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

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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

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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

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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

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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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