Comparison Criteria for

Environmental Chemical

Analyses of Split Samples

Sent to Different Laboratories

Corps of Engineers Archived Data

Clarence L. Grant, Thomas F. Jenkins and Anand R. Mudambi May 1996

SPECIAL REPORT 96-9
Abstract

Data comparison criteria were developed for quality control (QC) and quality assurance (QA) chemical analyses obtained during environmental studies directed by the U.S. Army Corps of Engineers (USACE). Archived results

for 124 sets of eight metals in soils, 69 sets of fourteen volatile organic

compounds (VOCs) in soils, 163 sets of total petroleum hydrocarbons

(TPH) in soils, 79 sets of six explosives in soils, and 153 sets of fourteen

VOCs in groundwater were analyzed statistically. Concentration ratios (QC1/

QC2 and QC/QA) were lognormally distributed and this was the model used

for comparisons. For both metals in soils and VOCs in groundwater, duplicate QC results should yield ratios between 0.50–2.00; less than 4% of the

archived results were outside these limits. For QC/QA ratios, the limits suggested are 0.40–2.50; only 10.2% of metals ratios in soils and 5.6% of

the VOCs in groundwater exceeded these limits. Considering that both methods

are multi-analyte, we find only 4.0% of the metals samples and 2.0% of

the VOC samples had more than one offending ratio per sample. Application of these limits to recent analyses produced very similar results. For

VOCs, TPH, and explosives in soils, temporary limits of 0.25–4.00 are

suggested with the understanding that improvements leading to tightening

should be energetically pursued. Even with these wide limits, approximately

42% of VOCs, 14% of TPH, and 11% of explosives QC/QA ratios must be

considered outliers. Here, too, recent analyses yielded very comparable

percentages. The situation for VOCs in soils requires immediate attention to

improve sampling and preparation procedures, but all methods can and

should be capable of producing improved agreement between laboratories.

Numerous recommendations are offered with this goal in mind.

For conversion of SI units to non-SI units of measurement consult ASTM

Standard E380-93, Standard Practice for Use of the International System

of Units, published by the American Society for Testing and Materials,

1916 Race St., Philadelphia, Pa. 19103.

This report is printed on paper that contains a minimum of 50% recycled

material.
Special Report 96-9

Comparison Criteria for

Environmental Chemical

Analyses of Split Samples

Sent to Different Laboratories

Corps of Engineers Archived Data

Clarence L. Grant, Thomas F. Jenkins and Anand R. Mudambi May 1996

Prepared for

OFFICE OF THE CHIEF OF ENGINEERS

Approved for public release; distribution is unlimited.

US Army Corps


of Engineers

Cold Regions Research &


Engineering Laboratory
PREFACE

This report was prepared by Dr. Clarence L. Grant, Professor Emeritus, University of New Hampshire, Durham, New Hampshire; Dr. Thomas F. Jenkins, Research

Chemist, Geological Sciences Division, Research and Engineering Directorate, U.S.

Army Cold Regions Research and Engineering Laboratory (CRREL), Hanover, New

Hampshire; and Dr. Anand R. Mudambi, Chemist, Chemical Data Quality Management Branch, HTRW Center of Expertise, U.S. Army Corps of Engineers, Omaha,

Nebraska.

Funding for this work was provided by Headquarters, U.S. Army Corps of

Engineers, Washington, D.C., through the Missouri River Division (USAED-MRD),

MIPR# GP95-11.

The authors acknowledge the useful comments provided by Martin H. Stutz, U.S.

Army Environmental Center, Aberdeen Proving Ground, Maryland, and Ann B.

Strong, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Mississippi, during review of this manuscript. They also acknowledge the initial guidance

provided by Kevin Coats of the Missouri River Division, Corps of Engineers, and Dr.

David Koran, Headquarters, Corps of Engineers, who helped formulate the goals of

this study. Brian Condike, formerly of the New England Division Laboratory and

currently with the Southwest Division Laboratory, and Dr. Bruce Heitke, Headquarters, Corps of Engineers, are also acknowledged for providing useful comments on

the initial draft of this report. The authors also are indebted to Gerald Roy,

Information Systems Branch, CRREL, for selection of the data management system

used to assemble and process the data used in this work, and to Jane G. Mason for

her help in assembling and editing the manuscript.

The contents of this report are not to be used for advertising or promotional

purposes. Citation of brand names does not constitute an official endorsement or

approval of the use of such commercial products.

ii
CONTENTS

Page

Preface ................................................................................................................... ii

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

Data comparison methodology ......................................................................... 2

Results and discussion ........................................................................................ 6

Metals in soils .................................................................................................. 6

VOCs in soils ................................................................................................... 8

TPH in soils ...................................................................................................... 12

Explosives in soils ........................................................................................... 15

VOCs in groundwater .................................................................................... 17

Conclusions and recommendations .................................................................. 18

General recommendations ............................................................................ 18

Metals in soils .................................................................................................. 18

VOCs in soils ................................................................................................... 19

TPH in soils ...................................................................................................... 19

Explosives in soils ........................................................................................... 19

VOCs in groundwater .................................................................................... 20

Summary ............................................................................................................... 20

Literature cited ..................................................................................................... 20

Appendix A: Data and sample calculations for chromium ........................... 22

Appendix B: Data for metals, VOCs, TPH, and explosives ........................... 27

Abstract ................................................................................................................. 32

ILLUSTRATIONS

Figure

1. Frequency histogram for soil Cr QC/QA concentration ratios ........ 2

2. Lognormal probability plots for soil and for groundwater

QC/QA concentration ratios .............................................................. 3

3. Distribution of soil As, Ba, Cr, and Pb QC/QA concentration

ratios outside the acceptable range of 0.30–3.00 as a

function of QA concentrations normalized to their

respective mean concentrations ......................................................... 6

4. Frequency histogram for soil VOC QC/QA concentration ratios.... 9

5. Lognormal probability plot for QC/QA concentration ratios of

all VOCs in soils with ratios between 0.10–10.0 .............................. 9

6. Lognormal probability plots for QC/QA concentration ratios

for soil VOCs ......................................................................................... 10

7. Correlation of log concentration ratios for total xylenes and

ethylbenzene in soil ............................................................................. 12

iii
Figure Page

8. Comparison of frequency histograms of soil TPH QC/QA

concentration ratios from NPD and MRD ........................................ 12

9. Lognormal probability plot for soil TPH QC/QA concentration

ratios between 0.25 and 4.00 for NPD results .................................. 13

10. Frequency histogram for soil TPH QC1/QC2 concentration ratios

from NPD .............................................................................................. 13

11. Lognormal probability plot for soil TPH QC1/QC2 concentration

ratios between 0.25 and 4.00 for NPD results .................................. 13

12. Correlation of soil TPH concentration ratios for CAS, Inc./NET

Pacific, Inc. with soil TPH concentration estimates reported by

NET Pacific, Inc. ................................................................................... 14

13. Frequency histogram for soil explosives QC/QA concentration

ratios from MRD .................................................................................. 15

14. Lognormal probability plot for QC/QA concentration ratios of

explosives in soils with ratios between 0.25–4.00 ............................ 16

iv
INTRODUCTION

In conjunction with hazardous waste remedial

activities, the U.S. Army Corps of Engineers

(USACE) requires that quality control (QC) and

quality assurance (QA) samples be collected and

analyzed by a contract laboratory and a USACE

laboratory, respectively. Procedures for these activities are prescribed in Chemical Data Quality

Management for Hazardous Waste Remedial Activities (USACE 1990). The QC and QA samples represent about 10% of the field samples and are

splits or co-located samples. Splits are used for

analyses such as metals in soils where bulk samples can be homogenized and subdivided in the

field without losses or contamination. Co-located

samples are required for volatile organic compounds (VOCs) in soil and water samples to reduce losses in handling, and are often preferred

for other analytes in groundwater and surface

water samples. Since co-located samples are not

homogenized, the contribution of natural sample

variability can increase differences compared to

splits. Field personnel also collect rinsate blanks,

trip blanks for VOCs, and background soil and

groundwater samples. The identity of all QC samples is kept blind to analysts and laboratory personnel until data are in deliverable form.

Acceptability of contractor laboratory data is

determined by a USACE QA laboratory. Guidelines exist for this assessment except for the numerical comparison of QC and QA analytical results from split or replicate samples. Currently,

limits for these comparisons are not based on technical criteria and vary from one QA laboratory to

another.

It was decided that an analysis of archived QC/

QA data might be useful in establishing some realistic numerical comparison criteria. The major

shortcoming of the archived data is the absence

of any basis for assuming that the QA results are

more accurate than the QC data being evaluated.

In fact, a contractor laboratory generating QC results on one project may serve as the QA laboratory on another project. Lacking evidence to the

contrary, we must assume similar uncertainties

associated with both data sets.

The archived data received were as follows:

a) Forty-nine data reports for metals in soils

from the New England Division (NED) laboratory, labeled M1-1 to M1-49, and 75 reports from

the North Pacific Division (NPD) laboratory, labeled M3-1 to M3-75. These results were from 37

project locations in eight states and involved 16

laboratories.

b) Sixty-six data reports of volatile organic compounds (VOCs) in soils supplied by the Missouri

River Division (MRD), labeled VS2-01 to VS2-66

(VS2-60 to VS2-66 used GC methods instead of

GC/MS), and three reports from the NPD, labeled

VS3-1 to VS3-3. These results were from 26 project

locations in 15 states and involved 21 laboratories.

c) One hundred and two data reports for total

petroleum hydrocarbons (TPH) in soils supplied

by the NPD, labeled TS3-01 to TS3-102, and 61

reports from the MRD, labeled TS2-01 to TS2-61.

Analysis was by Method 8015M, GC-FID or equivalent. These results were from 17 project locations

in three states and involved eight laboratories.

d) Seventy-one data reports for explosives in

soils supplied by MRD, labeled ES2-01 to ES2-71,

and eight reports from NPD, labeled ES3-01 to

ES3-08. Analyses were by method 8330. These results were from eight project locations in eight

states and involved eight laboratories.

Comparison Criteria for

Environmental Chemical Analyses of

Split Samples Sent to Different Laboratories

Corps of Engineers Archived Data

CLARENCE L. GRANT, THOMAS F. JENKINS, AND ANAND R. MUDAMBI
e) One hundred and thirty-four data reports

for VOCs in groundwater from the MRD laboratory, labeled V2-1 to V2-134, and 23 from the NPD

laboratory, labeled V3-1 to V3-23. These results

were from 39 project locations in more than 21

states and involved more than 15 laboratories.

DATA COMPARISON

METHODOLOGY

The first approach considered was a plot of

QC concentration estimates vs. associated QA estimates. This is a very useful comparison technique. A linear regression line can be fitted by

minimizing the residual sum of squares for the

QC estimates while assuming zero error in the

QA estimates. For an ideal system with perfect

agreement between QC and QA values, the fitted

model would be linear through the origin with a

slope of 1.00. The extent of departure from this

ideal can be used to calculate confidence limits

for individual measurements at some selected

probability level. Confidence intervals for individual measurements are called tolerance bands.

After reviewing the data sets submitted, we

decided that the concentration ranges were much

too wide to be fitted to individual models. Furthermore, there is no basis for assigning referee

status to the QA values, and the data are not normally distributed, which is the underlying assumption of this approach. It is also likely that

the more complex computations required by this

approach might impede routine usage. Consequently, we decided to examine QC/QA concentration ratios for between-laboratory comparisons

and QC1/QC2 ratios for comparison of withinlaboratory replicates. This approach is similar to

the first one considered except that no regression

model is fitted. When duplicate QC values were

given, the first listed value was always used for

the QC/QA computation. For metals in soils and

VOCs in groundwater, ratios below 0.30 and

above 3.00 were designated outliers and excluded

from further computations. Because of the larger

unavoidable uncertainties attached to the estimation of low concentration organic analytes in soils,

these limits were expanded to 0.25–4.00 for TPH

and explosives and to 0.10–10.0 for VOCs. While

this practice may seem arbitrary, exclusion of a

very few highly extreme values is necessary to

avoid unreasonable increases of standard deviations and the associated confidence bands for a

data set. Except for VOCs in soils and one set of

TPH results, the percent exclusions were always

less than 13% and often well below 10%.

We should not expect the QC/QA ratios to be

normally distributed using linear coordinates. A

factor of 2 difference in QC and QA concentrations would lead to a low side ratio of 0.50 and a

high side ratio of 2.00. Obviously these two values are not symmetrically distributed around the

ideal value of 1.00 for perfect agreement. A histogram of the soil Cr QC/QA results (Fig. 1) shows

that the typical shape of these distributions is

skewed toward the high end. This histogram suggests that a lognormal distribution should describe

the results. Simply stated, this means that the logs

of the ratios will form a normal distribution when

plotted as shown in Figure 1.

An effective method of testing the hypothesis

that the ratios are lognormally distributed is to

use Lognormal Probability graph paper in which

the ordinate is a % probability scale and the abscissa is a log scale. If the lognormal model is

correct, a straight line will result. To plot the Cr

ratios shown in Figure 1, we convert the number

of ratios in each cell to a cumulative probability.

From Figure 1 there are 16 ratios between 0.395

and 0.595. Since the total number of ratios was

116, the probability of values in this cell is

 

( )( ) . % 16 100

116 = 13 8 .

This value is plotted on the probability scale vs.

the upper boundary of the cell, 0.595. The next

cell (0.595–0.795) contained 22 ratios and the cumulative percent was

30

20

10

0

0.395-0.5950.595-0.7950.795-0.9950.995-1.1951.195-1.3951.395-1.5951.595-1.7951.795-1.9951.995-2.1952.195-2.395

Cr Ratios QC

QA

Number of Cr Ratios QCQA

Figure 1. Frequency histogram for soil Cr QC/QA

concentration ratios.

2
 

( )( ) . % 16 22 100

116

32 8 + = ,

which is plotted vs. 0.795, etc. (see Fig. 2c). Figures 2a, 2b, 2c, and 2d are log-cumulative probability plots of QC/QA ratios for soil As, Ba, Cr,

and Pb respectively. Although a few points deviate slightly from the linear ideal for lognormal

results, the fits are quite good and justify analyzing the data as being lognormally distributed.

Plots for QC1/QC2 ratios behaved similarly, as

expected.

Lognormal plots for individual values rather

than groups are illustrated using four VOCs in

groundwater (Fig. 2e–2h). Individual rather than

grouped probabilities were used because of the

relatively small number of ratios. Individual probabilities are calculated from

 

( . )( ) i

n

− 0 5 100

where n is the total number of ratios in a set and i

is the rank after the ratios are arranged from lowest to highest. Thus for 20 ratios ranging from

0.40 to 2.75, the first ranked one (0.40) would be

plotted on the abscissa vs. a probability of

 

( . ) ( ) . % 1 0 5 100

20

2 50 − =

and the next higher ratio would be plotted vs.

 

( . )( ) . % 2 0 5 100

20

7 50 − = ,

etc. Benzene (Fig. 2e) has several points at each

extreme that deviate a good deal from linearity.

However, a linear probability plot was even less

satisfactory. Chloroform and ethylbenzene (Fig.

2f and 2g) fit the lognormal model with only minimal scatter while toluene (Fig. 2h) provides a fair

fit. Other VOCs not shown behaved similarly to

the ones shown. In view of these VOC plots, those

for other analytes, and the expected distribution

of ratios, we decided to treat all ratios by the same

computational procedures. Since the lognormal

model will be used, we must compute geometric

means and geometric standard deviations as

shown below.

1. Compute QC1/QC2 ratios and their logarithms when QC1/QC2 results are available.

2. Compute mean and standard deviation of

the logs.

3. Compute 95% confidence limits for the mean

of the logs according to the equation

 

x

t S

n

. x ± 0 95

where 

x is the mean of the logs, t0.95 is student’s

t for n–1 degrees of freedom (d.f.) and a two-tail

alpha risk of 5% (α = 0.05), Sx is the standard

0.10 1.0 3.0

Arsenic Concentration Ratio QC

QA

99.9

98

90

70

50

30

10

2

Cumulative Probability (%)

0.10 1.0 3.0

Barium Concentration Ratio QC

QA

99.9

98

90

70

50

30

10

2

Cumulative Probability (%)

a. Soil/arsenic ratio. b. Soil/barium ratio.

Figure 2. Lognormal probability plots for soil and for groundwater QC/QA concentration ratios.

3
Figure 2 (cont’d). Lognormal probability plots for soil and for groundwater QC/QA concentration ratios.

0.10 1.0 3.0

99.9

98

90

70

50

30

10

2

Cumulative Probability (%)

Chromium Concentration Ratio QC

QA

c. Soil/chromium ratio. d. Groundwater/lead ratio.

0.10 1.0 3.0

Lead Concentration Ratio QC

QA

99.9

98

90

70

50

30

10

2

Cumulative Probability (%)

0.10 1.0 3.0

99.9

98

90

70

50

30

10

2

 Probability (%)

Benzene Concentration Ratio QC

QA

e. Groundwater/benzene ratio. f. Groundwater/chloroform ratio.

0.10 1.0 3.0

99.9

98

90

70

50

30

10

2

 Probability (%)

Chloroform Concentration Ratio QC

QA

deviation of the logs, and n is the number of logs

in the set.

4. Obtain the antilogs of the mean and the upper and lower 95% confidence limits to see if the

limits bracket the expected mean of 1.00. These

confidence limits define a range of values within

which we may reasonably assume (95% probability in this case) the true value lies if there were no

systematic error present. Since the expected true

mean for each set of ratios is 1.00, confidence limits that do not bracket 1.00 suggest some systematic bias in the experimental results.

5. Compute 99% tolerance limits for individual

ratios from 

x ± t0.99(Sx) for the logs.

6. Obtain antilogs for these tolerance intervals

to see if there are any values outside the limits.

4
These tolerance intervals represent the lower and

upper limits within which we may assume (99%

probability here to give minimal probability of an

outlier) that all individual ratios in a set should

fall. If an individual ratio is outside these limits,

it is assumed to be an outlier and it can be rejected. This decision is based on the assumption

that the lognormal model is correct.

7. Assuming the expected (ideal) mean log of

0.00, obtain antilogs for –t0.99(Sx) to + t0.99(Sx). Determine how many values are outside of these

limits. NOTE: Do not count the outliers excluded

before these calculations were started.

8. Repeat steps 2 through 7 for QC/QA ratios.

An example of these computations is presented

in Appendix A for the Cr results.

When both QC and QA laboratories reported

“less than” values, the ratios of these values were

checked to see if they were within a factor of 3.0

or 4.0, depending on the analyte. When one laboratory reported a value above the reporting limit

and the comparison result was below the reporting limit, they were still compared using the appropriate factor. For example, if one laboratory

reported 0.60 μg/g and the other reported not

detected (ND < 1.0 μg/g), they were in agreement. But if one reported 0.60 μg/g and the comparison value was ND < 5.0, they disagreed because the “less than” value was unacceptably high.

The most obvious disagreement would be if the

two values were 5.0 μg/g and ND < 0.80 μg/g, as

an example. Such comparisons were often unsatisfactory because the ranges of reporting limits

were sometimes as wide as a factor of 1000 or

more.

Inconsistency with significant figures was a

problem throughout. For example, the QA laboratory might report a result of 10.7 μg/g and the

QC laboratory might give the matching result as

8 μg/g. The result 10.7 μg/g implies uncertainty

at the tenth of a μg/g whereas the result of 8 μg/

g implies an uncertainty of ± 1 μg/g. We will address this problem with a recommendation in a

later section, but for the purpose of our computations we will treat 8 μg/g as though it is 8.0 μg/

g. Similar problems were present for the reporting limit values.

Some analytes were determined by more than

one method in different laboratories (inductively

coupled plasma and graphite furnace atomic absorption in the case of lead, for example). We originally intended to segregate results according to

method, but there were far too many data reports

lacking method information to permit this to be

done. In reality, we expect properly calibrated

methods to yield comparable accuracy, so when

numerical concentrations are reported using accepted methods, there should not be systematic

bias in the results. Of course, reporting limits will

vary for different methods.

0.10 1.0 3.0

99.9

98

90

70

50

30

10

2

 Probability (%)

Ethyl Benzene Concentration Ratio QC

QA

0.10 1.0 3.0

99.9

98

90

70

50

30

10

2

 Probability (%)

Toluene Concentration Ratio QC

QA

g. Groundwater/ethylbenzene ratio. h. Groundwater/toluene ratio.

Figure 2 (cont’d).

5
The original plan called for reporting limits to

be specified on all data reports for all analytes.

However, many data reports showed either no

reporting limits or only a few. The lack of complete data limited the comparisons of reporting

limits to some extent.

For VOCs in groundwater concentrations, numerical surrogate recovery results were sparse.

Often nothing was specified or the recoveries were

simply labeled “OK.” We had planned to use this

information to aid in identifying outliers (unacceptable results), but too little data were present

to permit this application.

A few values were flagged with “J” to designate that the value was estimated (below the reporting limit). However, these values yielded ratios that appeared to be about as reliable as the

bulk of the results and, therefore, they were used

in the comparison.

RESULTS AND DISCUSSION

The discussion that follows is based on the assumption that the lognormal model is adequate

for these sets of ratios. The null hypothesis then

is that geometric means of the ratios have an expected value of 1.00 and that 95% confidence limits based on logs should bracket 1.00 if there is no

bias between the QC and the QA laboratories.

Furthermore, 99% tolerance intervals based on

similar assumptions should include

nearly all individual ratios. Any ratios

that fall outside of the tolerance limits

should be flagged for further review.

Metals in soils

Results for As, Ba, Cr, and Pb are

summarized in Table B1. We see that

only 1.3% of the duplicate QC ratios

were outside the arbitrarily chosen 0.30–

3.00 range for classification as outliers.

For Ba, Cr, and Pb, the expected mean

ratio of 1.00 was bracketed by the 95%

confidence limits, but the lower limit for

As was slightly above 1.00, suggesting

a slight bias. The 99% tolerance limits

were all very similar, with only six values outside these boundaries. If we

choose 0.50–2.00 as the regulatory limits for all four metals, the outlying Pb

ratio is within tolerances and the total

number of excluded ratios is 8(3+5), or

3.4%. This small percentage of unacceptable values seems reasonable, and the 0.50–2.00

range of ratios is recommended for duplicate QC

samples. Incidentally, for the other four metals

with few values above reporting limits, all ratios

(12 Cd, 5 Hg, 1 Ag) were within this recommended

range. When this range was used to analyze the

most recent results from 104 samples (32 project

locations), only 2% of the QC1/QC2 ratios were

outside of these boundaries.

As expected, the QC/QA ratios were less reproducible than the duplicate QC ratios. In Table

B1 we note that 29 values (6.3%) were designated

outliers because they fell outside of 0.30–3.00.

These 29 outliers are well distributed among the

four metals, As, Ba, Cr, and Pb. The distribution

of outliers relative to QA concentrations is shown

in Figure 3. In order to plot results from four metals with very different concentration means, the

QA concentration for each outlier was normalized to the mean QA concentration for that metal.

Log-log scales were necessary to accommodate

the wide ranges of values. Although there are a

few more ratios below 0.30 than above 3.00 (16

vs. 13) and more ratios for concentrations below

the normalized mean QA concentration of 1.00

than above (19 vs. 10), there is little basis to suggest that outliers occur primarily in extreme concentration regions. In fact, most of the outliers

fall between one-half and two times the mean

concentrations. For the 23 ratios available for Cd,

Hg, and Se, three were outliers (all on the high

10

1.0

0.10

0.10 1.0 10 100

Normalized (to means) QA Concentrations

Range of

Acceptable Values

As

Ba

Cr

Pb

Concentration Ratios QCQA

Figure 3. Distribution of soil As, Ba, Cr, and Pb QC/QA concentration ratios outside the acceptable range of 0.30–3.00 as a function of QA concentrations normalized to their respective mean

concentrations.

6
side). There were no samples with Ag concentration above reporting limits for both QA and QC

laboratories.

We also determined whether the outliers were

for several metals in a few samples and whether

they occurred with certain laboratories. However,

the outliers were distributed among 25 different

samples. Two samples had outliers for three metals and three samples had two metals outside of

the limits. None of the latter five samples was

from the same project. We lacked necessary information to state with certainty that different laboratories were associated with each of the five

samples. In any case, we feel that outliers are occurring more or less randomly rather than as any

systematic problem. Sample handling and/or

analysis errors are likely causes.

Geometric means for all four metals were below the expected mean ratio of 1.00, but the 95%

confidence limits bracketed 1.00 for all but Ba.

The tendency of all the ratios to be below 1.00

indicates that QC laboratories are reporting

slightly lower concentrations than QA laboratories, but this pattern is neither marked nor clearly

established. The 99% tolerance intervals for Ba,

Cr, and Pb were very similar. The somewhat wider

limits for As is a reflection of its large standard

deviation. Only five additional values beyond the

original outliers are outside of these tolerance intervals.

It is instructive to consider the effects of imposing three possible sets of logarithmically distributed rejection limits. In Table B2 we see that

the arbitrarily chosen limits of 0.30–3.00 produced

6.3% exclusions with nearly equal numbers of

outliers below 0.30 and above 3.00 and reasonably similar numbers for each metal. If the limits

are relaxed to 0.25–4.00, only 4.4% of the ratios

are excluded. Two-thirds of the exclusions are on

the low side, i.e., cases where the QC concentration is much lower than the QA concentration. In the other direction, if we use limits of

0.40–2.50, 10.2% of the ratios would be rejected.

Once again two-thirds are on the low side, but

the pattern is quite different for As than it is for

the metals determined largely by plasma emission. The tendency for low QC results is absent

for As but quite pronounced for Ba, Cr, and Pb.

The most recent results (again, 32 project locations) contained 11.9% of the ratios outside 0.40–

2.50 with similar percentages of low and high

outliers.

Clearly the 0.40–2.50 limits would put pressure on laboratories to improve the accuracy of

their analyses. Nonetheless, we believe that this

range is reasonable for metals in soils and we

recommend that it be used. Rejection of 10% of

contract laboratory results would likely trigger

demands to provide proof that the QA concentrations are sufficiently reliable to be used as referee

values. We believe that this position is justified

regardless of what regulatory limits are chosen.

A further very practical question remains:

would an entire set of results for eight metals be

rejected just because the QC/QA ratio for one

metal was outside tolerance limits? We doubt it,

especially if the results for the offending metal

were low compared to concentrations of concern.

We suspect that rejection would occur only when

two or more ratios are outliers for a sample and

only when concentrations are high. For the

archived data, only five samples out of 124 (4.0%)

had more than one ratio per sample outside of

tolerance bands. Consequently, we believe ratios

should be flagged if they are outside 0.40–2.50

and examined by a QA manager to determine the

fate of the results. Better yet, an artificial intelligence computer program could probably be devised to do this task. With so few samples having

multiple outliers, it seems that the 0.40–2.50 range

for ratios is justified.

Although most results for Ag, Cd, Hg, and Se

were less than the reporting limit (ND<), some

insight can be gained by tabulating the reporting

limits (Table B3). A few values were unusually

high (27 of 722, or 2.4%) and were deleted without further consideration. Of the remaining values, eleven of the 17 unacceptable reporting limits were associated with only three samples, none

of which were samples with excluded As, Ba, Cr,

and Pb ratios. The concentrations of other metals

in these three samples were not unusual. While

matrix effects may be involved, we continue to

believe that an occasional faulty analysis is the

major culprit. After these exclusions, means and

medians were in reasonable agreement, indicating that the distributions were not seriously

skewed. Furthermore, the ranges and the means

for QA and QC laboratories were also in good

agreement. Even under these conditions, the ratios of QC/QA reporting limits were often outside 0.30–3.00 for specific samples. Whether this

is a problem depends on the relationship between

reporting limits and regulatory concentration values. When reporting limits are well below regulatory limits, there is less need for the reporting

limits to be in close agreement.

When one laboratory reported ND< and the

7
other gave a concentration above the reporting

limit, the ratios were often outside the factor of

3.00 criterion. By far the poorest metal in this respect was Cd, where 25 of 40 such ratios failed

this test. The failure rate for Ag was 6 in 16 pairs,

for Hg it was 4 in 27, and only 2 of 21 Se pairs

failed to be within a factor of 3.00. In general,

most values above reporting limits were from the

QA laboratories, although several were flagged

with “J,” meaning they were estimates but below

reporting limits. The QA laboratories may be providing a slightly more reliable analysis than the

QC laboratories, but there still is insufficient real

evidence to defend this statement.

VOCs in soils

For the 69 sets of VOCs in soils, there was a

total of 134 analyte pairs having both a QC and

QA concentration above reporting limits. Ethylbenzene with 25 pairs, toluene with 35 pairs, and

total xylenes with 36 pairs represented the bulk

of the data. The remaining 38 pairs were distributed among benzene (eight pairs), chloroform (one

pair), total 1,2-dichloroethenes (five pairs), methyl ethyl ketone (two pairs), tetrachloroethene

(11 pairs), and trichloroethene (11 pairs). Carbon

tetrachloride, chlorobenzene, 1,2–dichloroethane,

1,1-dichloroethene, and vinyl chloride yielded no

pairs. Although samples VS2–60 through VS2–66

used GC methods rather than GC/MS, there was

no apparent reason to treat those results separately.

Most analytes also had examples where either

the QC or the QA result was a real number but

the other was below reporting limits. In many

cases it was impossible to judge how well (or

badly) these pairs compared because reporting

limits were based on dilutions made to accommodate a high concentration analyte. For example,

benzene in sample VS2–17 was reported as 59.7

μg/kg by the QA laboratory (reporting limit of

6.1 μg/kg) while the QC laboratory reported <

2500 μg/kg. These results may agree, but it is

impossible to tell. Possibly this issue is unimportant given the very high concentration of xylene

in this sample. One thing is clear: reporting these

concentrations to three significant figures is a gross

misrepresentation! In other cases, such as methyl

ethyl ketone in sample VS2–18, the QA laboratory reported 271 μg/kg and the QC laboratory

reported <12 μg/kg. Although the disagreement

of these results could be represented by a ratio if

we assigned the QC result a value of 12 μg/kg,

we decided not to include such results in the statistical evaluation. Suffice it to say that such results occur with disturbing frequency.

Another common extreme is represented by

several analytes in VS2–33 where the QA laboratory reported < 1.1 μg/kg and the QC laboratory

reported < 7000 μg/kg. Such examples suggest

the need for standardization of reporting procedures for samples requiring dilution to permit determination of one or more high concentration

analytes. If such guidelines are already in place

but are misunderstood or ignored by laboratories, a training program might improve the situation. Alternatively, if low concentration analytes

really are of no interest when one or more analytes

are present at very high concentration, data compilations could be greatly simplified.

For the 134 QC/QA ratios with concentrations

above reporting limits, ratios ranged from 0.015

to 2830! The distribution of these ratios by analyte

and for the total is shown in Table B4. We note

that the distributions are very similar for individual analytes and for the group collectively. This

suggests that statistical analysis applied to the

entire group would adequately represent results

for VOCs in soils. It is also apparent that extreme

values are excessive. Over 40% of the ratios are

outside the limits 0.25–4.00 and 24% are outside

limits of 0.10–10.0. While we know of no standard for acceptability of analytical results on split

or co-located samples sent to different laboratories, it stretches the boundaries of credibility to

think that analysts would describe agreement

within a factor of 10 as acceptable quantitation.

Nonetheless, we will base statistical characterization on the 76% of ratios within 0.10–10.0.

A histogram of the logs of all 134 ratios is

shown in Figure 4. Logs of the 102 ratios between

0.10 and 10.0 form a tolerable approximation to a

normal distribution. Further support for the hypothesis of a lognormal distribution is provided

by the linear cumulative probability plot of these

logs on normal probability paper (Fig. 5). When

separate lognormal plots were prepared for

ethylbenzene, toluene, total xylenes, and the remaining analytes collectively (Figs. 6a–6d), all provided reasonable fits to this model.

Based on the lognormal model, the geometric

mean of the 102 ratios between 0.10 and 10.0 was

0.95 and the 95% confidence limits on this mean

were 0.77 to 1.16. The 99% tolerance intervals for

individual ratios were 0.062 to 14.4. The fact that

these intervals are considerably wider than the

boundaries used for editing suggests that some

values outside the range of 0.10 to 10.0 may also

8
30

20

10

0

Boundaries Corresponding to 

Ratios of 0.10 to 10.0

Number of Ratios QCQA

Logs of Concentration Ratios QC

QA

– 1.999 to – 1.800– 1.599 to – 1.400– 1.199 to – 1.000– 0.799 to – 0.600– 0.399 to – 0.200

0.001 to 0.2000.401 to 0.6000.801 to 1.0001.201 to 1.4001.601 to 1.8002.001 to 2.2002.401 to 2.6002.801 to 3.0003.201 to 3.400

Figure 4. Frequency histogram for soil VOC QC/QA concentration ratios.

98

95

90

70

50

30

10

5

2

99

– 0.8 – 0.4 0 0.4 0.8

QC

QA Log of VOA in Soil Concentration Ratios

Cumulative Probability (%)

arise as a consequence of random rather than systematic errors. The standard deviation is larger

than expected for a normal distribution and the

tolerance intervals are excessively wide.

What can we conclude from these computations? Even after excluding 24% of the ratios representing the most divergent values, the agreement between laboratories for the determination

of VOCs on split soil samples is so poor that it

would be inappropriate to establish acceptability limits based on this data. In a recent article

dealing with methods for regulatory analytical

environmental chemistry, Kimbrough and Spinner (1994) wrote, “If year after year certain methods produce wildly inaccurate results, then this

should be a signal that the method ought to be

evaluated and hopefully improved. The acceptance of very wide control limits basically sends the

message that if everybody is wrong, then everyone

is right.” (Italics are ours.) We heartily concur

with these statements and we believe that it is

time to make concerted efforts to improve the

situation. If the Corps wishes to use temporary

limits while procedures are being brought under better control, we suggest 0.25–4.00. Clearly

this will result in many values being questioned,

but wider limits will not encourage the needed

improvement.

Obviously, it is not a trivial task to significantly improve the procedures used to estimate

VOC concentrations in soils. Before considering our suggestions for improvement, let’s first

ask if there is more to learn from the archived

data. We failed to associate any specific laboratories with either good or bad results. In the absence of standards and with numerous laboratories participating (including some that serve either

the QC or the QA function on different projects),

it was impossible to establish a pattern. Clearly,

the more laboratories that submit results, the more

Figure 5. Lognormal probability plot for QC/QA concentration ratios of all VOCs in soils with ratios between

0.10–10.0.

9
Probability (%)

Ethylbenzene Concentration Ratios QC

QA

98

95

90

70

50

30

10

5

2

99

0.1 1.0 4.0

Probability (%)

Toluene Concentration Ratios QC

QA

98

95

90

70

50

30

10

5

2

99

0.1 1.0 10

a. Ethylbenzene.

b. Toluene.

Figure 6. Lognormal probability plots for QC/QA concentration ratios for soil VOCs.

likely we are to experience biases and the more

difficult it becomes to identify them.

What about the QC results from matrix spike/

matrix spike duplicates (MS/MSD)? We could find

no meaningful correlation between these results

and the degree of agreement between QC and

QA laboratories. Similar failure of MS/MSD results as a quality indicator for VOCs was recently

reported by Robertson et al. (1995). Generally

speaking, the MS/MSD results appeared to be

quite satisfactory and suggestive of much greater

reliability than was actually obtained on co-located samples. However, we must remember that

MS/MSD results are derived by spiking extracts

and, therefore, they omit all error associated with

sample collection, transport, storage, and extraction. Assuming that the solution used for spiking

is prepared independently from the calibration

standards (frequently untrue), MS/MSD results

can serve to validate calibration and to reveal specific interferences from the matrix under analysis.

Does this imply that calibration variations do not

contribute to the disagreement between laboratories? The answer is no. Significant bias may be

introduced from calibration errors, but we do not

believe it is the major source of error. In contrast

to the soil results, we found that most QC/QA

ratios for GC-MS determination of VOCs in

10
groundwater fell between 0.40–2.50. Nonetheless,

we recommend that the Corps start to use vaporfortified secondary soil standards of the type described by Hewitt (1994) and Hewitt and Grant

(1995). These fortified samples sealed in glass

ampoules are stable for extended periods and they

can be distributed to participating laboratories.

By using such secondary standards in an organized program of performance evaluation prior

to qualification and later as periodic QC samples,

the laboratory portion of the analytical procedure

(extraction plus analysis) can be evaluated.

We believe that the major losses are caused by

volatilization during sample collection and laboratory subsampling for analysis. Additional volatilization loss can occur during transport and storage, and biodegradation can also be a problem.

Recent articles (Siegrist and van Ee 1994, Hewitt

et al. 1995) document losses of a factor of 1000 or

more. This postulation is consistent with our observation in this study that samples containing

multiple analytes produce QC/QA ratios that are

consistently high or low for all analytes present.

In Figure 7 we illustrate this effect by plotting

logs of total xylene QC/QA ratios vs. logs of

ethylbenzene ratios for corresponding samples.

The correlation coefficient of 0.906 provides strong

evidence for the relationship between these re98

95

90

70

50

30

10

5

2

0.1 1.0 10

Probability (%)

Xylenes (total) Concentration Ratios QC

QA

99

Probability (%)

98

95

90

70

50

30

10

5

2

99

0.1 1.0 10

QC

QA Concentration Ratios for Several VOAs

d. Several VOCs in soils (benzene [seven],

1,2-dichloroethene [total] [five], methylethylketone [two], tetrachloroethene [eight],

trichloroethene [eight]).

c. Xylene (total).

Figure 6 (cont'd).

C

11
sults. Hewitt et al. (1995) state, “Only those sampling methods that maintain soil structural integrity, use a single soil transfer step, and preserve

against biological degradation should be

used when establishing VOC concentrations.” A tipless plastic syringe is suggested for obtaining soil samples, which

are immediately transferred to VOC vials containing either methanol or water

acidified with NaHSO4. Details of this

approach, which satisfies SW-846, Method

8260A, are available from Hewitt. We believe that it is imperative for the Corps to

consider this or alternative methods to

improve the quality of analytical results

for VOCs in soil.

In contrast to the 42% of archived QC/

QA ratios outside 0.25–4.00, only 23% of

the most recent results (44 samples from

11 project locations) were outside of these

limits. It is interesting to note that 20% of

the QC1/QC2 ratios were also outside

0.25–4.00 (no QC1/QC2 values were available in the original archived data). Of the

20%, 17% of the ratios were above 4.00,

i.e., the first value was much larger than

the second value. Perhaps VOCs were being lost during storage, thus causing the

second values to be low.

TPH in soils

The 102 data reports for TPH in soils

submitted by the North Pacific Division

(NPD) contained 83 pairs of replicate QC results

with concentrations above reporting limits, and

95 pairs with both QC and QA results above reporting limits. For these data, 6.0% of QC1/QC2

ratios and 13.7% of QC1/QA ratios were outside

0.25–4.00 (Table B5). We concede that these limits

are arbitrary, but they form a reasonable starting

point and the rejection rate for QC/QA is only

moderately greater than 10%, which we consider

an acceptable goal. As noted in the VOC section,

we believe that it is imperative to seek improvements in procedures that yield widely divergent

results.

In contrast to the results from contractors reporting to NPD, 28 of the 61 pairs (45.9%) of QC/

QA ratios from the Missouri River Division (MRD)

databank were outside 0.25–4.00. No replicate QC

values were included on these data reports. The

histograms of log QC/QA ratios shown in Figure

8 provide a very clear depiction of the difference

in dispersion for the two data sets. The lognormal model appears to be a reasonable fit to the

NPD QC/QA ratios between 0.25–4.00. This hypothesis is reinforced by the probability plot of

QCQA

15

10

5

0

MRD Results Boundaries Corresponding to

Ratios 0.25 to 4.00

Number of Ratios

25

20

15

5

0

– 2.399 to – 2.200– 1.999 to – 1.800– 1.599 to – 1.400– 1.199 to – 1.000– 0.799 to – 0.600– 0.399 to – 0.200

0.001 to 0.2000.401 to 0.6000.801 to 1.0001.201 to 1.4001.601 to 1.800

NPD Results

10

Logs of Concentration Ratios QC

QA

Figure 8. Comparison of frequency histograms of soil TPH

QC/QA concentration ratios from NPD and MRD.

3

2

1

0

– 1

– 2

– 2 – 1 0 1 2 3

Xylenes (total) Log Concentration Ratio QCQA

Ethylbenzene Log Concentration Ratio QC

QA

y = 0.134 + 0.795x ˆ

“r” = 0.906

Figure 7. Correlation of log concentration ratios for

total xylenes and ethylbenzene in soil.

12
these ratios on lognormal paper (Fig. 9). However, the histogram of ratios for MRD results

shows extreme dispersion, and the values between

0.25–4.00 form a rectangular distribution, thereby

precluding statistical analysis using a lognormal

model. The NPD results were probably more reliable than the MRD results because the former

tried to ensure that both QC and QA laboratories

used the same state-approved method. There is a

wide variation in both the extraction and analysis

procedures for TPH as a function of various state

requirements. We chose to analyze only the more

reliable NPD results; no further analysis was conducted on the MRD results. This decision was

rooted in the conviction that future aspirations

should at least be based on the best of past performances.

A histogram of the log QC1/QC2 results is

shown in Figure 10 and the lognormal probability plot appears in Figure 11. Both plots provide

only fair fits to the lognormal model and there is

some uncertainty about the correct location of the

straight line in Figure 11. Nonetheless, the expectation of lognormality and the absence of a better

model convinced us to proceed with this model.

The geometric mean of the 78 QC1/QC2 ratios

between 0.25–4.00 was 0.95 and 95% confidence

limits on this mean were 0.84–1.07 (Table B5). The

99% tolerance intervals for individual ratios were

0.22–4.07, which is in good agreement with the

arbitrary limits used to edit the data. The geometric mean of the 82 QC/QA ratios between

0.25–4.00 was 0.98 with 95% confidence limits of

0.84–1.15 and 99% tolerance intervals of 0.16–6.15.

Probability (%)

TPH Concentration Ratios QC

QA

98

95

90

70

50

30

10

5

2

99

1

0.1 1.0 5.0

Figure 9. Lognormal probability plot for soil TPH QC/QA

concentration ratios between 0.25 and 4.00 for NPD results.

Logs of Concentration Ratios

QC1

QC2

Number of Ratios

30

20

10

0

Boundaries Corresponding to

Ratios of 0.25 to 4.00

– 0.799 to – 0.700– 0.599 to – 0.500– 0.399 to – 0.300

– 0.199 to - 0.100

0.001 to 0.1000.201 to 0.3000.401 to 0.5000.601 to 0.7000.801 to 0.9001.001 to 1.100

QC1

QC2

Figure 10. Frequency histogram for soil TPH QC1/QC2

concentration ratios from NPD. Probability (%)

TPH Concentration Ratios

QC1

QC2

98

95

90

70

50

30

10

5

2

99

1

0.1 1.0 4.0

Figure 11. Lognormal probability plot for soil TPH

QC1/QC2 concentration ratios between 0.25 and 4.00

for NPD results.

13
The poor agreement of the tolerance intervals with

the editing limits implies that the standard deviation of the log ratios was larger than expected for

a good-fitting lognormal distribution. A look at

the histogram (Fig. 8) shows that there are more

ratios than expected toward the extremes and

fewer than expected in the center. Deficiency of

ratios near the ideal value of 1.00 (log = 0) may

be due to systematic bias as suggested in later

paragraphs.

As with the VOCs, we found no meaningful

correlation between either surrogate recoveries or

MS/MSD results with corresponding QC/QA ratios. In many cases MS/MSD results were absent

or were indicated to be unavailable because they

were diluted out of range. In other examples exhibiting very poor agreement between QC and

QA results, surrogate recoveries were excellent

and MS/MSD results were quite good. We must

ask whether MS/MSD results are providing

enough useful information to justify the cost. Perhaps the use of independently prepared secondary standards would offer greater cost/benefit.

More will be said on this later.

Some of the random error undoubtedly arises

as a consequence of sampling and splitting soils

with heterogeneously distributed petroleum hydrocarbons. Possibly contractor guidance can be

revised to reduce this source of error. Volatilization seems unlikely to be a major source of error,

but that too could be examined. Biodegradation

is another potential source of error. A well-planned

nested experimental design could be used to determine the magnitude of these errors prior to

instituting corrective measures.

The GC-FID method may be a significant

source of error for TPH determinations. Besides

inevitable random errors, the unique arrangement

followed by the Corps of Engineers may allow

systematic errors such as calibration or signal integration biases to appear as random error. We

refer to the use of a given laboratory for the QA

function on one project and for the QC function

on another project. Thus, if a laboratory reports

consistently low results, they would inflate QC/

QA ratios when serving as a QA laboratory but

they would depress ratios when acting as a QC

laboratory.

We believe that GC-FID for TPH is susceptible

to systematic error, especially in the integration

of peaks on chromatograms with broad undifferentiated background. This is particularly true for

weathered residues where the normal hydrocarbons, which produce a very recognizable pattern

in fuels, have been reduced relative to the

branched chain components. Evidence of bias is

found in Figure 12. NET Pacific, Inc., Santa Rosa,

California, was the QA or the QC laboratory for

all 95 QC/QA ratios computed from NPD results.

In 52 cases the other laboratory was CAS, Inc.,

Kelso, Washington. NET Pacific was the QA laboratory 28 times and the QC laboratory 24 times.

When we plotted the log of the concentration ratios for CAS, Inc./NET Pacific, Inc. vs. the log of

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

– 0.2

– 0.4

– 0.6

– 0.8

– 1.0

12 3 4 5

Log of TPH Concentration (ppm) Reported by NET Pacific, Inc.

Log of TPH Concentration Ratio CAS, Inc. NET Pacific, Inc.

y = 0.629 – 0.170x

"r" = – 0.439

Figure 12. Correlation of soil TPH concentration ratios for CAS, Inc./NET Pacific, Inc.

with soil TPH concentration estimates reported by NET Pacific, Inc.

14
the concentration reported by NET Pacific, Inc.

(Fig. 12), the correlation was significant at the

99.8% probability level. The fitted linear equation

indicates no bias (log ratio = 0.0) at a TPH concentration of 5600 ppm. At lower TPH concentrations, CAS, Inc. reports concentrations on sample

splits that exceed those reported by NET Pacific,

Inc., and the difference increases as concentration

decreases. For example, when NET Pacific, Inc.

reports a TPH concentration of 100 ppm, CAS,

Inc. reports, on average, a concentration of 200

ppm. Bias of this magnitude is unacceptable. Because of the numerous laboratories used to analyze samples, this problem is magnified several

times. In the case of the NPD results, the 43 ratios

not shown in Figure 12 contained results from

five other laboratories.

One possible way to identify the source of bias

and, hopefully, to eliminate it, is to prepare several typical soils fortified with known concentrations of TPH. To minimize potential error sources

such as volatilization and biodegradation, we suggest weighing portions of homogenized dry soils

into ampoules. Each ampoule would be spiked

with a known amount of TPH and sealed as described by Hewitt (1994). Perhaps three soils with

different clay and organic matter content could

be used. After participating laboratories analyzed

these secondary standards and submitted the results for statistical analysis, a general meeting

could be held to resolve causes of large differences. Ideally the number of participating laboratories would be minimized. Unfortunately,

there is no obvious way to compensate for the

effects of variable degrees of weathering, and

that may prove to be one of the largest sources

of error.

In summary, we think it is inappropriate to

suggest acceptability limits until the method as

currently practiced is brought under better control. If temporary limits are desired for the interim, we feel they should be no wider than 0.25–

4.00. When these limits were used for the most

recent results for 87 samples from 15 project locations, 5.5% of the QC1/QC2 ratios and 16% of

the QC/QA ratios were outside of these limits.

These results agree well with the 6.0% and 14%

estimates obtained for archived NPD data.

As soon as possible, secondary TPH standards

should be prepared for use in a study to define

and minimize bias. Such standards could also

become part of the routine QC program. A study

of sampling, sample splitting, and storage should

also be conducted. It is also important to ensure

that both QC and QA laboratories follow the same

extraction and analysis procedures. Finally, we recommend stopping the dual use of laboratories

for QA and QC functions.

Explosives in soils

The 71 data reports for explosives in soils submitted by the MRD contained a total of 65 pairs

with both QC and QA as real numbers. The distribution of these pairs among several analytes is

shown in Table B6. The MRD, Omaha, Nebraska,

laboratory served the QA function in all of these

results. Seven of the 65 pairs (10.8%) yielded QC/

QA ratios outside 0.25–4.00. However, clustering

around the ideal ratio of 1.00 was much better

than for VOCs or TPH; 72% were between 0.50–

2.00. Bias was suggested because 41 of the 65 ratios were less than 1.00. Since there was no apparent distinction in the distributions or geometric

means of the ratios for individual analytes, they

were treated as a single group. Figures 13 and 14

show a histogram and a probability plot, respectively, for the logs of the 58 ratios between 0.25–

4.00. The lognormal model once again provides a

reasonable fit.

Logs of Concentration Ratios QC

QA

20

15

10

5

0

Boundaries Corresponding to

Ratios of 0.25 to 4.00

QC

QA Number of Ratios

– 1.299 to – 1.200– 1.099 to – 1.000– 0.899 to – 0.800– 0.699 to – 0.600– 0.499 to – 0.400– 0.299 to – 0.200

– 0.099 to 0.000

0.101 to 0.200

0.301 to 0.0.400

0.501 to 0.6000.701 to 0.800

Figure 13. Frequency histogram for soil explosives

QC/QA concentration ratios from MRD.

15
98

95

90

70

50

30

10

5

2

99

Cumulative Probability (%)

– 0.8 – 0.4 0 0.4 0.8

QC

QA Log of Soil Explosives Concentration Ratios

The geometric mean of the 58 ratios between

0.25–4.00 was 0.91 with 95% confidence limits of

0.78 to 1.06. If we isolate the 40 ratios with Weston,

West Chester, Pennsylvania, as the QC laboratory,

the geometric mean for QC/QA is again 0.91.

Consequently, despite the high percentage of ratios below 1.00, we are unable to demonstrate statistically significant bias between the contracted

QC laboratories and the MRD QA laboratory. The

99% tolerance limits are 0.20–4.18, slightly wider

than the editing limits. As before, this minor discrepancy is attributed to a slightly imperfect fit of

the lognormal model.

Because a very high proportion of the concentrations are close to method reporting limits, it is

not surprising that there were 84 cases where either the QC or the QA laboratory reported a real

value while the other reported “less than.” Of

these, 47 were cases where the “less than” value

was smaller than the real value, but many of these

were in close agreement, e.g., < 0.25 vs. 0.29.

Whenever the real value was within a factor of 4

of the “less than” value, we considered the ratio

to be acceptable. For 36 of the 47 pairs in this

group, this criterion was met.

The “less than” value was greater than the real

value in 37 cases, e.g., < 0.50 vs. 0.39. We used the

same factor of 4 here although it is clear that this

factor could be exceeded without disagreement

of the values, e.g., < 5.0 vs. 0.27. The problem

here is that the “less than” value is too high. Of

the 37 pairs in this group, 27 were within a factor

of 4. Overall then, 63 of 84 pairs with only one

real value were in acceptable agreement. This situation could obviously be improved with better

interlaboratory agreement on reporting limits, but

current agreement is far superior to that for VOCs

and TPH.

MS/MSD results were provided in some cases,

especially those from the MRD laboratory. Unfortunately, we were unable to correlate those results with QC/QA ratios. We remain unconvinced

that the MS/MSD effort is cost-effective in this

program.

There were also eight data reports for the

Umatilla Washout Lagoon, submitted by NPD.

All samples showed extremely high RDX and TNT

concentrations requiring high dilution prior to

analysis of extracts. Consequently, they were examined separately. All QA results were generated

by Maxwell S3, San Diego, California, and all QC

results came from Precision Analytecs, Pullmann,

Washington. Concentration estimates for TNT

were missing for two QA samples due to high

dilution. Of the 14 pairs of results (eight RDX, six

TNT), 13 gave QC/QA ratios well under 1.00. The

geometric mean was 0.58 with 95% confidence

limits of 0.39 to 0.86. The substantial systematic

error between these two laboratories could arise

in various ways, including losses due to holding

time differences. Our research has also shown that

nitroaromatic compounds are susceptible to biodegradation (Grant et al. 1995). Sample heterogeneity, incomplete extraction, and calibration differences are additional potential sources of error.

A recent experiment involving the MRD laboratory and the Cold Regions Research and Engineering Laboratory (CRREL) provides evidence

to support the potential contribution of such error sources (Solsky pers. comm.* ). Seventeen soil

samples were split in the usual fashion and analyzed by MRD and CRREL. A total of 53 pairs of

results was generated for 2,4,6-TNT, 1,3,5-TNB,

2,4-DNT, RDX, and 1,3-DNB. Seven of the 53 ratios for MRD/CRREL were outside the limits 0.25–

4.00. The geometric mean of the 46 ratios inside

of the editing limits was 1.08, with 95% confidence limits for the mean of 0.95–1.23, and 99%

tolerance limits of 0.33–3.51. These limits are somewhat narrower than the ones reported for the previously described MRD results, but they exhibit

similar characteristics.

* J. Solsky, U.S. Army Corps of Engineers, Missouri

River Division, Omaha, Nebraska.

Figure 14. Lognormal probability plot for QC/QA concentration ratios of explosives in soils with ratios between 0.25–4.00.

16
When five of these samples were dried, ground,

and split prior to analysis by both labs, the 20

pairs of results for the same analytes yielded only

one ratio outside 0.25–4.00. In fact, the range of

the other 19 ratios was 0.44–1.16; the geometric

mean was 0.79, 95% confidence limits for the mean

were 0.70–0.89, and 99% tolerance intervals were

0.39–1.61. It is unclear why a significant bias existed between the laboratories when none was

present for the samples split in the normal way.

However, it is most important that the standard

deviation of the ratios for the dried and ground

samples was considerably less than for the original splits (significant at the 99% confidence level).

Interestingly, if the bias between laboratories is

corrected to a mean ratio of 1.00, the 99% tolerance intervals would become 0.49–2.04. This provides considerable evidence for the benefit of

sample drying and grinding prior to splitting.

In summary, there is considerable support in

these data for temporary acceptance limits of 0.25–

4.00 with the expectation of tightening in the future as procedures are improved. For the most

recent results for 19 samples from three project

locations, 13% of the QC/QA ratios were outside

0.25–4.00. This is similar to the original results

and indicates a need for further improvement.

Secondary standards prepared similarly to our

earlier recommendation for TPH should be used

throughout projects as a means of minimizing bias

between laboratories. We cannot isolate the contributions of error sources such as sample heterogeneity and biodegradation in these results, but

there clearly is a potential for improvement in

this aspect of the program.

VOCs in groundwater

For the 157 sets of VOCs in groundwater, we

discovered that four sets (V2-63 through V2-66)

were repeats of V2-56 through V2-59. Thus, there

were actually only 153 sets of results. For carbon

tetrachloride, chlorobenzene, 1,2-dichloroethane,

1,1-dichloroethene, and methyl ethyl ketone, there

were fewer than five pairs of values where each

was above the reporting limit. The number of pairs

above reporting limits for the other nine analytes

ranged from nine to 63. Twenty-three of the QC

laboratory samples included concentrations for

field duplicates, but results for most analytes were

below reporting limits.

Only 1,2-dichloroethene and trichloroethene,

with 11 and 14 pairs of duplicate QC results, respectively, provided enough evidence to judge the

limits to be expected for VOCs in groundwater.

One of the 25 ratios was excluded because it exceeded 3.00. The 95% confidence limits on the

geometric means bracketed the expected mean of

1.00 very nicely in both cases. However, the low

number of degrees of freedom in the standard

deviation estimates caused the 99% tolerance intervals to be wider than desired, especially for

1,2-dichloroethene (0.39–2.42). If one uses the acceptability criterion suggested previously for metals (0.50–2.00), all 24 ratios are within these limits. Although there are insufficient data to permit

evaluations of the other VOCs, there is nothing to

indicate that this range would be unsuitable for

them. On the contrary, the fact that these data

arise from a single multianalyte procedure suggests that the precision should be quite similar

for each of the analytes. This differs from the situation with metals where several methods are employed. Therefore, we recommend 0.50–2.00 as the

acceptability limits for duplicate QC ratios of

VOCs in groundwater. When these limits were

applied to the most recent results for 67 samples

from 17 project locations, only 3% of the ratios

were outside of this range.

The QC/QA ratio characteristics are summarized for nine VOCs in Table B7. Eleven of 321

ratios (3.4%) were excluded because they were

outside 0.30–3.00. Outliers were spread among

several VOCs and they occurred over a wide spectrum of concentrations. Geometric means for each

of the nine VOCs were close to the expected value

of 1.00, and the 95% confidence limits included

1.00 in every case. The 99% tolerance bands are in

reasonable agreement with each other except for

vinyl chloride, which offered insufficient data for

close tolerances (too few degrees of freedom).

Only three of the 310 ratios remaining after the

initial 11 exclusions were outside 99% tolerances.

As with metals in soils, let us examine the effect of using 0.40–2.50 as the acceptability criterion for QC/QA ratios. We find seven additional

exclusions beyond the 11 originally rejected values for 0.30–3.00 limits. Of these 18 unacceptable

ratios (5.6%), 11 are for ratios below 0.40 and seven

are for ratios above 2.50. Further insight can be

obtained from Table B8, which shows the distribution of ratios outside 0.40–2.50 with respect to

the number of analytes above concentration reporting limits for both QC and QA per sample.

Three samples have two outlier ratios each and

two of these three have no other analytes present

above reporting limits. The other 12 rejected ratios occur one per sample even though each of

these samples has acceptable ratios for one or

17
more other analytes. The three samples with two

outliers clearly require close scrutiny for likely

rejection of the entire analysis, but the 12 samples

with only one outlier may well be acceptable after examination of the whole set of analyses. The

pleasing result is that 90% of the samples had no

unacceptable ratios according to the suggested

limits. Further support for using limits of 0.40–

2.50 for QC/QA ratios is gleaned from the less

than 3% of outliers found in the most recent results for 67 samples from 17 project locations. The

much greater homogeneity of groundwater compared to soil and the reduced amount of manipulation during preparation and analysis must certainly account for the vastly superior VOC results

for groundwater.

Some VOC reporting limits were absent on the

data sheets and this reduced the effectiveness of

comparisons. The “practical quantitation limits”

for groundwater tabulated in Table D-4 of ER 1110-

1-263 (USACE 1990) are 5 μg/L for 12 of the VOCs,

10 μg/L for vinyl chloride, and 100 μg/L for methyl ethyl ketone. About three-fourths of the

samples apparently contained no analytes at concentrations requiring dilution prior to GC/MS.

For those samples, the ER 1110-1-263 criteria were

usually equaled or bettered. In general, however,

the reporting limits for QA laboratories were lower

by about a factor of 2 than corresponding values

from the QC laboratories. Thirty-five samples

listed one or both reporting limits above the ER

1110-1-263 recommendations, often by orders of

magnitude. More often than not the high reporting limits were from the QC laboratory while the

QA value was acceptable. Twenty-seven of these

samples contained one or more VOCs at concentrations high enough to have required substantial

dilution. It appears that the QC reporting limits

were derived from diluted samples while QA values were for undiluted samples. From a practical

perspective, the question is whether high reporting limits decrease the acceptability of concentration estimates when one or more VOCs are present

at very high concentrations. Greater concern about

reliability would attach to the eight samples where

high reporting limits cannot be readily explained.

CONCLUSIONS AND

RECOMMENDATIONS

General recommendations

1. To improve future evaluations, both QC and

QA laboratories should be required to submit the

method of estimation and numerical values of reporting limits for all analytes, actual surrogate

recoveries (“OK” is unacceptable), and to designate method of analysis for each analyte.

2. A convention for significant figures should

be adopted for all concentration reports. One possibility would be to use two significant figures

for all values below 100 (e.g., 0.72, 6.9, and 54)

and a maximum of three significant figures for

values of 100 or greater. This would alleviate the

problem of comparing pairs such as 6 and 6.25

(are they different?).

3. Standard practice should be adopted for reporting limits where dilutions are required. The

target reporting limits given in ER 1110-1-263

(USACE 1990) might be specified as the highest

acceptable values, assuming that these are low

enough for the intended use of the data. Special

exceptions could be made for a few samples with

unusual matrix problems. At the very least, both

QC and QA should specify reporting limits on

the same basis rather than one on the original

sample and one on a diluted portion.

4. A concerted effort is needed to reduce uncertainty in the QA values if they are to serve a

referee purpose. This effort might combine the

use of blind duplicates of selected samples, some

spike recoveries, and the regular analysis of certified samples that are as similar in matrix as possible to regular samples. Such an expanded internal QC effort by the QA laboratories would help

justify the use of their results for determining acceptability of analyses by other laboratories. Only

those laboratories with demonstrated excellence

should serve the QA function.

5. A policy for dealing with outlier ratios and

unacceptable reporting limits must be promulgated by USACE. Such a policy could likely be

incorporated into an artificial intelligence computer program to greatly reduce the time and cost

required to administer the policy.

6. Consideration should be given to using as

few contract laboratories as possible and to avoid

using a given laboratory for the QA function on

some projects and the QC function on others.

Metals in soils

Ratios of archived results for metals in split

soil samples were lognormally distributed for

those metals with sufficient numbers above concentration reporting limits (As, Ba, Cr, Pb). For

duplicate QC ratios, only 3.4% were outside the

limits of 0.50–2.00 while 10.2% of the QC/QA ratios were outside limits of 0.40–2.50. Only five

samples out of 124 (4.0%) had more than one metal

18
QC/QA ratio per sample outside of the 0.40–2.50

limits.

We recommend that

a) Acceptance limits for duplicate QC ratios be

set at 0.50–2.00.

b) Acceptance limits for QC/QA ratios be set

at 0.40–2.50. Application of these limits to recent

results confirms that these are workable comparison criteria for both QC1/QC2 and QC/QA.

c) Future results for multianalyte procedures

should be carefully analyzed with regard to the

number of rejects/sample. From such analysis, a

policy for dealing with such analyses should be

developed.

VOCs in soils

Archived results for VOCs in split soil samples

produced QC/QA ratios that were distributed lognormally as expected. However, over 40% of the

QC/QA ratios were outside the range 0.25–4.00

and 24% were outside 0.10–10.0. The magnitude

of this scatter is so large that it is impossible to

recommend effective limits of acceptability. Instead, we believe that steps are urgently needed

to improve data quality.

We recommend that

a) Temporary acceptance limits of 0.25–4.00 be

set, despite the possibility of up to 40% unacceptable data. Analysis of recent results using these

limits indicated some improvement, but more is

needed.

b) Secondary standards sealed in ampoules (see

Hewitt 1994, Hewitt and Grant 1995) should be

used for performance evaluation during laboratory qualification and as periodic QC samples.

c) The change with the greatest potential for

data quality improvement would be to start a program of improved techniques for sample collection, storage, and laboratory subsampling in

preparation for analysis. Specific recommendations can be found in Hewitt et al. (1995). An

instructional videotape might prove to be useful.

TPH in soils

Ratios of archived results for TPH in split soil

samples analyzed by QC and QA laboratories

were provided by the North Pacific Division

(NPD) and also by the Missouri River Division

(MRD). Nearly half of the QC/QA ratios for the

MRD results were unacceptably wide, so only the

NPD data was statistically analyzed. Although

86% of these ratios were between 0.25–4.00, the

standard deviation of the logarithms was larger

than expected for a lognormal distribution.

We recommend that

a) As with VOCs in soils, temporary QC/QA

acceptance limits of 0.25–4.00 be adopted while

procedures are being brought under better control, especially those at MRD. It is especially important to ensure that both the QC and the QA

laboratories use the same extraction and analysis

protocols depending on the requirements of the

applicable state method (if present).

b) Secondary standards sealed in ampoules

should be prepared and used to identify sources

of systematic error so they can be reduced to a

minimum.

c) A nested experimental design should be used

to evaluate the magnitude of errors in sampling,

sample homogenization and splitting, and storage. This information would be used to focus attention on any large random error sources.

Explosives in soils

Ratios of MRD archived results for explosives in split soil samples analyzed by QC and

QA laboratories were distributed lognormally,

with 89% of the 65 QC/QA ratios between 0.25–

4.00 and 72% between 0.50–2.00. Because many

concentrations were close to reporting limits,

there were 84 cases where either the QC or the

QA laboratory reported “less than” while the

other provided a real number. For these 84 pairs,

63 (75%) were considered to be in acceptable

agreement.

The NPD submitted a series of analyses from

the Umatilla Washout Lagoon where concentrations of RDX and TNT were very high. There was

a large bias (nearly a factor of 2) between the QC

and QA laboratories, possibly due to either calibration differences or incomplete extraction by the

QC laboratory.

We recommend that

a) Temporary acceptance limits for QC/QA ratios be set at 0.25–4.00. Application of these limits

to recent results yielded 13% of the ratios outside

this range. We believe this percentage can be significantly reduced.

b) Secondary standards containing known concentrations of explosives in dry soils and sealed

in ampoules should be used by participating laboratories on a regular basis in order to reduce

interlaboratory bias.

c) Sample collection, splitting, and storage procedures should be reviewed with the objective of

reducing variations, especially biodegradation

losses of nitroaromatics.

19
VOCs in groundwater

Ratios of archived results for VOCs in co-located groundwater samples were vastly superior

to results for many of the same analytes in split

soil samples. This finding reinforces the conclusion that the major problems with VOCs in soils

occur during sampling and sample preparation

because the determinative step is basically the

same for each. For the 25 duplicate QC results

reported, only one was outside of the limits 0.50–

2.00 while 5.6% of the QC/QA ratios were outside limits of 0.40–2.50. Recent results provided

even lower percentages (3%). Only three samples

out of 153 (2.0%) had more than one VOC ratio

per sample outside of the 0.40 to 2.50 limits.

We recommend that

a) Acceptance limits for duplicate QC ratios be

set at 0.50–2.00, the same as for metals in soils.

b) Acceptance limits for QC/QA ratios be set

at 0.40–2.50, the same as for metals in soils. The

recent data confirm the workability of those

ranges for both QC1/QC2 and QC/QA.

c) Like metals in soils, a policy must be promulgated for dealing with samples with more

than one outlier.

d) More attention needs to be given to consistency in the procedure for specifying concentration reporting limits.

SUMMARY

The objective of chemical environmental measurements is to provide valid characterization of

sites under investigation so that regulatory decisions can be made with confidence. To obtain the

accurate analyses required to realize this objective, the process of analysis must be considered

to start at the time of sampling. Procedures must

minimize volatilization and biodegradation losses

and contamination gains. This includes sampling,

sample transport, and sample storage. Analytical

validation must deal with the entire procedure,

i.e., extraction, determination, and data manipulation. These requirements are doubly important

in programs where many different laboratories

contribute to the databank.

In our judgment, ratios of chemical analyses

on split samples that differ by more than a factor

of 4, reported by two laboratories, are useless for

decision making whenever concentrations are near

regulatory limits. In reality, we dislike having limits greater than 2.5 and we believe that such agreement is well within reach with proper sampling.

Obviously there is no fundamental basis for either factor, but consider the dilemma when using

wider limits, such as 0.10–10.0. Assume a regulatory value of ten and one laboratory reports six

while the second reports 58. The analyses agree

within a factor of 10, but a valid regulatory decision is impossible. Admittedly, the problem is

minimized when results depart greatly from

regulatory limits, but it is around these limits

that disputes most often arise. Of course, this

problem is not eliminated with tighter limits,

but it certainly reduces the frequency of occurrence.

We reluctantly recommended temporary limits of 0.25–4.00 for VOCs in soils because this criterion may cause up to 40% of results to be unacceptable. However, we believe the results can be

dramatically improved by making fairly simple

changes in the sampling procedures. For TPH,

where the same criterion was recommended, it is

clear that the NPD laboratories have this analysis

under better control than the MRD laboratories.

Resolution of this difference should be the first

objective, to be followed by further improvements

in the procedures.

We believe that the U.S. Army Corps of Engineers should commit a significant portion of its

effort in chemical environmental measurements

toward the realization of improved data quality.

Some specific suggestions are enumerated in this

report. Clearly, close interaction with participating laboratory personnel is also needed. To do

otherwise and maintain the status quo seems certain to validate the Kimbrough and Spinner (1994)

quote, “The acceptance of very wide control limits basically sends the message that if everybody

is wrong, then everyone is right.”

LITERATURE CITED

Grant, C.L., T.F. Jenkins, K.F. Myers, and E.F.

McCormick (1995) Comparison of fortification vs.

field contamination for holding time estimates of

soils containing explosives residues. Journal of Environmental Toxology and Chemistry, 14: 1865–1874.

Hewitt, A.D. (1994) Vapor-fortified QA/QC soil

subsamples for the analysis of volatile organic

compounds. American Environmental Laboratory,

3(94): 1–8.

Hewitt, A.D. and C.L. Grant (1995) Round-robin

study of performance evaluation soils vapor-fortified with volatile organic compounds. Environmental Science and Technology, 29: 769–774.

20
Hewitt, A.D., T.F. Jenkins, and C.L. Grant (1995)

Collection, handling and storage: The keys to improved data quality for volatile organic compounds in soil. American Environmental Laboratory,

2(95): 25–28.

Kimbrough, D.E. and R. Spinner (1994) Performance-based methods for regulatory analytical

environmental chemistry. American Environmental

Laboratory, 11-12(94): 1–10.

Robertson, G.L., F.C. Garner, D.W. Hewetson

and G.A. Laing (1995) The effectiveness of matrix spike analysis in the contract laboratory program. Presented at the 9th Workshop of QAEM,

April 1995, Scottsdale, Arizona.

Siegrist, R.L. and J.J. van Ee, Eds. (1994) A symposium summary: Measuring and Interpreting

VOCs in Soils: State-of-the-Art and Research Needs.

EPA/540/R–94/506.

USACE (1990) Chemical data quality management

for hazardous waste remedial activities, 1 October 1990, U.S. Army Corps of Engineers ER 1110-

1-263.

21
APPENDIX A: DATA AND

SAMPLE CALCULATIONS FOR CHROMIUM

Table A1. Cr concentrations, ratios, and logarithms.

Concentra tions μg/ g Ra tios a nd loga rith ms

Sa mple

number QA QC1 QC2 

QC

QA

1

 

log QC1

QA 

QC

QC

1

2 

log 1

2

QC

QC

M1-1

M1-2

M1-3

M1-4

M1-5

M1-6

M1-7

M1-8

M1-9

M1-10

M1-11

M1-12

M1-13

M1-14

M1-15

M1-16

M1-17

M1-18

M1-19

M1-20

M1-21

M1-22

M1-23

M1-24

M1-25

M1-26

M1-27

M1-28

M1-29

M1-30

M1-31

M1-32

M1-33

M1-34

M1-35

M1-36

M1-37

M1-38

M1-39

M1-40

 21

 12

 8.8

 18

 26

 32

 30

 18

 34

 9.2

 2.6

 8.0

 33

 10

 18

 14

 2800

 J19

 J13

 J18

 J16

 14

 J17

 12

 27.8

 22

 11

 15

 16

 11

 17

 10

 16

 9.9

 12

 14

 18

 12

 15

 12

3.4

6.2

4.6

8.3

17.7

15.9

14.2

8.3

14.2

7.6

2.9

9.3

17.5

 19

13.1

22.7

38.3

18.6

15.4

28.4

25.9

16.9

30.5

12.6

 34

 24

 7

10.6

14.4

5.8

4.8

8.2

8.2

6.2

 10.8

 7.9

 15.5

 15.2

 12.8

 13

*0.16

0.52

0.52

0.46

0.68

0.50

0.47

0.46

0.42

0.83

1.12

1.16

0.53

1.90

0.73

1.62

*0.014

0.98

1.18

1.58

1.62

1.21

1.79

1.05

1.22

1.09

0.64

0.71

0.90

0.53

*0.28

0.82

0.51

0.63

0.90

0.56

0.86

1.27

0.85

1.08

*

–0.287

–0.282

–0.336

–0.167

–0.304

–0.325

–0.336

–0.379

–0.083

0.047

0.065

–0.275

0.279

–0.138

0.210

*

–0.009

0.074

0.198

0.209

0.082

0.254

0.021

0.087

0.038

–0.196

–0.151

–0.046

–0.278

*

–0.086

–0.290

–0.203

–0.046

–0.249

–0.065

0.103

–0.069

0.035

Continued

22
Table A1 (cont’d). Cr concentrations, ratios, and logarithms.

Concentra tions μg/ g Ra tios a nd loga rith ms

Sa mple

number QA QC1 QC2 

QC

QA

1

 

log QC1

QA 

QC

QC

1

2 

log 1

2

QC

QC

M1-41

M1-42

M1-43

M1-44

M1-45

M1-46

M1-47

M1-48

M1-49

M3-1

M3-2

M3-3

M3-4

M3-5

M3-6

M3-7

M3-8

M3-9

M3-10

M3-11

M3-12

M3-13

M3-14

M3-15

M3-16

M3-17

M3-18

M3-19

M3-20

M3-21

M3-22

M3-23

M3-24

M3-25

M3-26

M3-27

M3-28

M3-29

M3-30

M3-31

M3-32

M3-33

M3-34

M3-35

 18

 3.5

 20

 14

 14

 350

 10

 43

 11

 19

ND<0.9

9

 19.9

 16

 160

 9.0

 11

 5.5

 12

 25

 6.0

 28

 24

 27

 15.9

 32

 9.8

 44

 42

 16.7

 33.2

 9.3

 10

 10.2

 25

 13

 6

 13

 5

 9

 10.2

 42.4

 53

 31

 24

 16.3

 3.7

 13

 11.5

 13.7

 199

 7

 24

 13

 14.9

 2.5

 24.6

 13

 217

 8.0

 7.8

 6.6

 6.8

 6.0

 8

 52

 35

 40

 15

 41

 9

 50

 35

 15

 27

 15

 13

 14

 20

 17

 8.1

 6.9

 6.6

 9.5

 22

 31

 44

 23

 17

 16.4

 1.9

 24.2

 12

 10.9

 12.8

 8.6

 6.0

 6

 43

 31

 45

 38

 13

 36

 30

 13

 28

 13

 13

 14

 18

 16

 10

 7.8

 7.6

 10

 19

 30

 48

 24

 20

0.91

1.06

0.65

0.82

0.98

0.57

0.70

0.56

1.18

0.78

**

1.24

0.81

1.36

0.89

0.71

1.20

0.57

*0.24

1.33

1.86

1.46

1.48

0.94

1.28

0.92

1.14

0.83

0.90

0.81

1.61

1.30

1.37

0.80

1.31

1.35

0.53

1.32

1.06

2.16

0.73

0.83

0.74

0.71

–0.043

0.024

–0.187

–0.085

–0.009

–0.245

–0.155

–0.253

0.073

–0.106

**

0.092

–0.090

0.132

–0.051

–0.149

0.079

–0.247

*

0.125

0.269

0.164

0.171

–0.025

0.108

–0.037

0.056

–0.079

–0.047

–0.090

0.208

0.114

0.138

–0.097

0.117

0.130

–0.275

0.121

0.023

0.334

–0.136

–0.081

–0.130

–0.150

0.91

1.32

1.02

1.08

0.72

0.52

0.79

1.00

1.33

1.21

1.13

0.89

1.08

0.69

1.39

1.17

1.15

0.96

1.15

1.00

1.00

1.11

1.06

0.81

0.88

0.87

0.95

1.16

1.03

0.92

0.96

0.85

–0.042

0.119

0.007

0.035

–0.145

–0.288

–0.102

0.000

0.125

0.083

0.053

–0.051

0.033

–0.160

0.143

0.067

0.062

–0.016

0.062

0.000

0.000

0.04

0.026

–0.092

–0.053

–0.061

–0.022

0.064

0.014

–0.038

–0.018

–0.071

Continued

23
Table A1 (cont’d).

Concentra tions μg/ g Ra tios a nd loga rith ms

Sa mple

number QA QC1 QC2 

QC

QA

1

 

log QC1

QA 

QC

QC

1

2 

log 1

2

QC

QC

M3-36

M3-37

M3-38

M3-39

M3-40

M3-41

M3-42

M3-43

M3-44

M3-45

M3-46

M3-47

M3-48

M3-49

M3-50

M3-51

M3-52

M3-53

M3-54

M3-55

M3-56

M3-57

M3-58

M3-59

M3-60

M3-61

M3-62

M3-63

M3-64

M3-65

M3-66

M3-67

M3-68

M3-69

M3-70

M3-71

M3-72

M3-73

M3-74

M3-75

 38

 29

 48

 31

 39.5

 50

 13

 5.6

 15.1

 8.9

 5.6

 15.7

 16

 21

 13

 15

 11.5

 11.6

 8.2

 16

 12

 35.8

 24

 16

 49

 13

 21

 26

 37.2

 16

ND<5

25.4

 13

9.6

 25

16.9

11.2

8.6

22.6

 33.6

 26

 31

 32

 35

 26

 36

 11

 10.8

 16

 7.5

 9.5

 9.5

 12.9

 16.2

 11

 11

 6.9

 9.0

 5.8

 18.8

 10

 43

 11

 19.5

 44

 15

 22

 26

 37

 51

ND<10

 59

 24

22.5

 22

 14

9.1

9.7

68.7

 42.5

 24

 24

 13

 12.7

 18

 12.9

 15.6

 12

 8.2

 8.1

 9.2

 10.8

 8

 36

 12

 22.5

 51

 16

 23

 26

 35

 38

ND<10

 23

 39

 22

 13

 31.5

 53.7

0.68

1.07

0.67

1.13

0.66

0.72

0.85

1.93

1.06

0.84

1.70

0.61

0.81

0.77

0.85

0.73

0.60

0.78

0.71

1.18

0.83

1.20

0.46

1.22

0.90

1.15

1.05

1.00

1.00

*3.19

**

2.32

1.85

2.34

0.88

0.83

0.81

1.13

*3.04

1.26

–0.165

0.029

–0.176

0.053

–0.182

–0.143

–0.073

0.285

0.025

–0.074

0.230

–0.218

–0.094

–0.113

–0.073

–0.135

–0.222

–0.110

–0.150

0.070

–0.079

0.080

–0.339

0.086

–0.047

0.062

0.020

0.000

0.000

*

**

0.366

0.266

0.370

–0.056

–0.082

–0.090

0.052

*

0.102

1.08

1.29

0.85

0.85

0.89

1.00

1.04

0.92

0.84

1.11

0.63

1.74

1.25

1.19

0.92

0.87

0.86

0.94

0.96

1.00

1.06

1.34

**

2.57

0.62

1.00

1.08

2.18

0.79

0.035

0.111

–0.073

–0.070

–0.051

0.000

0.016

–0.038

–0.075

0.046

–0.200

0.241

0.097

0.077

–0.038

–0.062

–0.064

–0.028

–0.019

0.000

0.024

0.128

**

0.409

–0.211

0.000

0.032

0.399

–0.102

* Excluded from calculations because (<0.30 or >3.00).

** No ratio calculated because of ND< values.

24
SAMPLE CALCULATIONS FOR Cr DATA

Step 1. See Table A1 for ratios and logarithms.

Step 2. Mean of logs of

 

QC

QC

1

2

0 304

60 = 0 0051 1 01 +

=+ = . . ( . ). antilog

 

Standard deviation ( Sx ) . . = . − = 0 83787 0 00154

59

0 119

.

Step 3. 95% confidence limits on mean of logs are

 

+ ± =+ ± − ≤μ≤+ 0.0051 [ ] = (2.00)(0.119)

60 0.0051 0.0307; Prob 0.0256 0.0358 0.95.

Step 4. Taking antilogs, we get Prob 0.94 1.09 0.95 [ ] ≤μ≤ = .

The ideal or expected mean ratio of 1.00 is within the upper and lower boundaries for 95%

confidence level.

Step 5. 99% tolerance limits for logs are

 

+ ± =+ ± − ≤ ≤+ 0 0051 2 66 0 119 0 0051 0 3165 0 3114 0 3216 0 99 . ( . )( . ) . . ; . . . Prob [ ] xi = .

Step 6. Taking antilogs, we get Prob 0 49 2 10 0 99 [ ] . .. ≤ ≤ xi = .

Two of the 60 

 

QC

QC

1

2

 ratios are outside of these boundaries for individuals;

 

( ) M 67 2.57, M 74 2.18 3 3 −= −= .

Step 7. For the ideal mean log of 0.00, Prob [ ] − ≤ ≤+ 0 3165 0 3165 0 99 . .. xi =

antilogs give Prob 0 48 2 07 0 99 [ ] . .. ≤ ≤ xi = .

The same two ratios are still outside these boundaries.

Step 8. Mean of logs of 

 

QC

QA

3.712

116 0.0320(antilog 0.93) 1 = − =− =

 

Standard deviation (Sx ) . . = . − = 3 355082 0 118784

115

0 168

95% confidence limit on mean of logs

 

− ± =− ± 0 0320 1 98 0 168

116 . 0 0320 0 0309 ( . )( . ) . . .

 

Prob [ ] − ≤ ≤− 0 0629 0 0011 0 95 . .. μ = ; taking antilogs, we get

 

Prob 0 87 1 00 0 95 [ ] . .. ≤ ≤ μ = .

The expected mean of 1.00 is barely within the upper boundary for 95% confidence level.

99% tolerance limits for logs

 

− ± =− ± 0 0320 2 62 0 168 0 0320 0 440 . ( . )( . ) . . .

 

Prob [ ] − ≤ ≤+ 0 472 0 408 0 99 . .. xi = ; taking antilogs, we get

 

Prob 0 34 2 56 0 99 [ ] . .. ≤ ≤ xi = .

25
None of the 116 ratios are outside of these boundaries.

For the ideal mean log of 0.000

 

Prob [ ] − ≤ ≤+ 0 440 0 440 0 99 . .. xi = and, taking antilogs, we get

 

Prob 0 36 2 76 0 99 [ ] . .. ≤ ≤ xi = .

Again, none of the 116 ratios are outside the boundaries.

26
APPENDIX B: DATA FOR METALS, VOCS, TPH, AND EXPLOSIVES

Table B1. Summary of As, Ba, Cr, and Pb concentration ratios.

(a) for duplicate QC samples

Element

Tota l no.

of ra tios

QC1

/ QC2

No. of

ra tios

excluded

(outside

0.30–3.00)

Percent

exclusions

Geometric

mea n of

reta ined

ra tios

95%

confidence

limits on

mea ns

99%

tolera nce

limits for

individua ls

No.

previously

a ccepted

ra tios

outside

tolera nces

As

Ba

Cr

Pb

60

60

60

53

1

1

0

1

 1.7

 1.7

0

 1.9

1.06

0.97

1.01

1.05

1.01–1.13

0.91–1.03

0.94–1.09

0.98–1.12

0.53–2.11

0.53–1.77

0.49–2.10

0.57–1.93

2

1

2

1

Avg=1.3% Avg=1.02

(b) for QC/ QA comparison

Element

Tota l no.

of ra tios

QC1/ QC2

No. of

ra tios

excluded

(outside

0.30–3.00)

Percent

exclusions

Geometric

mea n of

reta ined

ra tios

95%

confidence

limits on

mea ns

99%

tolera nce

limits for

individua ls

No.

previously

a ccepted

ra tios

outside

tolera nces

As

Ba

Cr

Pb

113

121

121

104

10

 6

 6

 7

8.8

5.0

5.0

6.7

0.93

0.89

0.93

0.96

0.84–1.02

0.82–0.96

0.87–1.00

0.89–1.03

0.25–3.49

0.29–2.72

0.34–2.56

0.35–2.58

0

2

0

3

Avg=6.3% Avg=0.93

27
Table B2. Percentages of unacceptable QC/ QA ratios with three possible sets of rejection limits for

As, Ba, Cr, and Pb.

Rejection limits 0.25–4.00 0.30–3.00 0.40–2.50

Exclusions Exclusions Exclusions

Meta l Low High Low High Low High

As

Ba

Cr

Pb

 2

 4

 3

 4

 4

 2

 0

 1

 2

 4

 4

 6

 8

 2

 2

 1

 7

 8

 4

11

 9

 4

 2

 2

Subtotals 13 7 16 13 30 17

Grand totals 20 29 47

Percent exclusions* 4.4% 6.3% 10.2%

* Based on 459 total results.

Table B3. Summary of reporting limits for Ag, Cd, Hg, and Se.

Ag Cd Hg Se

Number of

va lues 

QA

105 

QC

93 

QA

76 

QC

60 

QA

85 

QC

101 

QA

98 

QC

104

Range μg/g

Exclusions

New range

Mean μg/g

Median μg/g

0.10–10

4

0.10–3.1

1.4

1.7

0.15–10

3

0.15–2.9

1.7

2.0

0.046–5.2

1

0.046–3.0

0.71

0.54

0.21–5.0

1

0.21–2.3

1.2

1.0

0.011–0.50

2

0.011–0.30

0.11

0.10

0.050–0.50

2

0.050–0.20

0.13

0.10

0.060–50

3

0.060–3.0

0.62

0.50

0.090–5.0

1

0.090–2.5

0.75

0.60

*Users guide

to CLP, μg/L

*SW-846

μg/L

10

7

5

4

0.2

0.2

5

2

*Values taken from page D-2 (Table D-3) of ER 1110-1-263 (USACE 1990).

The concentrations shown are for guidance; they will vary with different samples and different

matrices. Direct comparison of these values with the QC and QA is not possible because the

latter concentrations are μg/g in soil while the reference values are μg/L for an extract.

28
Table B4. Characteristics of QC/ QA ratios for archived VOC results for soils.

Eth ylbenzene TolueneXylenes

(tota l) Va rious* Summa ry

Number of pairs of real values 25 35 36 38 134

Number and % ratios

 outside the limits of 0.25–4.00 11 (44%) 14 (40%) 18 (50%) 13 (34%) 56 (42%)

Number and % ratios

 outside the limits of 0.20–5.00 11 (44%) 12 (34%) 12 (33%) 11 (29%) 46 (34%)

Number and % ratios

 outside the limits of 0.10–10.0 9 (36%) 6 (17%) 9 (25%) 8 (21%) 32 (24%)

* Consists of eight benzene, one chloroform, five 1,2-dichloroethene (total), two methyl ethyl ketone,

eleven tetrachloroethene, and eleven trichloroethene ratios.

Table B5. Characteristics of archived total petroleum hydrocarbon (TPH) results from NPD and MRD.

NPD results MRD results

 

QC

QC

1

2 

QC

QA 

QC

QA

Number of pairs of real values 83 95 61

Number and % ratios

 outside the limits of 0.25–4.00 5 (6.0%) 13 (13.7%) 28 (45.9%)

Geometric mean of ratios

 within the limits of 0.25–4.00 0.95 0.98 *

95% confidence limits on mean 0.84–1.07 0.84–1.15 *

99% tolerance limits around mean 0.22–4.07 0.16–6.15 *

* Not calculated due to extremely large % of values outside the range of 0.25–4.00.

29
Table B8. Distribution of VOC samples according to the number of analytes present and the number of QC/ QA ratios

outside 0.40–2.50.

No. of sa mples with ra tios outside 0.40–2.50

No. a na lytes a bove

reporting limits for

both QC a nd QA

per sa mple

Distribution of sa mples

a ccording to number of

a na lytes a bove reporting

limits per sa mple One outlier Two outliers

0

1

2

3

4

5

>5

13

50

32

32

21

5

0

0

0

3

5

3

1

0

0

0

2

0

1

0

0

Total = 153 Total = 12 Total = 3

TABLE B6. Characteristics of QC/ QA ratios for archived explosives results for soils.

RDX 1,3,5-TNB 1,3-DNB 2,4,6-TNT 2-Am-DNT 2,4-DNT Summa ry

Number of pairs of

 real values

3 13 3 21 14 11 65

Number and % ratios

 outside the limits

 of 0.25–4.00

1 (33%) 2 (15%) 0 (0%) 3 (14%) 0 (0%) 1 (9%) 7 (10.8%)

Number and % ratios

 numerically < 1.00 1 (33%) 9 (69%) 3 (100%) 15 (71%) 8 (57%) 5 (45%) 41 (63.0%)

Table B7. Summary of QC/ QA concentration ratios for nine VOCs in groundwater.

Compound

Tota l no.

of ra tios

QC/ QA

No. of

ra tios

excluded

(outside

0.30–3.00)

Percent

exclusions

Geometric

mea n of

reta ined

ra tios

95%

confidence

limits on

mea ns

99%

tolera nce

limits for

individua ls

No.

previously

a ccepted

ra tios

outside

tolera nces

 Benzene

 Chloroform

 Ethylbenzene

 Tetrachloroethene

 Toluene

 1,2-Dichloroethene

 Trichloroethene

 Vinyl Chloride

 Xylene

46

26

37

19

36

44

63

9

41

2

2

0

0

3

0

2

0

2

4.3

7.7

0.0

0.0

8.3

0.0

3.2

0.0

4.9

1.03

1.07

1.11

0.97

1.02

0.91

0.94

0.95

1.09

0.93–1.14

0.97–1.17

0.98–1.25

0.84–1.12

0.87–1.18

0.82–1.00

0.89–1.00

0.67–1.34

0.95–1.26

0.42–2.51

0.57–2.00

0.41–3.01

0.41–2.30

0.34–3.09

0.39–2.09

0.52–1.70

0.21–4.27

0.34–3.50

1

0

0

1

0

0

1

0

0

Total = 321 Total = 11 Avg = 3.2% Avg = 1.01 Total = 3

30
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May 1996

Comparison Criteria for Environmental Chemical Analyses of Split Samples

Sent to Different Laboratories: Corps of Engineers Archived Data USAED-MRD

 MIPR GP95-11

Clarence L. Grant, Thomas F. Jenkins and Anand R. Mudambi

U.S. Army Cold Regions Research and Engineering Laboratory

72 Lyme Road Special Report 96-9

Hanover, New Hampshire 03755-1290

Office of the Chief of Engineers

Washington, D.C. 20314-1000

Approved for public release; distribution is unlimited.

Available from NTIS, Springfield, Virginia 22161

Explosives Quality assurance TPH

Groundwater Soils VOCs

Metals Split sample analysis

UNCLASSIFIED UNCLASSIFIED UNCLASSIFIED UL

Data comparison criteria were developed for quality control (QC) and quality assurance (QA) chemical analyses

obtained during environmental studies directed by the U.S. Army Corps of Engineers (USACE). Archived results for

124 sets of eight metals in soils, 69 sets of fourteen volatile organic compounds (VOCs) in soils, 163 sets of total petroleum

hydrocarbons (TPH) in soils, 79 sets of six explosives in soils, and 153 sets of fourteen VOCs in groundwater were

analyzed statistically. Concentration ratios (QC1/QC2 and QC/QA) were lognormally distributed and this was the

model used for comparisons. For both metals in soils and VOCs in groundwater, duplicate QC results should yield ratios

between 0.50–2.00; less than 4% of the archived results were outside these limits. For QC/QA ratios, the limits suggested

are 0.40–2.50; only 10.2% of metals ratios in soils and 5.6% of the VOCs in groundwater exceeded these limits.

Considering that both methods are multi-analyte, we find only 4.0% of the metals samples and 2.0% of the VOC samples

had more than one offending ratio per sample. Application of these limits to recent analyses produced very similar

results. For VOCs, TPH, and explosives in soils, temporary limits of 0.25–4.00 are suggested with the understanding that

improvements leading to tightening should be energetically pursued. Even with these wide limits, approximately 42%

of VOCs, 14% of TPH, and 11% of explosives QC/QA ratios must be considered outliers. Here, too, recent analyses

yielded very comparable percentages. The situation for VOCs in soils requires immediate attention to improve

sampling and preparation procedures, but all methods can and should be capable of producing improved agreement

between laboratories. Numerous recommendations are offered with this goal in mind.

40