4.3 USE OF DISCRETE SAMPLES
A "discrete sample" refers to the collection of a small mass of soil, typically
100200g, from a single point within an area targeted for investigation. Discrete
samples have traditionally been used to help identify the lateral and vertical extent
of contamination. The use of discrete soil sample data is not recommended for final
decision making purposes as part of an environmental investigation (HDOH,
2015,b; Brewer et al. 2016;
see Subsection 4.1.2). Random, smallscale variability
of contaminant distribution and concentration in soil limits the reliability of
discrete sample data for estimating the extent of contamination that could pose
an unacceptable risk to human health and the environment.
It is also important to note that the HDOH Environmental Action Levels for soil
are not intended for direct comparison to individual, discrete sample data
points (HDOH, 2016; refer to Subsection
4.1 and Section 13) as well as the USEPA Regional
Screening Levels (USEPA, 2014). Action/screening levels
for directexposure, for example, assume random contact with soil throughout the
DU over many years. Comparison to the mean action level in designated Exposure Area
DUs is therefore appropriate (refer to Section 3; see also
USEPA, 1987, 2013b).
The concentration of a contaminant at any given discrete sample point within a DU,
whether it be above or below an action or screening level, is not relevant to the
overall risk posed by contamination for the DU as a whole (see also
HDOH, 2015b).
Existing discrete sample data and grids of discrete samples can, however, be useful
for designation of DUs for a more intensive, Multi Increment sample investigation.
For new projects, consider the collection of a large mass of soil from multiple
locations around a sample collection point (Figure 425
A&B).
Such ï¿½largemassï¿½ discrete samples will help improve the representativeness of the
resulting data for the associated grid point. For example, collect 12kg of soil
(recommended MI sample mass, minimum 300g; Subsection 4.2.3)
from multiple (e.g., 510+) points within a few feet of the grid point in order
to reduce Fundamental Error and capture random, smallscale variability of contaminant
concentrations over short distances (see Subsection 4.1.2).
Individual masses of soil should be collected in a similar manner as described for
MI increments, including proper shape, depth and mass (Subsection
4.2.5.2). Bulk samples to be screened in the field should be tested multiple
times until a representative mean can be determined, for example through use of
a portable XRF (Subsection 8.4.1). Samples submitted
to a laboratory for testing should be processed and tested following standard MI
procedures to ensure that representative data are obtained, including testing of
a minimum 10g mass (Subsection 4.2.6). Note that
the latter requirement could negate the costbenefit of implementing a discrete
sample grid approach to screen a site in comparison to the collection of MI samples
from reasonably small DUs. If samples are not processed for testing then this limitation
should be noted in the report and additional care taken in interpretation of the
data.

Figure 425 A&B. Collection of largemass discrete soil samples from multiple
locations around a single sampling point in order to improve data representativeness
(A: USGS 2016; B: see ERM 2008).

This approach reduces the susceptibility of traditional discrete soil samples to
random error and improves the ability to identify largerscale contaminant patterns
of interest. Note that these types of samples are sometimes informally referred
to as "composites" in USEPA and other field investigation guidance (e.g.,
USEPA 1989, USGS 2014, USGS
2016). Use of the term ï¿½compositeï¿½ is discouraged for projects overseen
by HDOH, however, due to potential confusion with more formal use of the term to
indicate the intentional mixing of soil from what would otherwise be considered
separate DUs (refer to Subsection 4.4.11).
Discrete soil sample data can in theory be used to estimate mean contaminant concentrations
for a targeted DU area provided that samples are collected in a manner consistent
with sampling theory (e.g., proper, size, shape, mass, etc.) and the data can be
demonstrated to be reproducible. As discussed below, however, this is unlikely to
be cost effective in comparison to the use of Multi Increment sample data to estimate
mean contaminant concentrations.
4.3.1 INTERPRETATION AND PRESENTATION OF ISOCONTOUR MAPS
Isocontour maps (e.g., concentration, thickness, etc.) based on discrete sample data
should not be used for decision making purposes without adjustment to reflect additional
site knowledge and professional judgment. This is due to the unreliability
of smallscale patterns and the reduced accuracy of isocontours based on traditional
discrete soil (and sediment) sample data as discussed above (HDOH
2015b, Brewer et al. 2016). Specific errors
often encountered in unadjusted, isocontour maps include:
 Artificial "hot spots" and "cold
spots" caused by random, smallscale variability of contaminant concentrations at
the scale of a discrete sample;
 Erroneous "zero" isocontours around
the perimeter of contaminated areas due a lack of outward data points;
 Inherent lack of precision of isocontour
placement.
Unrecognized, these errors can lead to a false sense of precision in computergenerated
isocontour maps and lead to erroneous decisions regarding the need to continue or
halt site investigations or remedial actions (HDOH, 2015b;
see also Subsection 4.1). This includes calls for remediation
of isolated "hot spots" based on single or small numbers of discrete samples and
premature termination of site investigations or remedial actions due to false "cold
spots" in the discrete sample data.
Isocontour maps should be adjusted to reflect site knowledge and professional judgment
not reflected in computergenerated maps. Such adjustments are not possible
in existing computer programs to the knowledge of HDOH and must be done by hand.
Boundaries between apparent largescale patterns should necessarily be dashed. Smallscale
heterogeneity within largerscale patterns generated by small numbers of discrete
sample points should not be presented on final maps included in the report.
For example, Figure 426 depicts a nineacre site
formerly used for storing and
mixing pesticides. The northern area of the site was known to be heavily contaminated
with arsenic based on previous collection of both discrete and Multi Increment samples.
The exact area of elevated arsenic was uncertain based on previous testing although
the area of the former mixing shed was most suspect. No obvious signs of contamination
were recognizable in the field.
A significant number of largemass, discrete surface soil samples (06 inches) were
collected from a 50foot grid across the site (ERM 2008).
Each discrete sample was collected from multiple points around each grid point in
order to help address random, smallscale heterogeneity and increase data representativeness
(see Figure 425b). Samples were analyzed using a portable
XRF. A subset of samples was analyzed in a laboratory for comparison. As can be
seen in the figure, the XRF helped to identify at least one large spill area of
arseniccontaminated soil in the northern part of the site. Smaller clusters of
discrete samples with higher reported levels of arsenic might or might not be reflective
of actual conditions in the field. False patterns of higher and lower levels of
contamination can be produced by samples that are too small to capture and smooth
out random heterogeneity of contaminant distribution in soil (see
Subsection 4.1; HDOH, 2015,b).
Three distinct areas of arsenic contamination are apparent in the figure (see Figure
426). The concentration of arsenic in the majority of discrete samples collected
from Area A is below a screening level 20 mg/kg, with occasional "outliers" that
exceed this value. Arsenic is randomly above 20 mg/kg in any given, discrete soil
sample collected from Area B. Arsenic is above 20 mg/kg in the majority of discrete
samples collected from Area C, with random "outliers" below this value.
Figure 426. Unadjusted Isoconcentration Map from Discrete Sample Arsenic Data at
a NineAcre Former Pesticide Storage Site
Redshaded areas denote total arsenic concentrations >20 mg/kg. Most contaminated
area corresponds to former pesticide mixing area denoted by red circle on 1979 aerial
(modified from ERM 2008). Three largescale areas of
arsenic distribution hypothesized (HDOH, 2015b): A)
Arsenic below 20 mg/kg in majority of discrete samplesize masses of soil; B) Arsenic
above and below 20 mg/kg in any given, discrete soil sample and C) Arsenic above
20 mg/kg in majority of discrete sample masses of soil. Smallscale patterns are
interpreted to be artifacts of random, smallscale heterogeneity and may or may
not be reproducible (see HDOH, 2015b).

Figure 427. Adjusted Arsenic Isoconcentration Map for a Former Pesticide Storage
Site
The adjusted map more accurately reflects the resolution of arsenic distribution
in soil across the site that can be reliably extracted from the discrete sample
data.
As discussed below, such maps can subsequently be used to help designate Decision
Units and carry out a more reliable and higher resolution Multi Increment sample
characterization of the site. Preliminary maps such as these could also be used
to carry out initial remediation actions, for example removal of soil from the heavily
contaminated area, followed up with a DUMulti Increment investigation to assess
the need for additional actions. This assessment requires significant experience
and professional judgment on the part of decision makers.

The appearance of seemingly isolated, "hot spots" and "cold spots" within largerscale,
distinct areas most likely reflect smallscale contaminant distribution that may
or may not represent true areas of higher or lower contamination that can be mapped
(see Subsection 4.1; HDOH, 2015b).
If grid points were moved over a few feet and new samples collected and analyzed,
then a similar largescale pattern would appear, but smallscale "hot spots" and
"cold spots" within these areas would be located in different places. This type
of field error is an artifact of the individual sample being too small to overcome
and capture random, smallscale heterogeneity of the contaminant in the soil. Attempts
to design remedial actions based on single samples or even small sets of discrete
sample data is highly unreliable and is not recommended or acceptable for final
decision making purposes.
Largescale patterns reliably identified by grids of discrete soil samples can,
however, be used in conjunction with other available information to designate DUs
for the collection of Multi Increment samples. Figure 427
presents an adjusted
map of arsenic distribution in soil that more accurately reflects the resolution
of arsenic distribution across the site that can be extracted from the discrete
sample data.
4.3.2 DESIGNATION OF DECISION UNITS
In spite of the limitations noted above, tight grids of discrete sample data utilizing
field screening tools can provide useful screening level data to help identify largescale
areas of contamination, and help guide a more thorough DUMIS investigation (refer
to Subsection 4.2). Examples of field screening tools include
portable XRay Fluorescence (XRF) instruments and immunoassay tests. Field screening
tools need to be reliable for the application employed, and those handling the tools
for site investigations should have experience with their use. Additional information
on use of field screening methods is provided in Section 8.
Continuing with the example presented above, Figure 428
depicts hypothetical DUs
designated for the former industrial facility based on a combination of historical
information, the results of the discrete soil sample study, proposed redevelopment
for oneacre residential lots, and optimization of potential remedial actions (for
example only; not included in original report).
Figure 428. Example DUs Designated for a Former Pesticide Storage Site
Based on a combination of historical information, the results of the discrete soil
sample study, proposed redevelopment for oneacre, residential lots and optimization
of potential remedial actions.

Oneacre DUs are designated in the lower area of the site, where historical information
and discrete sample data suggest minimal contamination (Area A in
Figure 428).
The DUs reflect hypothetical exposure areas for the planned residential redevelopment
of the site and the lowest recommended "resolution" for site characterization (see
Subsection 3.4). It is anticipated that remediation will
not
be required within this area. The DUs designated for Area B in
Figure 428 are intentionally
scaled smaller. This reflects the increased chance that some degree of remediation
may be required for this area and a desire to increase the resolution of the data.
This is done by reducing the sizes of DUs in order to optimize remediation and minimize
potential removal of otherwise clean areas of soil that are inadvertently included
with otherwise contaminated areas. This approach is also emphasized in Area C, where
both historical information and discrete sample data verify the presence of significant
contamination and the need for remedial actions. The use of small DU areas and volumes
ensures an adequate resolution of data for preparation of the most costeffective
remedial action plan possible. Refer to Subsection 3.4
for
additional information on DU designation for investigation and remedial purposes.
4.3.3 ESTIMATION OF MEAN CONTAMINANT CONCENTRATIONS IN RISK ASSESSMENTS
Discrete soil sample data have traditionally been used to estimate the mean contaminant
concentration for targeted exposure areas in environmental site assessments and
remedial actions (e.g., USEPA 1987,
2013b). The reliability of this approach was called into question by the
HEER Office in 2006, due to the inability to verify the field representativeness
of a single date set. Multi Increment sampling methods provide significant advantages
for estimation of contaminant means in comparison to discrete sample data, including:
 Consideration of sampling theory
to determine the mass of soil required to collect a representative sample and method
of sample collection and analysis;
 Improved coverage of the targeted
area (number of increments collected far greater than typical number of discrete
samples);
 Systematic and standardized approach
for sample collection in order minimize bias in the field (e.g., size, shape and
mass of individual increments);
 Reduced number of samples required
for analysis; general greater statistical precision of replicate samples (e.g.,
lower RSDs);
 Samples processed and subsampled
at laboratory in order to ensure representative data;
 Replicate sample data provide additional
information on field representativeness of samples and precision of data.
Nonetheless, mean contaminant concentrations for DUs can be estimated using discrete
sample data provided that a systematic approach is used collect and process the
samples in accordance with sampling theory, including sample shape and mass (refer
to Subsection 4.1 and 4.2, and
that
the data can be demonstrated to be representative of actual field conditions through
evaluation of replicate samples. Such quality control measures in the field are
critical to the overall quality and representativeness of the resulting data, and
go beyond simple consideration of the number of samples collected and the variance
between individual data points. The HEER office should be contacted to discuss the
collection and use of discrete sample in a risk assessment for a specific site.
An evaluation of the representativeness of a discrete sample data set should be
carried in the same manner as done for Multi Increment samples (see
Subsection 4.2). The accuracy of an estimated mean contaminant concentration
for a DU is evaluated in terms of precision, or reproducibility, and bias,
or systematic over or under estimation (ITRC 2012).
This is illustrated in Figure 429.
Figure 429. Four Possible Relationships between Bias and Precision (after ITRC 2012)
The center point of the target is the true mean. The mean estimated from a single
set of discrete samples, or a single Multi Increment sample, represents one point
on the target. The gray area around the point represents uncertainty in the estimate

In order for an estimated mean to be accurate, the data set must be both unbiased
and precise. Statistical analysis of a single set of discrete sample data only evaluates
the precision of the estimated 95% UCL in terms of the variance of the data set
provided and the statistical method used to evaluate the data. The number of discrete
samples included in a data set can be increased in order to decrease the variance
and provide an acceptable degree of precision.
Analytical precision only reflects one aspect of potential error, however. The complete
precision of the data set in terms of field representativeness cannot be evaluated
from a single set of discrete samples. This can only be evaluated through the collection
and comparison of replicate sets of samples, as done for Multi Increment samples
(See Subsection 4.2.7; see also
ITRC 2012). Complete replicate sets of discrete samples are rarely, if ever,
collected to test the quality of the estimated mean, however.
Past USEPA guidance has recommended that a minimum of 20 to 30 discrete samples
are required to adequately represent contaminant heterogeneity within a targeted
area (USEPA, 1992b):
 Data sets with 20 to 30 samples provide fairly consistent estimates of the mean
(i.e., there is a small difference between the sample mean and the 95 percent UCL).
Replicate Multi Increment data reviewed by the HEER Office, including a field study
carried out in 2014 (HDOH, 2015b,
b) as well as statistical simulations included in the ITRC ISM document
(ITRC 2012) suggest that error in terms of field representativeness
could still be substantial when a relatively small number of discrete samples (e.g.,
< 30) are used to characterize a targeted DU (see also
Subsection 4.2.2).
If discrete sampling is proposed for use at a site overseen by the HEER Office,
specific approaches to address both precision and bias in the data should be discussed
in the SAP (refer to Subsection 4.1). This should include
a review of sample collection approaches in terms of sampling theory (e.g., number,
size, shape, mass, etc.). Note that the mass of a discrete sample has been primarily
dictated by the needs of the laboratory for analysis (default 100 grams per sample
recommended; USEPA 1987), rather than sampling theory.
This issue should likewise be addressed in the SAP.
"Outlier" discrete sample data points (e.g., comparatively very high concentrations)
should not be omitted from a data set in order to force the data set to fit
a geostatistical model (USEPA 1989,
2006b, g; see also HDOH
2015b); (Note that this conflicts with recommendations in the USEPA
Pro UCL guidance; USEPA 2013b). The true mean is the
concentration of the target contaminant that would be reported if the entire DU
volume of soil could be tested as a single "sample." "Outliers" simply reflect a
high distributional heterogeneity of contaminant concentrations in the soil at the
scale a discrete sample and are an artifact of the sampling approach employed. The
omission of supposed outlier data points from calculations distorts the representativeness
of the data set and generates a technically unsupportable mean. For comparison,
MIS increments that fall on small but obviously contaminated areas of a DU would
not be excluded from the bulk Multi Increment sample. All discrete sample data must
be included in an estimate of the mean, with the precision of the data set as a
whole statistically evaluated. If additional sample points are required to improve
precision then the samples should be collected using Multi Increment sampling approaches.
