Data Quality Objectives Process Research Articles

Determining groundwater recharge for quantifying PFAS mass discharge from unsaturated source zones

AbstractSoil‐to‐groundwater contaminant mass discharge (Md) is the authoritative metric defining source strength at sites impacted by per‐ and polyfluoroalkyl substances (PFAS) and is increasingly being reported. Accurate estimates of groundwater recharge at representative spatial scales, however, is critical to quantitatively estimating Md, which to date has received comparatively little attention relative to PFAS‐specific partitioning and retention processes within unsaturated zone source areas despite a plethora of available literature. The objective of this review is to summarize the concept of Md as it applies to PFAS‐impacted sites, present standardized terminology, and collate published literature on groundwater recharge for a practitioner‐level summary of practical methods. Critically, the primary aim is to reduce uncertainty in recharge (and, thus, Md) estimates commensurate to a given site management application (i.e., the data quality objective process). Therefore, we propose a tiered system of increasing cost, complexity, and certainty depending on specific project requirements and site conditions and recommend applicable recharge methods for each tier based on these factors. Ultimately, a framework is presented for the assessment of PFAS‐impacted source areas based on the concept of Md.

Soil sampling plan design of key facilities for denuclearization based on data quality objective process

The possibility of denuclearization of the Korean Peninsula has been continuously debated, and the initiative participation of the Republic of Korea has necessitated preemptive measures against neighboring countries. In this study, we present a proposal for formulating a site survey plan when the amount of site information provided is insufficient and the accuracy of the information is not guaranteed. Considering a case wherein “a soil sample analysis is used to determine the presence or absence of nuclear activity” in a radiochemical laboratory, which is a typical key facility for denuclearization, the optimal soil sample collection plan is designed based on international guidelines and public information. In the event of denuclearization, a scenario that is not based on the expertise of the sample collector is set, and the data quality objective (DQO) process is applied to ensure reality. Consequently, the primary sample collection points can be derived in consideration of accessibility, and the sample collection scale can be adjusted according to the cost. By applying the DQO process to ensure sample representativeness and reality, reliable and resource-efficient soil sample collection can be achieved in radiochemical laboratories and other denuclearization facilities.

Ecological risk assessment for residual coal fly ash at Watts Bar Reservoir, Tennessee.

The Tennessee Valley Authority conducted a Baseline Ecological Risk Assessment (BERA) for the Kingston Fossil Plant ash release site to evaluate potential effects of residual coal ash on biota in Watts Bar Reservoir, Tennessee. The BERA was in response to a release of 4.1 million m(3) of coal ash on December 22, 2008. It used multiple lines of evidence to assess risks for 17 different ecological receptors to approximately 400000 m(3) of residual ash in the Emory and Clinch rivers. Here, we provide a brief overview of the BERA results and then focus on how the results were used to help shape risk management decisions. Those decisions included selecting monitored natural recovery for remediation of the residual ash in the Emory and Clinch rivers and designing a long-term monitoring plan that includes adaptive management principles for timely adjustment to changing conditions. This study demonstrates the importance of site-specific ecological data (e.g., tissue concentrations for food items, reproductive data, and population data) in complex ecological risk assessments. It also illustrates the value of the US Environmental Protection Agency's (USEPA) data quality objectives process in building consensus and identifying multiple uses of results. The relatively limited adverse effects of this likely worst-case scenario for ash-related exposures in a lotic environment provide important context for the USEPA's new coal combustion residue disposal rules.

An Approach for Addressing Hard-to-Detect Hot Spots

The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) survey approach is comprised of systematic random sampling coupled with radiation scanning to assess acceptability of potential hot spots. Hot spot identification for some radionuclides may not be possible due to the very weak gamma or x-ray radiation they emit-these hard-to-detect nuclides are unlikely to be identified by field scans. Similarly, scanning technology is not yet available for chemical contamination. For both hard-to-detect nuclides and chemical contamination, hot spots are only identified via volumetric sampling. The remedial investigation and cleanup of sites under the Comprehensive Environmental Response, Compensation, and Liability Act typically includes the collection of samples over relatively large exposure units, and concentration limits are applied assuming the contamination is more or less uniformly distributed. However, data collected from contaminated sites demonstrate contamination is often highly localized. These highly localized areas, or hot spots, will only be identified if sample densities are high or if the environmental characterization program happens to sample directly from the hot spot footprint. This paper describes a Bayesian approach for addressing hard-to-detect nuclides and chemical hot spots. The approach begins using available data (e.g., as collected using the standard approach) to predict the probability that an unacceptable hot spot is present somewhere in the exposure unit. This Bayesian approach may even be coupled with the graded sampling approach to optimize hot spot characterization. Once the investigator concludes that the presence of hot spots is likely, then the surveyor should use the data quality objectives process to generate an appropriate sample campaign that optimizes the identification of risk-relevant hot spots.

Translating Ecological Risk to Ecosystem Service Loss

Hazardous site management in the United States includes remediation of contaminated environmental media and restoration of injured natural resources. Site remediation decisions are informed by ecological risk assessment (ERA), whereas restoration and compensation decisions are informed by the natural resource damage assessment (NRDA) process. Despite similarities in many of their data needs and the advantages of more closely linking their analyses, ERA and NRDA have been conducted largely independently of one another. This is the 4th in a series of papers reporting the results of a recent workshop that explored how ERA and NRDA data needs and assessment processes could be more closely linked. Our objective is to evaluate the technical underpinnings of recentmethods used to translate natural resource injuries into ecological service losses and to propose ways to enhance the usefulness of data obtained in ERAs to the NRDA process. Three aspects are addressed: 1) improving the linkage among ERA assessment endpoints and ecological services evaluated in the NRDA process, 2) enhancing ERA data collection and interpretation approaches to improve translation of ERA measurements in damage assessments, and 3) highlighting methods that can be used to aggregate service losses across contaminants and across natural resources. We propose that ERA and NRDA both would benefit by focusing ecological assessment endpoints on the ecosystem services that correspond most directly to restoration and damage compensation decisions, and we encourage development of generic ecosystem service assessment endpoints for application in hazardous site investigations. To facilitate their use in NRDA, ERA measurements should focus on natural resource species that affect the flow of ecosystem services most directly, should encompass levels of biological organization above organisms, and should be made with the use of experimental designs that support description of responses to contaminants as continuous (as opposed to discrete) variables. Application of a data quality objective process, involving input from ERA and NRDA practitioners and site decision makers alike, can facilitate identification of data collection and analysis approaches that will benefit both assessment processes. Because of their demonstrated relationships to a number of important ecosystem services, we recommend that measures of biodiversity be targeted as key measurement endpoints in ERA to support the translation between risk and service losses. Building from case studies of recent successes, suggestions are offered for aggregating service losses at sites involving combinations of chemicals and multiple natural resource groups. Recognizing that ERA and NRDA are conducted for different purposes, we conclude that their values to environmental decision making can be enhanced by more closely linking their data collection and analysis activities.

Calculating Background Levels for Ecological Risk Parameters in Toxic Harbor Sediment

Establishing background levels for biological parameters is necessary in assessing the ecological risks from harbor sediment contaminated with toxic chemicals. For chemicals in sediment, the term contaminated is defined as having concentrations above background and significant human health or ecological risk levels. For biological parameters, a site could be considered contaminated if levels of the parameter are either more or less than the background level, depending on the specific parameter. Biological parameters can include tissue chemical concentrations in ecological receptors, bioassay responses, bioaccumulation levels, and benthic community metrics. Chemical parameters can include sediment concentrations of a variety of potentially toxic chemicals. Indirectly, contaminated harbor sediment can impact shellfish, fish, birds, and marine mammals, and human populations. This paper summarizes the methods used to define background levels for chemical and biological parameters from a survey of ecological risk investigations of marine harbor sediment at California Navy bases. Background levels for regional biological indices used to quantify ecological risks for benthic communities are also described. Generally, background stations are positioned in relatively clean areas exhibiting the same physical and general chemical characteristics as nearby areas with contaminated harbor sediment. The number of background stations and the number of sample replicates per background station depend on the statistical design of the sediment ecological risk investigation, developed through the data quality objective (DQO) process. Biological data from the background stations can be compared to data from a contaminated site by using minimum or maximum background levels or comparative statistics. In Navy ecological risk assessments (ERA's), calculated background levels and appropriate ecological risk screening criteria are used to identify sampling stations and sites with contaminated sediments.

A Methodology for Assessing Sample Representativeness

Assessing sample representativeness is a critical component of any environmental investigation and should be performed before any conclusions are reached. If the samples are not representative, any conclusions or decisions will be incorrect. A complete understanding of the data quality objective process, sample plan design, sample plan implementation, and quality control is required to assess sample representativeness. This article presents a methodology for the evaluation of sample representativeness.

Maintaining High-Volume, Low-Pressure Surface-Coating Regulatory Compliance Using the U.S. Environmental Protection Agency’s Data Quality Objectives Process

To effectively reduce the environmental compliance costs associated with meeting specific requirements under the Aerospace Manufacturing and Rework Facility’s National Emission Standard for Hazardous Air Pollutants rule, the U.S. Environmental Protection Agency’s (EPA) Data Quality Objective (DQO) process has been proposed as a suitable framework for developing a scientifically defensible surface compliance monitoring program. By estimating the variability associated with the air cap pressure of high-volume, low-pressure (HVLP) surface-coating spray equipment, the number of monitoring samples necessary for an affected facility to claim compliance with a desired statistical confidence level was established. Using data taken from the pilot test facility, the DQO process indicated that the mean of at least 21 HVLP air cap pressure samples taken over the compliance period must be ≤10 pounds per square inch (psig) gauge for the facility to claim regulatory compliance with 99.99% statistical confidence. Fewer compliance samples could be taken, but that decision would lead to a commensurate reduction in the compliance confidence level. Implementation of the DQO-based compliance sampling plan eliminates the need for an affected facility to sample all regulated HVLP surface-coating processes while still maintaining a high level of compliance assurance.

APPLICATION OF THE DQO PROCESS TO THE DELAWARE ESTUARY PCB TMDL PROGRAM

APPLICATION OF THE DQO PROCESS TO THE DELAWARE ESTUARY PCB TMDL PROGRAMWe applied U.S. Environmental Protection Agency's Data Quality Objectives (DQO) process to collecting the data needed for decision-making in support of the Stage 2 PCB TMDL program for the Delaware Estuary. The DQO process, which is a seven-step planning approach, was used to develop sampling designs for data collection activities that focused on the challenges of identifying significant sources...Author(s)Lawrence R. SandeenJeffrey D. WetheringtonDavid R. BlyeRao SankarmanchiSourceProceedings of the Water Environment FederationSubjectSession 2: Delaware Estuary PCB TMDL: A Case StudyDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2005ISSN1938-6478SICI1938-6478(20050101)2005:3L.192;1-DOI10.2175/193864705783967034Volume / Issue2005 / 3Content sourceTMDLS ConferenceFirst / last page(s)192 - 211Copyright2005Word count407

Development of a Hazardous Air Pollutants Monitoring Program Using the Data Quality Objectives Process

To effectively reduce the environmental compliance costs associated with meeting hazardous air pollutant emission requirements, the U.S. Environmental Protection Agency's Data Quality Objective (DQO) process has been proposed as a suitable framework for establishing a defensible monitoring program. Through the use of a hazardous materials pilot study, the variability in the composite vapor pressure for regulated handwipe cleaning solvents was established. These results served as inputs to the DQO process, which identified that for facility decision-makers to claim with a 99% confidence level that the facility is in compliance with the National Emission Standards for Hazardous Air Pollutants (NESHAP), a minimum of 12 handwipe cleaning solvent compliance samples (taken at random every 6 months) must have a composite vapor pressure equal to or below the regulatory limit of 45 mmHg at 20°C. Implementation of the DQO-based compliance-sampling plan eliminates the need for an affected facility to sample all regulated handwipe cleaning solvents while still maintaining a reasonably high level of confidence in the compliance status of its regulated sources. The approach described for designing a defensible compliance sampling plan can be extended to other aspects of the aerospace NESHAP rule, including compliance sampling for surface coating, chemical depainting, and hazardous waste disposal.

Total fat analysis in milk‐ and soy‐based infant formula powder by supercritical fluid extraction

AbstractA rapid method for the determination of total fat in infant formula powders using a commercially available supercritical fluid extraction (SFE) instrument was evaluated. The matrices examined were Standard Reference Material SRM 1846 Infant Formula (NIST) and commercial milk‐ and soybased infant formula powders. Method verification and validation were done by linear regression analysis using the Method of Standard Additions. A Data Quality Objectives (DQO) format was used to define and evaluate the performance characteristic parameters of the instrumental total fat analysis fy SFE. A peer validation study showed excellent agreement with the declared labeled percentage fat values and reproducibility among three participating laboratories. The laboratory relative SD (RSDR %) is within Horwitz's limits of acceptability and the HORRAT ratio criteria at the level of the analyte analyzed. Linear regression analysis of all infant formula matrices spiked with added fat showed that the SFE instrument response was due only to the added analyte. By integrating the DQO process with a readily available certified reference material, along with reproducibility indicated by two outside collaborating laboratories, we established verification and validation of the accuracy of the data obtained by SFE.

Using Continuous PM2.5 Monitoring Data to Report an Air Quality Index

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area’s (MSA’s) AQI.1 Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality. This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency’s (EPA’s) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity.2 The chosen measure of model adequacy is r2, the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greens-boro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r2 of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r2 of 0.86, based on 214 paired observations.

Application of intrawell testing of RCRA groundwater monitoring data when no upgradient well exists.

A statistical quality control approach to detect changes in groundwater quality from a regulated waste unit is described. The approach applies the combined Shewhart-CUSUM control chart methodology for intrawell comparison of analyte concentrations over time and does not require an upgradient well. A case study from the U.S. Department of Energy's Hanford Site is used for illustration purposes. This method is broadly applicable in groundwater monitoring programs where there is no clearly defined upgradient location, the groundwater flow rate is exceptionally slow, or where a high degree of spatial variability exists in parameter concentrations. This study also indicates that the use of the Data Quality Objectives (DQO) process can assist in designing an efficient and cost-effective groundwater monitoring plan to achieve the optimum goal of both low false positive and low false negative rates (high power).

Risk-based Decision Process for Arsenic in Soils: Implications of Conservative Protocols

Making defensible risk-based decisions is a complex process that incorporates risk assessment into a risk management framework. Many site investigations require additional study, negotiations and/or actions for arsenic detected in soil samples, in many cases where no process related sources are identified and no other chemicals of concern are identified. Regulatory agencies develop guidance to standardize approaches to risk-based site investigations that focus on achieving “safe” concentrations. For arsenic, the action level is frequently in the “gray region”, a U.S. Environmental Protection Agency (USEPA) term associated with a region of high uncertainty for risk management decisions in the “decision performance curve” associated with the data quality objective (DQO) process. Recognizing the conservative nature of the risk-based screening value for arsenic, approaches to enforce this level (or proof of comparability to natural background) may have numerous consequences including ineffective use of resources, stigmas on properties or actions at industrial or hazardous sites that are inconsistent with their regional setting. Florida has developed regulations and guidance on investigation of brownfield sites and has supported a study by the University of Florida (UF) to evaluate natural background concentrations in Florida soils. This paper discusses the sources of uncertainty near the soil cleanup target levels (SCTLs) in the Florida decision-making framework.

Data quality objectives in environmental research planning.

This paper presents highlights of a Data Quality Objectives course relating the Environmental Protection Agency's (EPA) seven step research planning process to research efforts at the U.S. EPA National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, in Duluth, Minnesota. Introductory materials were derived from "Guidance for the Data Quality Objectives Process, EPA QA/G-4." Case studies illustrate decisions that were made during the systematic planning process and subsequent experimentation. This paper demonstrates how the Data Quality Objectives Process clearly links research goals and objectives with the final product. Application of the process to environmental research ensures that environmental research data are of known, credible, defensible and usable quality.

Rethinking groundwater monitoring at the Hanford Site

AbstractGroundwater monitoring at Department of Energy's (DOE's) Hanford Site is a large, expensive undertaking serving multiple purposes, including compliance with regulations and DOE orders, remediation efforts under CERCLA, and sitewide risk evaluations. Like most large Federal facilities, the monitoring program currently in place has evolved and grown overtime as new requirements were established and groups were assigned to address them. DOE and its regulators simultaneously awakened to the fact that there was a need to reevaluate the monitoring activities at Hanford, to better integrate the program, to avoid duplicative sampling, to improve everyone's understanding of the performance of the network, and to evaluate whether adequate data could be collected for lower cost. This paper describes the approch that was developed to guide the rethinking effort with direct and extensive involvement of DOE, EPA, Washington Department of Ecology, Indian Tribes, and DOE Contractors, and how this approach was applied to a large portion of the site. Both the human element of the process (cultural change), as well as some of the technical details associated with the effort, including a flexible application of EPA's data quality objectives process, are discussed.

Determining an Optimal Sampling Frequency for Measuring Bulk Temporal Changes in Ground‐Water Quality

AbstractIn the Data Quality Objectives (DQO) process, statistical methods are used to determine an optimal sampling and analysis plan. When the DQO decision rule for instituting remedial actions is based on a critical change in water quality, the monitoring program design must ensure that this change can be detected and measured with a specified confidence. Usually the focus is on the change at a single monitoring location and the process is limited to addressing the uncertainty inherent in the analytical methods and the variability at that location. However, new strategies that permit ranking the waste sites and prioritizing remedial activities require the means for assessing overall changes for small regions over time, where both spatial and temporal variability exist and where the uncertainty associated with these variations far exceeds measurement error. Two new methods for assessing these overall changes have been developed and are demonstrated by application to a waste disposal site in Oak Ridge, Tennessee. These methods incorporate historical data where available and allow the user to either test the statistical significance of a linear trend or of an annual change compared to a baseline year for a group of water quality wells.

Optimizing data usability in the remedial process

AbstractLarge quantities of data are collected and evaluated throughout every stage of the remedial process. The usability of these data is often a function of the perceived “quality” of the data, with good data considered usable and bad data considered unusable. The assessment of data quality and usability has traditionally focused on analytical and other direct measurement errors and uncertainty. However, problems with the data that are measurement‐related are usually a relatively minor portion of the total error and uncertainty.Error and uncertainty are introduced throughout every aspect of any remedial process, including planning errors, measurement errors, and interpretation errors. Although each error component is important, the errors and uncertainty associated with the design, collection, and interpretation of data are often much greater than measurement‐related errors.Nevertheless, there is typically a disproportionate level of effort expended addressing the minor types of measurement errors when compared to other more important error components in the remedial process. However, the key to obtaining optimum data use requires the general redirection of Data Quality Assurance (DQA) activities from measurement‐related errors/uncertainty to other important planning and interpretation elements.The elements considered essential for developing an effective approach for maximizing data usability include: focused project planning activities stressing a rigorous data quality objective process and a geostatistical approach to the design of the data collection program; the development of sensible and appropriate data validation/review objectives; and the development of realistic error and uncertainty limits for measurement/analytical activities.

Implementing doe's streamlined approach for environmental restoration (SAFER)

AbstractThis article discusses how the Department of Energy (DOE) developed the Streamlined Approach for Environmental Restoration (SAFER) to effectively recognize and manage the uncertainties inherent in the environmental restoration process. SAFER integrates the Data Quality Objective (DQO) process with the Observational Approach. DOE and EPA agreed to a joint pilot project test of SAFER. In January 1994, DOE selected four facilities to host the SAFER Pilot Projects: Hanford in Washington, Savannah River Site (SRS) in South Carolina, Oak Ridge National Laboratory (ORNL) in Tennessee, and the Mound Plant in Ohio. The Hanford Pilot focuses on remedial design and remedial action at a reactor area. SRS hosted two pilot projects, both of which were in the remedial investigation (RI) planning stage. The ORNL SAFER pilot project addresses the RI/feasibility study (FS) for a series of surface impoundments. At the Mound Plant, two projects involve removal actions. The third Mound project involves transferring property to the City of Miamisburg. An independent performance measurement team is measuring success in both quantitative and qualitative terms.

Characterizing soils for hazardous waste site assessments

This paper provides a review and justification of the minimum data needed to characterize soils for hazardous waste site assessments and to comply with the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA). Scientists and managers within the regulatory agency and the liable party need to know what are the important soil characteristics needed to make decisions about risk assessment, what areas need remediation and what remediation options are available. If all parties involved in characterizing a hazardous waste site can agree on the required soils data set prior to starting a site investigation, data can be collected in a more efficient and less costly manner. Having the proper data will aid in reaching decisions on how to address concerns at, and close-out, hazardous waste sites.This paper was prepared to address two specific concerns related to soil characterization for CERCLA remedial response. The first concern is the applicability of traditional soil classification methods to CERCLA soil characterization. The second is the identification of soil characterization data type required for CERCLA risk assessment and analysis of remedial alternatives. These concerns are related, in that the Data Quality Objective (DQO) process addresses both. The DQO process was developed in part to assist CERCLA decision-makers in identifying the data types, data quality, and data quantity required to support decisions that must be made during the remedial investigation/feasibility study (RI/FS) process. Data Quality Objectives for Remedial Response Activities: Development Process (US EPA, 1987a) is a guidebook on developing DQOs. This process as it relates to CERCLA soil characterization is discussed in the Data Quality Objective Section of this paper.