United States
Environmental Protection
Agency
Environmental Monitoring
Systems Laboratory
Las Vegas NV 89193-3478
Research and Development
EPA/600/S4-89/032 Sept. 1989
&EPA Project Summary
Sampling Frequency for
Ground-Water Quality
Monitoring
Michael J. Barcelona, H. Allen Wehrmann, Michael R. Schock,
Mark E. Sievers, and Joseph R. Kamy
This project was initiated to collect
a benchmark water-quality dataset
end evaluate methods to optimize
sampling frequency as a network
design variable. Ground water was
collected biweekly for 18 months
from twelve wells at two sites in a
shallow sand and gravel aquifer in
Illinois. Sampling and analyses were
conducted for twenty-six water
quality and geochemical constituents
with careful quality control measures
to allow statistical analysis of vari-
ability in ground-water quality data.
The results demonstrate that natural
variability over time can exceed the
variability introduced into the data
from sampling and analysis pro-
cedures.
Natural temporal variability and the
highly autocorrelated nature of
ground-water quality data seriously
complicate the selection of optimal
Sampling frequency and the
identification of seasonal trends in
ground-water quality variables.
Quarterly sampling frequency is a
good initial starting point for ground-
water quality monitoring network
design, although bimonthly fre-
quency may be preferred for reactive
chemical constituents. Analysis of
data collected during this project
suggests that the collection of a
long-term (i.e., more than two years)
dataset is necessary to determine
optimal sampling frequency and to
identify seasonal trends in ground-
water monitoring results.
This Project Summary was devel-
oped by EPA's Environmental Monitor-
Ing Systems Laboratory, Las Vegas,
NV, to announce key findings of the
research project that Is fully docu-
mented in a separate report of the
same title (see Project Report order-
Ing information at back).
Introduction
There are two principal sources of
variability in ground-water quality data,
"natural" variability and variability resul-
tant from the network design and
operation The components of "natural"
variability arise from temporal or spatial
variability related to hydrologic processes
such as pumpage, recharge or discharge,
as well as influences of these processes
on the release and distribution of
chemical constituents from a variety of
chemical sources. The sources may be
natural mineral assemblages, precipita-
tion and percolation through the unsatu-
rated zone, in addition to numerous point
and non-point sources of chemical con-
taminants. In general, "natural" sources
of variability cannot be controlled
although they may be quantified through
effective monitoring network design.
Water-quality data variability may also
arise from the sampling and analytical
components of monitoring network
design. Sampling variability includes vari-
ations due to the selection of the
locations and construction of sampling
points in space, sampling frequency, well
purging, and the execution of the
sampling protocol. The sampling protocol
consists of the procedures used to
collect, handle, preserve, and transport
water samples to the analytical labora-
tory. Elements of the sampling protocol
have been evaluated for their relative
-------
contributions to variability or errors in
water-quality data in previous research.
Analytical variability in water-quality
data arises principally from the errors
involved in analytical methods and the
subsequent data processing steps. These
errors can be controlled once suitable
water-quality indicators or chemical con-
stituents have been selected and a
thorough data quality assurance/quality
control program has been designed and
executed.
This study was planned to control the
sources of variability in water quality data
which result from network design com-
ponents such as sampling location, freq-
uency, sampling methods and analytical
procedures. The sampling frequency was
held constant at a biweekly interval
between sample collection dates. The
benchmark dataset that resulted from this
experimental design could then be
analyzed to determine the optimal sam-
pling frequency for selected water-quality
variables at both uncontaminated and
contaminated study sites.
The full report describes the level of
QA/QC effort which is necessary to
achieve control over sources of error and
data variability due to sampling and
analytical operations. Discussions of tem-
poral variability in groundwater level and
water quality results are included in the
report to place the dataset in perspective.
The results and conclusions of the work
are supported by extensive references,
where the literature permits. The report
should be useful to the planning and
execution of regulatory and research
activities which demand the cost-effective
collection of high quality ground-water
quality data.
Variability in Ground-Water
Quality
The impact of the sources of variability
mentioned above will be influenced by
the hydrology of the ground-water
system. It is important to understand that
although aquifer hydraulic properties may
not vary significantly at a single measure-
ment point over time, spatial variability
may be substantial. Aquifer hydraulics
may be expected to influence chemical
constituent distributions in space and
time.
Temporal and spatial variations in
ground-water elevation may affect
ground-water flow rate and the direction
of movement. Such changes may influ-
ence the quality of the ground water in
the vicinity of a sampled well by directing
water from a different upgradient area or
changing the velocity with which dis-
solved constituents move along a flow
path. Examples abound in the literature
detailing ground-water response (i.e.,
elevation change) to a wide variety of
influences. In addition to seasonal fluctu-
ations produced in response to short-
term (i.e., months to one year) events,
ground-water levels also reflect changes
in long-term (i.e., years to decades) con-
ditions.
Temporal and spatial variability may
also result from sample collection and
measurement errors inherent to network
design and operation. This variability, or
"noise," in the data embodies the
stochastic distribution of possible values
for particular chemical constituents and
the effects of both determinate (i.e.,
systematic) and indeterminate (i.e., ran-
dom) error. Determinate error can be
measured as inaccuracy or bias if the
"true value" is known. Indeterminate
error can be estimated as imprecision or
irreproducibility if a sufficient number of
replicate determinations can be made to
faithfully estimate the mean or the "true"
value.
Statistical measures of short-term tem-
poral variability include seasonal effects
(e.g., consequences of recharge or temp-
erature effects) which can be assigned to
the seasons of the year, periodic effects
(e.g., consequences of anthropogenic
contaminant sources or pumping effects)
and serial correlation or dependence
effects which tend to make data points
following maxima or minima in temporal
data series higher or lower, respectively,
than one would attribute to random
processes alone. Trends in data, on the
other hand, are long-term variations com-
pared to those which may occur within a
hydrologic year.
Procedure
Field Sites
Two sites were chosen to enable the
isolation of the effects of network design
variables fro those due to natural or con-
taminant-related sources. The sites were
located over an alluvial sand and gravel
water table aquifer of moderate to high
yield. One site was in a pristine envi-
ronment far removed from any sources of
contamination in the Sand Ridge State
Forest near Havana, Illinois. The other
site was in an industrial environment
under the influence of a leaking anaero-
bic waste impoundment near Beard-
stown, Illinois.
Sand Ridge State Forest is an Illinois
Department of Conservation (IDOC) facil-
ity located 5 miles (8 km) southeast of
the Illinois River in the north-c
Havana Lowland. The Illinois State
Survey's experimental field site is Ic
in the middle of the State Forest
Havana, Illinois.
Three distinct horizons compris
unconsolidated deposits at Sand I
at the surface is 30 feet (9 m) of
sand (the Parkland sand); from 3D f
m) to a depth of 55 feet (17 m)
Manito Terrace of the Wisconsinai
wash, consisting of a sometimes
sometimes coarse sand to me
gravel; and from 55 feet (17 m) do
bedrock below 110 feet (34 m), anc
sibly as deep as 150 feet (46 m),
medium sand to fine gravel o
Sankoty sand (Kansan outwash).
Depth to the water table is greatei
30 feet (9 m) below the ground su
Ground-water movement is general
ward the Illinois River. The hydraulk
dient measured at the site in 198C
approximately 0.0016. Aquifer tests
ducted on the water supply wells
nearby state fish hatchery indicate
the hydraulic conductivity of the sam
gravel at approximately 100 feet (C
depth (in the Sankoty sand) is about
gpd/ft2 (0.094 cm/sec). Tracer e)
ments conducted in 1983 indicatec
lover hydraulic conductivities (frorr
to 1900 gpd/ft2, o.01 to 0.09 cm/sec
be exhibited by the finer-grained,
lower materials. Hydraulic conduc
values of 350 to 900 gpd/ft2 (0.02 tc
cm/sec) were obtained by emp
methods of analysis based on the
size distributions of shallow aq
samples. The porosity of the satu
terrace materials was found to be 25
The "contaminated" field sit
located in the vicinity of several
waste impoundments serving a
slaughtering facility approximately 1
(1.6 km) southeast of Beardstown, III
The field site lies two miles southej
the river, and it is only about 5 feel
m) higher than the floodplain. Farn
and wooded areas surround the fa
The unconsolidated deposits lying a
the bedrock consist of the clayey s
of the Beardstown Terrace on
Wisconsinan outwash plain. The be<
surface is of Mississipian age and li
about 100 feet (30 m) below the gr
surface.
Owing to land surface elev;
changes, depth to water varies from
15 feet (1.5 to 4.5 m) below the gr
surface. Similar to the Sand Ridge
regional ground-water flow is towarc
Illinois River (hydraulic gradient, 0.
Due to the presence of silt and clay
aquifer is less permeable than it is s
-------
Sand Ridge site. One falling head perme-
ability test produced a hydraulic conduc-
tivity value of only 130 gpd/ft2 (6 x 10-3
cm/sec).
Monitoring Wells
Bore holes for construction of all
monitoring wells were drilled with a 4.25-
inch (11 cm) inside diameter (I.D.) hollow-
stem auger. All auger flights, solid
samplers, well casing materials, and well
protectors were steam cleaned before
use or placement in the bore hole.
The construction details of the sam-
pling wells at both sites are identical in all
ways other than the length of casing and
casing materials in two wells at Beards-
town. One well at Beardstown vas con-
structed of stainless steel (SS) and one
other of polyvmylchloride (PVC). All of
the other sampling wells at both sites
were constructed with polytetrafluoro-
ethylene (PTFE-Teflon«, DuPont). All
wells have 2-inch (5 cm) I.D. flush-
threaded casing. Screens were 5 feet (1.5
m) long with 0.01-inch (0.02 cm) slot
openings. The four wells at Sand Ridge
were completed at depths of 35, 50, 65,
and 105 feet (10.6, 15.4, 20, and 32 m),
respectively.
The eight wells at Beardstown were
completed at several depths at locations
upgradient and downgradient from the
impoundment.
Results and Discussion
Five preliminary sampling runs were
completed between November 1985 and
March 1986. Then thirty-nine biweekly
sampling trips were conducted during the
period of March 10, 1986 through August
25, 1987. These field activities involved
purging and sampling the monitoring
wells 526 times and measuring more than
2,000 ground-water levels. Only two wells
were missed out of the 528 sampling
opportunities. Water samples were
collected for more than 26 analytical
determinations each, including major
cations, anions, TOC; TOX, pH, alkalinity,
and other species.
During the course of the study, more
than 55,000 analytical determinations
vere made on blanks, standards and
samples. The final dataset was 96%
complete, that is, 96% of the maximum
possible number of samples and subse-
quent analytical determinations were
successfully completed. Outliers were
screened successively at ±3 and ±2
standard deviations from the mean levels.
In most cases, this screening revealed
apparent errors in calculations, calibra-
tion, or data entry which were corrected
prior to data analysis. For all wells and
constituents, the maximum number of
samples which were identified as
possible outliers and for which no
documented error was identified was four
percent of the total. No adjustment was
made to apparent outliers for which no
documented error could be identified.
QA/QC analyses demonstrated that the
analytical methods were within control
limits and that good analytical perform-
ance was maintained throughout the pro-
ject period.
Estimation of Sources of
Variation
Generally, the natural variations in
water quality time series are of interest.
For instance, the difference between the
time series of a given contaminant at a
downgradient and an upgradient well may
give an indication of whether contaminant
release has occurred. However, the dif-
ference series is inevitably corrupted by
errors in the field data collection and
laboratory analysis procedures, both of
which introduce what may be considered
"noise" into the time series. Each of
these noise processes has a variance,
and the total variance is the sum of the
three variance terms, this model assumes
that the three sources of variation are
statistically independent. This is a rea-
sonable assumption because the sources
are physically independent and the
individual variances were calculated from
the analytical results from replicate con-
trol samples, lab and field spiked sam-
ples.
The results are summarized in Table 1
for three groups of wells (i.e., Sand Ridge
wells 1 to 4, Beardstown upgradient wells
5 and 6, and the Beardstown down-
gradient wells), for almost all of the
groups, and for almost all of the chemical
constituents, a high fraction of the total
variation was natural. In most cases the
combined lab and field variances were
below ten percent of the total variance.
This is consistent with the QA/QC data
analyses, which showed that the data
collection errors were generally quite
small. The entries in the table have been
separated into water quality parameters
and chemical parameters of geochemical
interest. The results confirm that if careful
sampling and analytical protocols are
used, the analytical and sampling errors
can be held to less than about 20%.
Therefore, the natural variability in the
major ion chemistry of the system can be
identified. For TOC and TOX it is clear
that "natural" sources of variability are
greater than the combined lab and field
variance. However, the level of overall
variability in TOX results was quite large
in comparison to the mean values for
each well. The significance of these
determinations at the microgram per liter
concentration level is doubtful.
The implication of the results of this
study is that network design optimization
efforts should focus primarily on the
natural or contamination source-related
variability. The use of field and laboratory
replication for purposes other than
QA/QC will be difficult to justify as long
as the sampling and analytical protocols
are in control. This conclusion must be
qualified, however. The chemical constit-
uents present at appreciable concentra-
tions (i.e., mg-L-1) at either site were the
major cations and anions and general
water quality indicators. The analytical
and sampling variances for trace organic
contaminants would be expected to be
higher, and their analytical recoveries are
frequently found to be a function of
concentration. For such contaminants, the
field and laboratory variations may not be
independent, which would violate a basic
assumption in this model.
Temporal Variations in Ground-
Water Quality
There are numerous examples of both
short- and long-term variability in ground-
water quality in the literature. Significant
short-term temporal concentration vari-
ability has been observed in low-yield
wells (i.e., monitoring and observation
wells) largely resulting from purging
effects. Similar variations from one to ten
times the initial or background concen-
trations have been noted in samples from
high-volume production wells due to
pumping rate, initial pumping after
periods of inactivity, and cone of depres-
sion development.
In general, the major ionic chemical
constituents determined in this study
showed differences between their overall
maximum and minimum values from the
mean for each well on the order of one or
two times the mean value. One or two
times the mean value places the vari-
ability noted in this study in the same
range as long-term, seasonal variability.
The magnitude of overall long-term varia-
tions observed in this study and the
literature is often much lower than those
noted for short-term variations due to
pumping and local recharge effects. The
magnitude of short-term concentration
variations noted in the literature strongly
suggests that the analysis of ambient
resource, water quality datasets must be
undertaken with careful attention to the
-------
Table 1. Percentage of Variance Attributable to Laboratory Error, Field Error, and Natural Variability by Chemical
and Site
Sand Ridge
Beardstown
(Upgradient)
Beardstown
(Downgradient)
Type of Parameter Lab
Field
Nat
Lab
Field
Nat
Lab
Field
Nat
Water Quality
SiO2m
o-P04,
T-PO4
cr
Ca
Mg
A/a
K
Geochemical
0.0
0.0
0.0
1.2
0.0
7.2
0.0
0.0
0.0
0.0
0.0
0.0
NA
1.2
NA
NA
45.7
20.0
NA
NA
100.0
100.0
100.0
97.6
100.0
92.8
54.3
80.0
100.0
100.0
0.1
0.2
0.0
0.0
2.8
0.0
0.0
0.0
0.0
33.9
NA'
NA
20.0
0.0
NA
3.3
2.3
2.2
0.3
NA
99.9
99.8
80.0
700.0
97.2
96.7
97.7
97.8
99.7
66.7
0.2
1.4
0.0
0.0
0.9
0.0
0.0
0.0
0.0
87.1
NA
0.0
6.8
0.0
NA
17.2
3.6
2.8
7.1
NA
99.8
98.6
93.2
700.0
99.1
82.8
96.4
97.2
92.9
72.9
NH3.
N02
S°
Fe2*
FeT
MnT
Contaminant
Indicator
TOC~
rox-
0.0
NA
NA
NA
0.0
0.0
Lab •<
15.4
0.0
0.0
NA
NA
NA
NA
NA
<• Field
100.0
NA
NA
NA
100.0
100.0
84.6
100.0
0.0
0.1
NA
0.0
0.0
0.0
Lab -i
29.9
12.5
0.0
NA
NA
0.1
0.0
40.1
• Field
100.0
99.9
NA
99.9
100.0
59.9
70.1
87.5
0.0
0.3
NA
0.0
0.0
0.0
Lab i
40.5
24.6
0.0
NA
NA
5.9
NA
73.6
<• Field
100.0
99.7
NA
94.1
100.0
26.4
59.5
75.4
*NA indicates that the number of observations on which the estimated variance was based was less than 5, or the
estimated variance was negative.
"True field spiked standards not available for these constituents, demanding combined estimates of laboratory and
field variability.
pumping procedures used in purging and
sample collection. This observation is
particularly critical in relatively sparse
datasets where annual "mean" concen-
trations may be determined from pro-
grams with low sampling frequency (i.e.,
annually, biannually, etc.). Similar cau-
tions in interpretations of long-term
datasets apply in the analysis of trends at
varying or unequal sampling frequencies
due to the relatively short duration of the
records in comparison to the length of
apparent annual to multi-year variations.
Sampling Frequency
The primary purpose of the project was
to investigate the optimal sampling fre-
quency for ground-water quality monitor-
ing. Strictly speaking, there is no
minimum sampling frequency. However,
there is a relationship between the infor-
mation content of the data and the
sampling frequency. The term "informa-
tion" is sometimes used loosely, but in a
statistical context, it can be given a more
precise definition, depending on the use
of the data. The most common definition
of information (e.g., in the Fisher sense)
is in terms of the variance of the mean,
Var(x) = o2/n, where x is the sample
mean, n is the sample size, and o2 is the
variance of the data. The reciprocal of the
variance of the mean is a measure of the
information content of the data. If the o2 is
large, or the sample size small, the infor-
mation content is low. While this defini-
tion of information applies to estimation
of the mean, the power of trend detection
(in space or time) is related to the vari-
ance of the mean as well.
Seemingly, the information content of
the data could be increased arbitrarily,
since it depends linearly on the sample
size. In practice, though, ground-water
quality data are correlated in time (auto-
correlated), and the autocorrelation
increases with the sampling frequency.
When the data are autocorrelated, the
variance of the mean can be reexpressed
as Var(x) =o2/nef, is an effective inde-
pendent sample size, which depends on
the autocorrelation. The value of nef is
always less than n, the actual sample
size, if the autocorrelation is positive, as it
usually is in practice. If the model that
describes the autocorrelation is the I
one Markov process, nef approaches
upper limit as the sampling frequer
increases, regardless of how large
becomes. The lag-one process has b<
found to provide a reasonable descript
of many water quality time series. II
often difficult to extend the analysis
water quality data beyond lag-one I
cause the autocorrelation function I
comes excessively noisy.
The ratio nef/n can be considered to
a measure of the loss of information c
to autocorrelation in the data. Althoi
nef always increases with n for posit
autocorrelation, nef may increase qi
slowly if the autocorrelation is high. I
this reason, one of the analyses a
ducted was to estimate a model of
serial dependence (i.e., autocorrelatii
in the observed chemical series.
To illustrate the effect of the au
correlation on sampling frequency,
solved for the sampling interval, in wee
that would result in ratios nef/n = 0.5, C
and 0.9. Alternatively, these can be int
preted as relative losses of informat
due to autocorrelation in the data of
-------
20, and 10 percent. The results are given
in Table 2. At Sand Ridge, the implied loss
of information was about 50 percent for
many variables at a weekly sampling fre-
quency, 20 percent for many variables at
sampling intervals in the range of 4-8
weeks, and 10 percent for the majority of
variables at a sampling interval of 8 weeks
or more.At the Beardstown wells, the loss
of information at high sampling frequencies
was much greater. At the upgradient wells,
which had the highest autocorrelation, the
inferred loss of information of 50 percent
occurred for several variables at a sam-
pling interval of over 26 weeks. Information
loss of between 20 and 10 percent was
inferred for some variables at sampling
intervals exceeding one year. This effect
was particularly evident for Na*. CI" and
well-head temperature (TEMPW) which
showed an increasing trend over the study
period.
The results of the study indicate that, for
the major chemical constituents (i.e., water
quality or contaminant indicator), quarterly
sampling represents a good starting point
for a preliminary network design. Some
estimated ranges of sampling frequency to
maintain information losses below ten
percent are shown in Table 3. This
frequency, of course, must be evaluated
with respect to the purpose and time-frame
over which the network will be conducted.
Under the conditions of this study,
sampling four to six times per year would
provide an estimated information loss
below 20% and minimize redundancy. The
results for reactive, geochemical constitu-
ents suggest that bimonthly sampling fre-
quency would be a good starting point if
chemical reactivity and transformation are
of concern.
Caution must be exercised in inter-
pretation of these results due to the effects
of seasonality and long-term trends. How-
ever, it should be clear that there is con-
siderable redundancy in the data at the
two-week sampling interval, and that, at
similar sites and for most of the variables
studied, operational sampling programs
would be inefficient at sampling intervals
more frequent than bimonthly.
It is important to emphasize that the
information from sampling depends on the
effective independent sample size, not just
the ratio net/n. Therefore, if the autocor-
relation is large so that a relatively low
sampling frequency is necessary to avoid
sampling redundancy, the total length of
the sampling period must be increased to
achieve sufficient information return. These
results cannot simply be interpreted to
Tiean, for instance, that quarterly sampling
.s adequate, unless that interpretation is
couched in terms ofthe time horizon of the
sampling program.
Table 2.
Sampling Intervals (in Weeks) for Given Ratio
of Effective to Independent Sample Size,
Based on the Estimated Lag One Markov
Model
0.5
0.8
0.9
Sand Ridge
NOZ+-N
Fe
PH
S"
NH3
SiO2
Mnr
Probe 02
t-PO4m
0-P04m
Eh
NO3N02-N
roc
S04m
Fer
K
Ca
Mg
ci-
Na
Alk
Ion Balance
Temp Cell
VOC
Cond
TOX
Temp Well
NVOC
Beardstown Upgradient
NO2+- N
Fe
pH
S'
NH3
SiO2
Mnr
Probe O2
t-PO4m
O-P04_
Eh
NOsNO2-N
TOC
SO4S
FeT
K
Ca
Mg
ci-
Na
Alk
Ion Balance
Temp Cell
VOC
Cond
TOX
Temp Well
NVOC
2
1
4
2
2
3
4
3
1
1
3
8
3
5
2
2
3
4
2
3
7
7
4
4
10
10
6
6
3
15
3
3
11
a
3
6
2
2
5
3
5
4
21
19
26
23
53
42
6
6
26
26
35
35
71
71
4
1
7
3
4
5
7
5
2
2
6
16
6
9
3
4
6
7
3
6
14
14
8
8
10
20
11
11
6
29
6
5
22
16
6
11
3
3
9
5
9
7
42
38
53
47
107
85
12
12
53
53
71
71
143
143
5
2
9
4
5
6
9
7
3
3
a
21
8
12
4
5
8
9
4
a
19
19
10
10
27
27
15
15
7
39
a
6
30
22
8
15
4
4
12
6
12
10
56
51
71
62
144
114
16
16
71
71
95
95
192
192
-------
Table 2. (continued)
0.5
0.8
0.9
Beardstown
Downgradient
N02+-N
Fe
pH
S~
NH3
S/O2
MnT
Probe O2
t-PO4m
0-P04m
Eh
TOO
S04m
FeT
K
Ca
Mg
ci-
Na
Alk
Ion Balance
Temp Cell
VOC
Cond
TOX
Temp Well
NVOC
3
4
2
6
2
2
2
3
15
23
5
3
5
4
6
7
6
5
8
5
8
8
10
10
8
8
9
9
5
8
3
11
4
4
3
6
29
47
9
6
9
7
11
13
11
11
16
11
16
16
19
19
16
16
18
118
6
11
4
15
5
5
4
8
39
62
12
7
12
9
15
18
15
14
21
14
22
22
25
25
21
21
24
24
Conclusions
Sampling and analytical errors car
controlled to within about ±20% of
annual mean inorganic chemical cons
ent concentration in ground water if
protocols are properly designed and <
cuted. The use of previously publis
guides for ground-water monitoring
provide reproducible, accurate results
such studies.
The results of the study concent
mainly on inorganic chemical constitui
in ground water. The statist!
characteristics of the time-series data
reactive chemical constituents (e
Fe(ll), sulfide, H2O2, 02 and N02')
close that temporal variability is o
lower than the magnitude of concentra
changes observed during purging
stagnant water prior to sampling. 1
means that improper well purging
result in gross errors and the introduc
of artifacts into ground-water quality d
sets.
Table 3. Estimated Ranges of Sampling Frequency (in Months) to Maintain Information
Loss at < 10% for Selected types ofChemical Parameters
Type of Parameter
Pristine Background
Conditions
Contaminated
Upgradiant
Downgradient
Water Oulaity
Trace constituents
Major constituents
2 to?
2 to 7
1 to2
2 to 38
2 to 10
2 to 10
Geochemical
Trace constituents
Major constituents
to2
to2
2
7 tO 14
1 to 5
1 to 5
Contaminant Indicator
roc
rax
Conductivity
PH
2
6 to 7
6 to 7
2
3
24
24
2
3
7
7
1
-------
Michael J. Barcelona, H. Allen Wehrmann, Michael R. Schock, Mark E. Sievers, and
Joseph R. Karny are with the Illinois Department of Energy and Natural
Resources, Champaign, IL 61820-7495.
Jane E. Denne is the EPA Project Officer (see below).
The complete report, entitled "Sampling Frequency for Ground-Water Quality
Monitoring," (Order No. PB 89-233 5221 AS; Cost: $28.95, subject to change) will
be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
Las Vegs, NV 89193-3478
United States Center for Environmental Research
Environmental Protection Information
Agency Cincinnati OH 45268
Official Business
Penalty for Private Use $300
EPA/600/S4-89/032
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