Quality Assurance and Quality Control of Geochemical 

Data: A Primer for the Research Scientist

By Nicholas J. Geboy and Mark A. Engle

Open-File Report 2011–1187

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia 2011

For product and ordering information:

World Wide Web: http://www.usgs.gov/pubprod

Telephone: 1-888-ASK-USGS

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment:

World Wide Web: http://www.usgs.gov

Telephone: 1-888-ASK-USGS

Suggested citation:

Geboy, N.J., and Engle, M.A., 2011, Quality assurance and quality control of geochemical data—A primer for the 

research scientist: U.S. Geological Survey Open-File Report 2011–1187, 28 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

1. Introduction................................................................................................................................................................ 1

2. Considerations Prior to Sample Collection ................................................................................................................ 1

2.1 What Constituents Are of Primary Concern? ....................................................................................................... 2

2.2 What Are the Proper Techniques and Laboratories to Determine the Constituents of Primary Interest?............. 2

2.2.1 Common Interferences for Inorganic Elemental Analysis on ICP-MS............................................................ 3

2.2.2 Common Interferences of Elemental Analysis by Emission and Absorption Spectrometry ........................... 3

2.2.3 Matrix Effects................................................................................................................................................. 4

2.3 What Are the Geochemical Abilities of the U.S. Geological Survey Energy Resources Program Laboratories 

in Denver?.................................................................................................................................................................. 5

2.4 How Much Sample Mass/Volume is Needed? ..................................................................................................... 5

3. Considerations During Sample Collection ................................................................................................................. 5

3.1 Sampling Solid Materials...................................................................................................................................... 6

3.2 Sampling for Organic Analysis ............................................................................................................................. 7

3.3 Sampling Water ................................................................................................................................................... 8

3.4 Sampling Atmospheric Particulate Matter ............................................................................................................ 8

3.5 QA/QC Samples to Collect During Sampling ....................................................................................................... 9

4. Tips for Sample Submittal.........................................................................................................................................10

4.1 Are the Samples Packed Properly? ....................................................................................................................10

4.2 What Else Needs to Be Included With the Unknown Samples?..........................................................................11

4.2.1 Reference Materials .....................................................................................................................................11

4.2.2 Duplicate and Replicate Samples.................................................................................................................12

4.2.3 Blank Samples .............................................................................................................................................12

4.2.4 Suggested Inclusion of QA/QC Samples to Accompany a Batch of Samples Submitted to an Analytical 

Laboratory .............................................................................................................................................................13

5. Post Laboratory Analysis ..........................................................................................................................................13

5.1 Using Field Blanks to Determine the Method Detection Limit..............................................................................14

5.2 Examples of the Importance of Scrutinizing Data with Respect to QA/QC..........................................................14

5.3 Dealing with Censored Data ...............................................................................................................................17

6. Closing Remarks ......................................................................................................................................................20

References ...................................................................................................................................................................21

Appendix 1. Detection Limits for Various Analytical Techniques .................................................................................23

Appendix 2. Where to Obtain Reference Materials......................................................................................................28

Figures

1. Data used to generate a rhenium (Re)-osmium (Os) isochron .....................................................................12

2. Vanadium, rubidium, and strontium concentration data in atmospheric particulate matter collected from 

Woods Hole, Massachusetts, in February 2008...........................................................................................15

3. Leachate data from coal combustion products by two different analytical procedures, which do not display a 

1:1 linear relation..........................................................................................................................................16

4. Leachate data from coal combustion products by two different analytical procedures following a 

transposition of laboratory identifications .....................................................................................................17
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Tables

 1. Sodium (Na) concentration data for atmospheric total suspended particulate matter from Woods Hole, 

Massachusetts, with summary statistics.......................................................................................................19

 2. Comparison of summary statistics for the dataset when arbitrary detection limits are set and four different 

nondetect statistical methods are employed.................................................................................................20

Appendix Figures

A1. Instrument detection limits for inductively coupled plasma-atomic emission spectrometry (ICP-AES).........23

A2. Instrument detection limits for inductively coupled plasma-mass spectrometry (ICP-MS)............................24

A3. Instrument detection limits for neutron activation analysis (NAA).................................................................25

A4. Instrument detection limits for X-ray fluorescence spectrometry (XRF)........................................................26

A5. Instrument detection limits for atomic absorption spectrometry (AAS) .........................................................27
v

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

SI to Inch/Pound

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32
1

Quality Assurance and Quality Control of Geochemical 

Data: A Primer for the Research Scientist

By Nicholas J. Geboy and Mark A. Engle

1. Introduction

Geochemistry is a constantly expanding science. More and more, scientists are employing 

geochemical tools to help answer questions about the Earth and earth system processes. Scientists may 

assume that the responsibility of examining and assessing the quality of the geochemical data they 

generate is not theirs but rather that of the analytical laboratories to which their samples have been 

submitted. This assumption may be partially based on knowledge about internal and external quality 

assurance and quality control (QA/QC) programs in which analytical laboratories typically participate.

Or there may be a perceived lack of time or resources to adequately examine data quality. Regardless of 

the reason, the lack of QA/QC protocols can lead to the generation and publication of erroneous data. 

Because the interpretations drawn from the data are primary products to U.S. Geological Survey 

(USGS) stakeholders, the consequences of publishing erroneous results can be significant. The principal 

investigator of a scientific study ultimately is responsible for the quality and interpretation of the 

project’s findings, and thus must also play a role in the understanding, implementation, and presentation 

of QA/QC information about the data. 

Although occasionally ignored, QA/QC protocols apply not only to procedures in the laboratory 

but also in the initial planning of a research study and throughout the life of the project. Many of the 

tenets of developing a sound QA/QC program or protocols also parallel the core concepts of developing 

a good study: What is the main objective of the study? Will the methods selected provide data of enough 

resolution to answer the hypothesis? How should samples be collected? Are there known or unknown 

artifacts or contamination sources in the sampling and analysis methods? Assessing data quality requires 

communication between the scientists responsible for designing the study and those collecting samples, 

analyzing samples, treating data, and interpreting results. 

This primer has been developed to provide basic information and guidance about developing 

QA/QC protocols for geochemical studies. It is not intended to be a comprehensive guide but rather an 

introduction to key concepts tied to a list of relevant references for further reading. The guidelines are 

presented in stepwise order beginning with presampling considerations and continuing through final 

data interpretation. The goal of this primer is to outline basic QA/QC practices that scientists can use 

before, during, and after chemical analysis to ensure the validity of the data they collect with the goal of

providing defendable results and conclusions. 

2. Considerations Prior to Sample Collection

Gathering meaningful data begins long before the laboratory analyst ever receives the samples. 

In the initial stages of any scientific study, a fundamental scientific question is posed along with a 

conceptual scheme of an acceptable approach to answer the research question. One piece of this scheme 
2

should include developing (even if informally) a sampling plan, which is a fundamental step in 

collecting relevant data (Luppens and others, 1992; Ramsey, 1997). When developing a sampling plan, 

it is important to keep the aims of the study in mind and to determine what constitutes a representative 

sample of the material being investigated. Questions to keep in mind include: How many samples are 

required to answer the scientific question posed? Do these samples need to meet some sort of temporal 

or spatial resolution? Do the samples need to be representative of something larger than themselves? 

What is the minimum mass or volume of each sample needed to perform all necessary analyses, keeping 

in mind reserve splits may be necessary? What is the minimum number of samples required for 

statistical significance? What sorts of field QA/QC samples (blanks, duplicates, and so forth) are 

appropriate? Some of these questions may be answered with the assistance of a literature search. Often 

other researchers have published information on proper sampling methods, preservation methods, 

potential pitfalls, and analytical techniques on similar topics or study areas. Field time is expensive, and 

developing a thoughtful sampling plan prior to collecting samples gives a researcher more time in the 

field to actually be sampling and ensures that the samples that were collected will appropriately address 

the scientific objectives of the study. 

2.1 What Constituents Are of Primary Concern? 

Just because an analytical test can be performed does not mean it has to or should be performed.

Identifying which elements or compounds are the most vital to answer the questions posed will 

streamline a study and make it more insightful. For example, if determining the major oxide 

concentrations within a suite of coals is all that is required to answer a scientific question, then 

performing a blanket analysis for the entire periodic table is unnecessary and may come at the expense 

of the required analyses. Practically speaking, sampling procedures and contamination sources vary 

widely depending on the analytes of interest. Selectively choosing which analyses best address the 

objectives of a study allows the analyst to home in on more precise data, because the key components to 

be tested will determine the best method to use. If it is ever unclear which constituents would best help 

answer a scientific question, searching the literature to see what has or has not worked for past 

researchers is a useful start. 

2.2 What Are the Proper Techniques and Laboratories to Determine the Constituents of Primary 

Interest? 

Any given constituent can be measured by using a variety of analytical techniques, both 

qualitative and quantitative. Qualitative techniques include electron probe microanalyzer (EPMA), 

X-ray diffraction (XRD), nuclear magnetic resonance (NMR), infrared spectroscopy (IR), and so forth. 

Quantitative techniques include inductively coupled plasma-mass spectrometry (ICP-MS), inductively 

coupled plasma-atomic emission spectrometry (ICP-AES), thermal ionization mass spectrometry 

TIMS), gas chromatography-mass spectrometry (GC-MS), X-ray fluorescence (XRF), neutron 

activation analysis (NAA), EPMA, and quantitative XRD, among others. These methods have varying 

detection limits, interferences, restrictions, and costs, and these factors may make data from specific 

methods unsuitable to provide the requisite precision or accuracy needed to answer the question at hand. 

To assist in selecting an appropriate method, detection limits for a variety of analytical methods can be 

found in appendix 1. 

As an example of where certain analytical methods are not appropriate for certain samples, 

consider a study involving measuring Hg in flue gas. Concentrations average 1,500 parts per million by 

volume for sulfur dioxide gas [SO2 (g)] in flue gas emissions. Some commercial types of mercury 
3

analyzers exhibit a strong interference in the presence of SO2 (g), so research must be done using only 

an instrument in which there is no interference or one which corrects for the interference. 

2.2.1 Common Interferences for Inorganic Elemental Analysis on ICP-MS 

Mass spectrometers, which represent the measurement portion of an ICP-MS, separate isotopes 

on the basis of their mass-to-charge ratio (m/e). Isobars are nuclides with the same number of nucleons 

and isobaric interferences occur in mass spectrometry when isotopes from different elements of equal 

m/e are present in significant concentrations in the same sample solution. Often, when an element 

isobarically interferes with another, a different isotope of the analyte element must be selected. For 

example, the most abundant isotope for calcium is 40Ca. ICP-MS utilizes an argon torch, and 

99.6 percent of argon has a mass of 40 atomic mass units (amu). Therefore, they cannot be distinguished 

from one another by a magnetic mass analyzer. Analysts can get around this problem by analyzing for 

the second most abundant isotope of calcium, 44Ca. Strontium, however, readily substitutes for calcium, 

so both are commonly found in the same sample solution. The most abundant isotope of strontium has a 

mass of 88, and 88Sr2+ has the same m/e as 44Ca+, so a magnetic analyzer on an ICP-MS cannot 

distinguish between them either. The 43Ca+

 ion, however, has no common interferences and, therefore, 

would be a better isotope to determine the concentration of calcium within a sample. Although the 

scientist submitting samples has little control over which isotopes the analyst uses, communicating the 

elements of interest and basic information about the matrix of the samples may help the analyst make a 

more informed decision. 

Isobaric interferences are a concern mainly when monoisotopic elements are involved. The 

monoisotopic elements are 9Be, 23Na, 27Al, 45Sc, 55Mn, 75As, 89Y, 103Rh, 127I, 133Cs, 141Pr, 159Tb, 165Ho,

169Tm, 197Au, and 232Th; no singly charged isotopes overlap with any of them. However, 206Pb2+ 

can 

interfere with 103Rh–, and some molecules formed in the plasma also can interfere (40Ar35Cl– overlaps 

with 75As–). If the scientist is confident there is no measurable chlorine in the sample, this interference 

with arsenic can be avoided by not using HCl in sample digestion or preservation. Although the analyst 

may be aware of these problems and know how to work around them, it is prudent for the scientist to 

also be aware of such issues and communicate with laboratory personnel to help avoid potentially 

interference-laden results. 

2.2.2 Common Interferences of Elemental Analysis by Emission and Absorption Spectrometry

Emission spectroscopy refers to analytical techniques that excite the atoms in a sample and 

determine concentrations by measuring the characteristic wavelengths emitted when the atoms relax.

These techniques include ICP-AES, which excites the atoms with heat, and an electron probe, which 

excites the atoms with electron bombardment. There are literally thousands of spectral interferences in 

emission spectroscopy. Most laboratories, however, are prepared for this interference and measure for 

characteristic lines that do not overlap significantly with other common elements. For ICP-AES, if there 

is any question about the reliability of a particular instrument or laboratory, the most recent spectral 

interference study results should be requested. These studies, performed annually, ensure there are no 

direct or wing overlaps or near neighbors that affect the background around specific analytes (elements 

of interest). 

Absorption spectroscopy is similar to emission spectroscopy. Like in emission spectroscopy, 

samples are excited to higher energy levels. As the name implies, however, characteristic wavelengths 

of the energy absorbed, rather than emitted, are measured and used to determine concentration. Atomic 

absorption spectroscopy (AAS) is one such technique and is used in the analysis of mercury in solid 

materials. Spectral interferences may also occur in AAS, but perhaps more important concerns are 
4

chemical interferences. Chemical interferences occur when elements being analyzed form molecules 

that are not fully atomized by the flame and thus do not absorb the characteristic wavelength of energy.

Common examples of chemical interferences occur when phosphate is present in a sample where Mg, 

Ca, or Ba concentrations are being determined or when silica is present in a sample where Mn is being 

analyzed. Other interferences and suggestions for overcoming them can be found in U.S. Environmental 

Protection Agency (2007). 

Although it is not necessary for all researchers to be fluent in the workings of ICP-AES, EPMA, 

or AAS, these analytical methods are commonly used by geoscientists, and thus it is helpful to have a 

basic understanding of them. Before submitting samples to a laboratory, it is important to consider what 

components need to be analyzed and what potential interferences are associated with each technique. 

Discussing standard operating procedures with the laboratory analyst or manager is a simple way to 

resolve any doubts about a method, determine the best way to proceed with a project, and ensure that 

both parties are on the same page with respect to what is in the samples and what could potentially go 

awry during the analysis. 

2.2.3 Matrix Effects 

A common group of interferences present in all of the above mentioned analytical techniques is 

referred to as matrix effects. The matrix, defined as all of the components of a sample other than the 

analyte, can affect the quality of the results obtained from the geochemical analysis if the sensitivity of 

the analyte is dependent on any component of the matrix. Chemical effects are an example of matrix 

effects. Another example of matrix effects occurs when using a plasma source (for example, ICP-MS

and ICP-AES). High concentrations of total dissolved solids in a sample can result in deposits accruing 

on the orifice of the sampling cone, which leads to signal drift over small time scales and loss of 

precision. 

Several protocols can be used for detecting and compensating for matrix effects. If extra sample 

is available for the analysis, a split can be taken from one or more samples and spiked with a known 

concentration of the analytes of interest prior to sample preparation and analysis. Analytical results from 

these splits, known as matrix spikes, are compared to the analysis of the original samples to determine 

how much of the spiked material was recovered. If the recovery of the analyte is outside the accepted 

limits set by that laboratory or study, then there are significant matrix effects, and the data for that 

analyte are considered to be questionable and further discussion with the laboratory may be required 

about possible methods to circumvent the problem. Even if the recovery rates fall within the acceptable 

limits, it does not prove an absence of matrix effects. Rather, it suggests that if matrix effects are 

present, they are no larger than those typically present for a variety of matrices that underwent the same 

preparatory and analytical procedures. Discrepancies can be attributed to matrix effects only if other 

QA/QC samples are falling within their accepted ranges and the concentration of the spike is much 

higher than the ambient analyte. If significant matrix effects are associated with the analysis, the effects

may be overcome by ensuring the samples and calibration standards have the same matrix. This 

technique is called matrix matching. With matrix matching, the calibration curve for the instrument has 

already taken into account all matrix effects and essentially corrected for them. It is important, therefore,

to inform the analyst of the nature of the samples submitted and to include reference materials with 

similar matrices. In some cases, a lack of clear communication with a laboratory about the nature of the 

samples can lead to unintentional matrix problems. While properly designed matrix spikes can be one of 

the best methods to identify potential problems with the analysis (as they include information on both 

the calibration and interferences), not all laboratories routinely run them and therefore their inclusion 
5

may be an important point of discussion with the laboratory prior to submitting the samples. More 

detailed information on matrix spikes can be found in U.S. Army Corps of Engineers (2005). 

Once the proper analytical method is identified and possible analytical interferences are 

considered, a laboratory must be chosen to perform that method. Many laboratories around the country 

can be used for common analyses. If the component of interest is more specialized, however, an 

appropriate laboratory must be identified. For example, the analysis for mercury isotopes would most 

likely require selection of a research laboratory employing a highly specialized analyst because of the 

complexity of the method. 

2.3 What Are the Geochemical Abilities of the U.S. Geological Survey Energy Resources Program 

Laboratories in Denver?

The USGS Energy Resources Program (ERP) laboratories in Denver, Colorado, have established 

standard operating procedures (SOPs) for the following methodologies, presented in no particular order: 

525 degrees Celsius (°C) and 750 °C ashing of coal; American Petroleum Institute (API) gravity; 

bitumen extraction by Soxhlet; fractionation of oil or bitumen extract by column chromatography; 

elemental analyzer determination for carbon, hydrogen, nitrogen, oxygen, and sulfur; gas 

chromatography (GC); gas quantification; GC of aromatic hydrocarbons; GC of saturate hydrocarbons; 

GCMS analysis of biomarkers; loss on ignition; low temperature coal ashing; microwave digestion; 

maltene preparation; direct mercury analysis in coal, rock, sediment, and soil by using a DMA-80; 

moisture in coal; trace element analysis by ICP-MS (Elan 6100); Optima isotope ratio mass 

spectrometer coupled with elemental analyzer for stable isotopes analysis on solid and liquids; 

PANalytical X-ray diffractometer; sample preparation for analysis by organic petrology; selenium 

analysis and digestion; sulfur analysis on a LECO-SC632; and trace digestion. Additional and updated 

information on the ERP laboratories (including instrument models) can be found on their Web page at 

http://energy.cr.usgs.gov/gg/geochemlab/index.html. 

2.4 How Much Sample Mass/Volume is Needed?

Collecting the appropriate mass or volume of sample is important not only to lower uncertainty 

but also to ensure there is enough sample to complete all desired analyses and QA/QC checks and keep 

a reserve split. As an example, for coal, ERP laboratories in Denver suggest 50 grams (g) of sample for 

major and trace elemental analysis. Some commercial laboratories suggest about 5 to 10 g to complete 

ultimate/proximate, F/Cl and loss on ignition (LOI) analyses. Further, 1 to 3 g are needed to make a 

pellet for organic petrography or microprobe work. Therefore, if coal or coal combustion products 

(CCPs) are being sampled, a minimum of 60 g is needed for total analysis. Keep in mind, however, that 

sample may be lost during grinding and other preparatory work, so it would be best to collect at least 

100 g. Additional sample may also be necessary for matrix spikes and reserve splits. Each study and 

medium are different, so research into analytical methodology is essential before any sampling event. 

3. Considerations During Sample Collection 

Sampling involves collecting both actual samples, which will help answer scientific questions, 

and QA/QC samples, which provide quantitative information on the quality of the analytical results. 

QA/QC samples, such as blanks and duplicates, can only add to the robustness of a study and are critical 

even despite a potential perceived lack of time and (or) money to collect and analyze them. They are 

important insofar as they can be used to account for any outside influences and offer insight into 

potential analytical problems. The usefulness of QA/QC samples depends on how honestly they are 
6

collected and treated. It may be tempting to treat QA/QC samples with extra care to, for example, make 

a blank contribution appear as low as possible. If an actual sample, however, is not treated with the 

same care, then the blank sample will not accurately reflect the reality of the sample. Each of the 

QA/QC samples described below should be treated like any other sample in terms of collection, storage, 

preservation, cleaning, labeling, shipment, and any other processes involved. 

Contamination of sample is not restricted to laboratory error. Understanding possible QA/QC

concerns during sampling can prevent inadvertently degrading a sample prior to returning from the 

field. For instance, in a study on the environmental effects of coal, the focus may be on arsenic, 

selenium, mercury, and polycyclic aromatic hydrocarbons (PAHs). Each of these constituents exhibits 

its own sampling and analytical challenges; however, the inorganic constituents will require much 

different sampling and preservation methods than the PAHs. If samples are collected using only 

methods for PAHs, they may not be suitable for further analysis of the three trace elements. In the same 

way, it could be a waste of resources to collect samples for measurement of elevated sodium, a major 

cation in water that can cause damage to soils in arid environments, using methods designed for lowlevel trace concentrations (for example, using a particle-free sampling suit, arm-length gloves, doublebagged TelfonTM sample bottles, and so forth). Regardless of the media and nature of the samples being 

collected, recognized standards should be reviewed, and method sections of recent papers may be 

checked to ensure established sampling protocols are being followed. 

General points to consider when sampling different media are given below. By no means are the 

following sections complete but rather offer ideas to consider when sampling. Some of the proper 

standards for sampling various media are identified. 

3.1 Sampling Solid Materials

For USGS ERP scientists, solid geomaterials are often synonymous with coal, CCPs, and, 

occasionally, overburden. Sampling coal for geochemical analysis can be done both in the field and 

onsite at a powerplant or core storage facility. In any case, coal samples can be stored in plastic bags 

within metal tins to minimize moisture loss, coal oxidation, and cross contamination among the 

samples. For temporary storage, plastic bags without the tins are acceptable; however, some researchers

have noted that over a period of years plastic bags deteriorate, leaving the samples exposed to each other 

and the atmosphere. Metadata must be obtained from the powerplant/mine with respect to coal type and 

grade, source, sampling date, time, preservation, and so forth. Some level of metal contamination is 

impossible to avoid if the coal that has already been mined and transported is stored in direct contact 

with metals and other contaminants. The assumption is that the level of metal contamination is so far 

below the concentration of elements within the coal that it is negligible, but this may not always be true. 

It is important to note weathering rinds and “nuggets” when preparing solid samples for analysis. 

Weathering rinds are not representative of the actual product that is being sampled but instead have 

exchanged components with the surrounding environment. Nugget effects occur when a component of 

the sample (such as a mineral grain) contains a disproportionate amount of a key element of interest or if 

it is heterogeneous on the scale of single sample volumes. For example, Illinois Basin coal may contain

trace amounts of sphalerite, which can include cadmium. If the aliquot of coal sampled contains an 

unrepresentatively high amount of sphalerite grains, the measured cadmium concentration would be 

misleadingly high. Although it is impossible to avoid all nuggets and rinds, their inadvertent inclusion 

may explain anomalously high data points that should not be considered representative of the sample. 

However, if the study is related to coal quality, rinds and nuggets should be included in the analysis 

because mining operations do not separate them prior to coal combustion. 
7

Sampling coal cores presents some unique concerns worth addressing. It can be difficult to 

quantify how much core sample is needed to be truly representative. Each core should be evaluated 

individually in terms of lithology and composition. Distinct megascopic lithologies or facies should be 

sampled separately. In the case of coal assessments, inorganic partings are sampled separately, but not 

discarded, as their inclusion with the coal will increase ash content and lower BTU output (in case the 

coal is not preferentially mined around the partings). Coal cores may also be contaminated from drilling 

fluids. The exterior of cores should be cleaned prior to any chemical analysis. If possible, it may be 

preferable to use the interior of the core for more sensitive analyses such as trace elements. A 

comprehensive review of sampling coal core is available in Manual on Drilling, Sampling, and Analysis 

of Coal (Luppens and others, 1992). 

Solid CCPs, including bed or bottom ash, fly ash, and flue gas desulfurization, among others, are 

also intrinsically contaminated to some degree. Nonetheless, it is important to store CCPs separately in 

sealable containers to minimize cross contamination. When splitting samples to achieve representative 

cuts, riffle splitting is the ASTM International standard (ASTM D2013). If low concentration isotopic 

analyses need to be done, however, it is better to roll, cone, and quarter the samples to prevent 

unnecessary contact with metal. 

The USGS occasionally engages in overburden studies that involve sampling solid materials 

from core. Overburden is material that rests above minable materials (that is, coal) that is removed prior 

to surface mining and then put back in place during the reclamation process as outlined in the Surface 

Mining Control and Reclamation Act of 1977. As with coal core sampling, it is important to note 

lithologic changes within the core and sample in a way that is representative of those changes. That way 

if one lithology is found to contain higher concentrations of toxic elements than other lithologies, it can 

be filled in first during reclamation, minimizing the risk of it being aerially exposed, and thus 

mechanically weathered into the environment. 

Appropriate procedures for sampling coal can be found in ASTM D7430-10b. In addition, 

ASTM D4596 (Coal Seam Face Channel Sampling) and D5192 (Coal Core Sampling) may be useful. 

Standards also are available from ASTM Committee D18 (soils) that apply to overburden and soil 

sampling. 

3.2 Sampling for Organic Analysis

Samples collected for organic analysis, such as surface waters, produced waters, and natural gas 

samples, are easily contaminated by oils, plastics, sunscreen, and many other common items not of 

concern when sampling for inorganics. Because most traditional sampling techniques involve plastic 

gloves, plastic bags, labeling tape, pencils/markers, and so forth, extra precaution must be taken to 

obtain meaningful organic data. 

Prior to being placed in sample bags, geologic samples should be wrapped in aluminum foil or 

other low organic carbon-containing material to prevent direct contact with the plastic. Aluminum foil is 

easily pretreated by combustion at 400 °C for 16 hours to oxidize any organic carbon present (Brocks 

and others, 2003). Nonetheless, many commercially available aluminum foils contain one oiled side.

The sample should be wrapped in such a manner that it does not touch the oiled side of the foil. When 

labeling samples, it is best to mark the sample name on the foil rather than on the sample itself. Both 

inks and pencil “lead” are organic and may leave trace amounts of residue behind. If gloves are 

required, appropriate gloves, such as nitrile, are recommended. 

Once collected, samples being prepared for organic analysis must be treated with caution. When 

glass fiber is used as a collection substrate of organic compounds, it should be combusted at 400 °C for 

16 hours or as indicated in appropriate methods. When solid samples are ground for organic analysis, 
8

the ring mill should be cleaned between samples by washing with water and grinding two or three times 

with quartz sand (which has been annealed at 900 °C for 24 hours) at 60-second intervals (Brocks and 

others, 2003). 

3.3 Sampling Water

ERP scientists collect water samples for a variety of reasons but especially when investigating 

the effects of energy resource use on the environment and human health. Although fresh and saline 

waters must be treated differently analytically, the QA/QC protocols during sampling are similar. 

Plastic, glass, or TeflonTM collection bottles should be precleaned with acid (if necessary) and triple 

rinsed with the water to be sampled prior to actual collection. It may be necessary to use opaque or 

amber bottles to prevent photochemical reactions from occurring, which could change the 

concentrations of vulnerable components. The amount of headspace may also be an issue for more 

volatile elements. If suspended particles are not of interest, it is prudent to pass the water sample 

through a 0.45 micrometer (μm) or finer filter during collection to prevent dissolved material from 

adsorbing onto the larger particles, which can be removed from the solution. To preserve the sample 

between collection and analysis, it is standard practice to acidify solutions for later elemental analysis 

with 0.1 molar (M) nitric acid (high purity, if necessary), unless the sample will be analyzed for anions 

or titrated to determine alkalinity, as the nitric acid will minimize solute deposition on the container 

walls. Hydrochloric acid should only be used if chlorine is not of interest to the research, while still

considering the ArCl interference on ICP-MS and ICP-AES discussed in section 2.2.1. Promptly 

refrigerating or placing samples on ice will slow biological activity, which may regulate trace metal 

concentrations such as Ni, V, Fe, and Mo. Saline waters from the sea, estuaries, or deep boreholes may 

have lower stability and, therefore, require more rapid preservation through acidification and (or) 

refrigeration.

If water samples are being collected for organic analysis, nitrile gloves should be worn. As with 

inorganic analysis collection, samples should be collected in amber or opaque bottles, filtered, and 

refrigerated. Samples may require preservation with dichloromethane or other antibacterial additive to 

prevent bacterial growth within the filtered sample. The scientist must be aware of situations that could 

affect the results, such as seasonal, biological, and meteorological events, which may prevent a sample 

from being representative or “background,” and many samples may be needed for both temporal and 

spatial comparisons. For more information on sampling water, the USGS has produced the National 

Field Manual for the Collection of Water-Quality Data (U.S. Geological Survey, 2006). 

3.4 Sampling Atmospheric Particulate Matter

Collecting particulate matter from the atmosphere requires pumping a known volume across 

impactors, which segregate particles on the basis of aerodynamic diameter, or through filters. Several 

different instruments can be used to collect particulate matter, each with its own characteristic particle 

size range and flow rate range. Flow rate through the samplers and duration of collection must be 

calculated prior to arrival in the field and adjusted onsite to ensure enough sample is amassed for 

chemical analysis. In addition, because filters are made of different media, their use depends on the 

nature of the compounds of interest. Commonly, cellulose and TeflonTM filters are used when sampling 

for trace metals, prefired quartz fiber is used when sampling for Hg, and prefired glass fiber is used 

when sampling for organics. 

Impactors and stages can be easily contaminated by atmospheric particulate matter prior to the 

start of collection. For studies focusing on trace elements or constituents that are prone to 

contamination, special care should be taken. Vinyl gloves and ultra-low particle (for example, Tyvek®) 
9

lab coats, which are approved for use in clean room settings, should minimize contamination of the filter 

during loading of filters into sample holders and stages; ideally, this process is done within a laminar 

flow hood. Transportation of loaded stages in multiple layers of bags or other containers prior to sample 

collection will also help minimize potential contamination. The physical sampler should also be wiped 

with a low-particle cloth immediately before the stages are put in place. The particular gloves, 

containers, and cleaning materials and solutions should be appropriate for the analytes of interest so 

they do not readily contaminate the sampling media. Ideally, the samplers should be positioned at least 

2 meters above the ground to ensure that a representative sample of the lower atmosphere is being 

collected that is not overly biased by large, saltating particles. Following collection, filters should be 

split (if necessary and with precleaned ceramic or metal scissors, depending on the compounds of 

interest) and stored in a freezer (if volatile compounds are being investigated) for preservation.

Many different methods can be used for sampling atmospheric particulate matter. A list of the 

various U.S. Environmental Protection Agency (EPA) standards is available (U.S. Environmental 

Protection Agency, 1999). 

3.5 QA/QC Samples to Collect During Sampling

• Field Blanks – A field blank is a sample or sampling media (for example, filter) that has been 

exposed to the same field and transport conditions as the samples and is used to distinguish ambient 

contamination, which is attributed to collection and transportation processes, from actual sample 

contribution. For example, if a water sample is collected from a well in Pennsylvania and then 

shipped to a laboratory, high purity water would be brought into the field, transferred to another 

collection vessel, and shipped to the laboratory with the aqueous sample from the well. Another 

example would be filters to collect particulate matter. Field blanks for the filters would be created by

placing the media into a filter holder and placing the holder at the sampling site but never running 

air through the filter. 

• Trip Blanks – These samples are nearly identical to field blanks except the samples or sampling 

media cases are never removed from their packaging. In the water example above, a trip blank 

consists of water brought from the laboratory to the field site but never transferred to a collection 

vessel. Comparing the trip blank to the field blank can help determine if contamination was 

contributed from the field environment or from the environment the samples are exposed to during 

transport. 

• Duplicates and Replicates – This report defines duplicates as samples collected in tandem with one 

another from the same source at the same time in the field. Replicates, on the other hand, are defined 

as repeat analyses performed on different aliquots of the same sample. Replicates may either be field 

replicates or laboratory replicates depending on the location of the sample analysis. Specific types of 

duplicates are discussed in more detail below. Duplicates provide information on both the 

heterogeneity of the sampling material (can be used to identify “nugget” effects) as well as the 

analytical precision. Replicates should be more representative of analytical variability; comparison 

of the two can be useful in quantifying and determining the source of analytical variability. Poorer 

precision in duplicates relative to replicates may indicate a lack of sample homogenization, and 

comparable results may indicate that analytical variability controls precision. 

• Site/Study Reference Sample – In the case of a long-term or multiple campaign study (or if a large 

number of samples require the set to be split up and submitted as smaller individual batches), it may 

make sense to collect an extremely large volume of a single sample. This sample can be 

homogenized and submitted to the laboratory with each batch of samples (discussed in section 4) 

and used to track analytical variation between or among batches. 
10

• Compound Loss Sample – Although field and trip blanks are important in identifying sources of 

contamination during sampling, there is also a potential to preferentially lose compounds from the 

sample during the sampling events. This is more important for highly volatile or short-lived 

compounds. Although rarely done, a previously collected sample or aliquot of a reference sample 

can be shipped to the study area and then brought back to the laboratory following the path of a trip 

blank. Comparison of pre- and post-sampling analyses of the sample can be used to provide 

information on possible compound loss and the efficacy of preservation techniques in field 

conditions. 

• Equipment Blanks – Equipment blanks reveal contamination from any equipment used during 

sample collection or preparation, such as from a total suspended particulate air sampler or from a 

water sampling pump and tubing. These samples can be among the most important QA/QC samples 

because they provide important information on how little or how much the sampling and preparation 

methods affect the results of the samples. In some cases, equipment blanks can be compared directly 

against field and trip blanks to provide quantitative information on contamination from sampling 

and preparation equipment. For instance, using the example of well sampling again, high purity 

water brought from the laboratory and pulled through the same pump used to sample the well can be 

used to directly estimate contributions of specific compounds in concentration units as a result of 

using the tubing and pump. In other cases, however, equipment blanks can provide only qualitative 

information on contamination from sampling. In the case of soil sampling, an equipment blank can 

be collected by rinsing off the unused sampling trowel with high purity water and collecting the 

rinsate. Comparison of the rinsate with unused high purity water may be helpful in identifying 

compounds on the trowel, which were added to the soil sample, but no direct determination of how 

much compound per sample can be made. 

• Cleaning Agent Blanks – If any agents are used to clean field or laboratory equipment, it may be 

worthwhile to have them analyzed, especially if trace work is being done. Cleaning agents may 

include water, anionic detergent, or commercial products such as surface cleaners. For example, if a 

product such as an all-purpose cleaner is used to clean a laminar flow hood under which samples are 

prepared for trace element analysis, it may be prudent to analyze the cleaner to make sure its residue 

is not likely to contaminate the samples. As with some of the equipment blanks, cleaning agent 

blanks are more typically qualitative but can provide important clues on the efficacy and 

appropriateness of cleaning methods. While cleaning agents are not the most likely source of 

contamination, they are worth investigating if blanks are higher than expected. 

4. Tips for Sample Submittal

The preparation of samples for laboratory submission includes proper packaging and providing 

appropriate QA/QC samples in the suite. Addressing the following questions when submitting samples 

can help ensure they do not become contaminated en route and that there is a sufficient accompaniment 

of QA/QC samples.

4.1 Are the Samples Packed Properly?

When samples are packed for shipment to the analytical laboratory, the samples should be stored 

in a way that will prevent them from having direct physical contact with each other, ideally in air tight 

containers. Ice packs should be used if the samples need to remain chilled during transport (such as 

organics or other volatile constituents), as the cold temperature slows oxidation and loss of some 

elements and inhibits biological activity. If the samples do need to remain chilled, they should be 
11

shipped at the beginning of the week to prevent a weekend arrival when the package may be left to sit 

on a warm loading dock for a day or two. Likewise, some solid samples should be stored in ways that 

minimize exposure to extreme hot or cold temperatures. Contact the laboratory to make sure that 

someone will be available to process the samples when they arrive. In addition, it is important to ship 

samples by a method that allows for tracking of packages so that misdirected parcels can be located. In 

some studies, background samples are collected in addition to potentially anomalous or contaminated 

samples. In this case, it may be helpful to ship these two groups separately to prevent contamination of 

the control group, especially by volatile compounds. 

4.2 What Else Needs to Be Included With the Unknown Samples?

Several key pieces of information can be gleaned from various types of QA/QC samples 

submitted to and provided by the laboratory. The following sections discuss how reference materials 

offer insight into how true (that is, accurate) analytical results are, how repeat measurements show how 

reproducible (that is, precise) analytical results are, and how blank samples can identify and track 

potential sources of contamination. 

4.2.1 Reference Materials

Reference materials (RMs) are samples that have been tested in many different laboratories or by 

several different analytical techniques and given a certified value with an uncertainty. When used 

appropriately, RMs reflect the accuracy of the analytical results. By inserting RMs in sample sets, the 

results from RMs can be compared to the expected values so the researcher can assess the accuracy of 

the analytical values for the unknown samples. To be effective, however, appropriate RMs must be 

used. RMs chosen should be of comparable composition and also of comparable concentrations and 

similar matrices. For example, if samples are freshwater lacustrine waters, seawater RMs are not likely 

to provide meaningful information. Likewise, if samples are anticipated to contain 50 parts per billion 

(ppb) Hg, a RM with 5,000 ppb Hg would be inappropriate. In some cases, RMs are provided in a 

different physical state than the samples in the set and are therefore clearly known to be RMs to the 

analyst. For example, there are no RMs for particulate matter collected on filters, so solid RMs in jars 

are provided for analysis with the filters. When possible, however, blind standards may provide a better

check of analytical accuracy. Blind standards are RMs that are given a sample sequential identification

so the analyst is unaware that it is a certified RM, thus providing the scientist with an independent check 

on the data. Submittal of blind RMs is not an attempt to trick the analyst but a step to ensure that all 

samples (unknowns and RMs) are treated the same. This practice eliminates the subconscious “handling 

with kid gloves” of RMs. A list of sources for many RMs can be found in appendix 2. 

It may also be important to develop an in-house standard (for example, site/study reference 

sample) that can be submitted with every sample set over a period of years or decades. To this end, 

Eastern Energy Resources Science Center (EERSC) scientists have collected approximately 30 pounds 

of Appalachian Basin feed coal from a powerplant in Kentucky. The homogenized powder is not a 

calibrated standard but can be included with other coal sample submissions, essentially acting as both a 

long-term duplicate and an in-house standard. Tracking value changes in this one sample over a long 

period will offer information on long-term instrument drift and reliability, true analytical uncertainties, 

cross checking between laboratories (if more than one laboratory is used or a need to switch laboratories 

arises), and quality assurance of other data. If no in-house standard is available, a previously measured 

sample can be resubmitted. 
12

4.2.2 Duplicate and Replicate Samples

Several types of duplicates are available, each of which offers useful information, but all are 

measures of different types of precision. Total analytical duplicates are two samples collected in tandem 

from the same source at the same time and treated individually during preparation and analysis. Results 

for total analytical duplicate samples do not need to be identical but should behave in some predictable 

manner. Even if the exact data reported for duplicates vary widely, if they fall on the same isochron or 

mixing line, it may simply be a case of natural variation or heterogeneity and not necessarily an 

analytical error (fig. 1). 

Figure 1. Data used to generate a rhenium (Re)-osmium (Os) isochron. Samples G1, G2, and G3 are total 

analytical duplicates. Although the samples do not reproduce well, they behave in a predictable manner (falling on 

the same isochron), suggesting sample heterogeneity rather than analytical error. Data from Geboy (2006); Ma = 

mega annum.

Replicates are aliquots of the same sample analyzed repeatedly on the instrument and are 

expected to reproduce results consistently. The standard deviation among replicate samples reflects the 

precision (or lack thereof) for that particular analysis and that particular sample. For example, if two 

aliquots of the same digestate are analyzed 10 samples apart from each other during analysis with 

widely different results, a large uncertainty will be associated with that sample that may affect what 

conclusions can be drawn from the data generated during that analysis. As with RMs, duplicate and 

replicate samples should be submitted as blind samples when possible. 

4.2.3 Blank Samples

Blank samples are another vital component of any comprehensive dataset. Blanks offer insight 

on potential sample contamination sources and the appropriateness of methods and can be used to

correct quantitative and isotopic data. Like duplicates, there are several forms of blanks. Blanks that are

collected in the field have already been discussed, but there are also laboratory blanks. Total analytical 

blanks are processed side-by-side with samples but without the sampled material (or use a very clean 
13

substitute for the sample, such as 18.2 MΩ-cm water). Total analytical blanks are useful in that they 

reveal background levels and possible contamination for the entire laboratory process. If total analytical 

blanks are sufficiently below the concentrations measured in samples, the technique is a good fit for the 

study. If they reveal a significant contribution to one or more component(s) being analyzed, then 

troubleshooting may be necessary. 

Troubleshooting requires comparison and investigation into reagent blanks and labware blanks. 

By analyzing reagents and labware used in the preparation work, the scientist can determine the source 

of the contamination and adjust the method or clean/replace the contaminating step accordingly. Once 

total analytical blanks are at sufficiently low levels, they are used to correct concentrations and, if 

applicable, isotopic ratios in the unknown samples. Blank samples are effective only if they are 

collected in the same manner as regular samples. Like RMs, scientists may subconsciously use more 

care with blank samples to minimize their contribution. If blanks are prepared more cautiously than 

samples, they are potentially undervaluing actual external contributions. 

4.2.4 Suggested Inclusion of QA/QC Samples to Accompany a Batch of Samples Submitted to an 

Analytical Laboratory

In order to provide confidence in data, Reimann and others (2008) suggest that all actual samples 

should be randomized to spread out any temporal systematic biases such as instrument drift. For 

example, consider a job that includes fly ash and bed or bottom ash samples. All of the fly ash samples 

are numbered sequentially, followed by all of the bed or bottom ash samples. If there is a drift in the 

instrument, which causes a slow decrease in signal strength over time, the fly ash samples (which were 

all analyzed early in the run) will be artificially higher than the bed ash samples (which were analyzed 

toward the end of the run). Randomizing samples helps to disperse this bias across all sample types. In 

addition to randomizing samples, Reimann and others (2008) suggest that sample sets include 

• Field duplicates (5–10 percent of samples)

• Certified RMs (at least 1 percent of samples) 

• In-house internal laboratory standard (10–20 percent of samples)

• Analytical duplicates (10–20 percent of samples)

These numbers cannot be met for all sample sets. In the case of coal cores or soil water, 

duplicates can be difficult, if not impossible, to obtain. The above percentages are simply suggestions 

and represent an ideal case. In instances where analytical techiques are particularly difficult or prone to 

contamination, an even higher frequency of QA/QC samples may be necessary.

5. Post Laboratory Analysis

Once results are returned from the laboratory, it is the responsibility of the scientist to review the

results and check for discrepancies and QA/QC issues as soon as possible. If errors or questionable data 

are found, reporting them promptly may allow the analyst to rerun samples before they are disposed of. 

The laboratory report should routinely include internal QA/QC runs, such as calibration and standards, 

which bracket submitted samples. If these data are not included, they should be requested immediately. 

Determining percentage recovery of blind standards and comparison of duplicates will also offer insight 

into the quality of the data. If recovery is good (90–110 percent) for most components but poor for one 

or two, then data for those one or two components may not be reliable, and their inclusion in the results 

should be reconsidered. Results, even if tenuous, should never be eliminated or destroyed. Rather, data 

can be excluded from interpretation with an explanation of why they are excluded. If a long-term 

internal RM is being used, its values should be plotted every time it is analyzed and compared to 
14

previous analyses. This long-term plot can be especially useful if there has been a change in analyst, 

instrument, or laboratory used. 

5.1 Using Field Blanks to Determine the Method Detection Limit 

Several different thresholds can be used to evaluate the minimal meaningful quantitative value 

of data, such as the instrument detection limit (IDL) and the method detection limit (MDL). Analytical

instruments will produce a small signal (that is, background noise) even when no sample has been 

injected into the machine. The IDL typically is defined as three times that of background. These 

detection limits are the ones reported by manufacturers and are often highly optimistic, meaning they 

are the best limits ever achieved under ideal running conditions. In addition to instrument noise, most 

samples have other sources of uncertainty associated with collection and sample preparation; therefore, 

the IDL is often much lower than the true detection limit of an entire method. A more realistic way of 

determining the detection limit for a sample is to calculate the MDL, which is defined as the minimum 

concentration of a component in a particular sample that can be measured and reported with 99 percent 

confidence that the concentration of the analyte is above zero. When results are returned from a 

laboratory, detection or reporting limits are included for each analysis. These limits are typically a form 

of the MDL, but it is important to confirm this with the analyst. National Energy Technology 

Laboratory (NETL) in Pittsburgh, Pennsylvania, for example, uses three times the standard deviation of 

10 replicates of blank solution (Kristen Carlisle, National Energy Technology Laboratory, written 

commun., February 17, 2011). The USGS Minerals Resources Program laboratories in Denver calculate 

detection limits as three times the standard deviation of eight replicates of blanks that have gone through 

the same physical and chemical laboratory procedures as the samples (Paul Lamothe, USGS, written

commun., February 17, 2011). These detection limits do not, however, take into account contribution 

from outside sources before they arrive in the laboratory. The best practice, therefore, is to calculate the 

MDL as three times the standard deviation of all the collected and analyzed field blanks or total 

analytical blanks, because the method begins long before samples are handled by the analyst. 

Consider five field blanks that were collected during a recent field campaign. The main 

constituent of interest is lead (Pb). The results for the blanks returned from the laboratory are 0.2883, 

0.1146, 0.0761, 0.1298, and 0.0986 microgram (μg). The standard deviation of these five blank values is 

0.0843 μg. These blanks have recorded any contaminant contribution from the entire process of 

collecting, preparing, and analyzing the samples, meaning they represent the minimum amount of Pb 

that would be found in a sample that does not actually come from the sample itself; the blanks act as the 

chemical equivalent of a tare on a balance. Also, remember that (assuming normality) 99 percent of all 

values will fall within three times the standard deviation; therefore, a MDL of 0.0843 μg times three is 

equal to 0.2529 μg and is the minimum amount of actual contribution from the Pb sample that can be 

measured and reported with 99 percent confidence. MDLs are unique to each method and analyte and 

must be calculated by the researcher once results are obtained from the laboratory. 

5.2 Examples of the Importance of Scrutinizing Data with Respect to QA/QC

Case Study 1 – Samples of atmospheric particulate matter were collected in Woods Hole, 

Massachusetts, during the last week of February 2008. The aerosols were collected over approximately

12-hour periods in fractions of less than 2.5 μm (PM2.5) and total suspended particulates (TSP).

Although the samples were collected continuously, the scientist sent the filters in two different batches 

several months apart to the same laboratory to be analyzed for trace elements. The results for three 

elements are shown in figure 2 with the vertical dash line separating the two different jobs.
15

A severe drop in concentrations (in most cases, a full order of magnitude) of all elements 

occurred in both fractions between the two subsets. It is unlikely that an environmental change occurred, 

coinciding with where the scientist arbitrarily chose to divide the time series. Rather, an analytical error 

is more reasonable. After contacting the analyst, there was no obvious explanation for the discrepancy. 

Whether because of an undocumented dilution, an instrumental error, or an incomplete digestion, the 

QA/QC on these data are suspect, and therefore, they were not used in developing conclusions in the 

study. 

Figure 2. Vanadium, rubidium, and strontium 

concentration data in atmospheric particulate 

matter collected from Woods Hole, 

Massachusetts, in February 2008. The dashed 

line is total suspended particulate data, and the 

solid line represents fine particulate matter data.

The red dotted line indicates the method 

detection limit. Note the abrupt decrease in 

concentrations between the first and second 

subset. Unpublished data from Mark Engle. 
16

Case Study 2 – Leachates from coal combustion products were analyzed for trace elements using 

both ICP-AES and ICP-MS. This test was designed to compare results of the two analytical methods for 

elements that were successfully analyzed by both instruments. The leachate samples were prepared 

using two different separation standards: toxicity characteristic leaching procedure (TCLP; U.S. 

Environmental Protection Agency, 1992) and synthetic groundwater leaching procedure (SGLP; as in 

Hassett, 1994). Ideally, both analytical procedures should yield similar results across most elements for 

each of the leaching standards. That is, copper concentrations from a TCLP leachate measured with 

ICP-AES should be similar to copper concentrations from a TCLP leachate measured with ICP-MS. 

Although ICP-AES more accurately measures certain elements than ICP-MS, and vice-versa, there is 

not a resolvable difference for many elements; therefore, if plotted against each other, ICP-AES data 

and ICP-MS data should have an approximately linear relation with a slope equal to one. The results 

received from the laboratory are plotted in figure 3. 

The data do not show any sort of predictable relation. Initially, this scatter suggests that either 

one of the standard leaching protocols or one or both of the analytical techniques encountered 

significant problems. After looking more critically at the data, the scientist noticed that if the laboratory 

IDs for a run of samples were transposed (that is, SGLP and TCLP samples were switched), the data 

would plot in a more agreeable manner, as seen in figure 4. Following a consultation with the analyst, 

who checked his laboratory notebook, a data transfer error was found. It appears both standards and 

analytical methods produce similar results with the notable exception of arsenic (and possibly nickel, 

cobalt, and selenium). In this case, a careful review of the data saved the time and money of 

unnecessary troubleshooting. 

Figure 3. Leachate data from coal combustion products by two different analytical procedures, which do not 

display a 1:1 linear relation. Data from Jones and others (2010). 
17

Figure 4. Leachate data from coal combustion products by two different analytical procedures following a 

transposition of laboratory identifications. With the notable exception of arsenic (As), the data now approximately

reflect the expected 1:1 linear relation. Data from Jones and others (2010); Ni = nickel; Co = cobalt; Se = selenium.

5.3 Dealing with Censored Data

Once the data have been verified by the scientist, they need to be put into context and 

interpreted. This process sometimes requires statistical treatment of the data, which can be complicated 

by the presence of censored data. Often, some fraction of the analytical results for a given constituent 

will fall below the IDL or MDL. Data that are below detection are referred to as nondetects or “censored 

data” and are reported by the analyst as not detected or <X, where X is the appropriate detection limit.

Handling these data correctly is vital in assuring the quality of any conclusions drawn from the data. 

Traditionally, scientists have substituted a nominal value, such as zero, one-half, or two-thirds of 

the detection limit, for the censored values and then proceeded with interpreting the data and calculating 

summary statistics. Over the last decade, however, statisticians and chemometricians have commented 

on this substitution method; for example, “...substituting values for nondetects should be used rarely, 

and should generally be considered unacceptable in scientific research. There are better ways” (Helsel, 

2006, p. 2434). The argument is made that substituting censored values is a misrepresentation that

amounts to falsifying data and can lead to serious errors in any statistical information derived from that 

dataset. Rather, it has been suggested that censored values can be treated statistically by using several 

methods, including maximum-likelihood estimates (MLE), Kaplan-Meier (K-M) survival curves, and 

regression on order statistics (ROS). The following section briefly explains how these methods can be 

applied to estimate summary statistics from datasets that contain nondetects. In addition, methods have 

been developed for regression/correlation and statistical comparison among groups but are not discussed 

here (Helsel, 2005). 
18

To determine which method is most suitable for a particular dataset, the important things to 

consider are the amount of data available, the percentage of the sample set that is censored, and the 

distribution of those data reported above detection limits. The K-M method is a nonparametric analysis 

based on the probability of a sample “surviving” detection and will accommodate datasets regardless of 

their distributions (that is, Gaussian, lognormal, bimodal, random, and so forth). The limitation of the KM method is that it cannot determine the median if one-half the data (or more) are censored. Likewise, it 

cannot determine the first quartile if more than 25 percent of the data are censored, and so on. In 

addition, if there is only a single detection limit for the component being analyzed and none of the 

samples exhibit a detected concentration below that detection limit, the K-M method is essentially 

equivalent to a substitution method. In these situations it may be more prudent to employ either MLE or 

ROS methods. Both of these methods, however, also come with restrictions and assumptions. MLE is 

applicable only to datasets that closely follow a given distribution (for example, normal, log normal, and 

so on) as it fits a curve to the detected values and extrapolates to points below the detection limit.

Summary statistics are then generated for the curve that fits that distribution. This method is most 

effective with larger sample sizes (n > 50). The ROS method is similar but applies the distribution only 

to the fraction of censored values. Data points above the detection limit are used in combination with 

that “below detection” distribution to generate summary statistics. The ROS method works well with 

smaller datasets (n < 50). Helsel (2005) provides a full review of these methods. If more than 80 percent

of the data are censored or if the dataset is small or both, then all of these statistical methods for 

censored data may yield tenuous results that may not provide meaningful statistics. Then again, if more 

than 80 percent of the data are censored, it may also mean the analytical method is inappropriate. In 

addition, Olea and others (2008) have suggested applying bootstrapping methods to estimate summary 

statistics for datasets that are treated using compositional data methods. Several statistical packages, 

including Minitab, NCSS, S-PLUS, and the NADA library for R, can execute one or more of these 

methods. 

As an example of how the substitution method can alter true statistics, consider sodium 

concentration data collected from atmospheric total suspended particulate matter in Woods Hole, Mass.

(table 1). In actuality, none of the data were reported below the MDL, so the “true” summary statistics

can be determined: median of 590 ng m–3; mean of 658 ng m–3; and a standard deviation of 321 ng m–3. 

As an exercise, the detection limit is arbitrarily set to different concentrations to censor different 

percentages of the data. In each of these cases, the censored data are replaced with one-half the arbitrary 

detection limit, and statistics were determined using the substitution (SUBS), K-M, ROS, and MLE 

methods (table 2).

The summary statistics generated from the substitution method agree with the actual summary 

statistics when only 17 percent of the data are censored, but as the detection limit rises (and the 

percentage of data falling below that detection limit increases), substitution produces drastic negatively 

biased results. The calculated median deviates from the actual median by almost 25 percent and 

40 percent when 50 percent and 57 percent of the data are censored, respectively, while the ROS 

method deviates by only <1 percent and 3 percent, respectively. These data are lognormal (they are 

linear on a log-probability plot), so either the MLE or ROS could be applied. Again, the ROS method is 

preferred with smaller datasets, and these results support that preference. The ROS method provides 

fairly consistent median, mean, and standard deviation results even when 70 percent of the data are 

censored. Table 2 also illustrates that the K-M method cannot determine a median (that is, a second 

quartile) when 50 percent or more of the data fall below detection. As pointed out above, in this 

particular usage where there is a single detection limit and no samples with detected values below that 

limit, the K-M method behaves as a variation of the substitution method. 
19

Table 1. Sodium (Na) concentration data for atmospheric total suspended particulate matter from Woods Hole,

Massachusetts, with summary statistics.

3 [ng/m , nanograms per cubic meter]

Sample ID Na (ng/m3) Sample ID Na (ng/m3) 

08-MA-06TF 876 08-MA-100TF 1,440

08-MA-09TF 72.6 08-MA-03TF 495

08-MA-10TF 1,400 08-MA-60TF 245

08-MA-12TF 277 08-MA-62TF 169

08-MA-13TF 282 08-MA-63TF 438

08-MA-14TF 713 08-MA-64TF 555

08-MA-15TF 852 08-MA-65TF 887

08-MA-22TF 747 08-MA-66TF 576

08-MA-24TF 711 08-MA-68TF 515

08-MA-26TF 841 08-MA-72TF 995

08-MA-30TF 644 08-MA-07TF 475

08-MA-34TF 1,110 08-MA-82TF 790

072908-1 574 08-MA-83TF 527

08-MA-95TF 603 08-MA-85TF 569

08-MA-98TF 475 08-MA-92TF 890

Median 590

Mean 658

Standard Deviation 321
20

Table 2. Comparison of summary statistics for the dataset when arbitrary detection limits are set and four 

different nondetect statistical methods are employed. 

[K-M, Kaplan-Meier; ROS, regression on order statistics; MLE, maximum likelihood estimates; SUBS, substitution method; 

NA, not applicable] 

Detection limit Percent 

censored K-M ROS MLE SUBS

300 17 Median 576 590 518 590

Mean 696 678 695 648

Standard Deviation 273 293 623 334

500 30 Median 576 590 480 590

Mean 715 672 651 636

Standard Deviation 258 298 595 337

600 50 Median NA 595 350 451

Mean 751 668 587 600

Standard Deviation 233 301 790 351

710 57 Median NA 609 337 355

Mean 811 674 556 609

Standard Deviation 198 296 730 355

800 70 Median NA 584 233 400

Mean 899 652 469 590

Standard Deviation 159 306 821 320

6. Closing Remarks

While it may be easy or convenient to trust analytical data, it is not always prudent. Some data 

are clearly erroneous and, although not used, represent a waste of effort and funding. Even more 

problematic, however, are incorrect data that are inadvertently published. Following proper QA/QC and 

sampling methods from the inception of a project through its completion can greatly increase confidence 

that the data are reliable. As geochemical tools become more available, careful QA/QC procedures 

become even more important. Analytical techniques are becoming more refined, and detection limits are

being reduced. With this refinement, even minor contamination can cause differences in data that can 

drastically change interpretations and conclusions. Rapidly improving geochemical techniques require

that more time be spent reviewing recent literature prior to the proposal of a study. Published method 

sections that are only a few years old may have been subsequently improved upon and, therefore, 

rendered obsolete. 

Keeping lines of communication open with the laboratory is beneficial to all parties. Laboratory 

managers and analysts may have first-hand experience with techniques that have or have not been 

successful in the past. Also, the more information that is given to the laboratory, the better the 

laboratory can tailor the analyses to help achieve the goals of the study. It also helps if a rapport exists 

among all people involved with the study so that, if problems do arise, they can be handled in a 

coordinated, professional, and timely manner. 
21

Developing a study, collecting and processing samples, analyzing for key elements and 

compounds, and reviewing results all require appropriate QA/QC protocols. QA/QC work may be 

iterative and may not end even when the study ends. While this primer is not intended to be a 

comprehensive guide to QA/QC within the USGS Energy Resources Program, it may offer insight into 

good QA/QC practices in general. Ultimately, the QA/QC work may provide information as important 

as the original data themselves. At the very least, closely following QA/QC guidelines will lead to the

reliable data that are expected by USGS stakeholders and the public. 

References 

Brocks, J.J., Buick, Roger, Logan, G.A., and Summons, R.E., 2003, Composition and syngeneity of 

molecular fossils from the 2.78 to 2.45 billion-year-old Mount Bruce Supergroup, Pilbara Craton, 

Western Australia: Geochimica et Cosmochimica Acta, v. 67, issue 22, p. 4289–4319. 

Fitton, Godfrey, 1997, X-ray fluorescence spectrometry, in Gill, Robin, ed., Modern analytical 

geochemistry: Essex, U.K., Addison Wesley Longman, p. 87–115. 

Geboy, Nicholas, 2006, Rhenium-osmium age determinations of glaciogenic shales form the 

Mesoproterozoic Vazante Formation, Brazil: University of Maryland, Master’s Thesis, 99 p. 

Gill, Robin, ed., 1997, Modern analytical geochemistry: Essex, U.K., Addison Wesley Longman, 327 p. 

Hassett, D.J., 1994, Scientifically valid leaching of coal conversion solid residues to predict 

environmental impact: Fuel Processing Technology, v. 39, issue 1–3, p. 445–449. 

Helsel, D.R., 2005, Nondetects and data analysis—Statistics for censored environmental data: New 

York, Wiley, 250 p. 

Helsel, D.R., 2006, Fabricating data—How substituting values for nondetects can ruin results, and what 

can be done about it: Chemosphere, v. 65, no. 11, p. 2434–2439. 

Helsel, D.R., and Hirsch, R.M., 2002, Methods for data below the reporting limit, Chapter 13, in

Statistical methods in water resources, U.S. Geological Survey Techniques of Water-Resources 

Investigations, book 4, chap. A3, p. 357–376. 

Jarvis, K.E., 1997, Inductively coupled plasma-mass spectrometry (ICP-MS), in Gill, Robin, ed., 

Modern analytical geochemistry: Essex, U.K., Addison Wesley Longman, p. 171–187. 

Jones, K.B., Ruppert, L.F., Swanson, S.M., and Affolter, R.H., 2010, Trends in elemental leaching from 

coal combustion products from two coal-fired power plants: Geological Society of America Abstracts 

with Programs, v. 42, no. 5, p. 610. 

Luppens, J.A., Wilson, S.E., and Stanton, R.W., eds., 1992, Manual on drilling, sampling, and analysis 

of coal: Philadelphia, Pa., American Society for Testing and Materials, 61 p. 

Olea, R.A., 2008, Inference of distributional parameters of compositional samples containing 

nondetects: Proceedings from the 3d Compositional Data Analysis Workshop CoDaWork ’08, Girona, 

Spain, May 27–30, 2008, 20 p. 

Parry, S.J., 1997, Neutron activation analysis, in Gill, Robin, ed., Modern analytical geochemistry: 

Essex, U.K., Addison Wesley Longman, p. 116–134. 

Ramsey, M.H., 1997, Sampling and sample preparation, in Gill, Robin, ed., Modern analytical 

geochemistry: Essex, U.K., Addison Wesley Longman, 327 p. 

Reimann, C., Filzmoser, P., Garrett, R., and Dutter, R., 2008, Statistical data analysis explained: West 

Sussex, U.K., John Wiley & Sons, Ltd., 343 p. 

Rowland, A.P., 1997, Atomic absorption spectrometry and other solution methods, in Gill, Robin, ed., 

Modern analytical geochemistry: Essex, U.K., Addison Wesley Longman, p. 67–86. 

Skoog, D.A., Holler, F.J., and Crouch, S.R., 2007, Principles of instrumental analysis (6th ed.): 

Belmont, Calif., Thomson Brooks/Cole, 1039 p. 
22

U.S. Army Corps of Engineers, 2005, Environmental quality—Guidance for evaluating performancebased chemical data: Pub #EM 200-1-10. 

U.S. Environmental Protection Agency, 1999, Compendium of methods for the determination of 

inorganic compounds in ambient air: EPA/625/R-96/010a, accessed July 1, 2011, at 

http://www.epa.gov/nrmrl/pubs/625r96010/iocompen.pdf. 

U.S. Environmental Protection Agency, 1992, EPA Test Method 1311—TCLP, Toxicity characteristic 

leaching procedure, accessed July 1, 2011, at 

http://www.ehso.com/cssepa/TCLP_from%20EHSOcom_Method_1311.pdf. 

U.S. Environmental Protection Agency, 2007, Method 7000B—Flame atomic absorption 

spectrophotometry, accessed July 1, 2011, at 

http://www.epa.gov/osw/hazard/testmethods/sw846/pdfs/7000b.pdf.

U.S. Geological Survey, 2006, Collection of water samples: U.S. Geological Survey Techniques of 

Water-Resources Investigations, book 9, chap. A4, accessed July 1, 2011, at 

http://pubs.water.usgs.gov/twri9A4/. 

Walsh, J.N., 1997, Inductively coupled plasma-atomic emission spectrometry (ICP-AES), in Gill, 

Robin, ed., Modern analytical geochemistry: Essex, U.K., Addison Wesley Longman, p. 41–66. 
23

Appendix 1 

Detection Limits for Various Analytical Techniques

The figures in this appendix present detection limits for various analytical techniques. For the 

convenience of the user, each analytical technique has its own periodic table; shading is used to identify 

the order of magnitude to which that technique can measure each element. The detection limits are 

labeled in parts per million (ppm) and parts per billion (ppb), which are equivalent to milligrams per 

liter (mg/L) and micrograms per liter (μg/L), repectively, for samples in solution and micrograms per 

gram (μg/g) and nanograms per gram (ng/g), respectively, for solid samples. 

 

Figure A1. Instrument detection limits for inductively coupled plasma-atomic emission spectrometry (ICP-AES). 

From Walsh, 1997; ppm, parts per million; ppb, parts per billion. 
24

Figure A2. Instrument detection limits for inductively coupled plasma-mass spectrometry (ICP-MS). From Jarvis, 

1997; ppm, parts per million. 
25

Figure A3. Instrument detection limits for neutron activation analysis (NAA). From Parry, 1997; ppm, parts per 

million; ppb, parts per billion. 
26

Figure A4. Instrument detection limits for X-ray fluorescence spectrometry (XRF). From Fitton, 1997; ppm, parts 

per million. 
27

Figure A5. Instrument detection limits for atomic absorption spectrometry (AAS). From Rowland, 1997; ppb, parts 

per billion. 
28

Appendix 2 

Where to Obtain Reference Materials

Company/Agency Type of Resource Material

U.S. Geological Survey, Mineral Resources Hard Rock Samples

Nevada Bureau of Mines and Geology Precious Metals

National Institute of Standards and Technology Ores and Sediments, Natural Gas, Water

European Commission, Institute for Reference Materials and 

Measurements Water, Soil and Air Quality

U.S. Geological Survey, Water Resources Aqueous Trace Elements, Major Ions, 

Nutrients and Mercury

Inorganic Ventures Inorganics

National Research Council of Canada Biological and Hydrological

Coal Standards Coal and Coal Components

Scott Specialty Gases Natural Gas and Methane

Koehler Instruments Petroleum

Quality Assurance Resources Coal