United States
Environmental Protection
Agency
Environmental Monitoring
Systems Laboratory
Las Vegas. NV 89193-3478
Research and Development
EPA/600/S4-88/040 May 1989
Project Summary
Evaluation of Control Chart
Methodologies for RCRA Waste
Sites
Thomas H. Starks
This report is a discussion of
decision rules relating to the
monitoring of ground water at
hazardous waste sites that are
subject to regulation under the
Resource Conservation and Recovery
Act of 1976 (RCRA). The final rule for
RCRA regulations 40CFR part 264 was
published October 11, 1988
(53FR39720). Understanding the
complexity of the monitoring problem
and the diversity of RCRA sites, the
final rule wisely allows the
owner/operator to choose,
conditioned on EPA approval, a site-
specific "statistical procedure."
Analysls-of-varlance, tolerance
intervals, prediction intervals, and
control charts are included as
acceptable methods for "statistical
procedures." These methods are
discussed to facilitate the choice of
decision rules. A nested random-
effects model for ground-water
quality parameter measurement is
suggested and decision procedures
are developed In terms of that model.
Particular attention is paid to the
possible application of industrial
quality control strategies to the
ground-water monitoring problem. A
decision procedure that changes
over time as more information about
well and aquifer characteristics
accumulate is proposed. This
procedure involves the use of outlier
tests and of Shewhart-CUSUM
quality control strategies.
This Project Summary was
developed by EPA's Environmental
Monitoring Systems Laboratory, Las
Vegas , NV, to announce key findings
of the research project that Is fully
documented In a separate report of
the same title (see Project Report
ordering Information at back).
Introduction
Under the Resource Conservation and
Recovery Act of 1976 (RCRA), the U.S.
Environmental Protection Agency has
developed regulations for landfills,
surface impoundments, waste piles, and
land treatment units that are used to
treat, store, or dispose of hazardous
wastes. The regulations include
requirements for the monitoring of
ground water in the top aquifer below the
hazardous waste site (HWS). This
monitoring involves the drilling of
background well(s) and compliance wells
at the HWS, and the sampling and
analysis of well water at regular time
intervals to help determine whether
leachate from the HWS has entered the
aquifer. There are several as yet
unsolved problems in this monitoring
program. They include determination of
appropriate methods for obtaining
accurate measurements of some
constituents such as volatile organics,
specifications for well construction,
detection and accommodation of shifting
direction and rate of aquifer flow, and
development of good decision rules
based on measurements of water
samples drawn from wells near the HWS
for determining when additional
regulatory action may be required. This
paper discusses the problem of
developing good decision rules and
recommends that the development be
based on a realistic model for the
ground-water measurements. A nested
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random-effects model is suggested and
slatisticaf procedures based on that
model are formulated and criticized.
Industrial quality control strategies are
considered in terms of their possible
application to the ground-water
monitoring decision problem,
Procedure
During the first year this project
developed the appropriate components
of variation model for the RCRA
ground-water tesi problem. Such a
model is the evaluation criterion for any
proposed RCRA ground water test. The
second year the implicit variance models
assumed by the various proposed test
strategies for the RCRA problem were
explicitly derived and compared. The
third year the most promising test
procedure, control chart strategy, was
evaluated against simulated data
representing the most frequent RCRA
data problems and different values of
parameters critical to the test procedure.
Control chart strategy is evaluated on
simulated "reaJ world" data. Several
available sets of "real world" data were
examined for evaluating proposed RCRA
statistical decision procedures. The sets
an had one flaw for this use in that the
state of the site (in-control or no leak,
out-gf-contfol or leak) was not known
or recorded. Thus only simulated data
sets could represent the desired state
and relevant RCRA-type probiems
(multiple wetls, correlated samples, etc.).
The contro! chart strategy was evaluated
for two factors related to the algorithms
and five factors rotated to frequent
statistical problems with environmental
monitoring data. Each factor was
simulated for both states (in-control,
out-of-control). The factors evaluated
in the design of the simulation
experiments were (1) parameter
estimates and (2) length of learning
period for the Snewhart-CUSUM control
chart strategy and RCRA-type data
complications such as: (3) multiple wells,
(4) correlated samples. (5} negatively
skewed data (e.g., data overcorrected by
transformation), (6) positively skewed
data (e.g., monitoring data is often
positively skewed, requiring trans-
formation), and (7) multiple ground-
water quality parameter data. The criteria
of evaluation are long average-run-
length for the in-control state and short
average-run-length for the out-of-
control state.
Summary and Conclusions
All statistical decision procedures are
based on assumed measurement
models. Decision procedures based on
unrealistic models will not succeed in
providing answers to ground-water
monitoring decision problems, no matter
how simple or elegant the procedures
may be. It is essential that a realistic
workable model for the measurements
be formulated and used both in
construction and evaluation of decision
procedure. A nested random-effects
model is presented to illustrate a model
approach and to indicate the difficulties
inherent in developing good statistical
procedures for monitoring of ground-
water quality. Obviously, no statistical
measurement mode! is as complex as
the system in nature, but the model for a
decision procedure should be as
reasonable and as simple as possible.
Any decision procedure, based on
measurements of the quality of the
ground water taken in each sampling
period, where decisions are made at the
end of each sampling period as to
whether of not additional regulatory
actions are required, is by definition a
quality control strategy. In addition, for a
quality control strategy, one is interested
in the distribution of run lengths in both
in-control and out-of-control
situations. That is, a good decision
procedure {quality control strategy) is
one with large average in-control run
lengths and small average out-of-
control run lengths. Hence, consideration
in comparing decision procedures for
RCRA sites should be given to the
distributions of their run lengths rather
than to their probabilities of Type I and
Type (I errors on individual applications
of the decision rule in each sampling
period. (However, the two types of
criteria are obviously not unrelated). In
choosing a quality contro! scheme for
use at RCRA sites it is reasonable to
consider quality control schemes that
have been used successfully in other
settings, particularly in industrial settings,
The formulation of good decision
procedures for determining when
increased monitoring activity is needed
at a hazardous waste site (HWS) is
extremely difficult because of the high
cost, slow acquisition, low precision, and
multfvariate nature of ground-water
monitoring data along with system
instability due to intrusions on the aquifer
caused by man outside the HWS. The
first three of these problems force the
initiation ol quality control strategies
before good (highly precise) estimates of
measurement distribution parameters
can be obtained. With good estimates of
ths measurement distribution param-
eters, it is possible to mathematically
derive the run-length distributions f
various quality control strategie
However, without these good estimates,
is necessary to employ Monte Car
techniques to estimate the distribute
properties of run lengths when tr
process is in-control (i.e., site is n
leaking into aquifer and plume is passir
through one or more weii sites). Tr
Monte Carlo analysis of the Shewhar
CUSUM quality control technique
indicates the type of results that can t
obtained with this method and ah
indicates that the method is reasonab
robust with respect to left-skewei
non-normal probability distributions •
measurements. However, the techniqi
is not robust with respect to lack >
independence between measurement
in particular, its in-control run-lengf
characteristics are shortened by positK
serial correlations.
The Monte Carlo simulation resul
and methods discussed in this repo
provide a basis for comparison an
evaluation of all other decisio
procedures, since similar Monte Car
simulations can be performed on ar
decision procedure to obtain estimates «
the run length distribution of such pn
cedures.
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Thomas H. Starks is with the University of Nevada, Las Vegas. NV89154.
George T. Flatman is the EPA Project Officer (see below).
The complete report, entitled "Evaluation of Control Chart Methodologies for
RCRA Waste Sites" (Order No. PB 89-138 4161 AS; Cost: $13.95, 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 Vegas, NV 89193-3478
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
t
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