences among wells at SWSA 6 can be attributed to thegroundwater quality regimes alone, for example, alkalinity and sulfate. Other differences can beattributed to whether the well is an up-gradient well or a perimeter well (Table 8). Somedifferences are more complicated, as indicated by the significant interactions identified in Table 9.The existence of a significant interaction implies that statements about well type differencesdepend upon the groundwater quality regime considered. Other observed differences that are notstatistically significant or not accounted for by graphical inspection are attributable to the naturalvariation in SWSA 6 groundwater.

Results from parametric and nonparametric analyses were similar for most constituents. Theexceptions can be attributed to violation of assumptions. Some differences were detected by neithermethod but could be readily seen in graphical displays. This reinforces the need for visual datachecks to verify assumptions and to support formal statistical tests.

ACKNOWLEDGMENTS

The authors wish to thank K. L. Daniels, W. M. McMaster, G. K. Moore, and R. K. Owenbyfor helpful discussions that initially motivated and later improved the work presented here. We areespecially grateful to I. L. McCollough for maintaining the groundwater quality data base used inthese analyses.

LITERATURE CITED

Becker, R. A., and Chambers, J. M. 1984. S, An Interactive Environment for Data Analysis andGraphics, Wadsworth, Belmont, Calif.

Boegly, W. J., Jr. 1984. Site Characterization Data for Solid Waste Storage Area 6,ORNL/TM-9442, Martin Marietta Energy Systems, Oak Ridge Natl. Lab.

Brown, M. B., and Forsythe, A. B. 1974. "Robust Tests for the Equality of Variances," / . Am.Stat. Assoc. 69, 364-67.

Conover, W. J. 1980. Practical Nonparametric Statistics, 2d ed., Wiley, New York.

Conover, W. J., Johnson, M. E., and Johnson, M. M. 1981. "A Comparative Study of Tests forHomogeneity of Variances, with Applications to the Outer Continental Shelf Bidding Data,"Technometrics 23, 351-62.

Davis, E. C , and Solomon, D. K. 1987. A Summary ofORNL SWSA-6 Waste Inventory: 1972Through 1986. ORNL/RAP/LTR-87/6, Martin Marietta Energy Systems, Oak Ridge Natl.Lab.

127

Davis, E. G, ct al. 1987. Summary of Environmental Characterization Activities at the Oak RidgeNational Laboratory Solid Waste Storage Area Six, Fiscal Year 1986 Through 1987,ORNL/RAP/LTR-87/68, Martin Marietta Energy Systems, Oak Ridge Nad. Lab.

Everitt, B. 1974. Cluster Analysis, Heinemann Ed., London.

Levene, H. 1960. "Robust Tests for Equality of Variances," pp. 278-92 in Contributions toProbability and Statistics, ed. I Olkin, Stanford U. Pr., Palo Alto, Calif.

Miller, R. G., Jr. 1981. Simultaneous Statistical Inference, 2d ed.. Springer Verlag, New York.

Neter, J., and Wasserman, W. 1974. Applied Linear Statistical Models, Irwin, Homewood, 111.

SAS Institute, Inc. 1985a. SAS™ User's Guide: Basics, Version 5 Edition, Cary, N.C.

SAS Institute, Inc. 1985b. SAS™ User's Guide: Statistics, Version 5 Edition, Cary, N.C.

Shapiro, S. S., and Wilk, M. B. 1975. "An Analysis of Variance Test for Normality (CompleteSamples)," Biometrika 52, 591-611.

Snedecor, G. W., and Cochran, W. G. 1980. Statistical Methods, 7th ed., Iowa St. U. Pr., Ames,Iowa.

U.S. Environmental Protection Agency (EPA) 1989. Statistical Analysis of Ground-WaterMonitoring Data at RCRA Facilities, Interim Final Guidance, EPA/530-SW-89-026.

ANALYSIS AND EVALUATION OF GROSS RADIOACTIVITY DATA

Marie F. Tardiff and Dennis A. WolfOak Ridge National Laboratory*

P.O. Box 2008Oak Ridge Tennessee 37831-6102

ABSTRACT

U.S. Department of Energy (DOE) orders require that gross radioactivity analysesbe used only as trend indicators unless supporting analyses provide a reliablerelationship to specific radionuclides. A historic part of the airborne missionssampling program at the Oak Ridge National Laboratory (ORNL) has been the analysisof particulate samples for gross alpha and gross beta. These samples are routinelyanalyzed 8 days after collection in order to reduce the impacts of snort-lived, naturallyoccurring isotopes upon the results.

The purpose of this investigation was to characterize the short-lived component ofthe gross alpha and gross beta signatures for ORNL emission point sources. Genericdecay curves are presented based upon multiple counts of samples collected over1 year. Isotopes that match the half-life signatures are proposed, and a program ispresented that will verify these inferences. The consequences of this investigation arediscussed relative to the ongoing sampling program including die potential forassociating these signatures with specific isotopes.

INTRODUCTION

Airborne effluents from the major stacks at ORNL with a potential to emit radionuclides aresampled continuously for estimating emissions source terms. This information is used inconjunction with dispersion models to estimate the potential radiation dose to the public due toairborne emissions from the facility. Emissions samples are captured sequentially via paper filtersfor particulates, activated charcoal filters for adsorbable gases, silica gel columns for tritiated watervapor, and flow-through gross beta-gamma chambers to estimate noble gases. Particulate samplesare used to quantify alpha and beta emitting isotopes; adsorbable gas samples are used to quantifybeta-gamma emitting isotopes. The sequence of the collecting media and the counting chamber isdesigned to provide samples of generic emissions types (e.g., particuiates, adsorbable gases,tritiated water vapor, and noble gases) in an order that minimizes interferences and samplingmethod artifacts.


129

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The emphasis of the airborne emissions monitoring program at ORNL has historically beenlong-lived isotopes because they are persistent in the environment and, consequently, may havesignificant off-site dose impacts across multiple exposure pathways. Routine monitoring programstypically relied upon generic measurement systems for the alpha and beta emitters because theidentification of individual isotopes of these types is complex and expensive. The approach hasbeen to measure gross alpha, which is the sum of all alpha activity in the sample, and gross beta,which is the sum of all beta activity in the sample. The measurement and quantification ofindividual gamma emitters is straightforward and inexpensive and, therefore, has typically beenconducted on an individual isotope basis.

The activity or rate of decay of the short-lived radionuclides is substantially higher than thelonger lived radionuclides because of the basic physics of the isotopes. The resultant effect is forthe signatures of the short-lived isotopes to overwhelm the signatures of the isotopes that aredosimetrically more significant. The laboratory analysis design has compensated for the highactivity of the short-lived isotopes by allowing the particulate samples to decay for 8 days prior togross counting, thereby reducing the contribution of short-lived isotopes to the gross signatures.Consequently, the gross alpha and beta results have provided an indication of trend forradionuclides that have a half-life of several days or longer.

Two limitations are associated with using the gross measurement approach. Each isotope has aunique energy associated with its nuclear decay and, consequently, has a unique efficiency ofdetection. This is not a problem when the material of interest contains a single isotope or amixture of known proportion. The typical design of airborne effluent treatment and dischargesystems consists of combining multiple sources into a common treatment system with ventilationthrough a single stack or vent. This usually results in multiple constituents of indeterminantproportions. The second limitation to the gross measurement approach is a consequence of the firstlimitation; assessments of environmental impacts and potential dose to humans depend uponunique properties of each isotope that cannot be determined with gross measurements.

Recent changes in the regulations for radioactive airborne emissions have addressed theseissues. Demonstrations of compliance to the regulations must be made with isotope-specificinformation unless the effluent consists of a single isotope or a stable ratio of multiple isotopes.Additionally, it is necessary to investigate the short-lived component of the radioactivity signaturesfor the particulate filters in order to make a comprehensive estimate of the isotopic componentsand to assess off-site dose impacts.

We examined the gross alpha and gross beta signatures of particulate filters after short periodsof decay in order to assess the magnitude of the short-lived signature and to determine whetherthis short-lived signature was associated with a specific isotope or a mixture of isotopes. Finally,we determined whether the short-lived particulate component of the particulate emissions wasbeing quantified through its parents or daughters on other sampling media.


The data used in this analysis were obtained from the paniculate sample filters, which are thefirst collecting media of the sampling train. The samples are collected continuously, and the mediaare exchanged weekly. Routine sample analyses include a gross alpha and gross beta analysis,which are nondestructive counting techniques, 8 days after collection. The sample analysisprogram was augmented with gross alpha and gross beta measurements within 24 hours of

131

collection and 2 days after collection in order to obtain information regarding the decay dynamicsof the short-lived sample constituents. This program was continued for 1 year, 33 usable data setswere produced.

The sample counting system is a Tennelec model LB 5100 automated gas flow proportionalalpha beta analyzer. This system is calibrated with natural uranium for the alpha channel and *°Srand ^Y for the beta channel.

RESULTS

Radioactive decay is an exponential phenomenon with each radioactive isotope having aunique rate of decay. The rate of decay for an isotope can be expressed either as its half-life or asits decay constant. The half-life is the time required for a radioactive substance to lose 50% of itsradioactivity through radioactive decay. This time is independent of the total initial mass. Thedecay constant is the fraction of nuclear transitions per unit time. The relationship between half-life and the decay constant is shown in Eq. 1.

X - In 2fTlB . (1)

Log-linear plots of the activity data for total gross alpha and total gross beta versus time areshown in Figs. 1 and 2 for one emission point source. As described in this section, each filter wasmeasured three times; the data plots show each of the measurement triplets connected by a singleline. The slope of each line segment is the decay constant for that line segment. The steeper theslope of the line segment, the faster the rate of decay and, consequently, the shorter the half-life. Ifthe activity for a sample were due to a single isotope, then the semilog plot for the three pointswould be a straight line. The elbow effect shown by the line segments for each of the samplesindicates that there are multiple isotopes involved in the total signature. The initial segment of theplots has a steeper slope than the subsequent segment because the activity of the short-livedisotopes initially dominates the gross signature.

The emphasis of this investigation is the data for the first two measurements for each sample.These data represent an approximation of the rate of decay for the short-lived radionuclides in thesample. The third data point for each sampSe is the data presently being used in the airborneemissions monitoring program. The log plots show that the gross signature between measurementpoints one and two result from radionuclides with a shorter half-life than the data from points twoand three.

An average decay constant for the short-lived gross activity can be estimated by computing theslope of the line segment connecting the first and second measurements. These constants can thenbe converted into half-lives using the relationship in Eq. 1. The average half-lives for the short-lived gross alpha and gross beta were 10.8 h and 11.3 h, respectively. Figure 3 shows box plotdistributions of the estimated gross activity half-lives.

The semilog plots of the data show that the rate of decay decreases with time. Thisphenomenon is represented by the elbow-shape of the line segments joining the data triplets. Ifdata were collected more frequently, tne shape of ihe plots would resolve to a smoothly decreasingexponential function. Depending upon the number of radionuclides generating the gross activity

132

° 0 50 1 0 0 1 5 0 2 0 0 2 5 0

T i m e S i n c e S a m p l e C o l l e c t i o n (h)

Fig. 1. Total alpha activity (station 3039-2).

realization and the half-life of the shortest lived isotopes, the time from collecting the sample untilthe first measurement may be the primary determinant of the estimated half-life for the data from aparticular sample.

A plot was constructed of the time lag from the sampling event to the first analysis versus theestimated half-life resulting from the first two measurements in order to determine whether thetime lag to analysis was an artifact of the analytical design that was controlling the estimate of thegross half-life. The plots are shown in Fig. 4. The discontinuity in the time data for both plots is aresult of the samples being counted by the laboratory on the day they are received or on thefollowing day. The lack of slope in the plots shows that the estimate of half-life is independent ofthe time lag to the first analysis. This result can be corroborated by plotting the time lag to thefirst analysis versus the estimated initial gross alpha and gross beta activities. These plots areshown in Fig. 5. The lack of slope in the plots shows that there is no relationship between thelevel of initial activity and the time lag to analysis.

133

cra

U

10

4J

oE -

in

o

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0

T i m e S i n c e S a m p l e C o l l e c t i o n ( h )

Fig. 2. Tola! beta activity (station 3039-2)

Stability of the signatures is of primary importance in assessing the feasibility of using grossmeasurements as surrogates for isotopic analyses. The half-life estimates and 95% confidenceintervals for the means across 33 weeks of data averaged 10.8 ± 0.8 h for gross alpha and11.3 ± 1.5 h for gross beta. These means and confidence intervals can be used to identifycandidate isotopes that are responsible for the gross activity.

The estimated gross alpha and gross beta half-lives were compared to alpha and beta emittingisotope half-lives in the Radiological Health Handbook. Four candidate beta emitting isotopeswere identified that have half-lives within the 95% confidence interval of the gross beta signature.They are 77Ge (11.3 h), 93Y (10.3 h), 2MBi (11.2 h), and 212Pb (10.6 h). No alpha emitting isotopeswere identified that matched the gross half-life confidence interval of 10.0 to 11.6 h.

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•oID

01

A l p h a B e t a

G r o s s A c t i v i t y

Fig. 3. Distribution of half-life estimates from gross activity (station 3039-2).

DISCUSSION

Of the four candidate isotopes that match the gross beta half-life, 93Y is a fission product, 77Geand 2WBi are accelerator products, and 212Pb is a daughter in the natural thorium decay series. It ishighly unlikely that either a fission product or accelerator products would be consistently presentin this emission source over the course of a year. Isotopes associated with the thorium decay seriesare generally present in natural materials from the decay of thorium, uranium, and radium isotopes.Historic research missions at ORNL included investigations of various uranium and thorium fuelcycles. Residual amounts of these isotopes in the research facilities would provide a continuoussource of daughters, including 212Pb. Figure 6 shows the thorium decay series from 232Th to thestable element 2O8Pb.

(0-H

oCM

tu

o4J

0)

H

to

o4J

CP

OCM

9 . 5 1 0 . 0 1 1 . 0 1 2 . 0 10 11 12 13 14

E s t i m a t e d A l p h a H a l f - L i f e ( h ) E s t i m a t e d B e t a H a l f - L i f e (h)

Fig. 4. Effect of analysis lag time on estimaled half-life (s&tion 3039-2).

to

(O

c

uwM

- H

lu

o

o

m

o

<u6

0 100 200 300 400 500

E s t i m a t e d Alpha A c t i v i t y (Bq)

inu

u,Q4-1

tn

o\

100 300 500 700

E s t i m a t e d Beta A c t i v i t y (Bq)

Fig. 5. Effect of analysis lag time on estimated gross activity at time of sample collection (station 3039-2).

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ALPHA DECAY

BETA DECAY

216pQ

0.15S

212pB

10.6H

2 1 2B!

60.6M

3.05M

212RO

NIL

208p B

STABLE

Fig. 6. The thorium decay chain.

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The gross alpha signature is less easily resolved. The decay of 2l2Pb (Fig. 7) follows twoseries, either 212Pb, 212Bi, 212Po, and 208Pb or 212Pb, 212Bi, 2O8T1, and 208Pb. The two series account for64% and 36% of the total decay, respectively. As shown in Fig. 7, the nuclear transition sequencesthat occur in these series are beta beta alpha and beta alpha beta. Additionally, the half-lives of the212Bi, 212Po, and 2O8T1 are very short relative to the half-life of 212Pb.

<KRn-22055.61 s

^Pb-212]10.643 h

Roalpha Idecay y

betadecay

64.07%

Fig. 7. The ̂ n decay chain shows how 22ORn and its daughters are related.

A short-lived parent that has much shorter-lived progeny will achieve a transient equilibriumstate after about 7 half-lives of the daughters in which the parent activity decreases measurablyover time and the progeny appear to decay at the same rate as the parenL This is because eachparent nuclear transition is followed almost immediately by the transitions of the daughters. Whenevaluated in the time perspective of the parent decay rate, there is no apparent buildup ofdaughters, and the daughters appear to decay at the same rate as the parent. Figure 8 shows aschematic representation of these relationships.

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1000

100

§< 10

1

EQUILIBRIUM

0 2 4 6 8 10 12 14 16

TIME (t) * half fives of daughterFig. 8. General parent-daughter relationship in transient equilibrium.

The half-lives of 212Bi and 212Po are 60.6 min and 0.3 us, respectively. These half-lives aresufficiently shorter than the parent 212Pb activity of 10.6 h to produce the transient equilibriumphenomenon. Hence, the gross alpha signature of the samples has an apparent half-life equivalentto 212Pb.

Additional corroborating evidence for the gross activities being from the 212Pb decay chain isavailable from the ratio of beta emitting daughters to alpha emitting daughters. As noted in Fig. 7,the decay sequence is beta beta alpha 64% of the time and beta alpha beta 36% of the time. Ineither case, there are two beta-emitting nuclear transitions for each alpha-emitting nucleartransition. If the gross activities of the samples are due to 2)2Pb and its progeny, then the ratio ofbeta activity to alpha activity should be 2 : 1. Figure 8 shows a plot of beta activity versus alphaactivity for each of the 33 data sets and a line drawn through the origin with a slope of 2. Theratio of the activities appears to be remarkably constant and independent of the magnitude of thesignatures. There is a consistent bias in the relationship to less than the theoretical ratio of 2.

Subsequent to the initial investigation, several samples were evaluated using gammaspectrometry in order to identify the isotopes that are contributing to these gross signatures. These

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measurements showed 212Pb and its progeny to be the source of the gross signatures. Theimplication of these results is that if the gross signatures from the paniculate filter and the noblegas chamber are quantified and converted into emission estimates along with the isotopic analysesof the charcoal filters, it is likely that the signature for the 2l2Pb decay chain is being quantified bythree separate methods, and the emissions are overestimated.

The departure from the theoretical activity ratio of 2 : 1 can result from the presence of otherisotopes in the sample and the efficiencies of the gross counting systems in quantifying thecalibration standards relative to the 212Pb decay chain. Table 1 shows the energies of the calibrationsources and the energies of 212Pb and its progeny. The relative differences in the energies betweenthe calibration sources and the decay chain could result in the bias that is shown in Fig. 9. Thepresence of other isotopes resulting in the consistent bias shown in Fig. 9 is very unlikely.

cr

oor-

oo

ooID

Line with Slope of 2

o>i o

-H

•H

U o

(0

0) Om o

CM

OO

100 200 300

Alpha A c t i v i t y (Bq)

Fig. 9. Gross beta versus gross alpha activity (station 3039-2).

4 00

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Table 1. Decay energies

Calibrationsources

Chain

CONCLUSIONS

Betaemitters

"Sr

212Pb2 1 2Bi

*~n

Energy(MeV)

0.2/0.9

0.10.70.6

Alphaemitters

a«U

2l2Bi2 1 2Po

Energy(MeV)

4.2

6.18.8

1. Sequential gross analyses of samples can be used to estimate generic decay curves andhalf-lives.

2. The estimates of half-lives based upon gross radioactivity data can be evaluated for theirindependence from the time to the first analysis and the magnitude of radioactivity.

3. The short-lived gross alpha and gross beta signatures for this emission point are predictablyassociated with 2l2Pb and its daughters. This conclusion is supported by the generic half-life of thegross beta signature, the ratio of alpha emitters to beta emitters in the 2l2Pb decay series, and thetransient equilibrium of the progeny.

4. The relationship among the 212Pb signature of the charcoal filter sampling system, thepaniculate filter system, and the noble gas counting chamber is probably predictable and needs tobe determined in order to avoid quantifying multiple realizations of the same activity andoverestimating emissions.

THE CLINCH RIVER RESOURCE CONSERVATION AND RECOVERYACT FACILITY INVESTIGATION: OVERVIEW AND PRELIMINARY

SCOPING OF THE OFF-SITE CONTAMINATION OF SURFACEWATER ENVIRONMENTS DOWNSTREAM FROM THE

U.S. DEPARTMENT OF ENERGYOAK RIDGE RESERVATION*

Bruce L. Kimmel and Curtis R. OlsenEnvironmental Sciences DivisionOak Ridge National Laboratory1"

Oak Ridge, Tennessee 37831-6351

ABSTRACT

Operations and waste disposal activities at the U.S. Department of Energy's OakRidge facilities have introduced a variety of contaminants (e.g., radionuciides, metals,and organics) into local streams that drain into the impounded Clinch and Tennesseerivers. The Clinch River Resource Conservation and Recovery Act FacilityInvestigation focuses on evaluating the potential risks to the environment and tohuman health and welfare that are associated with (1) contaminant releases to theClinch River from the Oak Ridge facilities and (2) long-term contaminantaccumulations in the river and reservoir sediments downstream from the Oak RidgeReservation (ORR).

We have conducted a preliminary sediment survey, using 137Cs activity as ascoping tool, to assess (1) the distribution of contaminants in the sediments of WattsBar Reservoir, the first impoundment downstream from the ORR, and (2) the retentionefficiency of the reservoir for particle-associated contaminants. Watts Bar Reservoirwas impounded in 1942, just prior to the development of the Oak Ridge complex, andtherefore, retains in its sediments a long-term history of contaminants released fromthe ORR.

Approximately 190 surface-sediment grabs and over 60 sediment cores werecollected from Watts Bar Reservoir and analyzed for 137Cs, a primary particle-reactiveradionuclide released from the Oak Ridge National Laboratory. A number of coreshave also been analyzed for total mercury to assess downstream contaminationresulting from mercury releases from the Oak Ridge Y-12 Plant. The highestdischarges for both Cs and Hg occurred during the mid-1950s. The verticaldistributions of 137Cs and Hg are strongly correlated (i2 = 0.87), and bothcontaminants exhibit a large subsurface peak coincident with their peak dischargehistories. The sediment depth of the subsurface peaks varies with location in the

'Project sponsored by the Office of Environmental Restoration and Waste Management, U.S. Department of Energy,under Contract No. DE-AC05-84OR21400 with Martin Marietta Energy Systems, Inc.

^Managed by Martin Marietta Energy Systems, Inc., under contract DE-AC05-84OR21400 with the U.S. Departmentof Energy.

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reservoir and depends on the rate of sediment accumulation. We estimate that about1.07 x 1013 Bq (290 Ci) of nlCs now reside in the Wads Bar Reservoir sediments;this amount represents about 85% of the decay-corrected total of about 1.24 x 1013 Bq(335 Ci) of Cs released into the river system since 1949. Using the correlationbetween the distribution of 137Cs and Hg, we estimate that about 75 metric tons of Hghave also accumulated in the Watts Bar Reservoir sediments.

PRELIMINARY SCREENING ANALYSIS OF THE OFF-SITEENVIRONMENT DOWNSTREAM OF THE

U.S. DEPARTMENT OF ENERGYOAK RIDGE RESERVATION*

B. G. Blaylock, F. O. Hoffman, and M. L. FrankEnvironmental Sciences DivisionOak Ridge National Laboratory*


ABSTRACT

Operations and waste disposal activities at the Oak Ridge Y-12 Plant, the OakRidge National Laboratory, and the Oak Ridge K-25 Site, located on the U.S.Department of Energy (DOE) Oak Ridge Reservation (ORR) in eastern Tennessee,have introduced airborne, liquid, and solid wastes into the surrounding environment.Some of these wastes may affect off-site areas by entering local streams that ultimatelydrain into the Oinch River. Previously reported concentrations of radionuclides,metals, and organic compounds in water, sediment, and biota of the Clinch River andWatts Bar Reservoir suggest the presence of contaminants of possible concern to theprotection of human health and the environment.

A preliminary screening was conducted of contaminants in the off-site surfacewater environments downstream of the DOE ORR. This screening analysis representspart of a scoping phase of the Clinch River Resource Conservation and RecoveryFacilities Investigation (CRRFI). The purpose of this preliminary screening analysis isto use existing data on off-site contaminant concentrations to identify and prioritizepotential contaminants of concern for further evaluation and investigatioa The primaryobjective of this screening analysis is to ensure that CRRFI sampling and analysisefforts focus on those contaminants that may possibly contribute to human health orenvironmental risk.

Prepared for U.S. Department of Energy Office of Waste Management and Environmental Restoration under budgetand reporting codes CD 10 72 and GF 72.

^Managed by Martin Marietta Energy Systems, Inc., under contract DE-AC-05-84OR21400 with the U.S. Departmentof Energy.

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ASSESSING RADIOLOGICAL IMPACTS OF U.S.DEPARTMENT OF ENERGY FACILITIES

J. P. Witherspoon and F. R. O'DonnellEnvironmental and Health Protection Division


ABSTRACT

Potential radiation doses from releases of radionuclides from the U.S. Departmentof Energy (DOE) facilities must be assessed for routine facility operation. Both theEnvironmental Protection Agency (EPA) (Clean Air Act) and DOE have radiation doselimits for maximally exposed off-site individuals, and compliance with these limitsmust be demonstrated annually.

Radiation doses are calculated with approved environmental transport anddosimetry models such as AIRDOS-EPA. Site-specific data that are required as inputinto the model include source terms (annual radionuclide releases), description andlocation of emission structures (stacks, vents, etc.), and annual meteorological data andlocal demographic information. In addition to quantities of radionuclides released, thechemical form and particle size of airborne materials are important.

The extent to which variation in the above data input can affect calculatedradiation doses will be discussed. In addition, the use of monitoring data to supplementdose calculations will be considered.

'Managed by Martin Marietta Energy Systems, Inc., under contract DE-AC0S-84OR21400 with the U.S. Departmentof Energy.

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ECOLOGICAL AND ENVIRONMENTAL RISK ANALYSIS: AFRAMEWORK FOR DATA ANALYSIS AND INTERPRETATION'

Steven M. BartellEnvironmental Sciences DivisionOak Ridge National Laboratory*

Oak Ridge, Tennessee 37831-6036

ABSTRACT

Ecological and environmental risk analyses provide an operational paradigm forforecasting and evaluating the uncertain effects of anthropogenic disturbances to theenvironment. Sampling error, spatial-temporal heterogeneities, and the naturalfluctuations that characterize ecological and environmental measurements requiremethods of risk estimation that directly consider these sources of variability. The workreported here will describe differently scaled models for predicting ecological risk inrelation to toxic chemical exposure for populations of fish. Contributions of varioussources of uncertainty to risk estimates will be quantified in an example involvingchloroparaffin risk in a hypothetical system.

Risk analysis can serve as a conceptual framework for addressing sources ofuncertainty in the analysis and interpretation of surveillance data. Risk analysis isfrequently based on the comparisons of variable data with uncertain model predictions.A method that quantifies the relative contribution of model uncertainty and datavariance in evaluation of risk estimates will be presented. This method will be used toevaluate predictions of adverse effects on populations of freshwater plankton exposedto phenolic compounds in experimental ponds.

Research sponsored jointly by the U.S. Environmental Protection Agency under Interagency AgreementDW89930690-01-0 with the U.S. Department of Energy under Contract No. DE-AC05-84OR21400 with MartinMarietta Energy Systems, Inc.

fManaged by Martin Marietta Energy Systems, Inc., under contract DE-AC05-84OR21400 with the U.S. Departmentof Energy.

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