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«Guidance for Obtaining Representative Laboratory Analytical Subsamples from Particulate Laboratory Samples EPA/600/R-03/027 November 2003 Guidance ...»

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Guidance for Obtaining

Representative Laboratory

Analytical Subsamples from

Particulate Laboratory

Samples

EPA/600/R-03/027

November 2003

Guidance for Obtaining

Representative Laboratory

Analytical Subsamples from

Particulate Laboratory Samples

by

Robert W. Gerlach

Lockheed Martin Environmental Services

and

John M. Nocerino

U.S. Environmental Protection Agency

223CMB03.RPT ™ 1/8/04

Notice

The U.S. Environmental Protection Agency (U.S. EPA), through its Office of Research and Development (ORD), funded the work described here under GSA contract number GS-35F-4863G (Task Order Number 9T1Z006TMA to Lockheed Martin Environmental Services). It has been subjected to the Agency’s peer and administrative review and has been approved for publication as an EPA document.

iii Foreword The basis for this document started in 1988. We were in a quality assurance research group dealing with the analysis of many different kinds of samples. Historically, the focus of our work was on the analytical method, and sampling was pretty much taken for granted. However, it soon became clear that sampling is perhaps the major source of error in the measurement process, and, potentially, sampling is an overwhelming source of error for heterogenous particulate materials, such as soils. It was also clear that classical statistical sampling theory was not adequate for such samples. Simple random sampling may work for very “homogeneous” samples, for instance, marbles of the very same size, weight, and shape where the only difference is the color of the marble. But the color of the marble is not a factor that contributes to the selection process of that marble! To be an effective sampling method, the factors that contribute to the selection process must be considered.

We knew that geostatistics offered some answers, such as the sample support (mass, volume, and orientation) and particle size (diameter) make a difference. That is only common sense. The larger the mass of the sample, the closer it should resemble the composition of the lot that it came from. But, taking ever larger (or more) samples was not a practical answer to getting a representative sample. Less intuitive may be that most of the heterogeneity should be associated with the larger particles and fragments.

However, grinding an entire lot of material to dust was also not a practical alternative.

We searched for a non-conventional statistical sampling theory that actually takes into account the nature of particulate materials and, in 1989, we hit “pay dirt.” Dr. Francis Pitard offered a short course at the Colorado School of Mines on the Pierre Gy sampling theory for particulate materials. Dr. Pitard had taught this course many times before to mining students, but this was his first offering directed toward the environmental community. Although this theory was developed in the mid-1950s by the French chemist, Pierre Gy, the theory was not widely known to those outside of the mining community, and it was seemingly only put into practice by a few mining engineers where the bottom line really counts, namely, gold mining. Dr. Pitard had the foresight to see the importance of introducing this theory to the environmental sciences.

Needless to say, we came back from the short course very excited that we had found our answer. But it was a hard sell. Over the ensuing years, we were only moderately successful at transferring this technology to the environmental community so that it might be implemented. We started by sponsoring a couple of short courses given by Dr. Pitard and we distributed some technical transfer notes. Although this theory has proven itself in practice many times over in the mining industry, there has been very little published with substantiating experimental evidence for this theory (it has been virtually nonexistent in the environmental arena). The effectiveness of the Gy theory, and the extent to which it is applicable, was also not well-established for environmental samples. Therefore, we were compelled to start a research program to explore the effectiveness and the application of the Gy theory for all types of environmental samples, and, where there are limitations, to expand upon the theory. Such a research program would not only help to provide the needed (and published) experimental verification of the Gy v theory, but it should also give credence to the theory for those not yet convinced (and justify the application) of this theory for the environmental sciences.

We started our experimental investigations on the various Gy sampling theory errors, using fairly “uncomplicated” matrix-analyte combinations, as applied to obtaining a representative analytical subsample (the material that gets physically or chemically analyzed) from a laboratory sample (the bottle that comes to the laboratory containing the sample to be analyzed). We felt that this was the easiest place to start, using our limited resources, while still producing an impact. The weakest link, and the potential for the most error, could very well be from taking a non-representative grab sample “off the top” of the material in the laboratory sample bottle! (By the way, the Gy theory defines what a representative sample should be.) The result of our ongoing investigations is the first version of this guidance document. We welcome any (constructive) comments.





This document provides general guidelines for obtaining representative samples for the laboratory analysis of particulate materials using “correct” sampling practices and “correct” sampling devices.

However, this guidance is general and is not limited to environmental samples. The analysis is also not limited to the laboratory; that is, this guidance is also applicable to samples analyzed in the field. The information in this guidance should also be useful in making reference standards as well as taking samples from reference standards. Similarly, this guidance should be of value in: monitoring laboratory performance, creating performance evaluation materials (and how to sample them), certifying laboratories, running collaborative trials, and performing method validations. For any of those undertakings, if there seems to be a lot of unexplained variability, then sampling or sample preparation may be the culprit, especially if one is dealing with heterogeneous particulate materials.

The material presented here: outlines the issues involved with sampling particulate materials, identifies the principal causes of uncertainty introduced by the sampling process, provides suggested solutions to sampling problems, and guides the user toward appropriate sample treatments. This document is not intended to be a simple “cookbook” of approved sampling practices.

The sections of this guidance document are divided into the following order of topics: background, theory, tools, observations, strategy, reporting, and a glossary. Many informative references are provided and should be consulted for more details. Unless one is familiar with the Gy sampling theory, correct sampling practices, and correct sampling devices, it is strongly recommended that one reads through this document at least once, especially the section on theory. The glossary can easily be consulted for unfamiliar terms. If one is familiar with the Gy sampling theory and is just interested in developing a sampling plan, or simply wants to answer the question, “How do I get a representative analytical subsample?”, then go ahead and jump to the section on “Proposed Strategies.” This section gives a general and somewhat extensive strategy guide for developing a sampling plan. A sampling strategy can be general, and not all of it, necessarily, has to be followed. However, a sampling plan is necessarily unique for each study. Any sampling endeavor should have some sort of sampling plan.

The basic strategic theme in this document is that if “correct” sampling practices are followed and “correct” sampling devices are used, then all of the sampling errors should become negligible, except for the minimum sampling error that is fundamental to the physical and chemical composition of the material being sampled. Since this minimum fundamental sampling error can be estimated before any sampling takes place, one can use this relative variance of the fundamental error to develop a sampling plan.

vi At first, it may seem that following this guidance is a lot of effort just to analyze a small amount of material. And, when one is in a hurry and has a large case load, it may seem downright overwhelming.

But, remember that the seemingly simple task of taking a small amount of material out of a laboratory sample bottle could possibly be the largest source of error in the whole measurement process. And not taking a representative subsample could produce meaningless results, which is at the very least a waste of resources and, at the very most, could lead to incorrect decisions.

Remember that sampling is one of those endeavors that you “get what you pay for,” at least in terms of effort. But, with the right knowledge and a good sampling plan, the effort is not necessarily that much.

It pays to have a basic understanding of the theory. Become familiar with what causes the different sampling errors and how to minimize them through correct sampling practices. For example, always try to take as many random increments as you can, with a correctly designed sampling device, when preparing your subsample; and if you can only take a few increments, then you are still better off than taking a grab sample “off the top” from the sample bottle, and you will at least be aware of the consequences. Be able to specify what constitutes a representative subsample. Know what your sampling tools are capable of doing and if they can correctly select an increment. Always do a sample characterization (at least a visual inspection) first. At a minimum, always have study objectives and a sampling plan for each particular case. If possible, take a team approach when developing the study objectives and the sampling plan. Historical data or previous studies should be reviewed. And be sure to record the entire process!

An understanding of the primary sources of sampling uncertainty should prevent unwarranted claims and guide future studies toward correct sampling practices and more representative results. Best wishes with all of your sampling endeavors.

–  –  –

The authors express their gratitude to the following individuals for their useful suggestions and their timely review of this manuscript: Brian Schumacher (U.S. EPA), Evan Englund (U.S. EPA), Chuck Ramsey (EnviroStat), Patricia Smith (Alpha Stat), and Brad Venner (U.S. EPA). The authors also convey their thanks to Eric Nottingham and the U.S. EPA National Enforcement Investigation Center (NEIC) for the use of their facilities in performing many of the laboratory experiments pertinent to this guidance.

Dedication

This sampling guidance document is dedicated to Dr. Pierre Gy to commemorate his fifty years toward the development and practice of his sampling theory and to Dr. Francis F. Pitard for his diligence in proliferating the Gy sampling theory and other theories for particulate sampling, for his lifetime of dedication to correct sampling practices, and for pointing those of us in the environmental analytical sciences in the right direction. The authors sincerely hope that this work expresses our gratitude and not our ignorance. We also dedicate this manuscript to all of those individuals that are involved with sampling heterogenous particulate material and we welcome any suggestions for improvement to this work.

Abstract

An ongoing research program has been established to experimentally verify the application of the Gy theory to environmental samples, which serves as a supporting basis for the material presented in this guidance. Research results from studies performed by the United States Environmental Protection Agency (U.S. EPA) have confirmed that the application of the Gy sampling theory to environmental heterogeneous particulate materials is the appropriate state-of-the-science approach for obtaining representative laboratory subsamples. This document provides general guidelines for obtaining representative subsamples for the laboratory analysis of particulate materials using the “correct” sampling practices and the “correct” sampling devices based on Gy theory. Besides providing background and theory, this document gives guidance on: sampling and comminution tools, sample characterization and assessment, developing a sampling plan using a general sampling strategy, and reporting recommendations. Considerations are given to: the constitution and the degree of heterogeneity of the material being sampled, the methods used for sample collection (including what proper tools to use), what it is that the sample is supposed to represent, the mass (sample support) of the sample needed to be representative, and the bounds of what “representative” actually means. A glossary and a comprehensive bibliography have been provided, which should be consulted for more details.

–  –  –

Please note that there is a glossary in the back of this guidance document that should help the reader understand unfamiliar words or concepts. For more extensive explanations of sampling topics that are not covered in this text, please refer to the bibliography.

1.1 Overview Unless a heterogeneous population (for example: a material, a product, a lot, or a contaminated site) can be completely and exhaustively measured or analyzed, sampling is the first physical step in any measurement process or experimental study of that population. The characteristics of collected samples are used to make estimates of the characteristics of the population; thus, samples are used to infer properties about the population in order to formulate new hypotheses, deduce conclusions, and implement decisions about the population. The assumption is that the samples both accurately and precisely represent the population. Without special attention to that assumption, sampling could be the weakest link leading to the largest errors in the measurement process or the experimental study.



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