MANAGER'S GUIDE
TO STORE!
U.S. Environmental protection Agency
Washington, D.C. 20460
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TABLE 'Off CONTENTS
CHAPTER PAGE
PROLOGUE i
1 INTRODUCTION 1-1
WHAT IS STORET? . 1-1
PRACTICAL APPLICATIONS 1-3
HOW DOES STORET WORK? 1-3
ARE STORET DATA RELIABLE? 1-4
STORET AND THE ANALYST 1-5
WHERE DOES THE MANAGER'S GUIDE FIT IN? 1-6
ADDITIONAL DETAILS 1-8
2 MONITORING PROGRAMS 2-1
AMBIENT MONITORING PROGRAMS 2-3
INTENSIVE SURVEYS 2-4
EFFLUENT AND BIOLOGICAL MONITORING PROGRAMS 2-6
DATA ANALYSIS TECHNIQUES 2-9
2-1 Identification of station codes 2-9
used by a specified agency
2-2 Identification of stations in a 2-11
specified area
2-3 Identification of parameters 2-13
sampled
2-4 Retrieval of raw data 2-15
2-5 Determination of sampling patterns 2-17
over time
2-6 Plotting locations of monitoring sites 2-19
2-7 Retrieval of intensive survey 2-23
information
2-8 Summarizing monitoring activities 2-25
3 EXISTING WATER QUALITY AND HISTORICAL TRENDS 3-1
EXISTING WATER QUALITY 3-1
HISTORICAL TRENDS 3-2
PROJECTIONS 3-6
DATA ANALYSIS TECHNIQUES 3-7
3-1 Transfer of USGS flow data to 3-7
STORET stations
3-2 Calculation of equivalent loads 3-9
3-3 Assessing existing conditions in 3-11
. ' terms of standards violations
3-4 Generation of area-shaded maps 3-13
3-5 Illustration of historical trends 3-15
using statistical summaries
3-6 Plotting trends over time 3-17
3-7 Generation of trend maps 3-19
3-8 Plotting stream profiles 3-21
3-9 Linear regressions of concentration 3-23
versus time
3-10 Formatting STORET data for input into 3-25
SAS (Statistical Analysis System)
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3-11 Output of STORE! data on punched 3-27
cards
4 POLLUTION SOURCES AND CONTROL PROGRAMS 4-1
IDENTIFICATION OF WATER QUALITY PROBLEMS 4-1
LOCATION AND CHARACTERIZATION OF POLLUTION 4-2
SOURCES
CAUSE AND EFFECT RELATIONSHIPS 4-4
EVALUATION OF CONTROL ALTERNATIVES 4-8
DATA ANALYSIS TECHNIQUES 4-9
4-1 Use of multiple station plots to 4-9
assess cause and effect
4-2 Retrieval of in-plant data 4-11
4-3 Retrieval of permit and 4-13
effluent data
4-4 Generation of effluent reports 4-15
4-5 Location and characterization of 4-17
municipal dischargers
4-6 Retrieval of data on selected communities 4-19
or facilities
4-7 Identification of stations that 4-21
sample weather data
5 BIOLOGICAL MONITORING 5-1
BACTERIA 5-1
CHLOROPHYLL 5-2
FISH KILL'S 5-4
DATA ANALYSIS TECHNIQUES 5-5
5-1 Statistical summaries of bacteriologic 5-5
data
5-2 Using bacterial data to assess the 5-7
source of fecal contamination
5-3 Retrieval of fish kill data 5-9
6 LAKE WATER QUALITY 6-1
EXISTING WATER QUALITY 6-1
EVALUATION OF CONTROL ALTERNATIVES 6-3
DATA ANALYSIS TECHNIQUES 6-5
6-1 Identification of lake stations 6-5
6-2 Retrieval of National Eutrophication 6-7
Survey data
6-3 Displaying lake stratification 6-9
6-4 Using contour maps to illustrate lake 6-11
water quality
APPENDIX
A BIBLIOGRAPHY A-l
B GLOSSARY B-l
C ADDITIONAL SOURCES OF INFORMATION C-l
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PROLOGUE
The Manager's Guide to STORET identifies applications of EPA's
STORET system to the requirements of the water quality management
program. It is intended to help the water quality manager to
reduce time-consuming and manpower-intensive manipulations of raw
data and to simplify preparation of reports and graphics.
fhe Guide describes data analysis techniques applicable to
programs initiated under the Federal Water Pollution Control Act,
as amended, with emphasis on Section 305(b). Many of the
techniques described will also be applicable to programs
initiated under the Toxic Substances Control Act (TSCA) and the
Resource Conservation and Recovery Act (RCRA) and to the functions
of the Office of Drinking Water and the Office of Solid Waste.
Separate chapters of the Guide are devoted to:
Monitoring Programs
Existing Water Quality and Historical Trends
Pollution Sources and Control Programs
Biological Monitoring
Lake Water Quality
Each chapter begins with a brief narrative that provides
background information and describes general STORET applications
to water quality management problems. Following the narrative are
individual descriptions of specific STORET data analysis
techniques, including example outputs. A glossary of technical
terms used throughout the Guide is" appended, as are a
bibliography and a listing of information sources pertinent to
water quality data analysis.
Throughout the Guide, the technical perspective is that of the
manager. No previous experience with STORET is assumed and no
attempt is made to explain specific system language or syntax.
Summary information on specific data analysis techniques is
included to facilitate communication between managers and their
analysts. For those who require more detailed information, cross-
references are provided to the STORET User Handbook.
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MANAGER'S GUIDE
TO
STORET
CHAPTER
1
INTRODUCTION
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CHAPTER 1
•INTRODUCTION
WHAT IS STORET?
STORET is a computerized data base utility maintained by EPA for
the STOrage and RETrieval of parametric data pertaining to the
quality of the waterways within and contiguous to the United
States. Since its inception in the early 1960s, the original
data base has evolved into a comprehensive system, capable of
performing a broad range of reporting, statistical analysis and
graphics functions, while continuing ^to serve in its original
role as a repository of parametric water quality data. STORET is
accessed by hundreds of users, utilizing computer terminals
located throughout the country.
The system is comprised of several individual but related files,
which contain various types of information, including:
Geographic and other descriptive data about the sites where
water quality data have been collected, referred to in STORET
as "station" data
Data related to the physical characteristics and chemical
constituents of the water, fish tissue, or sediment sampled,
referred to in STORET as "parametric" data
- Information on municipal waste sources and disposal systems
Data on pollution-caused fish kills, and
Daily stream flow data.
The data contained in STORET are collected, stored, and used by a
variety of Federal, State, and local government agencies and
their contractors, as depicted in Figure 1-1. Data and retrieval
requests are usually entered at computer terminals, and users
have the option of routing job output either to their own
keyboard terminals or to a remote printer (the central printer or
another specified remote printer). Output from the central
printer is sent to the user through the mails. It is also
possible to place job output on cards or microfilm and to store
output on tape or disk.
1-1
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SITE SELECTION:
SAMPLE COLLECTION:
SAMPLE ANALYSIS:
•DATA TABULATION:
1 DATA ENTRY:
STORAGE:
ET
RETRIEVAL
MEDIA:
FORMATS:
•REVIEW:
Hard Copy Cards
Tape Disk
Microform
Terminal
Maps Graphs Statistics
FIGURE 1-1
FROM DATA TO INFORMATION
1-2
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PRACTICAL APPLICATIONS
When used efficiently, STORET's data analysis capabilities can
greatly simplify the job of the water quality manager.
Knowledgeable selection of data and output formats can help the
manager fulfill reporting requirements and expedite decision-
making processes. Among other things, STORET data can be used to:
Fulfill 305(b) reporting requirements
Update State and area-wide water quality management plans
Provide background information for research studies
Summarize compliance with standards and criteria
Assess the availability of data on priority pollutants
Evaluate the effectiveness of water pollution control
programs, and
Check on NPDES permit compliance.
The user must realize, however, that the data in STORET are only
as useful as the monitoring plans that were used to collect these
data. There may not be enough information available to answer
every water quality question.
HOW DOES STORET WORK?
To store, retrieve, summarize and display STORET data, analysts
make use of a collection of customized computer programs and
keywords. Because the Water Quality File (WQF) is the largest
and most widely used of the STORET files, its programs are
generally the most flexible and the most sophisticated.
Using appropriate combinations of keywords, the user can easily
determine what data are available in the Water Quality File to
answer a given question. Flexible retrieval routines permit the
user to restrict the data retrieved according to:
Geographic area
Type of collection site (stream, lake, groundwater, etc.)
Time period
Depth, and/or
Parametric values.
1-3
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If sufficient data are present, the user has a choice of formats
in which to summarize and display the information. Output format
is controlled by specifying the WQF program to be used.
Alternative outputs include:
Tabulations of raw parametric data, for specified parameters
or for all parameters sampled at selected stations
Listings of sampling station information
Statistical summaries of parametric data
Graphical plots of variations in parametric values over time
or along a waterway
Location maps of specified geographic areas showing sampling
station locations
Summaries of parametric values in violation of standards
Contour, area-shaded, or trend maps showing variations in
parametric values over a specified geographic area
Linear regression plots and statistical calculations showing
relationships between specified variables
80-column punched cards containing station codes and
parametric data, and
A disk or magnetic tape containing STORET data that have been
reformatted to be compatible with other programs.
Program-specific keywords allow the user to further manipulate
the output format in terms of scale, statistical functions,
plotting symbols, and other variables. There are also program-
specific keywords that limit the data retrieved to values meeting
other user-specified criteria.
ARE STORET DATA RELIABLE?
STORET data are collected and entered into the system by a
multitude of Federal, State, and local government agencies and
their contractors, often over lengthy historical periods. To a
large extent, the reliability of the data is dependent on the
level of care employed by those agencies in the processes of
sampling, laboratory analysis, and data entry. EPA has little,
if any, control over those processes.
However, recognizing that the usefulness of the system hinges on
the reliability of its data, the Agency has taken steps to
1-4
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enhance the data entry procedure such that only high quality data
will be entered into STORET:
For the 187 most frequently used parameter codes, each value
is automatically checked at the time of data entry against
preestablished highest-acceptable and lowest-acceptable
values for that parameter; values that fall outside those
limits will not be stored without the use of an override
code.
Agencies may supply their own upper and/or lower limits —
these user-supplied edit checks can be input at the time of
data entry, or can be stored at individual stations or at
special cross-reference stations.
Extreme values that may bias a statistical summary may be
eliminated from a user's retrieval through the use of
program-specific keywords that can establish maximum or
minimum values for the parameters to be retrieved.
To clarify the circumstances surrounding sampling or analysis
procedures, users may store an alphabetic "remark code" with
any parameter value. (Remark codes may indicate, for example,
that a stored value is known to be less than or greater than
the actual value or that the value is estimated.)
Finally, another series of alphabetic codes is being
developed, which will serve to indicate the level of quality
assurance used in sampling and analysis; when this capability
becomes available, users will be able to retrieve data based
on the level of quality assurance used.
If, after all of these capabilities have been explored, a user
retrieves a value that appears to be in error, STORET also
provides a mechanism whereby the name, address, and telephone
number of the agency that stored the data can be located and the
reasons for an abnormally high or low value can be discussed. If
necessary, the agency that stored the value can change it.
STORET AND THE ANALYST
STORET is not a substitute for the professional judgement of the
analyst. Proper formulation of STORET retrieval requests and
subsequent interpretation of the printed output can only be
accomplished by an experienced water quality analyst. STORET is
merely a tool. Its capabilities complement, but cannot be
substituted for, professional judgement and experience.
STORET is a powerful utility. It can eliminate time-consuming
and manpower-intensive manipulations of raw data and can produce
sophisticated plots and maps that otherwise would require
1-5
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personnel with special graphics skills. In addition, STORET
permits the sharing of data among users, thus minimizing the need
for duplicate monitoring and record-keeping efforts.
None of these capabilities, however, can or should be used in a
vacuum. Familiarity with local conditions and general knowledge
of aquatic biology, chemistry, and physics are all essential to
their appropriate application. In devising a STORET retrieval
and evaluating its output, the analyst must be aware of the
influence of a multitude of variables, including, but not limited
to:
The physical and chemical characteristics of the parameters
being measured
Local geographic and demographic features
Stream flow
Sampling and laboratory analysis methods used
Related point and nonpoint sources, and
Statistical methodology.
In each of the following chapters of this Guide, descriptions of
specific STORET capabilities are prefaced with several pages of
narrative delineating how these and other related considerations
can affect water quality data analysis.
WHERE DOES THE MANAGER'S GUIDE FIT IN?
This Guide is designed to bridge a gap between Federally
legislated water program requirements and the detailed
descriptions of computer programs and keywords contained in the
STORET User Handbook. (Figure 1-2 illustrates where the Guide
fits into the flow of information.) In view of the trend toward
consolidation of Federal water quality management, reporting
requirements in the States' biennial 305(b) reports,
responsiveness to program guidance for 305(b) reporting has been
emphasized. Individual chapters cover the subjects of monitoring
programs, existing water quality and historical trends, pollution
sources and control programs, biological monitoring, and lake
water quality.
1-6
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EPA
HEADQUARTERS •
REPORTS
REQUIRED
UNDER
P.L32-500
WATER QUALITY
' MANAGEMENT
PROGRAM GUIDANCE
WATER
QUALITY
MANAGER
MANAGER'S
GUIDE TO
STORET
DATA ANALYSIS
NEEDS
STORET
PROGRAMS
AND
KEYWORDS
STORET
USER
HANDBOOK
RAW DATA
GRAPHS
MAPS
STATISTICS
FIGURE 1-2
WHERE DOES THE MANAGER'S GUIDE FIT IN?
1-7
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Each of the following chapters is comprised of several pages of
narrative, followed by a series of one- or two-page descriptions
of applicable STORET capabilities. The narrative portion
addresses general data analysis questions pertinent to the
problem area under consideration. Individual techniques are
presented in a carefully structured outline format, and each is
described on a separate page, allowing for selective reproduction
and for insertion of updates reflecting changes in program
requirements and/or STORET capabilities.
Throughout this Guide, the information provided is of a very
general nature. The analysis techniques described represent only
a sampling of the system's many potential applications in the
area of water quality management. Adaptation or expansion of the
methodologies outlined in order to meet individual needs is
encouraged.
ADDITIONAL DETAILS
EPA Headquarters provides extensive operational support for the
STORET user community, through the STORET User Assistance
Section, Monitoring and Data Support Division. User Assistance
personnel are available by telephone from 8 am to 5 pm eastern
time, Monday through Friday, to answer user questions. During
those hours, users may call toll free ((800) 424-9067). Local
users may wish to call the Washington, D.C. number ((202) 426-
7792).
The STORET User Handbook contains complete documentation on how
to use the system. Copies of the Handbook are distributed to all
new users. A current list of Handbook owners is used as a
mailing list for updates, periodicals, memos, and other items
that may be made available to STORET users.
User Assistance personnel also periodically conduct basic and
advanced STORET training seminars. (Prerequisites for the
advanced seminar are completion of the basic seminar and at least
6 months' experience as an active STORET user.) In addition, an
annual 3-day users' meeting provides a forum for users from
across the country to exchange ideas and share experiences with
the use of the system.
Representatives of Federal, State, interstate and local
government agencies all are eligible to become STORET users.
Depending on the affiliation of the user, there are several
methods of monetary compensation to EPA for the use of the
system.
EPA supports its contracted hardware vendor by assigning each
program office an ADP suballowance; one of these assignments is
for State usage of STORET. Each year this suballowance is
distributed among the States through their respective EPA
1-8
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Regional Offices. A prospective State user should contact his or
her Regional STORET representative for further details.
Federal agencies may compensate EPA for their STORET usage by
means of an interagency agreement. These agreements may be
negotiated by the appropriate Regional office or by EPA
Headquarters in Washington, D.C. Agreements that cross EPA
Regional boundaries, or are on a national level, should be
negotiated through EPA Headquarters.
For further information on funding or on how STORET can help you
fulfill your water quality data analysis needs, contact your
Regional STORET representative. STORET User Assistance in
Washington, D.C. ((800) 424-9067) can furnish you with the name
and telephone number of your representative.
1-9
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MANAGER'S GUIDE
TO
STORET
CHAPTER
2
MONITORING PROGRAMS
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CHAPTER 2
MONITORING PROGRAMS
Water quality monitoring activities conducted.by the States, EPA
Regions, and other agencies and organizations are an integral
part of the water quality management program mandated by the
Federal Water Pollution Control Act (P.L. 92-500). The data
collected in the course of such efforts form the basis for all
subsequent management planning and decision-making. In addition,
retrospective evaluations of an agency's monitoring program can
help provide a framework for later analysis of historical data.
A monitoring program description is required as part of the
States' biennial 305(b) reports, and related assessments can
complement activities conducted under Sections 104(b), 201, 208\
303(e>, and 314.
EPA's Basic Water Monitoring Program (1978) distinguishes four
types of water quality monitoring:
Ambient monitoring, the collection of uniform data on
representative parameters for the assessment of long-term
progress toward national water quality goals
Intensive surveys, which provide greater volumes of data over
shorter time spans, in order to answer specific water quality
management questions
Effluent monitoring, including both self-monitoring and
compliance monitoring activities conducted in conjunction
with the NPDES permit program, and
Biological monitoring, a pilot program designed to assess the
effects of water pollution on aquatic life.
Data collected in all four types of monitoring efforts are
accommodated by the STORET system, and can be retrieved
separately or in combination, using techniques described in this
and subsequent chapters.
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Ehrfl! PFDT£.Cri3N
STORE! SYSTEM
FIGURE 2-1
AMBIENT STREAM MONITORING NETWORK FOR THE STATE OF MICHIGAN
2-2
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AMBIENT MONITORING PROGRAMS
In describing an ambient monitoring network the analyst should
include, at a minimum, information on station siting, parameters
sampled, and frequency of observations. Efforts of all agencies
that monitor water quality in a particular geographic area should
be reviewed, and quality assurance measures used in sampling and
analysis should be considered, if that information is available.
All of this data can be entered into and retrieved from STORET.
In defining monitoring stations to the Water Quality File, future
retrieval requirements should be kept in mind. For example, if
stations are numbered sequentially, it is possible to identify
them at retrieval time using a range of either primary or
secondary station codes, instead of individual agency and station
code pairs. Sequential numbering in downstream order can further
facilitate both retrieval submission and subsequent
interpretation of analysis results. In naming a new station, its
relationship to the existing network must be carefully
considered, so that logical retrieval mechanisms can be
maintained and duplicate naming avoided. For this purpose, a
current listing of all station codes previously assigned by a
given agency should be maintained and referred to when
necessary.l
Stations for routine ambient monitoring should be sited to insure
a representative sampling of both problem areas and clean water
areas, as well as a variety of land use and water use types. To
assess the spatial distribution of monitoring stations stored in
STORET, the user may retrieve station descriptions or map station
locations for a particular geographic area of interest.2 Figure
2-1 shows the location of stations in the ambient monitoring
network maintained by the State of Michigan.
Similar techniques can be used to identify sampling redundancies
among stations maintained by different agencies in the same
geographic area. Where overlaps are found, interagency
agreements can be initiated to insure that the goals of all
agencies involved can be met in a cost-effective manner.
One likely source of additional water quality data is the United
States Geological Survey .(USGS), which maintains an extensive
water quality monitoring network, including stations sited to
give a balanced picture of the quality and quantity of water in
the Nation's streams. The USGS also maintains a benchmark system
that assesses only those basins as yet undisturbed by man. New
technique 2-1: Identification of Station Codes Used by a
Specified Agency.
technique 2-2: Identification of Stations in a Specified Area
and Technique 2-6: Plotting Locations of Monitoring Sites.
2-3
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water quality data collected by the USGS are routinely entered
into STORET, including the widely used flow data. Data collected
at USGS stations can often be useful to other agencies and should
be considered when reviewing regional monitoring efforts.
Once key stations have been identified, a complete monitoring
program description also demands an assessment of the parameters
sampled and the frequency of sampling.3 Minimum sampling
frequencies and parameter coverage specified by EPA in the Basic
Water Monitoring Program (1978) may be used as a point of
comparison to assess completeness of coverage and identify areas
where sampling should be expanded or consolidated.
The effectiveness of quality assurance programs used in both
sampling and laboratory analysis procedures should also be
considered, so that data collected by agencies with inadequate
quality control programs or values that have been stored with
remark codes can be given the appropriate weight in analyses.
The analyst should express his reservations, if any, concerning
data collected by the monitoring network under review.
Since one of the goals of EPA's ambient monitoring program is
uniformity of data collection, with a view to aggregation of data
on a national scale, the location and number of routine
monitoring stations are not likely to vary dramatically over time.
Once the ambient network has been reviewed adequately, using
appropriate STORET capabilities, subsequent reports need only
describe modifications to that framework. Any expansion or
alteration of the monitoring network or parameters sampled should
be specified, and the impetus behind those changes explained. It
may not be necessary, however, to reproduce State-wide maps or
station descriptions in every review cycle.
INTENSIVE SURVEYS
Although routine ambient monitoring is still a critical part of
any complete monitoring program, provisions of P.L. 92-500 have
caused a shift in emphasis toward greater utilization of
intensive surveys. The Basic Water Monitoring Program (1978)
suggests that intensive surveys be conducted at least once every
5 years on every river, lake, estuary, bay or aquifer where waste
loads are allocated or significant water quality changes have
occurred or are probable. Adherence to these guidelines would
result in the conduct of approximately 300 such surveys annually.
'Technique 2-3: Identification of Parameters Sampled; Technique
2-4s Retrieval of Raw Data; and Technique 2-5: Determination of
Sampling Patterns over Time.
2-4
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EFFECTIVE DISTANCE (MILES)
10"
ID'1
10° 10'
10'
1 1
THERMAL
__
|
FLOATABLE!
1
•ACTERIA •
SUSPENDED SOLIDS
1 1
DISSOLVED OXYOCN
.
1
NUTRIENTS
TOIIIC CFFCCT*
J
DISSOLVED SOLIDS
1
10"
IO
10'
IO 10 IO
0f
1
THERMAL
FLOATABLtS
_
1
•ACTMIA
1
ots
SOLVED OXYOTM
SUSPENDED SOLIDS
_
1
NUTRItHTS
ACUTE^C
1
1
w'ssi
ILVEDSO
JM
1 1
"LONO TERM "*
TOIIIC EffECT
DAY 1 MONTH] TEAR
WEEK SFflSON DECADE
TIME SCALES
FIGURE 2-2
TIME AND SPACE SCALES FOR ASSESSMENT OF WATER QUALITY PROBLEMS
2-5
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When intensive surveys have been conducted within an area and
time period of interest, details must be reviewed in the State's
biennial 305(b) report. The same data can be used in 201, 208,
and 303(e) studies as a basis for interpreting ambient monitoring
data, identifying areas of water quality degradation, and
analyzing cause and effect relationships.
STORET has special capabilities for the storage and retrieval of
intensive survey stations and data. These capabilities can be
used in conjunction with the techniques mentioned under Ambient
Monitoring Programs to characterize the purpose, methodologies,
and conclusions of all intensive surveys conducted in a
particular area and time period of interest.4
Because intensive survey data are often collected in order to
answer specific water quality management questions, the analyst
will ordinarily be attempting to reach some conclusion about the
meaning of the data collected. Both the time and space scales
used in the survey are critical to the validity of those
conclusions. Whereas ambient monitoring is designed to provide
uniform, representative water quality data, intensive survey
sampling must be conducted where and when the data collected are
most likely to provide conclusive evidence in support of
decision-making processes.
For example, a station set up to monitor bacterial pollution
should be sited relatively close to the source of the problem,
because of the rapid bacterial die-away rate. Alternatively, the
siting of stations sampling dissolved oxygen is more dependent on
stream and waste characteristics. Temporally, the critical
period for dissolved oxygen is likely to be during low-flow
summer months and, if algae are present, the critical time of the
day is near dawn. Figure 2-2 (Hydroscience, 1976a) summarizes
the appropriate time and space scales for assessment of various
types of water quality problems. Using this information, coupled
with knowledge of chemical reaction rates and characteristics of
the geographic area under investigation, the analyst can assess
the appropriateness of the time and space scales used in the
survey before attempting to interpret the data collected.
EFFLUENT AND BIOLOGICAL MONITORING PROGRAMS
Both effluent and biological monitoring programs were given
impetus by the enactment of P.L. 92-500. The NPDES (National
Pollutant Discharge Elimination System) program authorized by;
Section 402 calls for self-monitoring by dischargers as well as
compliance monitoring by the States. Biological monitoring is'
mandated in Section 502, which calls for a "determination of the
effects on aquatic life. . .in receiving waters due to the
*Technique 2-7: Retrieval of Intensive Survey Information,
2-6
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discharge of pollutants". Retrievals of effluent and biological
data from STORET are discussed in Chapters 4 and 5 of this Guide,
respectively.
2-7
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WATER QUALITY DATA ANALYSIS :
TECHNIQUE •
2-1
IDENTIFICATION OF STATION CODES USED BY A SPECIFIED AGENCY
Water quality monitoring stations may be identified in STORET by
a primary station code and up to three secondary station codes.
This technique lists all of the primary and secondary station
codes currently in use by a given agency. The output listing can
be used to determine how many stations are maintained by that
agency and to avoid duplication in station naming.
TECHNIQUE: Use the Water Quality File retrieval
program STA.
DATA REQUIREMENTS: Enter the agency code for the agency of
interest.
OUTPUT: A listing of all primary and secondary
station codes associated with the specified
agency code will be printed, including an
indication as to whether the parametric
data associated with those stations are
available on-line or have been archived.
Station codes are listed in alphabetical
order, reading left to right and top to
bottom.
DOCUMENTATION: Part WQ, Chapter RET, Section 6.
NOTES: The agency code is the only valid station
identification keyword for the STA program;
no data selection keywords are valid.
More than one agency code may be specified,
if desired.
2-9
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EXAMPLE:
This example shows the first page of output
from program STA. The retrieval was
restricted to stations maintained by the
State of Michigan (agency code 21MICH). In
this case, the characters "DOS" preceding
each station code indicate that these are
secondary station codes (S) that are
available on-line (00).
21MICH
UOS ACU0504.1
OOS AC00524.0
COS AC00540.1
COS AC00571.U
COS AC00601.0
COS AC00644.1
OOS AC00690.0
OOS AC00735.U
003 AC00780.1
OOS AC00786.0
OOS ACOOV91.1
OOS ACOOB46.0
0 OS AC00974.U
OOS AC01001.0
OOS AC01047.0
OOS AC01146.0
OOS ACU1281.3
OOS AC01400.0
UOS AC016BO.O
COS AC018GO.O
OOS AC01930.0
OOS AC02000.2
OOS AC02300.0
OUS AC02516.2
005 AC02b80.3
OOS AC03395.0
OuS AC04260.2
OOS AC04990.3
OOS AC05660.U
OOS AC08659.1
OOS AC09600.3
OOS AC17260.1
OOS AC20044.0
OOS AUS002
OOS AUS008
OOS BA0010
OOS BA0017
OOS BA0023
COS BA0035
OOS BA0047
OOS BAOOS9
OOS BA0067
OOS BA0080
OOS BA0100
OOS BA0109
OOS BA0122
OOS BAD 139
OOS bC0013
OOS B03820
OOS B04240
OOS CCCP001
OOS CPCN020
OOS DECC001
OOS DEDP001
DOS DETPD01
OOS DEWS010
OOS DI0006
OOS 010013
OOS AC00505.0
OOS AC00525.0
OOS ACOOS40.2
OOb ACOOS7S.O
OOS AC00602.0
OOS AC00665.0
OOS AC00695.0
OOS AC00735.1
OOS AC00781.U
OOS AC007S8.1
OOS ACOOB25.0
OOS AC008S7.0
OOS AC00975.1
OOS AC01001.1
OOS AC01062.0
OOS AC011SO.O
OOS AC01281.4
OOS AC0143B.O
OOS AC01722.0
OOS AC01880.0
OOS AC01930.1
OOS AC02D30.0
OOS AC02400.0
OOS AC02560.0
OOS AC02S80.4
OOS AC04001.0
OOS AC04320.0
OOS AC05220.0
OOS AC05660.1
OOS AC08850.0
OOS AC09711.0
OOS AC17260.2
OOS AC20044.1
OOS AUS003
OOS AUS009
OOS BA0011
OOS BA0018
OOS BA0025
OOS SAO 038
OOS BA0048
OOS BA0060
OOS BA0069
OOS BA0081
OOS BA0102
OOS BA0110
OOS BA0123
OOS BA0142
OOS B01450
OOS U03901
OOS B04330
OOS CCCP010
OOS CPCS010
OOS DECC010
OOS DEDP010
OOS DETP010
OOS OI0001
OOS DI0007
OOS DI0014
OOS AC00505.1
DOS AC00527.U
OOS AC00545.0
OOS ACD0580.U
OOS AC00617.U
OOS ACD0670.0
OOS AC00695.1
OflS ACU0735.2
OOS AC00781.1
OOS AC00788.2
OOS AC00830.0
OOS ACU0857.1
OOS AC00975.2
OOS AC01020.0
OOS AC01075.0
DOS AC01200.1
OOS ACU1320.0
OOS AC016DO.O
OOS AC01744.0
OOS AC01900.0
OOS AC01930.2
OOS AC02030.1
OOS AC02SOO.O
OOS AC02571.0
OOS AC02860.0
OOS AC04150.0
OOS ACU4320.J
005 ACU5220.1
OOS AC07730.U
OOS AC08970.0
OOS AC10130.0
OOS AC17260.3
OOS AC20044.2
OOS AUS004
OOS AUS010
OOS BA0013
OOS BA0019
OOS BA0027
OOS BA0039
OOS BA0052
OOS BA0062
OOS BA0070
OOS BA0087
OOS BA0104
OOS BA0113
OOS BA0124
OOS BA0143
OOS 801460
OOS B04000
OOS B04340
OOS CCCP020
OOS CWPP001
OOS DECP001
OOS DERP001
OOS DEWN001
OOS DI0002
OOS DI0009
OOS DI001S
UOS AC00506.0
OOS AC00530.0
OOS AC00545.1
OOS AC00584.U
OOS AC00629.0
OOH ACOO(>70.1
OOS AC00696.I1
UOS ACU0748.U
OOS AC00785.C
OOS AC00790.U
OOS AC00830.1
OOS AC00890.U
OOS AC00975.3
OOS AC01C20.1
OOS AC01090.U
OOS AC01 270.0
OOS AC01327.I)
OOS AC01610.0
OOS AC01775.0
OOS AC01920.0
OOS AC01956.0
UOS AC02030.2
OOS AC02500.1
flOS AC025SO.»
OOS AC02650.1
OOS AC0423Q.O
UOS ACU4865.0
OOS AC05370.0
OOS AC07730.1
OOS AC096DO.O
OOS AC10131.0
OOS AC17360.0
OOS AL0023
OOS AUS005
OOS BA0001
OOS BA0014
OOS BA0020
OOS BA0029
005 BA0040
OOS BAOOS3
OOS BA0063
OOS BA0072
OOS BA0089
OOS BA0105
OOS BA0116
OOS BA0130
OOS BA0149
OOS B02500
DOS B04010
OOS B04360
OOS CCTE010
OOS OtPPOlO
OOS DECP002
.OOS DERP010
OOS DEWN010
OOS 010003
OOS DI0010
OCS DI0016
OOS ACOObD8..4C.U
OCS ACOOb'in.il
OCS ACOObfiO.l
OOS ACOO(>44.t;
(-'db ACUOfi77.u
COS ACOOV24 .i
OOS ACUR/PO.U
OOS ACDO'/PS.y
UOS ACUOY91.U
OOS ACUOt?4S.U
oos Acun'j3(j.i'
OOS ACUKlOC.I-
nns jvc«in2c.<
COS »C01125.0
UOS y>C01^Cl./
UCS AC'UJ3U.J
UOS ACClhlO./
oos Ac^iven.i
OOS ACri!i2n.2
oos AC02nro.i
OOS AC0211 0. J
OOS .1>C02bKi.I
UOS ACU2h£0.^
uos ACo?y^i-.(!
nos ACo^uGc.i
uns Acuiasii.i
POS AC05652.1'
nns AC08F»59.<;
uos ACODfcnn.?
OOS AC17120.U
OOS AC1E770.1
ons AUSOOI
005 AUS007
nns RAHOOS
OOS BAC016
OOS BAtirjZ
OCS bAC033
COS RAOO'1
OOE B/CC58
OOS BAOOf>G
OOS BAH077
OOS BAOC91
OOS BA010E
OOS 8A0119
OCS BA0135
OOS BA0251
OOS B03S10
COS E04J1C
OOS CCA0010
cos cpcuuin
OOS OCTP010
OOS DECP020
COS DERP030
OCS DEhSOCl
OOS IHOP05
OCS DI0012
OOS DIC018
2-10
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
2-2
IDENTIFICATION OF STATIONS IN A SPECIFIED AREA
This technique identifies stations maintained by an analyst's own
agency and/or other agencies that sample water quality within a
particular geographic area of interest. The station
identification information retrieved can be useful in describing
an overall monitoring program.
TECHNIQUE:
Use the Water Quality File retrieval
program INDEX.
DATA REQUIREMENTS:
Enter station identification keywords to
specify the geographic area of interest.
OUTPUT:
Modified station identification information
for each station selected will be printed,
including: State name; State and county
codes; agency code; primary and secondary
station codes; major, minor, and terminal
basin codes; latitude and longitude
coordinates; river mile index (if stored);
and station type.
DOCUMENTATION:
Part WQ, Chapter RET, Section 6.
NOTES:
This program retrieves no parametric data.
If necessary, the retrieval can be
restricted to print information only on
stations maintained by a specified agency
or agencies.
2-11
-------
EXAMPLE:
This example shows the first page of output
from the Water Quality File retrieval
program INDEX. In this case, the retrieval
was restricted to stations located in
Gogebic County, Michigan.
SI/CO I LOCATION
STATION TYPE
USER CODE STATION
LAT/LONC
INDEX
BASIN CODE
STORAGE DATE
SECONDARY STATIONS
MILESLV1 LV2
"AJ/MIN/TERM
LV5 LV6 LV7 LVS LV9 LVIC LVJ1
MICHIGAN 2*053 MONTREAL n. AT AURORA ST.
/TYPA/AMBNT/STHEAN
21MICH 270009 INOOP8
46 27 01.0 090 10 42.0
INDEX
MICHIGAN 26053 MONTREAL R. AT COUNTY RD.
/TYPA/AMBNT/STREAM
21MICH 270010 IN0099
4S 26 23.5 090 10 1)0.0
INDEX
MICHIGAN 2bOS3 MONTREAL It. AT COUNTY RU.
/TY PA/AM BNT/STH EAM
21MICH 270011
16 23 16.0 090 08 28.0
INDEX
MICHIGAN 26053 MID OK ONTONAGON H. AT US-45 BH
/TYPA/AHbNT/STREAM
21MICH 270012 BA0135
46 16 28.0 089 10 39.0
INDEX
26053 PRESQUE ISLE R. AT CO. RD. BR.
/TYPA/AHBNT/STREAM
21MICH 270013
46 22 36.0 089 41 20.0
INDEX
MICHIGAN 260S3 JACKSON CR. AT M-28 BRIDGE
/TYPA/AMBNT/STHEAM
21MICH 270014
4u 30 5V. 0 089 52 50.0
INDEX
MICHIGAN 26053 MONTREAL RIVER NEAR IRONWOOD, MI
/•f YPA/AMBNT/STR EAM
112WRD U402B100
46 30 26.0 090 13 47.0
INDEX
2-12
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
2-3
IDENTIFICATION OF PARAMETERS SAMPLED
Summary information is provided to help review parameter coverage
in an analyst's own network and/or at stations maintained by
other agencies in a specific area of interest.
TECHNIQUE:
Use the Water Quality File retrieval
program INVENT or INV120.
DATA REQUIREMENTS;
Enter station identification and data
selection keywords to define geographic
area and time period of interest.
OUTPUT:
For each station selected, station
identification information and summary
statistics on all parameters sampled will
be printed. In addition, a gross summary
that combines data from all stations
retrieved will be produced.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 6.
Unless the retrieval is restricted to a
manageable number of stations and/or
parameters, this program could generate
significant volumes of output. It is
advisable to retrieve data for key stations
only.
The example shows output from the program
INVENT; if INV120 is used, the two columns
listing the coefficients of variation and
standard errors will be omitted.
It is also possible to suppress individual
station summaries and to print only a
single aggregation of data from all
stations retrieved {refer to Technique 2-8:
Summarizing Monitoring Activities).
2-13
-------
EXAMPLE:
This example shows INVENT output for
station 070858, which is maintained by the
U.S. Forest1Service {agency code 14AGNFS9).
The retrieval was limited to data collected
in 1978 and 1979.
/mA/AMMit/STREAM
PARAMETER NUMBER
00004
00010
00020
00025
0003*
00042
00043
00044
0004$
00052
000*1
000*$
00076
OOOBO
000« 1
00095
00300
00301
00*03
OO410
OO600
00610
00*15
O0»25
O0*30
OO650
00900
01355
STREAM
MATER
AIR
IAROM1RC
MIND
ALTITUDE
CLOUD
CLOUO
PRtClP
RELATIVE
STREAM
STREAM
TURi
COLOR
if COLOR
CNOUCTVY
00
00
LAB
T ALK
TOTAL N
NH3-N
ND2-N
TOT KJfL
N02CN03
T PC*
TOT HARD
ICE
WIDTH
TEMP
UUP
PRESSURE
OIR.FROM
fffT
TWf
AMOUNT
TDT DAI
MUftlOlTI
FtOH.
STAGE
TRB1DMTR
fT-CO
fT-CO
AT 25C
SATUR
PM
CA103
N
TOTAL
TOTAL
N
N- TOTAL
P04
CAC03
COW*
FEET
CENT
CENT
MM OF Mt.
NORTH-0
A8 HSL
HMD CODE
HMD CDDE
IN
PERCENT
INST-CFS
FMT
HACN f-TU
UNITS
UNITS
H1CRONHO
MC/L
PERCENT
so
MG/l
MS/I
HC/L
MS/I
MG/L
NC/L
M6/L
MC/L
10
15
15
15
|5
15
15
1 5
13
15
10
15
14
13
I
14
15
15
14
13
3
3
|
3
14
14
4
1
MEAN
30.2000
13.266T
14 .3000
29 .90 19
178.667,
1332.67
3.53333
4 .53333
.139231
59.7333
183.967
87 .8200
1. 74285
103.846
37.COOO
77.8571
10.1667
98 .2465
7.27142
27.2461
1.0O66T
. 1 30000
.002000
.1)86666
.096428
.067500
45.6500
6.0OOOO
07OB9B C90704089I4) 402010404141 PfRCH PIV.
46 31 10.0 C88 ?9 <5.0 3
10.9MI.O79DES. FROM N28CFH16N J.
26O13 MICHIGAN
LAKE SUPERIOR 22(400
STURGEON RIVER-PERCH RlVFFR
I4ACNFS9 781227
0000 CLASS 00
VARIANCE
52.0121
67.0647
101.63*
.03B225
9108.82
49.14JB
5.98096
8.55240
•055QDB
5*2.640
1095 1 .5
.544643
.47*037
1674.31
465.827
4.30B14
28.3214
.092961
69.2295
.484433
.002100
.573033
.004(155
.000479
38.6237
STAN rev
7.21194
8.1V942
10.0815
. 19*5 13
95.«401
7.01020
2.445*0
2.92445
.234537
23.7200
104.649
. 737999
.691402
40.41F4
2 1.^830
2,075*1
5.32179
.30*895
P. 320*3
.696012
.04^826
. 7569(9
.064681
.021894
6.21480
CDEF VAR
.2380C6
.617293
.704997
.C0653P
.534IFO
.0052*0
.697! 5)
.645099
1.6B452
.597099
.568849
.COD4C4
.3967C7
.394029
.277213
.20415«
.054168
.041931
.3053*0
,f914C3
.3525C6
.£9374"
.122622
.324357
.136140
STAND tf
2.2F.C62
2.11450
2 .6C302
.050481
24.6425
I.P1002
.631451
.7«50B9
.0*5049
*. 12*49
33.C93*
. 190551
.184785
1 1 .3487
5.76F30
.535919
1.374 OX
.0814I<7
2.30767
.401843
.026458
.43704f
.018*23
.005851
3.10740
PAI1KUM HINIPUM
44.0000
22.0000
27.0000-
30.1600
360.000
1350.00
8.00000
9.00000
.730000
95.0000
449.500
t*8 .9006
3.70000
I 95.0OO
37 .0000
131.000
13.5000
109.600
T. 70000
39.BOOO
1.68000
.180000
.002000
1.61000
.27COOO
.111000
94.0OOO
6.00000
22.COOO
.000000
.1001*02
29.37CO
.000000
1330.00
.OOOOOO
.000000
.000000
19.00OO
79.P900
86.1000
.900000
40.000O
37. (1000
48.0000
C.I 0000
89.7000
6.80000
11.5000
.290000
.090000
.002000
.100000
.020000
'.027000
40.0000
6.00000
Ri <-• FA 11
7»/04/?»
7F/03/CI*
7*/OS/»f
7F/O3/CI
7C/03/Cf
7P/03/0*
7*/03/Cf
7C/03/C*
7f/03/C»
if/ei/(f
7C/04/24
7»>/03/lt
78/03/tt
7^/03/ri-
79/02/J I
If/03/11
7F/C3/«f
7£ /C3/< ^
If /03/< t
7f /€*/;«
7P/05/C^
7B /O^ / f *i
79/02/27
7P/0' /(•?
7P/03/CI
7f/03/C^
78/0*/74
78/03/C!
END OAlt
78/C9/26
79/C4/1I
79/C4/1I
79/04/11
79/0*/ll
79/04/11
79/0«/ll
79/04/11
79/M/ll
79/C4/M
IB/C9/26
79/04/11
79/02/27
78/09/26
79/C2/J7
79/C2/J7
79/04 /I J
79/04/11
79/C2/27
79/C2/27
79/02/27
79/02/27
79/C2/27
79/C2/27
79/C2/27
74/C2/27
79/02/27
78/03/00
2-14
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
2-4
RETRIEVAL OF RAW DATA
This technique provides maximum detail about parametric data
stored in the system. It is most useful if the geographic area
and time frame under consideration are limited.
TECHNIQUE:
Use the Water Quality File retrieval
program ALLPARM.
DATA REQUIREMENTS:
Enter appropriate station identification
and data selection keywords to define the
geographic area and time period of
interest. Up to 2,000 parameter codes can
be used if data on specific parameters are
required.
OUTPUT:
For each station retrieved, output will
include station identification information
and tabulations of all raw parametric data
collected in the time period specified.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 6.
Unless stations and data to be retrieved
are limited carefully, this type of
retrieval can produce an unmanageable
volume of data.
Not all general retrieval keywords are
valid with the ALLPARM program; consult the
STORET User Handbook for details.
2-15
-------
EXAMPLE:
This example shows the first page of
ALLPARM output for station 070858. The
retrieval was limited to data collected in
1978 and 1979.
070658 090704089141 402010404141 PERCH
4« 31 10.0 088 39 45.0 3
10.9MI.079DEG. F
26013 MICHIGAN
LAKE SUPERIOR 220800
STURGEON RIVER-PERCH R I VERB
14AGNPS9
78122? DEPTH 0
/TYPA/AMBMT/STREAM
INITIAL DATE 78/03/08
INITIAL TIME-DEPTH-BOTTOM
00004
00010
00011
00020
00025
00036
00042
00043
00044
00045
OOOS2
00061
00065
00076
oooao
00095
00300
00301
00403
00410
00600
00610
00625
00630
00650
00900
01355
STREAM
HATER
MATER
AIR
amcMTRC
WIND
ALTITUDE
CLOUD
CLOUD
PRECIP
RELATIVE
STREAM
STREAM
TURB
COLOR
CNDUCTVy
DO
DO
LAB
T ALK
TOTAL N
HH3-N
TOT KJEL
NO21NO3
T PO4
TOT HABD
ICE
WIDTH
TEMP
TEMP
TEMP
PRESSURE
DIP. FROM
FEET
TYPE
AMOUNT
TOT DAK
HUMIDITY
FLOW,
STAGE
TBBIOHTR
PT-CO
AT 25C
SATUR
PH '
CACO3
N
TOTAL
N
N-TCTAL
P04
CAC03
COVES
PEET
CENT
FAHN
CENT
MM OF HQ
NORTH -0
AB MSL
MHO CODE
HMO CODE
IN
PERCENT
INST-CFS
FEET
HACK FTU
UNITS
MICRON HO
MG/L
PERCENT
SU
MG/L
MG/L
MG/L
MG/L
MG/L
MG/L
MG/L
SEVERITY
INITIAL DATE
INITIAL TIME-DEPTH-BOTTOM
00004
00010
00011
00020
0002S
00036
00042
00043
00044
00045
00052
00061
00065
00076
00080
00081
STREAM
WATER
HATER
AIR
BARONTRC
HIND
ALTITUDE
CLOUD
CLOUD
PRECIP
RELATIVE
STREAM
STREAM
TURB
COLOR
AP COLOR
WIDTH
TEMP
TEMP
TEMP
PRESSURE
OIR.FRCM
FEET
TYPE
AMOUNT
TOT DAY
HUMIDITY
PLOW,
STAGE
TRBIDMTR
PT-CO
PT-CO
(SAMPLE CONTINUED ON NEXT
FEET
CENT
FAHN
'CENT
MM OF HG
NORTH -0
AS MSL
WHO CODE
HMO CODE
IN
PERCENT
INST-CFS
FEET
HACK PTU
UNITS
UNITS
PAGE)
1100 0000
0.0
32.0
10.0-
30
225
1350
1
1
0.00
'19.0
a?.o
2.7
40
131
13.5
95.9
T T
0.2
0.03
*
78/07/0?
1045 0000
21.0
69.8
23.0
30
200
1330
f,
6
0.45
94.0
87.5
1.6
74
RIV.
78/04/24 78/05/09
1230
44
6.0
42.8
10.0
30
90
1350
3
5
49.0
446
88.6
3.7
75
48
12.5
105.2
7.5
12
0.3
0.08
40
78/07
1230
27
22.0
71,6
27.0
30
180
1330
5
T
0.73
70.0
164
88.0
1.3
115
0000 1105 0000
34
9.0
48.2
7.0
29
270
1330
5
9
0.17
95.0
233
87.5
1,9
80
65
10.2
92.4
7.6
26
1.05 C
0.180
0.950
0.0
0.06
54
/25 78/08/09
0000 1110 0000
25
19.0
66.2
19.0
.30
360
1330
a
4
0.00
40.0
79
86.1
0.9
104
78/05/24
1015 ODOO
22
22.0
M.6
23.0
30
0
1330
1
1
33.0
146
87.5
1.8
85
72
8.7
103.5
7.5
35
1.69 C
0.120
1.610
0.1
0.06
42
78/08/25
1000 0000
17.0
62.6
20.5
30
45
1330
3
9
0.00
84.0
88.9
1.3
195
76/06/02
1030 0000
40
14.0
57.2
11.0
30
270
1330
4
3
0.05
51.0
243
69.5
9.3
94.4
78/09/0?
1045 0000
26
20.0
66.0
20.0
30
180
1330
3
2
0.00
94.0
158
88.2
1.4
120
••8/06/02
1035 0000
1.6
16D
63
6.9
21
0.1
0.07
78/09/26
0900 0000
26
13.0
55.4
16.0
30
180
1330
0
6
0.38
64.0
123
88.0
1.6
76
78/06/15
0955 0000
26
14.0
57.2
16.0
30
140
1330
2
2
0.03
60. 1)
124
88.0
1.6
130
71
10. 8
109.6
-•.3
27
O.U
0.05
79/02/2"1
1000 0000
O.U
32.0
B.O
30
180
1330
0
0
0.00
38.0
88.0
1.6
3?
70/06/27
1231) OOOU
33
21. U
69.8
24. U
30
270
1330
b
T
0.60
57.0
125
8"1.!)
1.4
96
60
8.b
102.7
7.3
29
0.1
0.07
79/04/11
0930 UUUO
1.0
33. U
o.u
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fa
133U
6
6
0.00
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74
2-16
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
2-5' • . .-.
DETERMINATION OF SAMPLING PATTERNS OVER TIME
The frequency of sampling is important in any monitoring program
description. This technique tabulates the number of observations
in each season or defined time period for selected parameters.
TECHNIQUE:
Use the Water Quality File retrieval
program MEAN with the variable date group
keyword to summarize data by season; use
the statistical function "number of
observations".
DATA REQUIREMENTS:
Enter appropriate data selection and
station identification keywords to define
geographic area and time period of
interest. Up to 10 parameter codes can be
used per retrieval.
OUTPUT:
For each station retrieved, the output will
include station identification information,
plus number of observations stored under
specified parameter codes in each season.
If specified, associated raw data and/or
seasonal statistics can also be printed.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 6.
It is possible to define date groups other
than season (e.g., month or quarter), if
desired.
Outliers may be eliminated by using MEAN
program-specific keywords.
2-17
-------
EXAMPLE:
This example shows the number of
observations for six different parameters
collected at station 030009 in the four
seasons of the year, from the winter of
1976-1977 to the fall of 1978. A yearly
summary for 1977 also is included.
STORET RETRIEVAL DATE 79/OB/27
/TYPA/AMBNT/5TPEAM
DATE TIME DEPTH
FROM OF
TO DAK FEET
76/12/21
VARIABLE NUMBER
77/03/20
77/03/21
VARIABLE NUMBER
77/06/20
77/06/21
VARIABLE NOHSEH
77/09/20
77/09/21
VARIABLE NUMBER
77/12/20
77/01/01
YEAR NUMBER
78/01/00
77/12/21
VARIABLE NU*I3ER
78/U3/20
7U/03/21
VARIABLE NUMBER
78/06/20
78/06/21
VARIABLE NUMBER
78/09/20
78/09/21
VARIABLE NUMBER
78/12/20
DOU10
WATER
TEMP
CENT
3.00000
3.00DOD
3.00000
3.0DDOO
12.0000
3.00000
3.00000
2.00000
4.00000
i
00076
TURB
TR8IDMTR
HACK rru
3.00000
3.000DO
3.00DOO
3.00DOO
12.0000
3.00000
3.00000
2.00000
4.00000
00095
CNDUCTVY
AT 25C
1ICROHHO
3.00000
N
3 . 0 0 0 U Ov
3.00000
3.0000D
12.0000
3.00000
3.00000
2.00000
4.00000
00060
STREAM
FLO'V
CFS
3.00000
3.00000
3.0000C
3.00000
12.0000
3.00000
3.00000
2.00000
-1^00000
030009
42 38 53.3 086 11 53,0 2
IG/L SU
3.000BO 3.00000
3.DOOOO 3.0000D
3.00000 3.00000
3.00000 3.0000U
12.0000 12.0000
3.00000 3.00000
3.00000 3.00000
2.00UUO 2.00000
4.00000 4.00000
2-18
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
2-6
PLOTTING LOCATIONS OF MONITORING SITES
This technique produces a reference map of all STORET stations
maintained by a given agency or in a specified geographic area.
The output helps identify any gaps or redundancies in a
monitoring network, and could be used to illustrate the
monitoring program description normally provided in a State's
305(b) report.
TECHNIQUE:
Use the Water Quality File retrieval
program LOG; specify labeling of stations
with a cross-reference number.
DATA REQUIREMENTS:
Enter station identification keywords to
define the geographic area and/or agency of
interest.
OUTPUT:
A map of the area specified will be
printed, with tags showing sampling station
locations, and a listing of station
information corresponding to those tags.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Sections 6 and 7.
In order to achieve sufficient resolution
of station symbols, it may be necessary to
make separate retrievals for portions of
the area of interest.
The NOPAR retrieval program may be used to
screen stations before printing, based on
user-specified sampling criteria.
2-19
-------
EXAMPLE:
This map shows the locations of monitoring
stations maintained by the State of
Michigan within a specified latitude/
longitude polygon. Tags on the map
correspond to station descriptions listed
by the LOG program, as shown on the next
page.
PROTECTION BGENO
STORE! SYSTEM
ru JSOOOO OR 3.9S N 1 1« /INCH
1 4Z M6 SS MiN
2-20
-------
This listing is the first page of station
information corresponding to the map tags
on the preceding page. For each station,
the data provided includes agency code,
station code, latitude and longitude, a
brief location description, State and
county codes, county name, and State name.
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790002
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CASS RIVER AT STUDOR ROAD.
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SACINAU RIVER AT COURT ST FRIDGE
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CASS R. AT RCDAfWER RD.
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CASS R. AT FAVETTE STREET
CASS R. DHNSTR BRIDGEPORT MNTP
CASS R. DHNSTR FRANXENNUTH NNTP
SNIAHASSEE R 100TD UPSTR FLINT H
FLINT RIVER AT FERGUSON BAYOU
SACINAM RIVER AT KICKS PARR
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N.6R. CASS R. AT H-81 OR.
CASS R. AT HURDS CORNER ROAD.
PERRY CREEK AT ORMES ROAD.
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SAGINAM CO.
SACINAH CO.
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SACINAM CG.
SAGINAM CO.
SACINAM CD.
SAGINAM CC.
SAGINAM CO.
SAGINAM CD.
SAC1MAH CO.
SACINAN CO.
SACINAN CC.
SACINAM CO.
SACINAM CD.
SAG f NAM CO.
SAGINAM CO.
SACINAN CO.
SAG1NAH CO.
SANILAC CO.
SA.NILAC CO.
SANILAC CO.
SANILAC CO.
SANILAC CD.
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SANILAC CD.
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SANILAC CO.
7USCOLA CO.
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i
Ml
MI
Ml
SACINAM
SAGINAM
Ml
HI
HI
HI
Ml
HI
MI
MI
HI
HI
MI
HI
HI
NI
Ml
HI
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HI
HI
NI
MI
Ml
HI
HI
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HI
HI
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2-21
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
2-7
RETRIEVAL OF INTENSIVE SURVEY INFORMATION
This technique permits the analyst to list all intensive surveys
stored in the system that meet user-specified criteria for
geographic location, date, parameters measured, and/or other
important factors. It can provide a specific breakdown of
intensive survey activity in a limited area of interest or a
broad, nation-wide review.
TECHNIQUE:
Use the Intensive Survey Directory File
retrieval procedure ISFIND.
DATA REQUIREMENTS:
In response to system queries, enter
appropriate criteria to characterize the
intensive surveys to be retrieved. Valid
retrieval criteria include geographic
location, date, parameters or groups of
parameters measured, survey purpose,
pollution source or problem evaluated, land
'and/or water use, water body type, sample
type, and source of funding.
OUTPUT:
For each survey that meets specified
retrieval criteria, a list of intensive
survey numbers will be printed;
corresponding agency and station codes may
also be listed, if desired. Intensive
survey numbers are in the form yyssnn,
where yy is the year in which the survey
was begun, ss is the FIPS State code, and
nn is a unique two-digit number within the
State and year.
DOCUMENTATION:
Part WQ, Appendix G.
NOTES:
This technique retrieves only descriptive
intensive survey information stored using
the new storage procedure ISDESC.
Parametric data for the stations identified
using this procedure may be retrieved using
standard Water Quality File retrieval
programs.
2-23
-------
Until this new capability has been fully
implemented, data available for application
of this technique may be limited.
EXAMPLE: When this capability is operational, the
analyst will be able to request, e.g., a
listing of all intensive surveys conducted
in the State of Michigan for the purpose of
assessing eutrophication problems. Output
would be a list of the unique numbers
associated with surveys meeting those
criteria, with or without agency and
station codes.
2-24
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
2-8
SUMMARIZING MONITORING ACTIVITIES
This technique can be used to tabulate the number of stations
added to and deleted from a monitoring network within a specified
time frame, as well as the numbers of observations and numbers of
samples collected during that time. In addition, summary
information is provided about each parameter sampled. The output
can be used to give a broad picture of an agency's sampling
activities without necessitating the printing of information for
each individual station.
TECHNIQUE:
Use the Water Quality File retrieval
program INVENT or INV120; request that
individual station summaries be suppressed.
DATA REQUIREMENTS:
Enter appropriate station identification
and data selection keywords to define the
geographic area and time period of
interest.
OUTPUT:
The number of stations added to and deleted
from the monitoring network specified will
be tabulated, as well as the number of
observations and samples collected and the
period of record for stations deleted
during that time frame. A second table
summarizing the parametric data collected
will also print.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 6.
The example shows output from the program
INVENT. If the INV120 program is used, the
two columns listing the coefficients of
variation and standard errors will not
print.
2-25
-------
EXAMPLE:
These two computer printouts summarize the
monitoring activities conducted by the
State of MichiganXover the entire period of
record.
<1960
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
TOTAL
STA BEG
58
24
11
11
24
31
20
204
366
557
65
145
215
168
460
481
289
230
158
102
1
3620
STA END
11
4
0
7
2
13
0
44
68
52
31
124
136
406
438
757
535
326
214
332
120
3620
1 OF OBS
4033
2311
1153
1314
7994
8268
7830
12773
25453
31913
52073
58707
69899
79734
94247
138528
77327
63923
69375
55919
8706
871500
I OF SAMPLE
940
676
287
344
798
1130
1016
2166
3447
6135
5105
5625
6362
6726
7117
9579
5051
3485
4362
3047
455
73853
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0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
END-PERIOD OF RECD IN VRS
<.5
7
4
0
1
0
13
0
40
61
32
22
24
51
101
328
360
245
216
135
98
1
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0
0
6
2
0
0
4
5
17
5
27
20
25
35
63
113
12
23
42
5
405
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3
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0
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0
0
0
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2
3
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65
280
75
334
177
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56
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54.6250
10.5300
19.5750
31. 151)3
3.33V92
10 . 14!>7
95.7600
46.62H2
7.64470
197.363
. 900000
1568.07
13.UOUO
2634 . b3
857 . fils
12.6619
10.7557
7 , 42b6'J
90.0069
23.5612
9.00000
477.501
700.1)00
7603.08
8.45967
2.79743
1.02657
1B.70W
8.55997
4. 63V 50
4.28145
.137531
7»93
.055110
.B6V27b
.49J128
.0606/2
41.20J4
7.29421
1.43496
1.7983B
i.90088
2.U8681
.79736B
L. 28166
.425523
2.73527
.916134
.361877
.577191)
.214111
8H. yl 64
.S63537
.37062?
.82 9503
1.36256
.657413
.177212
2.13933
2.08168
STAND ER
6.60016
230.616
.036669
9.57726
6.68214
4.49970
.062060
.089455
.18*313
14.3BB7
. J3bl 36
.U5723*
.971162
.034644
D63.470
151.348
43.S370
. 27fil4ti
.741632
.150276
2.J8B36
. b7.&236
1.914B5
8.3U18
1452.40
.1)16177
.250551
.070012
10.6304
.10E479
,b07672
.242137
.043023
.800034
.624544
.581537
6808.67
MAXIMUM
200.000
.000000
16144.0
396.000
74.0000
30.0000
30.0000
36.5000-
24.4000
43.6000
1100.00
100. UOU
BO. 01)00
360. UOU
.yfiuoon
4200000
13.0000
239000
•nso.ua
680.000
2b0.uOn
410.000
720.001)
400. UOI)
12.UOOO
20(1000
700.001)
25BOO.ll
3(1.0000
6.201)00
1.70000
260000
25.0000
7.80000
79.2000
.900000
75.0000
24.5000
2600.00
660000
MINIMUM
i.cnooo
.000000
5.00000
.nooooo
30.0000
.330000
9.00000
.^Qt)E+01
.000000
.0000110
4.UOUOO
.000000
.000000
.000000
.840000
.UOuOOO
13.1)01)0
.UOODOO
1.40UOO
.000000
.110000
.100000
6.00000
.000000
4.110000
7.101)00
700.1)00
71.0000
.oooooo
. bOOOOO
.700000
.000000
l.UQODO
2.JDOOO
.bOOOOO
.050000
1.70000
11.6000
.000000
200.000
BEG DATE END DATE
71/07/13 71/09/09
74/12/12 74/12/12
69/08/12 76/09/22
20/06/25 79/05/08
74/05/50 74/05/50
74/05/50 74/05/50
74/05/50 74/05/SO
20/U6/25 73/09/^7
74/02/06 75/12/05
71/07/Ul 78/05/02
66/04/15 58/jJ/lO
20/06/25 79/03/il
20/OS/i5 76/07/29
20/06/25 76/06/24
74/02/20 74/U2/20
66/04/2S 70/U9/JO
71/06//2 71/06/22
55/uS/jl 7B/09//6
73/11/20 76/09/26
67/07/10 74/11/19
Vj/ll/oS 75/UG/jO
V3/09/26 79/04/17
50/07/06 79/05/08
61/07/10 79/04/16
72/03/U2 72/U3/U3
61/00/<4 79/01/17
75/07/23 75/07/23
73/06/22 73/08/22
50/07/07 79/05/08
V6/07/20 76/08/17
76/09/15 76/09/15
59/07/28 79/1)4/17
73/D7/02 74/09/04
60/08/29 60/08/29
74/07/08 78/08/23
67/12/20 70/09/17
76/07/20 76/09/15
78/05/23 78/05/23
63/03/11 79/04/17
73/08/22 7B/CI1/24
2-26
-------
MANAGER'S GUIDE
TO
STORET
CHAPTER
3
EXISTING WATER QUALITY
AND
HISTORICAL TRENDS
-------
CHAPTER 3
EXISTING WATER QUALITY AND HISTORICAL TRENDS
In 1972, the Federal Water Pollution Control Act set as national
goals the achievement of "fishable/swimmable" water quality by
1983 and zero discharge of pollutants to U.S. waters by 1985. To
assess progress toward these goals, Section 305(b) of the Act, as
amended, requires that each State prepare a biennial report to
the Congress that will include, among other things, a description
of the current water quality within the State and an analysis of
the extent to which pollution control programs have been
successful and can be expected to facilitate the achievement of
the 1983 and 1985 objectives.
This type of reporting requires quantitative assessments of both
existing water quality and historical trends. STORET provides the
planner with efficient mechanisms for both types of analyses.
Projections of future water quality, while they will necessarily
be more qualitative in nature, can also draw upon STORET data to
a certain extent.
EXISTING WATER QUALITY
There is no uniform definition of the time period to be
considered in evaluations of "existing" water quality. Analyses
may be based on a minimum of one year's collection of data, so
that seasonal variations will be taken into account, or only on
data collected during critical time periods, so that worst-case
conditions can be assessed. For the biennial 305
-------
might be conducted using only data collected in the immediate
vicinity of point source outfalls or near specified land uses
(e.g., to evaluate the effects of urban or agricultural runoff).
In any case, where assessments of existing conditions are based
on data collected at critical times or locations, selection
criteria should be clearly identified.
Once data selection criteria have been defined, the analyst must
decide what type of data summary best suits his present
information requirements. For 305
-------
TABLE 3-1
PROPOSED 305(B) REPORTING FORMAT
Segment Class! fic»t1on/R«mar*s
01 -Current Appearance of the stream Is
still extremely poor du* to
upstream segment. Class A.
Ol-Projected Natural conditions Influence
violations. Class A.
02-Current Problems are from upstream
segment. Class SA.
irl-rrojtcted insufficient data to fully
evalirata. Segment Is used for
Mater supply. Class SA.
^
1
0
*
0
0
A
0
Rh
5a j
>- Ck
00
J
NU
CS
t
2
C
3
W
CS
I
CS
in— *
££•
at-
i
3
HU
CS
"T
2
CS
u
3
0
HU
CS
— 1
2
D
MU
<
D:
t'
3
3
CS
U
t
3
CS
U
3
D
HU
CS
3
0
cs
Cl
o
sr */?
§3
Ml/I
3
HU
t
3
HU
3
CS
HU
I
2
CS
o
-lot
oo
S3
O t/»
3
HU
CS
u
t
3
CS
2
HU
-
1
->
MU
NOTES TO TAOLE: Ocgrce of problw
3-Kajor: Repeated standards violations or other severe effects (I.e. large algal blooms).
Z-HedaraU: Occasional standards violations or other effects,
1-Mlnor: Some affects but uses generally not Impaired.
0-tlona: Ho noticeable affects.
X- Insufficient information for assessments.
* Unknown
Sources
Point Hon-potnt
^
0
A
0
0
A
0
o
:s
ss
3
CS
T
2
CS
0
A
0
a
Is
II
3
HU
t
1
HU
0
A
0
KJ_
n£
M U
fcSL
£S
0
t
0
0
A
0
3
a—»
X***
P£
ss
J-» */»
o****
x
A
0
3
HU
— )
0
Historical Trends
HU - Municipal U - Urban 0-- Individual t latprovemenl '
} -Industrial A - Aarjettjttre W - Solid Haste Blsposal J, Oegredatlon
CS - Cwnblned Sewer Overflow 5 ~ u , tur8 0 - Construction _) No Change
j I WXlc Modification ' ' ™" (*"™ A ™ Unknown
I
w-
-------
It is relatively simple to normalize STORE! data with respect to
.flow — by requesting that values be reported in terms of
equivalent loads (mass as a function of time, commonly reported
in pounds per day) instead of in terms of raw concentrations.4
PARAMETER GROUPS
AND
1 . Thermal
2. Dissolved Oxygen
Depletion
3. Nutrients
4. Bacteria
5. Suspended Solids
6. Dissolved Solids
7. pH
8. Oil and Grease
9. Heavy Metals
10. Pesticides
11. Other Toxics
TABLE 3-2
AFFECTED BY FLOW, TEMPERATURE
SEASONAL VARIATIONS
Floif Temperature
X X
* X
X X
X X
X
X
X
X
X
X
X
Seasonal
X
X
X
X
X
The normalization of water quality data with respect to
temperature is more complicated. For dissolved oxygen (DO)/
values are best normalized with respect to water temperature by
computing the dissolved oxygen deficit (the difference between DO
saturation and observed DO values). Although STORET does not
provide for this type of arithmetic calculation, the analyst may
retrieve values for both observed DO and DO saturation and elect
to do the arithmetic himself. If the necessary calculations are
'Technique 3-2: Calculation of Equivalent Loads.
3-4
-------
to be performed by computer, STORET output can be obtained on
tape, disk, or punched cards.9
Seasonal variations are best accounted for by restricting a
retrieval to data collected during critical time periods.
Once the data have been normalized as much as possible, it may be
necessary to aggregate and summarize the normalized values in
order to depict trends on the scale required. For instance, for
305(b) reporting, trends are to be reported by parameter group,
within individual stream segments.. If data are available for
more than one station on that segment, or on more than one
parameter in a specified group of parameters, it will be
necessary either to aggregate the data; to choose the station and
parameter that are most representative or for which the greatest
amount of data are available; or to calculate trends for each
station and each parameter, and then compare results to arrive at
the appropriate trend symbol required on the reporting matrix
(see Table 3-1).
Because STORET has such a wide variety of techniques that can be
used for the purposes of trend analysis, a decision also must be
made as to the output format that best suits present water
quality management requirements. It is possible to illustrate
trends in STORET data using statistical summaries, maps, or any
of three different types of digital or line printer plots.*
Depending upon the intended application of the analysis results,
and the amount of data available, the analyst can judge which
output format is most appropriate.
After trends have been measured, it may also be necessary to
determine whether the observed upward or downward tendency is
statistically significant. In many cases, assessments of
statistical significance require analytical capabilities not
directly available through the Water Quality File programs, but
such procedures are available through standard, commercially
available statistical packages like SAS.7
technique 3-10: Formatting STORET Data for Input into SAS
(Statistical Analysis System) and Technique 3-11: Output of
STORET Data on Punched Cards.
•Technique 3-5: Illustration of Historical Trends Using
Statistical Summaries; Technique 3-6: Plotting Trends over Time;
Technique 3-7: Generation of Trend Maps; Technique 3-8:
Plotting Stream Profiles; and Technique 3-9: Linear Regressions
of Concentration Versus Time.
7Technique 3-10: Formatting STORET Data for Input into SAS
(Statistical Analysis System).
3-5
-------
PROJECTIONS
In addition to assessments of existing and historical conditions,
water quality management program requirements may also call for
projections of future water quality. While it is usually not
appropriate simply to extrapolate historical trends into the
future, historical data can still be used to advantage in this
type of analysis — either to provide a baseline from which
qualitative predictions can be made, or to show how developments
similar to those expected in the target area have affected water
quality in other, similar areas. Where plans have been made for
land use changes, hydrologic modifications, water quality control
measures, or other pertinent developments, historical data and
engineering expertise can often be used to estimate what water
quality parameters will be affected, the direction and magnitude
of that change, and where the effects are most likely to be felt.
Because expert judgement plays a large part in qualitative
assessments of this type, the role of the analyst is particularly
critical in this phase of water quality management.
In summary, STORET is'a valuable tool, which allows the water
quality planner to evaluate extensive volumes of data in a
systematic manner. General analyses can be conducted over large
spatial areas, or specific problem areas can be evaluated in
detail. The numerous combinations of spatial and temporal
coverage that can be requested, combined with STORET's
statistical and plotting capabilities, provide the analyst with
an efficient, time-saving mechanism for the evaluation of
historical trends in water quality.
3-6
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-1
TRANSFER OF USGS FLOW DATA TO STORET STATIONS
Flow data collected at USGS gaging stations and stored in the
STORET Flow Data File can be used to estimate streamflow at
established Water Quality File stations. Once flow has been
stored, it can be retrieved using any WQF program that retrieves
data, including programs that calculate loadings.
TECHNIQUE:
Use the Flow Data File retrieval program
FLOSTR.
DATA REQUIREMENTS:
Enter beginning and ending dates, STORET
agency and station codes, USGS station
codes, and weighting factors relating the
flows of the USGS and STORET stations.
OUTPUT:
No printed output is produced. Instead,
flow values are automatically stored at the
designated WQF station, under parameter
code 60. After the weekend STORET update
run, flow data may be retrieved using valid
WQF procedures.
DOCUMENTATION:
Part FL, Chapter 3.
NOTES:
The Flow Data File contains data that have
not yet been verified by Geological Survey
personnel, including all values collected
during the current or immediately preceding
water year. If desired, these unverified
data may be excluded from a retrieval.
Since USGS provides EPA with updates to the
Flow Data File only twice a year, data for
recent months may not be available.
The analyst may transfer flow data only to
stations maintained by his own agency.
3-7
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-2
CALCULATION OF EQUIVALENT LOADS
For purposes of comparison, it is often desirable to normalize
parametric water quality data with respect to stream flow by
calculating equivalent loads. For any samples that include flow
measurements, STORET can calculate and print parametric values in
terms of equivalent loads.
TECHNIQUE:
Use the Water Quality File retrieval
program MEAN. Retrieve data for any flow
parameter and for other parameters of
interest. Request calculation and printing
of loadings with or without corresponding
raw data values.
DATA REQUIREMENTS:
Enter appropriate station identification
and data selection 'keywords to define
geographic area and time period of
interest. Parameter codes for flow and for
the other parameters under consideration
must also be included.
OUTPUT:
Equivalent loads for each parameter
specified will be tabulated, with or
without corresponding raw data values.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 6.
Loadings are reported in units of pounds
per day, picocuries per day, or number of
organisms per day, as appropriate.
Loadings can be calculated using the MEAN,
PLOT or MSP retrieval programs. STORET
cannot calculate or plot linear regressions
of loadings.
3-9
-------
EXAMPLE:
This example shows individual values for
flow, and both concentrations and
loadings for three other parameters, as
measured at station 510014.
/TYPA/AHBNT/STREAM
510014
44 14 54.4 086 19 24. SI 2
NANISTEG R AT MAPLE ST BRDC; CITY UF MANISTEE
26101 MANISTEE CO., MI
MAJ BASIN: LAKE MICHIGAN 1)81900
MIN BASIN: MANISTEE RIVER
21HICH
0000 FEET DEPTH CLASS 00
DATE
FROM
TO
77/10/04
77/11/08
77/12/19
78/01/12
78/02/14
78/03/21
78/04/03
78/05/03
78/06/06
78/07/25
78/OS/09
78/09/12
78/10/18
78/11/07
TIME DBPTH
or
DAY VEET
1115
1330
0820
1030
1030
1325
1140
1050
1355
1145
1210
1250
0940
0820
00061
STREAM
FLOW,
1HST-CFS
2010.00
1920.00
7360.00
2460.00
2010.00
4210.00
2740.00
2140.00
2070.00
1530.00
1730.00
00940
CHLORIDE
CL
MG/L
30.0000
27.0000
20.0000
32.0000
25.0000
36.0000
32.0000
30.0000
16.7000
18.9000
36.0000
26.0000
29.0000
30.0000
00940
CHLORIDE
CL
LB/D
325244
279613
. 1270E+07
331717
390293
726649
443368
192763
211020
297089
242612
03501
BETA
TOTAL
PC/L
4.00000
1.00000
2.00000
1.00000
2.00000
03501
BETA
TOTAL
PC/0
19670.1
18006.5
20599.8
3743.20
31616
FEC COLI
HPM-FCBR
/10BML
410.000
130.000
210.000
140.000
120.000
90.0000
130.000
80.0000
100.000
140.000
400.000
700.000
650.000
450.000
31616
FEC COLI
MFM-FCBR
NO./U
.2016E+14
.61C6E+13
.2520E+14
.7222E+13
.4425E+13
.1338E+14
.5362E+13
.5235E+13
. 7090E-H3
.1497E+14
.2962E+14
3-10
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-3
ASSESSING EXISTING CONDITIONS IN TERMS OF STANDARDS VIOLATIONS
This technique is particularly useful for 305(b) reporting, where
.the severity of water quality problems is to be assessed in terms
of frequency of standards violations.
TECHNIQUE:
Use the Water Quality File retrieval
program STAND; specify the printing of a
violations summary.
DATA REQUIREMENTS:
Enter station identification and data
selection keywords to define geographic
area and time period of interest. Also
enter up to 50 parameter codes and values
for the standards or the criteria to be
used as a point of comparison.
OUTPUT:
Output will include station identification
information for each station retrieved and
a summary of standards violations for each
parameter specified.
DOCUMENTATION:
Part WQ, Chapter.RET, Section 6.
NOTES:
If State standards are not in the same
units as stored data, stored values may be
converted using user-supplied conversion
factors.
Instead of, or in addition to, a violations
summary, it is possible to print a criteria
summary, or raw values on violations and
supporting values, or raw values on
violations only.
To assess the extent of violations in a
stream segment, it is possible to aggregate
data from all stations on that segment and
to print one violations summary for the
entire segment.
3-11
-------
EXAMPLE.* This example shows summary output from
the Water Quality File retrieval program
STAND. Standards violations that occurred
between October 17, 1977 and January 8,
1979 at station 740022 are summarized.
/NTRTHT/1NTAXE/8TREAM
NO or VALUES
HIM
MEDIAN
NO Of V10LB
PMC INT VIOL
MINIMUM VIOL
MIAN VIOL
MAXIMUM VIOL
M2N CRITERIA
MAX CNITBNIA
SUMMARY Or
01045 ' 71900
IRON MCRCURY
ft, TOT HG, TOTAL
UG/L UO/L
c s
IS!. 2 0.1800
(0.0 0.2000
1 0
17. 0.
SSO.O 0.0
9(0.0 0,0
560,0 0.0
STN l.SUNMARY.l
740022 R00230 INSCR1
42 59 13.5 082 25 29.0 1
WATER INTAKE FROM ST. CLAIR R.
26147 MICHIGAN
CITY OF PORT HURON WTP 06 IS
ST. CLAIR RIVER BASIN
21MICH
0033 rtET DEPTH CLASS 00
VIOLATIONS ON SAMPLES COLLECTED PROW 77/10/17 TO 79/01/08
00410
T A IK
CAC03
NO/L
(
tl.lt
79. SO
0
0.
0.0
0.0
0.0
90.00
00400
PH
su
(
«.2SO
I.3SO
9
31.
1,700
1.800
8,906
• lino
32730
PXINOLS
TOTAL
UQ/L
(
1.6(7
l.SSO
4
(7.
1,100
1.429
4.000
1,000
t!
3-12
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-4
GENERATION OF AREA-SHADED MAPS
An area-shaded map can provide an effective visual summary of
geographical variations in existing water quality. This
technique can be particularly useful for highlighting existing or
potential problem areas.
TECHNIQUE:
Use the Water Quality File retrieval
program MSP. Specify that a water quality
map is to be plotted, and that data are to
be aggregated by State, by county, by a
major, minor, or subbasin, or by a
rectangular "cell" defined in seconds of
latitude and longitude.
DATA REQUIREMENTS:
Enter station identification keywords to
define the geographical area of interest
and parameter codes corresponding to the
water quality parameters to be examined.
Program-specific keywords may be used to
establish the time period of interest (if
other than the period of record) and up to
four data value intervals corresponding to
an equal number of shading types.
OUTPUT:
For each plot requested, an area-shaded map
is produced, illustrating spatial
variations of a single function of a single
water quality parameter.
DOCUMENTATION:
Part WQ, Chapter RET, Section 7.
NOTES:
Unless a standard map scale is specified, a
default (probably non-standard) scale will
be selected, based on the size of the area
to be plotted; the maximum size of any
STORET map is 24" X 49".
Up to 25 plots may be specified in a single
retrieval request.
3-13
-------
If the range of data values to be plotted
is unknown, the user may request that the
system supply the appropriate intervals by
dividing the data retrieved into four
groups, each having an approximately equal
number of observations.
EXAMPLE:
This map shows variations in maximum
chromium levels across the United States.
Data were aggregated by county.
SUSPHNCED FRHCTIOM OF
UO/L
3-14
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-5
ILLUSTRATION OF HISTORICAL TRENDS USING STATISTICAL SUMMARIES
Trend analysis is used for various purposes, notably for a
State's 305(b) report. This technique computes yearly
statistical summaries of parametric data and allows comparison of
those summaries to identify trends.
TECHNIQUE:
Use the Water Quality File retrieval
program MEAN, and request that all
parametric data be converted to loadings;
use the statistical functions number of
observations, mean, and standard deviation.
Print yearly summaries for the period of
record and compare yearly means to assess
trends.
DATA REQUIREMENTS:
Enter station identification keywords to
define the geographic area of interest, up
to 10 parameter codes per retrieval,
including one flow parameter, and a
sufficient number of observations for
reliable trend analysis.
OUTPUT:
For each station retrieved, this technique
calculates the number of observations, the
mean, and the standard deviation of
loadings for all parameters specified,
summarized for each year in the period of
record.
DOCUMENTATION:
Part WQ, Chapter RET, Section 6.
NOTES :
It is possible to aggregate data from
several stations if trends must be assessed
in terms of stream segments or reaches, as
recommended in the 305(b) reporting
guidance.
A similar statistical summary technique can
be used for bacteriologic parameters, by
calculating a geometric mean instead of an
arithmetic mean (refer to Technique 5-1:
3-15
-------
Statistical Summaries of Bacteriologic
Data).
MEAN program-specific keywords allow the
user to eliminate outliers.
If sufficient data are available, the
analyst may choose to summarize values
collected during critical periods only.
EXAMPLE:
This example shows yearly numbers of
observations, mean values, and standard
deviations for stream flow and loadings of
eight other parameters as measured at
station 510014. The retrieval was
restricted to observations gathered from
1974 through 1977.
/TYPA/AMB.1 r/S T«EAM
OATU
FRO 4
TO
74AI1/U1
Y.EAK
75/01/00
YBAK
76/01/00
70/Ul/Ol
Y£A1>
77/ul/OO
77/lU/Ul
^EAfc
7o/Ul/UU
THE DEPTH
OF
DAY FEET
MU^llER
^EAN
STAND DEV
•11M3ER
HEA'l
STA>:0 DEV
^'U'l^CS
MEAN
STAND DCV
v3U'-i3Eil
•1EAM
STAND DEV
00060
STREA1
FLCM
CFS
11,001)0
2230.91
356-326
10.UOOO
2660.30
744.605
12.0UUO
3075.42
1340.10
5.UL/UUU
29bO.au
1857.49
00310
BOD
5 CA*
L^/0
11.0000
19*26.0
1000S.O
'lU.OOOO
24000.8
11677,2
ll.uooc
2 1 6d 9 . 8
2S227.7
5.0001)0
1S741.3
1W&.3
U0335
con-
LO^LEVEL
L1/C
1C. 0000
249280
171593
12.0000
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3-16
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-6
PLOTTING TRENDS OVER TIME
Graphical summaries can provide highly effective illustrations of
water quality trends. This technique calculates and plots
loadings as a function of time.
TECHNIQUE:
Use the Water Quality File retrieval
program PLOT; request that loadings be
calculated before plotting.
DATA REQUIREMENTS:
Enter appropriate station identification
keywords, up to 10 parameter codes per
retrieval, including one flow parameter,
and a sufficient number of observations to
show meaningful trends.
OUTPUT:
For each station and parameter specified, a
plot of loadings vs. time (in days) will be
printed.
DOCUMENTATION:
Part WQ, Chapter RET, Section 6.
NOTES:
It is not possible to calculate statistical
summaries (e.g., means or standard
deviations) of parametric data using the
PLOT program; loadings plotted represent
values for individual samples.
Each plot may represent data from one
individual station or an aggregation of
data from a group of stations.
If plots are requested from more than one
sampling station, it is possible to specify
that the same scale be used in each case.
STORET can also plot raw concentrations or
logarithms vs. time (as opposed to loadings
vs. time) if necessary.
If sufficient data are available, the
analyst may choose to plot only those
3-17
-------
values collected during critical time
periods.
EXAMPLE:
This plot shows total alkalinity loadings,
as measured at station 510014 from 1972
through 1977. A slight increase in
alkalinity over that time period is
evident.
STflflET
510014
44 14 54.4 086 19 24.9 2
HfiNlSTEE « BT MRPLE ST BROG: CITY OF MR
26iot MflNisree co.. HI
MflJ BflSlN: LRKE MI CHICRN 081900
MIN BRS1N: MfiNISTEE RIVER
21MICH
OQQO FEET DEPTH CLflSS 00
ZOO 400 800 800 1000 1200 UOO 1800 1800 ZQQC
3-18
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-7
GENERATION OF TREND MAPS
For purposes of illustration, it is possible to generate maps
with appropriate arrows in each latitude/longitude cell,
depicting the magnitude and direction of change in water quality
from one block of time to another.
TECHNIQUE:
Use the Water Quality File retrieval
program MSP; request two separate water
quality maps, each depicting mean values of
the same parameter in the same geographic
area, but for different time periods.
Designate one map "early" and the other map
"late". Use the trend mapping keyword to
combine the two map specifications and
produce one trend map.
DATA REQUIREMENTS:
Enter station identification keywords to
define the geographic area of interest, and
one parameter code.
OUTPUT:
A trend map, with appropriate symbols
plotted in each latitude/longitude cell, is
plotted to show where parameter
concentrations increased, decreased, or
remained the same.
DOCUMENTATION!
Part WQ, Chapter RET, Section 7.
NOTES:
It is possible, using the MSP program, to
plot trends in terms of logarithms or
loadings, and the use of this capability
may be desirable in certain situations.
If data for the parameter in question are
not stored for both specified time periods,
no symbol will print in the corresponding
latitude/longitude cell.
3-19
-------
EXAMPLE:
This map shows trends in observed levels of
a single parameter in the State of
Michigan. Arrows indicate the magnitude
and direction of change, circles indicate a
change less than 10 percent, and blank
spaces indicate insufficient data.
3-20
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-8
PLOTTING STREAM PROFILES
This technique depicts, on a single plot, changes in the value of
a single parameter along a waterway in two different time
periods. Visual comparisons can illustrate trends over time.
TECHNIQUE:
Use the Water Quality File retrieval
program MSP and request a single multiple
station plot. On the left-hand y-axis, plot
mean concentrations for a single parameter
at selected stations and during a selected
time period. On the right-hand y-axis, plot
mean concentrations of the same parameter
at the same stations, but over a different
time period. Specify that the two y-axes
have the same scale.
DATA REQUIREMENTS:
Enter appropriate agency and station code
pairs or river mile indexes to define
geographic area of interest. Also use
appropriate data selection keywords to
define the time periods in question, and
the parameter codes for the parameters of
interest.
OUTPUT:
A single multiple station plot is produced,
comparing changes in parameter values along
a waterway for two different time periods
— one plotted on the left, and the other
on the right, y-axis.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 7.
If river mile indexes (rmi) are not stored
at the stations of interest, the plot can
be made mileage-linear by inserting
appropriate mileages at each station.
Instead of plotting mean concentrations, it
is possible to plot mean loadings (if flow
data are available) or geometric means.
3-21
-------
Any number of stations may be specified but
a maximum of 30 stations can be plotted on
a single page of output. Plots of data from
more than 30 stations will continue on the
following page.
EXAMPLE:
This example shows variations in levels of
fecal coliforms at State sampling sites
located along the length of Michigan's Cass
River. Geometric means of data values
collected prior to 1973 are plotted using
the symbol "X", and geometric means of
values collected during 1973 and 1974 are
plotted using the symbol "0". Note that, in
general, values from the later time period
are lower.
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3-22
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-9
LINEAR REGRESSIONS OF CONCENTRATION VERSUS TIME
This technique calculates and plots a least-squares linear
regression of raw concentrations of a single parameter vs. time
at a single station. Statistical summary information also is
provided on a separate page of output. The regression line can
be an effective visual representation of water quality trends,
and accompanying statistics provide valuable supporting evidence.
TECHNIQUE:
Use the Water Quality File retrieval
program REG to calculate and plot a
regression of parameter values vs. time
(Type 1 regression) at a single STORET
station.
DATA REQUIREMENTS:
Enter a single agency and station code pair
for each plot requested. At least 20
observations for the parameter of interest,
collected over a period of less than 20
years, are required.
OUTPUT:
For each regression analysis requested,
this program produces a one-page
statistical summary and one to four pages
of graphic output (scatter diagrams
produced by a line printer). Intercepts of
the regression line are represented by
asterisks (*) and, if a value has been
provided for a quality standard line, its
intercepts are represented by hyphens (-).
Multiple values occurring at the same point
are represented by alphabetic characters.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 7.
It is also possible to calculate and plot
parameter vs. parameter regressions, using
values collected at the same station (Type
2) or at different stations (Type 3).
3-23
-------
A maximum of 10 stations and 10 parameters
may be included in a single retrieval
request.
The REG program plots only raw values;
loadings and logarithms must be plotted
using other WQF retrieval programs.
EXAMPLE:
This example shows variations in chloride
levels from 1975 through 1979 at a single
sampling site. The regression line (drawn
in by the analyst) shows that chloride
levels have declined slightly over that
period.
7.0000 +-
C
H
L
0 S.9250 X
R
I
D
E
4.U500 +
C
L
2.7000 + "—+ X + X + + +
1975. 1976. 1977. 1978. 1979. 1980.
TIME IN YEARS
3-24
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-10
FORMATTING STORET DATA FOR INPUT INTO SAS
(STATISTICAL ANALYSIS SYSTEM)
Although STORET has broad capabilities for routine statistical
analysis and graphical displays, certain types of analyses cannot
be done directly using STORET programs. Most of these functions,
including the plotting of statistical summary information for the
purposes of trend analysis, can be accomplished through an
interface with SAS (Statistical Analysis System). This technique
describes the mechanism used to produce a SAS-compatible input
file that contains data retrieved from the Water Quality File.
TECHNIQUE:
Use the Water Quality File retrieval
program RET to produce listings of raw data
values, including sampling dates, times,
depths, and values of requested parameters;
request that these data be reproduced in a
machine-readable input file on disk or tape
in a format compatible with SAS.
DATA REQUIREMENTS:
OUTPUT:
Enter appropriate station identification
and data selection keywords. Up to 50
parameter codes can be specified.
No hard-copy output is produced. Instead,
this technique produces an input file
containing parametric data on up to 50
parameters for all stations in the Water
Quality File that satisfy the station
identification criteria specified. The
file created for SAS input contains a 305-
character record for each sample, which
includes agency and station codes, along
with composite data and remark codes, if
stored. A 305^character delimiter record
follows each data record. All records are
in a condensed IBM hexadecimal format;
parameter values are expressed in IBM
internal floating point binary format.
DOCUMENTATION:
Part WQ, Chapter RET, Section 7
3-25
-------
NOTES: The same technique can be used to produce
input files in a variety of other formats.
It is also possible to produce machine-
readable input files using output from the
MEAN or MSP programs.
3-26
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
3-11
OUTPUT OF STORET DATA ON PUNCHED CARDS
If the analyst wishes to input STORET data into his own programs,
retrieval output may be transferee! either to an input file on
disk or tape/ as described in the previous technique, or to
punched cards. This technique punches 80-column cards containing
station codes, sampling dates and raw data.
TECHNIQUE:
Use the Water Quality File retrieval
program PUNCH.
DATA REQUIREMENTS:
Enter station identification and data
selection keywords to define the geographic
area and time period of interest. Up to six
parameter codes can be specified.
OUTPUT:
A deck of 80-column punched cards is
produced containing the primary station
number, the date and time of sampling, six
data values, and a sequence number. Missing
values will appear as 9999E-25. The
parameter code is not punched, but
parameter values are punched in the order
specified in the retrieval request.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 6.
Unless otherwise specified, this program
will also produce a printout of punched
values.
Card output from this program is not
interpreted.
3-27
-------
MANAGER'S GUIDE
TO
STORET
CHAPTER
4
POLLUTION SOURCES
AND
CONTROL PROGRAMS
-------
CHAPTER 4
POLLUTION SOURCES AND CONTROL PROGRAMS
The -identification of the point and non-point sources of existing
and potential water quality problems and the development of plans
for water pollution control are key elements of the Federal water
quality management program. Cause and effect analyses of this
nature are required as part of the management processes mandated
by P.L. 92-500, including the implementation of areawide
management plans (Section 208), basin plans (Section 209), and
permit programs (Section 402), and the fulfillment of periodic
reporting requirements (Section 305(b)).
Four interrelated data analysis tasks are involved in the
elucidation of cause and effect relationships in the context of
the water quality management program:
The identification of present and projected water quality
problems, in terms of individual parameters or groups of
parameters
The location and characterization.of potential point and non-
point sources
The establishment of correlations between observed problems
and their probable sources, and
The evaluation of alternative structural and nonstructural
measures for their control or elimination.
IDENTIFICATION OF WATER QUALITY PROBLEMS
From the point of view of water quality control and management,
it is most practical to examine water quality problems in terms
of specific parameters — constituents that may enter a waterway
either as a result of man's activities or as part of a natural
phenomenon. Both current water quality problems and problems
projected under anticipated conditions of population growth and
industrial development must be taken into account in such
analyses.
To a large extent, STORET capabilities for the identification of
problem parameters are covered in Chapter 3. For many purposes,
4-1
-------
assessments of the severity of a given problem are best
illustrated by summarizing standards violations.1
As with all water quality data analysis techniques, the validity
of problem assessments is dependent on the selection of
appropriate time and space scales. Appropriate time scales
should take into account the critical period for the parameter in
question, its relative chemical reactivity, and the nature of the
problem (e.g., long-term accumulations vs. the results of an
accidental spill). Selection of space scales also is dependent
on parameter reactivity and the type of problem under
consideration. I
LOCATION AND CHARACTERIZATION OF POLLUTION SOURCES
Concurrently with the identification of problem parameters, the
analyst should attempt to identify and characterize all potential
point and non-point sources of water pollution. For point
sources, this means identification of all major dischargers,
including municipal and industrial facilities. For non-point
sources, it means identification of dominant land uses in the
area, as well as individual sites that could be associated with
runoff-related problems. In addition, a comprehensive non-point
source inventory requires collection of a wealth of local
meteorologic, geographic and demographic information.
STORET has a variety of storage and retrieval capabilities to aid
the analyst in the identification and characterization of point
sources. Within the Water Quality File, in-plant sampling data
may be stored at stations labeled with a special station type
code.2 In addition, capabilities are available for the storage
and retrieval of NPDES (National Pollutant Discharge Elimination
System) permit conditions, compliance monitoring data, and
discharger self-monitoring data — all under State agency codes
assigned specifically for effluent data storage.' Using a special
STORET command procedure, the analyst may also retrieve permit
conditions and effluent data in any one of four alternative
formats designed for the reporting of permit violations.4
'Technique 3-3: Assessing Existing Conditions in Terms of
Standards Violations.
technique 4-2? Retrieval of In-plant Data.
'Technique 4-3: Retrieval of Permit and Effluent Data.
*Technique 4-4: Generation of Effluent Reports.
4-2
-------
TABLE 4-1
WATER QUALITY PARAMETERS AND POLLUTION PROBLEMS
Parameters
Total dissolved solids
and chlorides
Temperature
Carbonaceous BOD &
COD, total carbon
Organic nitrogen
Ammonia
Nitrite and nitrate
Phosphate
CCE (carbon chloroform
extractables)
Toxic metals and
inorganics
Toxic organics
Bacteria
Viruses
Floating substances
Suspended solids
Color and turbidity
Water Quality & Water Use Problems
Agricultural, industrial and
domestic water supply
Dissolved oxygen; aquatic
balance
Dissolved oxygen; nutrient
Dissolved oxygen; nutrient
Dissolved oxygen; nutrient
Nutrient; dissolved oxygen;
water supply
Nutrient
Water supply; food chain
Water supply; food chain
Water supply; food chain
Water supply; recreational usage
Water supply; recreational usage
Recreational usage
Recreational usage; dissolved
oxygen; nutrient; light
limitations
Recreational usage; light
limitations
4-3
-------
Further information about municipal dischargers is stored in
STORET's Municipal Waste Inventory File (MWIF).» The MWIF
contains extensive information about the facility location, its
treatment processes, projected needs for expansion and upgrading,
and related data on construction grants.
Additional data on water and sewage treatment facilities are
contained in the City Master File.* Because some of the above-
mentioned capabilities for storage ^nd retrieval of effluent data
are relatively new, it may also be necessary to consult data
bases outside of'STORET. Potential sources of this type of
information include, among others, State or Regional automated
effluent data systems, manual files of discharger monitoring ;
reports, and EPA's Permit Compliance System. For specific
references to alternative sources of effluent data, refer to
Appendix C$ Additional Sources of Information.
STORET contains only a limited amount of data applicable to non-j
point source assessments. Some meteorologic data are stored in "
the Water Quality File under appropriate parameter codes and at1
stations labeled with appropriate station type codes.7 Population
figures (results of 1960 and 1970 censuses) are stored in the ,
City Master File.* For further, more detailed information, it
will be necessary to consult Federal, State, and/or local
repositories designed specifically to handle data on land uses,
soil types, geomorphology and other non-point source related
subjects.
CAUSE AND EFFECT RELATIONSHIPS
Once problem parameters have been identified and potential point
and non-point sources have been inventoried, the task of
establishing meaningful correlations between the two data sets
remains. As a starting point, Table 4-1 presents a preliminary
list, relating specific groups of parameters to potential water
quality problems and water use impairments. This list is not
exhaustive, but it does provide a general guide for the
development of more detailed analyses.
5Technique 4-5: Location and Characterization of Municipal
Dischargers.
•Technique 4-6: Retrieval of Data on Selected Communities or
Facilities.
'Technique 4-7: Identification of Stations that Sample Weather
Data.
•Technique 4-6: Retrieval of Data on Selected Communities or
Facilities.
4-4
-------
For 305(b) reporting purposes, it is recommended that the analyst
indicate the general type(s) of point or non-point sources
responsible for observed standards violations in each of 11 major
parameter groups. Specifically, the 305(b) guidelines suggest
characterizing point sources as municipal, industrial, or
combined sewer overflows and non-point sources as urban,
agricultural, silvicultural, mining, hydrologic modifications,
individual, solid waste disposal, construction, or "other". (See
Table 3-1.)
The first step in the application of STORET capabilities to cause
and effect analyses might be to examine the Water Quality File
for intensive survey data. Because intensive surveys are often
conducted for the express purpose of defining causative
relationships, identification of surveys conducted recently in a
specific geographic area could avoid major duplications of
effort.'
Once that possibility has been explored, more sophisticated data
analysis techniques may be brought to bear, keeping in mind
fundamental physical, chemical, and biological principles. One
of the most basic of these concepts is the principle of
conservation of mass. Simply put, this principle states that
mass is neither created nor destroyed in any transformation of
matter. When applied to water quality management, it requires
that the total mass of a pollutant input into a water body be
accounted for in any explanation of its subsequent fate.
Application of this principle can help the water quality analyst
evaluate the impact of a pollution source. Theoretically, the
difference between the mass (equivalent load, usually expressed
in pounds per day) of a specified parameter measured upstream of
a major point source and the mass measured downstream should .be
equivalent to the mass discharged by the point source. A
demonstration of that type of equivalency can indicate that no
other significant source of the parameter in question is acting
within the selected ranges of time and space.
Appropriate time and space scales for this type of analysis vary,
depending on the reactivity of the parameter to be measured, as
illustrated in Figures 4-1 and 4-2. For conservative parameters,
such as total nutrients, the data to be evaluated can be
collected at reasonable distances upstream and downstream of the
point source, as depicted by points A and B in Figure 4-1.
Substances that may generally be considered conservative include
total nitrogen, total phosphorus, total dissolved solids, and
some heavy metals. For highly reactive substances, such as fecal
coliforms, data should be collected as close as possible to the
point of .discharge, as indicated by points C and D in Figure 4-2.
•Technique 2-7; Retrieval of Intensive Survey Information.
4-5
-------
w
u
o
H
<
-------
Parameters generally regarded as reactive include col iform
bacteria, biochemical oxygen demand, and ammonia.
Figures 4-1 and 4-2 illustrate typical water quality profiles of
parameter concentration vs. river mile for multiple point sources
of conservative and reactive parameters. Figure 4-1 shows the
additive input of multiple point sources of a conservative
substance, while Figure 4-2 shows the fluctuations in parameter
concentration expected after multiple inputs of a reactive
substance.10 This type of plot can help pinpoint the source of
observed problems and the location of that source in relation to
problem areas on the waterway.
In the generation of such a plot, the averaging of data over
various spatial and temporal scales may be desirable, and STORET
allows the analyst considerable flexibility in that area. Data
averaging minimizes random data variability and can provide a
better overall picture of water quality trends over time and/or
space.
Data retrieved for this type of analysis can also be used in
combination with effluent data to estimate the concentration of a
discharge constituent in the immediate vicinity of the outfall.11
This value can be estimated using the following formula:
CQ - CuQa + W
Qu + Qw
Where:
C0 « in-stream concentration at the outfall
Cu = upstream concentration
Qu » upstream flow
Qw « waste stream flow
W = mass discharged
C0 should represent the maximum concentration of the parameter
in question in that portion of the receiving stream. The
relative contribution of the point source to the overall
pollution problem can be inferred by comparing Cu to C0.
4-1: Use of Multiple Station Plots to Assess Cause
and Effect.
^Technique 4-3: Retrieval of Permit and Effluent Data.
4-7
-------
EVALUATION OF CONTROL ALTERNATIVES
Once the probable source of an observed water quality problem has
been determined, it is appropriate to consider alternative
structural and non-structural plans for its control. Such
evaluations may be based strictly on the informed judgement of
experienced personnel or, if enough data are available, on the
application of more quantitative techniques, such as mathematical
water quality modeling. Mathematical modeling can provide the
water quality analyst with a firm technical basis for assessing
alternative pollution abatement programs. Using estimated future
loading conditions, models can be helpful in classifying segments
as water quality or effluent limited, as required under Sections
208 and 303(e), and in developing wasteload allocations in those
segments classified as effluent limited.
The complexity of mathematical models can vary from simple desk-
top calculations to complex three-dimensional, time-variable
eutrophication models. The acquisition of prototype data for
model calibration and verification is essential to the
development of a valid mathematical water quality model, and the
amount of prototype data required is usually directly
proportional to the model's complexity. STORET can provide the
analyst with a valuable source of this prototype data and can
help evaluate the adequacy of existing data to develop a given
model.
In the absence of sufficient data to develop a mathematical
model, alternative proposals for water quality control must be
evaluated qualitatively, using experience and technical
knowledge.
Once the relative effectiveness of various structural and non-
structural controls has been assessed, it is still necessary to
put feasible alternatives in a cost-benefit context. Because
STORET contains no information on construction costs or related
data, this type of evaluation must use data obtained from other
sources. Once again, the role of the water quality analyst in
this process is critical.
4-8
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
4-1
USE OF MULTIPLE STATION PLOTS TO ASSESS CAUSE AND EFFECT
This technique produces plots of concentration vs. river mile for
selected parameters and stream segments. Cause and effect
relationships can be inferred by correlating peaks in plotted
values with the locations of point or non-point sources.
TECHNIQUE: Use the Water Quality File retrieval
program MSP to generate station- or
mileage-linear plots of mean concentrations
for parameters of interest. Indicate that
each parameter should be plotted on a left-
hand axis, to avoid plotting two parameters
on the same graph.
DATA REQUIREMENTS: Enter agency and station codes and, if
possible, relative mileages for each
station to be retrieved (or river mile
index for the segment of interest). A
maximum of 50 parameter codes may be
specified.
OUTPUT: One mileage- or station-linear plot of mean
concentrations will be produced for each
parameter requested.
DOCUMENTATION: Part WQ, Chapter RET, Section 7.
NOTES: It is also possible to plot mean loadings
or geometric means, if desired, but the
maximum number of parameters that may be
plotted would decrease to 10.
Any number of stations may be specified,
but data from a maximum of 30 stations can
be plotted on a single page. If more than
30 stations are retrieved, the plot will
continue on subsequent pages.
4-9
-------
EXAMPLE:
.This plot shows variations in mean
chloride levels as measured at State
stations along the length of Michigan's
Cass River. Stations are plotted in
upstream order. For purposes of
illustration, the analyst could insert
appropriate symbols along the x-axis to
indicate the locations of waste treatment
plants.
STOKt! MSHN Gil/23/79
MULTIPLE SU1IDN Kill (MSP)
FfiOH 670614 TO J'jDJll
24 31 «(,
PIC I NO. 1
LIU Slbt
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*.3CI«01
4-10
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
4-2
RETRIEVAL OF IN-PLANT DATA
Stations that monitor water quality within man-made facilities
(e.g., treatment plants or industrial sites) are labeled with the
station type code PIPE. This technique extracts data on all PIPE
stations located in a particular geographic area. A comparison
among data collected at in-stream stations upstream and
downstream of the facility and data collected at these PIPE
stations could help establish cause and effect relationships.
TECHNIQUE: Use the Water Quality File retrieval
program ALLPARM; extract all stations
labeled PIPE; request printing of raw data
and station descriptive paragraphs.
DATA REQUIREMENTS: Enter appropriate station identification
and data selection keywords to define the
geographic area and time period of
interest.
OUTPUT: For each station meeting the selection
criteria, the following information will be
printed: station identification
information, a descriptive paragraph (if
stored); and tabulations of raw data
collected during the specified time period.
DOCUMENTATION: Part WQ, Chapter RET, Section 6 (ALLPARM
Program).
Part WQ, Appendix F (Station Type Codes)
NOTES: Additional station type codes may be stored
at PIPE stations to indicate at what stage
of treatment the sample was taken and what
kind of facility is involved.
Not all general retrieval keywords are
valid with ALLPARM; refer to the STORET
User Handbook for details.
4-11
-------
EXAMPLE:
Appropriate combinations of station type
codes may be used to further restrict
station retrievals to outfalls.
The ALLPARM program output below includes a
descriptive paragraph and the first page of
raw data for station DETWWTP020.
/TYPA/MUN/TpEATD/OUTFL/PIPE
OETMXTPOPO
42 16 3J.4 «3 T6 }e.~ I
DETROIT HI
26163 MICHIGAN
GREAT LAKES-ST.LAkfFt.NCF.
LAKE EC IE
115GLRES 770412
COOO FEET CEPTH CLASS '0
INSCRIPTION
THIS DATA IS FROM SELF MONITORING REPORT FILED BY TOT CITY Cr
UMTD FDR NPOES PEWIT? HK0228C2. IT HIY NOT REFLECT F INAl .OFFICIAL .
REPORTS TO THE PERMIT ISSUING AGENCY.DUE TO THE LANGUAGE OF THF PEFP1T.
ITS PURPOSE IN STORET IS INFORMATIONAL iNO 5TATISICAL. PIFASE CONTACT
B.M.BUCKLEY AT 3)3-226-7611 FDR NODE INFDFMATION. fSI. 4/1/77
THIS SAMPLING POINT REPRESENT AN ARITHMETIC COMPOSITE OF INO INFLUFKS.
/TYPA/MUN/TREATO/OUTFL/FIPE
PARAMETER
C0310 BOD
CO* 00 PH
C053C RESIDUE
00535 RESIDUE
COS 50 CIL-CRSE
C0665 PKOS-TOT
C-OT20 CYANIDE
CO? 16 CALCIUM
00927 MCNSIUM
CO»29 SODIUM
C0940 CHLORIDE
(1002 ARSENIC
C1007 BARIUM
01027 CADMIUM
CIO 3? CHROM1UH
01034 CHROMIUM
CIO42 COPPER
01051 LEAD
01055 MANCNESE
01067 NICKEL
C1092 ZINC
C11O5 ALUMINUM
3161* FEC COLI
3JT3C PHENOLS
SOOSO CONDUIT
71900 MERCURY
74010 IRON
5 OAT
TOT NFLT
VOL NFIT
Tflf-SlIlT
CN-10T
CA-TOT
MC.tOT
Hi, IDT
a
AS .TOT
SA.T01
CO. TOT
HEU-VAL
CD .TOT
CUttOT
Pl.TOT
P*
Ml .TOTAL
IN. TOT
Al.TOT
XPNECMFO
TOTAL
FLOW
HE. TOTAL
n
HC/L
SU
MG/L
NE/L
MB/I
MC/t f
MG/l
HC/L
HC/L
«6/L
NC/L
tie /i
UG/L
UG/L
UG/L
UC/L
UG/L
UC/l
UG/l
UC/L
UC/L
UC/L
/100ML
UG/L
MCD
UG/L
MC/L
NUWfR
7*3
808
818
774
602
791
7*4
117
117
117
799
122
111
to?
5
BIB
817
709
tlT
816
8|0
1D9
8J5
BOS
912
$08
846
MEAN
69 .5233
7.3033K
122. *«
70.S543
26.1907
3.41779
.060345
»9.3298
18.513?
114.850
165.972
7.24672
241.962
19.8072
S6.DOOO
236.846
265.716
121.400
2(3.162
387.696
9*7.469
1347.39
363.536
143.227
918.261
.44Z5I4
13.8632
OETMHTPC20
42 16 35.4 C«3 06 28. C 1
DETROIT Ml
26163 MICHIGAN
GREAT LAKES-ST.LAMRFNCE
LAKE ERIE:
IISGLRF.S 770413
0000 CLASS CO
VARIANCE
5*4.565
.113054
2263.78
683.405
211.270
1. 482M
.004232
392.615
23.4878
T5B1 .»
9010.?*
57.7eco
B757.t6
930.015
173D.OO
11803.3
11295.8
7230.55
134S3.9
I81ZV.2
185293
565670
1363255
9648.01
34299.2
.176609
38.5478
STAN 06 V
23.7606
. 33623$
47.5T92
26.1420
14.5351
1.21772
.065056
19.8145
4 .84642
»7.073B
»4.9239
7. 601 32
93.5834
J3.0J71
41.5933
108.643
106.262
85.0326
116.170
134.649
430.456
752.111
11*7.59
98.2243
165.200
.420249
6.2T669
COEf VAR
.341764
.C4*03P
.3F8934
.3TC523
.554974
.3562C7
1.C78C7
.333972
.2617(3
.75R15f
.'71925
I.C48«3
.38*737
1.22411
.742737
.4567Cf
.3999«--
.7C0431
.410084
.347295
.449577
.5«*20C
3.2 11 7S
.685796
.201686
.949685
.447852
STAND ER
.843763
.011629
1 .66357
. 939655
.513253
.T43297
.002385
1 .831*5
.44IX?2
9 .C49W
3.35P16
.688191
t.?8255
.6T9412
16.6(11
3.79862
3-71*33
3.19347
1C .7353
4.71351
1'.1247
72.P39I
40.6501
3.4619f
6.I326C
. l 18646
.217347
MAIINUH
173.100
8.30000
48?. 000
192, "00
K9.000
12.230C
1.02000
142. OCC
2».7"OC
700. OOC
978. OOO
34.0000
600.000
47S.r:00
100.000
820.000
890.000
14-0. OC
teo.ooo
1960.00
7200.00
5300.00
24000.0
RCO.OOC
1447. CO
6.61000
4J.9/100
MINIMUM BEf DATE
11.0000 75/C1/C1
6.50000 75/OI/f 1
18. COOO 75/Cl/Cl
7.00000 7S/01/C2
2.4COOO 75/01/1 1
.200000 75/01/C1
.010000 75/01/d
14.5(00 75/OI/?<
1.6-000 75/01/2(
54.5OOO 15/C1/21
53.00OO 7
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
4-3
RETRIEVAL OF PERMIT AND EFFLUENT DATA
Special conventions exist for the storage and retrieval of NPDES
permit conditions and/or effluent data from the Water Quality
File. The output of the RET program has been modified especially
for display of NPDES data.
TECHNIQUE:
Use the Water Quality File retrieval
program RET; restrict the retrieval to
stations stored under special agency codes
assigned for discharger and compliance
monitoring data in the State{s) of
interest.
DATA REQUIREMENTS:
Enter appropriate station identification
and data selection keywords to define the
geographic area and time period of
interest. Up to 50 parameter codes may be
specified.
OUTPUT:
For each station (facility) selected,
tabular listings of permit conditions
(including alphanumeric data for number of
exceptions and frequency of analysis,
etc.), followed by actual values, will be
tabulated in order of pipe number.
DOCUMENTATION:
Part EF, Chapter 3.
NOTES:
All of the other Water Quality File
retrieval programs also may be used with
effluent data; if program MEAN is used, the
data will be summarized by pipe within the
facility.
It is also possible to specify retrieval of
more than one composite value type (e.g.,
average concentrations and minimum
loadings).
4-13
-------
EXAMPLE:
This example shows actual composite
values for five parameters, as measured
at three different outfalls (P014, P101,
and P102) from the Western Electric
Company to Little Alamance Creek, North
Carolina. The composite value type is
indicated by a two-letter code under
"time of day": high (HO, low (LC)f and
average (AC). Pipe number appears in the
depth column.
6l(j»£l HLTklEVAL LATE 7»/"7/13
/TSfFA/INt/N'riTMT/OUTFL/PIPE
LATfc
I nOM
1O
7o/ul/0l
i-KV)-
77/iu/tU
Cr (V) ->
77/lOAl
57/11/01
Clr (V) -
77/11/ul
77/12/iil
Ck* (V) ""
77/12/ul
7fc/UlXW-t
Cf i v ) *•
7a/u2/ul
Cf (V) *
7o/l>2/0l
7,,/ui/ul
Ct (vj -
77/lu/ol
Cir (V ) —
77/lu/ul
i-uOlU
•UNb 1JEHH 'AAIER
Of TliKP
OAV Ffcfct CENT
V114
hC 3i.u
tlt/1
AC
LC
uC
Hul
AC
i :
hC
i-Jul
AC
LC
liC
HH
AC
LC
HC
FlUl
AC 1.1
LC 0.0
bC 3.3
Flul
AC 7.1
LC 1.1
ilC 12.1
frlui
AC
LC
HC
UU3UC
\Xi
KC/L
lu.b
0.7
b.4
^.3
9 . 4
b.S
11.2
10. 5
a. 3
11.2
10.1
i.b
1D.B
1U.5
1U.4
10.7
S.2
b.e
K.6
00400
tti
su
7.SU
7.10
0. 7u
7.bU
e.eO
b« 30
7.30
tt . 10
7.BO
ti.iU
7.1u
6. BO
7.90
b.90
7.30
6.70
7.7U
7.4U
7.10
ti.20
Jlblb
FEC CCLI
MFK-FCUd
/10UML
40
bU
lio
4u
liU
60
CO
su
au
20
6D
IK
40
NCOOU33U1 I31b3tjotu J2o3wwuli 17ul
3b 03 10. 0 07;> 37 4g.u i
HESTEnN tLECTnIC CO
370bl NC
KECVG S1KK - LITTLE ALAi-.AJ.Ct Ck-GUlL o'il-
EPKC 751230
U9!)!( FLE1 iitPTii CLAao Uu
SOUSU
CONLUIT
FLOh
HCD
O.U26
U.UOb
O.Oiu
4-14
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
4-4
GENERATION OF EFFLUENT REPORTS
If NPDES permit conditions and parametric effluent data have been
stored in the Water Quality File for the discharger in question,
this technique can be used to retrieve those data and to
summarize the number of permit violations.
TECHNIQUE:
Use the STORET command procedure named
EFFRPT, with print option 3, to obtain the
maximum amount of detail.
DATA REQUIREMENTS:
Enter agency and facility (station) codes
for the discharger(s) in question.
Beginning and ending dates may be
specified, if required.
OUTPUT:
For each facility specified, all parametric
effluent data and permit conditions will be
tabulated by pipe number and by reporting
date^ithin pipe number. Data will include
(if stored) high, low, and average
parameter concentrations; high, low, and
average loadings; number of exceptions,
frequency of analysis, and sample type
(grab or composite); and number of
violations, if any.
DOCUMENTATION:
Part EF, Chapter 3.
NOTES:
There are three additional print options
with the command procedure EFFRPT: a
summary of violations by facility, a
summary of violations by pipe, and a
tabulation of all data available for
parameters in violation only.
If no data are reported for a parameter
that is subject to permit conditions, a
question mark (?) will appear instead of a
number of violations.
4-15
-------
EXAMPLE:
This technique is valid only for data
stored under agency codes beginning with
the letters "EF".
This example shows permit conditions and
DMR (discharge monitoring reports required
by the NPDES program) values for seven
parameters, as measured at discharge pipe
no. 102 from the Western Electric Company.
** STOUT EFFLUE1T
•EPD«T
FACILITY NC0003301 NESUR1
DISCH**tE PIPE ID
«po*riii« »mt
•tltNEIEK
CODE
09119 9DD "IT
* OUT t»S/L 31«
03«03 PH ><«T
SU MR
03400 RESIDUE "It
tmi 16/L 51«
OSS 30 RESIDUE PM
13r 1FLT HC/L 0«»
OJM* TDT 1 1
KEPO»THE 0«Tcl
CDD£
09910 BOD P).} .- — -. 1/7 Cp
219.0 «S9.9 799.3 *- — — 1 VI3KII3NIS1
i.OO — 9.39 -- -- -- 1/7 CP
7.10 7.4} S.?0
>79 »73 913
12B 159 199 -- " — I VMlMiaMi)
9.»00 11.109 IZ.I99
9.9*9 "- — -- I/I S"
9.90S 0.02k 9.J49
" — »39 — •" •- 1/»0 }d
NO REPORTED 3»t«
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
4-5
LOCATION AND CHARACTERIZATION OF MUNICIPAL DISCHARGERS
Data from STORET's Municipal Waste Inventory File {the "245"
file) can be used to determine the location of sewage treatment
facilities and outfalls in a particular area of interest and to
obtain other types of facility information.
TECHNIQUE:
Use the job control language listed in the
STORET help data set named RETMUNJ. Using
the appropriate control statement, specify
that data are to be retrieved by State and
county; by State and major/minor basin; by
State and community; by State only; by
major/minor basin only; by enforcement
area; or by State, agency and storage date.
Request that output be printed in a format
that provides descriptive information on
each facility and its discharges.
DATA REQUIREMENTS;
Enter appropriate codes to define retrieval
criteria and output format.
OUTPUT?
For each facility that satisfies the
retrieval criteria, descriptive information
on that facility and its discharges will be
printed. If a specific piece of information
was not available at the time of data
entry, the letter "X" will appear.
DOCUMENTATION:
Part MWIF, Chapter 3.
NOTES:
The Municipal Waste Inventory File contains
no historic parametric data.
The example shows only one of a series of
14 alternative output formats, all of which
are based on information contained on EPA
Form 245. Refer to the STORET User Handbook
for descriptions of all possible formats.
4-17
-------
EXAMPLE: This example summarizes MWIF data on the
Bessemer waste treatment facility in
Gogebic County, Michigan, including data
on location, type of discharge, and
facility design.
MLu.ICli-AL rtAb'it, FACILITY DATA STCRET SYSTEM
UnAAT W)
1. LOCATION
STAliij (2b) MICHIGAN
MUMlCHrALITY: BESSEMLK
BASIN: (2215 ) 1 7uUt>171(U
StoSA: COUNTY: (053) GOGEBIC
STATE REGION NUM6t,k: CONGRESSIONAL DISTRICT NUMBER: 11
2. FACILITY L1SCHAKGE
RECEIVING WATER; KALLANbEK CREEK TO BLACK RIVER
STATE uISChARGE PERMIT NUMBER:
MULTI-POINT IJliCHArfGii: NO
iNTLKsTATE : NO
OUTFALL TO OPEN WATEft BODY: inC
LAT/LO^G i;iSCHA«Gi: POINT: 4625.23/uy UU3UO
LlS'iANCL OUTFALL FhOm ShCRE:
OUTFALL SUBSURFACE: X
'ErtFOhCEMEiNil CONFERENCE: YES (04o)
GRANTS
FACILITY liE
t-XISTING TREATK£NT: (b4 ) SECONDARY-STANL.RATE TR FL
SERVING COMMUNITY: NO
CENSUS POPULATION: 2, BUS POPULATION SERVED: 2,550
TYPE SEWER SYSTEM: COMBINED
YEAR PLANT BEGAN: 1936 YEAR OF MAJOR REVISION: PL
ESTIMATED ANNUAL COST C&M ($1000): X
AVERAGE CAILY FLOW (MGD)
DESIGN: .550 ACTUAL: .670 % INDUSTRIAL: X
INFLUENT (MG/L)
DESIGN BOD: OU0295 ACTUAL BOD: OOU173
% INDUSTRIAL: X SUSPENDED SOLIDS: X
EFFLUENT (MG/L)
TREATED BOD: 000029 SUSPENDED SOLIDS: 000023
% NITROGEN REMOVAL: X % PHOSPHOROUS REMOVAL: X
ALPHA TREATMENT CODES: SH GH CM FTR NM DCRH BC
REMARKS: ACT SLDGE & P REM PL
4. DATE RECORD REPORTED: APRIL 24, 1972
5. DATE OF THIS REPORT: JULY Itt, 1979
4-18
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
4-6
RETRIEVAL OF DATA ON SELECTED COMMUNITIES OR FACILITIES
STORET's City Master File (CMF) contains unique identification
codes for most cities, communities, water treatment facilities,
and municipal sewage facilities in the United States. Also
associated with each individual community are geographic
information and population data.
TECHNIQUE:
Use the job control language provided in
the STORET help data set named RETCMFJ, and
specify geographic area of interest.
DATA REQUIREMENTS:
Enter a valid City Master File control
statement specifying that data are to be
retrieved by State and county codes; by
State and basin code; by State and city
codes; by State codes only; or by basin
codes only.
OUTPUT:
Output for each community/facility located
in the area specified will include a unique
numerical identification code, community
name, county code and name, Congressional
district, 1960 and 1970 census figures,
population size group codes, basin codes,
latitude and longitude, and study category.
DOCUMENTATION:
NOTES:
Part CM, Chapter 3.
The City Master File is maintained by EPA
Headquarters staff; to add, delete or
change CMF data, contact STORET User
Assistance personnel in Washington, D.C.
No parametric data are stored in the City
Master File.
4-19
-------
EXAMPLE:
This example lists data stored in the CMF
on communities/facilities located in
Gogebic County, Michigan. CMF output also
includes a page explaining the various
codes and abbreviations used in the
tabulations.
MSIER CUT Pitt 01REC10*T — Kit KUAN 1261 IV/OR/2! P»M 1
cim cmmmiir cot CHUN IT
029000001 AN>ll IOCI1IDN 093 COCfBIC
4*
099000001 M»EM* 053 COCtt 1C
44
093100001 ICSJEItR IMP 053 COCEBIC
304100001 IRKIN (UP 093 GOCtBIC
3*0900000 COCmC COUN1Y 093 COCCtlC
< 15 100001 1RUNTI* DIM 053 WjttBIC
*»
476000001 IRONHODU 053 COCfBIC
476100001 IRONHODO IktP 053 COCEBIC
41*100002 IIONMUDO 1»P 093 COCEBIC
974000001 KMENISCti IkP 093 (OCEIIC
*•
H6000001 HAHSAY 053 COCCBIC
44IESSEKR INP
»6IOOOOI kM>A*>ANTU 093 COCEBIC
46
920*00000 THOMSIOrl 093 HtEBIC
969000001 MM t- It I II 093 COCEBIC
976900000 M1ERSKEE1 093 COCEBIC
10111 FOR 51(11 16
1U1AL MMIIR RECORD* IN FILE 16
01 SIR Mil 1460 1410 m CA 0 M Ml SUB LIWC C
11 290 290 10 22 19 462000
•94000
II 1,304 2.109 33 22 19 462654 *
900300 *
11 2,0«3 1.100 32 22 19 4*2000
•4<000
11 *41 949 21 22 19 462000
• 94000
11 24,370 20.676 99 21 14 462000
•94000
11 1.000 1 92 22 15 462000
•94000
11 10.265 6.111 99 22 19 442I12 6
900424 «
11 2.911 2,296 39 22 19 4620OO
•94000
11 22 19
11 B12 619 21 22 14 «6224« 4
• 94416 *
II Iil9t I.06B 12 22 1» 462(12 *
9000OO *
11 9 2 22 19 462000
•94000
11 I 100 22 19 4»IO* C
•99910 C
11 1.231 2.7S7 33 22 19 »62»«2 6
B99994 •
11 I 900 22 12 4*1*0* C
•9104* C
11 •*« III 21 22 12 4*2000
•41000
5TUOT
CI1E6OR1
00002
C0002
00012
00002
00002
00002
00002
00002
C0002
COC02
00002
00002
00002
M •
4t ICCCND 4
•• *
•• S1UOT CATICORYI CODE IfCORO NO.
4* (PII1N1 PUSIIIOM 129-1321
6* INOUSfRIAL IHPLCHENIAIIIM.. OOOO4
•4 HM1CIPAL IMPLCHtNIATION. .. OOOIO
•• imuiiRiu. nisit INVINIOOT. 00040 0901-1199
** HIM DtAlNMf INVENTORT.... 00400
*6
6* « • LAI/LONt UPOA1CO BY 111*! DI1A
•6 . IBLINII - CENtiR at CUUN1T LII/LONC
44 C • LII/IDNG OF CUT CHANCED BY NO OFFICE
44
•• fttfll COUINCI CON1RAC1 AMARO& COOf '
•4 ; * UNDER 499 UNUtt 900
44 2 • SOO - 449 500 - 999
64 « . 9,goo - 9.994 2.500 - 4,999
44 9 . 10.000 - 24.999 5.000 - 9.999
64 * . 25,000 - 49,999 10.000 - 24.999
6* 7 • 90.000 - 99(999 29.000 - 19,949
44 » - 100.000 - 249,994 90,000 - 49.999
44 9 . 290.000 - 494.»»V 100.000 - 249,994
44 0 » 500.000 I UVER 290.000 1 OVER
44 - IB1ANRI CUT ONLf
46 | * HA1ER DISTRICT
44 3 - FIRE. SCHOOL, DIS1RICIS. ETC.
4446444444446*4 446644 666644 446*646464464»466446*444««6«««* 6664
/C/ CITY
/CIIHH 5.ERV DIS1/ lunHUNIM *t*VIUS OJMkiLT
/CURR INSI/ lUIKlllONAL INMI1UU
/ELEfl SCH/ LLENtNlART 5CHOOI
/FMSC/ Mil «IR>S &IN11ACY CISIHUI
/C S1A/ OA5 SIAIItIN
/CIN ilA/ ttNllOIIMC ilATKN
1*1 HADLtT
/H SCh/ ttlLH S1HLUI
/NOUS IUIH/ HOUSING AUIK1M1IT
/HOUS1NC UM/ ^UUSlhb UMT
/IMP OIS1/ IMPOUNDED Dliieill INTIrlNNENI
/Jl AL/1N/ JUIM AUIHU. I1V
/NUN DIS1/ tiAIMIININCf D1S1KICT MDHIIIKIM.
/MUN IUIH/ PU4ICIPIL lUTHUtlll* 0111
/N5&/ MUNICIPAL 5INL-k SlMktl ttlVI!IUN
/NU1 ¥1ft CL'/ Mill UAt UAUIl COHPANT
/PUD/ PMLIC 01 11 III DIJTklCI lljtt/jl
/SSh DliP 01ST/ St«tk CISHUSIL (•ISIUCI il
/iTP/ SSHH. 1REA1PENI PLANT
/iHTP CT«/ 5N3PPINC C(Mt«
/ST HDSP/ 51ATI NO^PIIAL
/iT P>/ 5UII hAKK
/SUBO/ *b6DlHISlUN
l\l 1M«
/IMP/ IIMN&hlP
/Ift PK/ ThllLEf PAK.
/UN 1C AREA/ UMNtl^PORAII; ARIA
/UtIL Olttf UTHIK OISISICI
/VLT fclR IUIH/ VALLEY HAItk AUTHOH111
/VIC1N CfF/ VILIN11V OP
/•/ VILLAI.I
/«» OliP Pll/ irAillMIER 1 ISPtlSAt Pit
/KIR SEN 01ST/ .111. [ SEMEK 015IBICI
/MIR ISSOC/ hAIEfc Ai&OCtAllUN
/UTR 5AK OIST/ NAIEk 5AN11AKT OIS1HC1
/MIR H$/ IfAICR MH!,
I
4-20
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
4-7
IDENTIFICATION OF STATIONS THAT SAMPLE WEATHER DATA
The Water Quality File contains a limited amount of weather data
that can be useful for non-point source assessments. Stations at
which such data are stored should be labeled with the appropriate
station type code(s).
TECHNIQUE:
Use the Water Quality File retrieval
program ALLPARM. Restrict the retrieval to
stations in the geographic area of interest
that are labeled with at least one of the
level 5 station type codes RUNOFF (monitors
stormwater), PRECIP (monitors rainwater),
or MET (monitors meteorologic data).
Request printing of raw data and
descriptive paragraphs.
DATA REQUIREMENTS:
Enter station identification and data
selection keywords to define geographic
area and time period of interest.
OUTPUT:
For each station retrieved, output will
include a descriptive paragraph (if
stored), station identification
information, and raw data on all parameters
sampled.
DOCUMENTATION:
Part WQ, Chapter RET, Section 6 (ALLPARM
Program).
Part WQ, Appendix F (Station Type Codes).
NOTES:
Not all general retrieval keywords are
valid with ALLPARM; refer to the STORET
User Handbook for details.
Other sources of weather data are listed in
Appendix C: Additional Sources of
Information.
4-21
-------
EXAMPLE:
If desired, output may be restricted to
data on specific parameters related to
weather conditions.
This example includes a descriptive
paragraph and the first page of raw data
stored at station CA0101, which was
established for the storage of data on
urban rainfall and runoff quality.
/LNO/PRECIP
CA0101
37 4B 15.0 122 26 43 .C ?
SIN FRANCISCO BAKER SI
06075 CAL1FCRNIA
CALIFORNIA 141?
SAN FRANCISCO BAT EtcIUN
J2CACITH 7(0707
0999 fltl DEPTH CUSS CO
DESCRIPTION
DATA FOR THI! !1 All Oh HERE ASSEMBLED AS PART CF E»A PROJECT 6B-301.496,
ESTABLISHMENT DF IN URBAN RAINFALL-FUWFF-OUAL IT* DATA BASE, f OR fUSTHf
INFORMATION, !EE THE FINAL REPORT BY K.C.HUBER AND J.P.HEANE1. «U*f>AN
RAINFALL-RUNOFF-BUAL1TY OATA BASE-, E,PA-6CO/8-77-C09, JWIY 1977.
/LNO/PRfctlP
PAR AM 1ER
00061
00093
00095
00099
00101
C01G2
1.0103
00104
00129
C0310
00340
Ob* GO
00*10
C0530
00535
005*5
00*52
00610
006*0
31505
31615
JC507
STREAK
SOL IOS
CNOUCTVY
TOUCH T
SOLIDS
SCLIOS
• SOL 1C S
SOLIDS
«£CIP
BOD
COO
PH
T ALK
RES I DUE
RESIDUE
RESIDUE
riL-GRSI
NH3-N
N03-N
TOT COL1
HI COL I
PHOS-T
flOK.
H.OAT
AT 25C
96HRS
X ON
* ON
* OK
x oh
INT LOCI
5 MY
HI LEVEL
CAC03
10T NFIT
VOL NHT
SETTLBLE
10T-HEJN
TOTAL
TOtAl
WN CONF
HPNECNtO
DRTHO
INST-CFS
Ht/L
N1CROHHD
I
74U FlLT
14U FlLT
5U Fill
.45U F1L
IN/HR
HG/L
nc/L
su
MG/L
WC/L
HG/L
Ml/L
HG/L
HG/L
M/L
/100NL
/100ML
MG/L P
NUMBER
4 1
43
47
r>
4 7
47
33
31
274
17
46
IB
32
47
47
47
42
47
47
33.
45
KttH
13.3814
1.932^5
734.636
96.0000
44.6978
27.2S72
6.60303
6.22258
.056167
29 .6470
122.565
6 . 97777
52.4437
Kb. t f3t
51.9149
1.86595
11.9285
1.48170
6.48722
l.COOOO
l.COOCO
.910661
CAC101
37 4B 15.0 122 26 43. 1 i
SAK FRANCISCO BAKER fj
06075 TAHFtRMA
CALIFORNIA • 14i?
SAN FRANCISCO. (AY FEGJO*
22CAC1TY 7BC1C7
(999 CLASS 00
VARIANCE
184.330
6.91264
• 110F.ce
80.00(jC
1270.21
832. 5!"
I2B.131
41.6824
.0074^-
417.369
11900.C
.066862
766.268
4854.39
1088.82
2.5136C
354. 7*8
.737944
16.2736
.oocooo
.cooooc
.467723
STAN OEV
13.5768
2.A7919
332C.71
8.94427
35.6400
28.6533
11.31"fi
6.45619
.086316
20.4296
109. Of 7
.262416
27.7176
69.6735
32.9973
1.5*543
18.8353
• 8KI1036
4.03405
.corOGO
.OCCCOQ
.683537
CPEF VIK
1.C146C
1.36C48
4.52019
.093169
.797355
1.C5T31
1.71429
1.C3754
1.48391
.689094
.690035
.C3J607
.528522
.803793
.6356C3
.849664
l.579vl
.179766
* 62 J 846
.750595
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2.1204
(4{ /94p
484. 37t
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4 ,2l*6f
1.97i47
1.15957
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4.81315
.231259
2.9H35
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7.4 {.CO
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1 .61000 69/C4/1
1.00000 69/14/i
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6V/ 11 /4.5
69/12 /^5
69/1I/C5
69/I1/.5
69/11 /C5
69/11/^.5
69/II/C5
69/1IK5
69/11 /!.$
69/1I/C5
69/1J/-5
69/11/1.5
69/ 11 /<.^
69/11 /-5 -
69/1I/C5
69/11 /<.-!
69/11 1-.-:
69/ll/w5
69/11/V5
69/11/.5
69/11/1.5
4-22
-------
MANAGER'S GUIDE
TO
STORET
CHAPTER
5
BIOLOGICAL MONITORING
-------
CHAPTER 5
BIOLOGICAL MONITORING
The establishment of a comprehensive biological monitoring
program that complements physical/chemical water quality
monitoring efforts is a relatively new emphasis of the water
quality management program. This new direction is reflected both
in the Basic Water Monitoring Program (1978), which proposes a
pilot biomonitoring program, and in the most recent guidance for
preparation of the States' 305(b) reports, which devotes a
separate section to specifications for describing biological
monitoring and reporting on its results.
Because STORET was originally conceived as a data base for
physical/chemical parametric data, its capabilities for storage
and analysis of biological data are limited, particularly where
hierarchical taxonomic information is required. The greatest
volume of biological data contained in the Water Quality File at
this time relates either to bacteria counts or to chlorophyll
determinations. In addition, data on all pollution-caused fish
kills reported to EPA are contained in STORET's Fish Kill File.
BACTERIA
The bacteriologic data stored in the Water Quality File can be
manipulated using any of the standard STORET retrieval programs
and in most of the applications described in the preceding
chapters of this Guide. There is one significant difference,
however, between bacteria counts and values of common physical
and chemical parameters. Unlike other parameters, bacteria
concentrations may vary by orders of magnitude within relatively
brief spatial and temporal spans. For this reason,
representations of trends in bacteriologic parameters are more
clearly illustrated on a logarithmic scale, and statistical
summaries of these data are properly performed on the logarithms
of the stored values (i e>, a geometric mean is preferable to an
arithmetic mean). Several of the WQF programs allow calculations
of common logarithms prior to statistical analysis or plotting,
and this capability should be utilized where appropriate.1
Bacteriologic parameters for which the greatest numbers of
observations are stored include fecal and total coliforms, fecal
streptococci, and total plate counts. The analyst can take
advantage of the relatively large volume of data on these
parameters to perform a specialized type of cause and effect
'Technique 5-1: Statistical Summaries of Bacteriologic Data.
5-1
-------
analysis. It is commonly agreed that the value of the ratio of
fecal coliforms to fecal streptococci is dependent on the source
of the bacteriologic contamination. Ordinarily, a ratio greater
than 4 indicates recent human pollution, whereas a ratio less
than 1 indicates animal, or livestock, pollution.2
Like many other parameters stored in the Water Quality File,
bacteria counts may be entered under any one of several
individual parameter codes, depending on the method of analysis
used, and this variability must be taken into account when
reviewing tabulations of bacteriologic data. For instance, the
membrane filter (MF) technique for assessing coliform
contamination commonly yields lower values than the most probable
number (MPN) technique. Reasons for this discrepancy include the
safety factor built into the statistically-based MPN tables; the
stress induced by disinfection or by discharge of fecal coliforms
into a saline environment; and the stress induced by the drying
process in the membrane filter test.
CHLOROPHYLL
Chlorophyll measurements are commonly available from the Water
Quality File. Frequently used parameter codes in this group
represent levels of chlorophyll "a", chlorophyll "b", chlorophyll
"c", pheophytin, total chlorophyll, and total algae.
Because algae are the primary food producers in aquatic
communities, and because chlorophyll is an indication of the
amount of free-floating alga biomass present in a water body,
chlorophyll is often used as a measure of eutrophication.
Various levels of chlorophyll "a" have been used as water quality
objectives throughout the country, but the acceptable level of
chlorophyll varies considerably, depending on the type of water
body (lake, river, or estuary), water use, and region of the
country (see Figure 5-1). For example, the objective for a rich,
productive fishery like the San Joaquin Delta in California is
much different from the objective for an oligotrophic lake like
Lake Superior (Hydroscience, 1976b). Eutrophication
determinations are discussed in greater detail in Chapter 6.
'Technique 5-2: Using Bacterial Data to Assess the Source of
Fecal Contamination.
5-2
-------
Ul
t
K
U
(MICROGRAMS PER L
POTOMAC
'ESTUARY
1
rn WESTERN
i
o —
-AKE
ERIE
'EUTROI
9Hir"
APPROX.
"OBJECTIVE"
SAN
JOAQUIN
DELTA, C
'EXCELI
ALIF.
ENT"
APPROX.
"OBJECTIVE"
LAKE
SUPERIOR
"EXCELLENT"
APPROX.
1
"OBJECTIVE"
SO
25
PHYTOPLANKTON CHLOROPHYLL o
FIGURE 5-1
COMPARISON OF REGIONAL CHLOROPHYLL
'A" OBJECTIVES
-------
FISH KILLS
The 305(b) guidance specifically states that major fish kills and
other large-scale impacts should be discussed in the State's
report on its biological monitoring program. The requisite ;
information for such a discussion should be readily available to
the analyst in a simple, usable format through STORE!"s Fish kill
File, which contains information on the cause and location of
pollution-caused fish kills, as well as data on the number and
kinds of the fish killed.3
Other sources of information may be necessary for reporting on
species diversity, and for devising other summary reports on
aquatic macroinvertebrates, fish, and shellfish that may be
required for water quality management purposes. At present,
large volumes of biological data are being collected in the field
and stored in manual files or other types of data bases because
of STORET's limitations with respect to biological data. At this
time, these files and others like them are the primary source for
biological water quality data.
^Technique 5-3: Retrieval of Fish Kill Data.
5-4
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
5-1
STATISTICAL SUMMARIES OF BACTERIOLOGIC DATA
Because bacteria concentrations vary by orders of magnitude, data
for those parameters are most appropriately analyzed in terms of
logarithmic values. This technique computes statistical
summaries of the logs of bacteriologic data and allows comparison
of yearly geometric means for the analysis of trends.
TECHNIQUE: Use the Water Quality File retrieval
program MEAN and the statistical functions
number of observations, mean, and standard
deviation. Specify that these functions be
performed on the logarithms of the stored
values. Compare yearly geometric means to
assess trends.
DATA REQUIREMENTS: Enter appropriate station identification
keywords and a sufficient number of
observations for reliable trend analysis.
Parameter codes for up to 10 bacteriologic
parameters may be specified.
OUTPUT: For each station retrieved, yearly
statistical summaries of the logs of the
data collected will be tabulated.
DOCUMENTATION: Part WQ, Chapter RET, Section 6.
NOTES: Statistical summaries may be printed for
individual stations or for an aggregation
of data from a number of stations (e.g.,
all key. stations on the segment in
question).
MEAN program-specific keywords allow the
user to eliminate outliers.
If desired, the data summarized may be
restricted to samples collected during
critical periods {e.g., summer months).
5-5
-------
EXAMPLE:
This example shows yearly numbers of
observations, geometric means, and
standard deviations of bacteria data
collected at station 510014. Data
indicate an upward trend in bacteria
levels (particularly fecal coliforms) at
this station.
510014
44 14 54.4 086 19 24.9 2
MANISTEE R AT MAPLE STREET BRDG
26101MANISTEE CO., MI
CITY OF MANISTEE 0819
MANISTEE RIVER BASIN
21MICH
0000 FEET DEPTH CLASS 00
DATE TIME DEPTH
FROM OF
TO DAY tEET,
74/01/01
YEAR
75/01/00
75/01/01
YEAR
76/01/00
76/01/01
YEAR
77/01/00
77/01/01
YEAR
78/01/00
78/01/01
YEAR
79/01/00
79/01/01
YEAR
80/01/00
NUMBER
MEAN
STAND DEV
NUMBER
MEAN
STAND DEV
NUMBER
MEAN
STAND DEV
NUMBER
MEAN
STAND DEV
NUMBER
MEAN
STAND DEV
NUMBER
MEAN
STAND DEV
31616
FEC COLI
MFM-FCBR
/100ML
LOG
11.0000
27.3285
3.37516
10.0000
84.8704
3.72427
12.0000
95.1319
2.74378
12.0000
206.168
1.73477
12.0000
201.600
2.19579
4.00000
267.767
1.36380
31679
FECSTREP
MF M-ENT
/100ML
LOG
2.00000
22.3606
3.12067
12.0000
21.2834
3.00696
12.0000
42.4631
2.98835
7.00000
48.5257
3.01256
/TYPA/AMBNT/STREAM
5-6
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
5-2
USING BACTERIAL DATA TO ASSESS THE SOURCE OF FECAL CONTAMINATION
It is possible to determine whether observed bacterial pollution
problems are due primarily to human or to animal wastes by
calculating the ratio of fecal coliforms (FC) to fecal
streptococci (FS). It is commonly agreed that an FC/FS ratio
greater than 4 indicates that pollution is most likely due to
human wastes and that an FC/FS ratio of less than 1 indicates
wastes of animal origin. This technique calculates FC/FS ratios,
using available data for fecal coliforms and fecal streptococci.
TECHNIQUE:
Use the Water Quality File retrieval
program MEAN. Extract stations that have
values stored for both fecal coliforms and
fecal streptococci and request calculation
of the FC/FS ratio. Specify printing of
individual samples rather than statistical
summary information.
DATA REQUIREMENTS:
Enter appropriate station identification
and data selection keywords to specify the
geographic area and time period of
interest. Parameter codes for fecal
coliforms, fecal streptococci, and the
FC/FS ratio must be specified.
OUTPUT:
For each station retrieved, station
identification information and tabulations
of raw FC and FS data, as well as the
calculated FC/FS ratio will be printed.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 6.
If printing of individual samples is not
specified, summaries by year and period of
record will be calculated, as well as a
summary of data from all stations
retrieved.
The MEAN program may also be used to
calculate dissolved oxygen saturation
(using values for temperature and dissolved
5-7
-------
oxygen) and unionized ammonia (using values
for temperature, pH and total ammonia).
5-8
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
5-3
RETRIEVAL OF FISH KILL DATA
STORET contains a separate Fish Kill File that stores information
on fish kills that have occurred within the United States as a -
result of a variety of industrial, municipal, agricultural, and
transportation-related operations. Data may be retrieved from
this file by State, county, city, basin, period of record, or
pollution cause code.
TECHNIQUE: Use the job control language listed in the
STORET help data set named FKRETRV.
Indicate the criteria by which data are to
be reported.
DATA REQUIREMENTS: Enter appropriate codes to specify the
geographic area, time period, or pollution
cause of interest.
OUTPUT: Five tables will be printed, including: a
summary of all kills of more than.100,000
fish; codes used for pollution cause, kill
severity, and area affected; a listing of
all kills retrieved, by city within each
State; a summary by State and month of the
.year; and a summary by number of. fish
killed.
DOCUMENTATION: Part FK, Chapter 3.
5-9
-------
EXAMPLE:
This example summarizes fish kills reported
throughout the country in 1960-1964. The
retrieval was not limited geographically.
The tables below detail major kills and
kills reported in the State of Alabama.
Tables on the following pages show
reporting codes and summary information.
MAJOR
L«« u SIHAM
KILLS — 100.0WJ OR OVER
"•EAR OK IK
SIAU
««e« «*«««* ««*««««*«*««*4** «*««»***«****«*«««»«««««»*«»*«*,«««*•««««»*«*
COOS* KIVIH
HHISKH LKUTE
SHASTA LAKf
COASTAL KAURS
LCS ANGELES HARB
LUS ANGEIES H»R8
SANTA tiifcfc HANBO
LOS ANGELl S H»HB
LOS 1Mb IE* tUKB
LOS INGE U 5 MAK6
LOS AMGIIES HAR6
ANACPSIIA R1VEK
LAKf JESSUP
UAHIAKA fcESFRVU!
MILNfR kliVI,
SNAKt/MILNEt kES
RIIE» CHEK
SPOUN *IV(P
CDUH CRIEK
IlllM'li klVthi
SAMGAMDN HVEK
XISHHAUKtt StE B
COTTUNNOOO nvtR
COTItlNMdOf' MVfR
ARUAf.SAS RIVEK
LEVtl Ck K FK (.»
ANNISTON
ELAINE
•IE DOING
SAN OIK.U
SAN PtDKJ
SAN PEOtiO
SANTA BARbAhA
•mMTICTOK
MILKING lUh
KILNlNGIOh
-ILMlNGltlN
oisr m CIIL
i»NfURC
^AHtAMA OAHtl
dURLET
itURLET
ritGERhkN
OAMJNOA
HAMPSMlkQ
CKtVE CGUtf
SPR INf-F ItLD
STCAMtlSL
COITONHCI ftLLS
LMPUBJA
f(l*1 OCLU
JHltt CUT
ALABAMA
AKKAKV1S
CALI, .HM»
CALItDRMA
CAUFCSMA
CALlFUdNIA
tiLlFDRM*
CALIFOSKH
CALIFURMt
CAIIFOSMA
CAIIKJUMA
DISIHC) liF CULUMD1A
HDR1CA
HAWAII
IOAHU
IDAHO
1LAHO
ILLINOIS
ILLIIlUI:.
ILLINOIS
ILLINOIS
ItLINUIS
KANSAS
KANSAS
KANSAS
KANSAS
TEAK
*•**«*«***
1961
l«»i
1963
19ft?
1961
1961
196+
196J
19A3
1961
1964
1962
19«t
196)
1961
196J
Hkl
1962
)96»
1961
196]
1963
196*
1964
194*
1963
Nunatft OF
FIW
OPERATION
««*««»«••• . .«««««*««**««•«**»«««***«««»
200.00}
1JU.OOJ
DO tOOJ
' 37.800,005
IJO.^OJ
277, 80)
2.000.UOJ
642 ,000
252. 00 J
Mn.OuO
i«v,OU'J
^.J8i),UUO
1.000.003
I, 000 .000
I'-KJ.OG-J
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176,t>^j
l»i,«51
•>,38T,i<)J
l^J.Jb)
22b,617
3*O.OOJ
2 43 .LCI
l6^
-------
POLLUTION CAUSE CGUES AS USED IN THE
HSH KILL REPORTS
CAUSE:
10 AGRICULTURAL OPtKAlIONS
11 PtSTICIOtS
12 FtRllL12tHS
13 MANURE, SUP, HI-CLOT DRAINAGE, ETC.
20 IMUJSTKiAL UK RAT IONS
21 nlNlMC
22 FOOD C KTNDKfO PRODUCTS
23 PAPtk L ALLUD PfcDDUCTS
<>» CHEMICALS
25 PflhULkUM
2b HLTALS
27 CUMBINATH-NS
?8 OlHtK
30 MUNICIPAL UKPAT1GNS
31 StkfrRAGt STSTt«
32 REFUSE [. ISPtlSAl.
33 UAUk SYSUH
3« SHlr-MINb POOL
3% PUH(K
TKANSPbKlAIIIjK
41 VAIL
*2 TkUCK
^3 BAktK Irk t-DAl
** PIPt LR't
5C OTHtfC
90 UNKNUHIv
StVERITY: AftLA AFFECTED:
2 HMVY LAST £ COLUMNS IK REPORT:
3 MIJOf«ATb
1 LIGHI «DA«=DAYS "HR
5-11
-------
~ MIMKR OF F
STATES
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
OEIAMARE
DISTRICT OF COLUMBIA
FLORIDA
CEOM1A
HA HA II
IOAMO
ILLINOIS
INDIANA
IONA
KANSAS
KENTUCKY
LOUISIANA
MINE
MART LAND
MASSACHUSETTS
M1CH1CAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
MEVAOA
Nttr HAMPSHIRE
NEW JERSEY
N6N MEIICO
MEM TORII
NOItTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOOTH CAROL IDA
SOUTH DAKOTA
VCMyCCCKC
iciuvEiaEC
IE US
UTAH
VERMONT
V1RCINIA
HASKINCTQN
NEST VIRMNIA
MISCOHSIN
NYONINC
»••» TOTALS
SK KILL REPORTS
JAN
*
2
i
i
l
5
I
I
3
i
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z
»
J
6
13
2
1
t
»4
.....
KB
Z
»
1
1
2
2
3
2
2
I
1
3
7
2
1
3
3
3
1
T
1
1
1
I
«I
MR
1
I
1
1
J
1
t
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t
t
3
>
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I
16
l»
1
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1
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73
APR
ft
1
6
t
2
t
1
4
i
l
l
»
4
1
1
*
1
3
3
11
1
24
1
«
2
1
117
MAY
Z
2
Z
• *
4
1
1
1
10
*
T
3
1
3
23
T
2
2
1
6
10
37
1
IT
3
11
1
ZO6
JUN
ft
IB
13
2
3
5
Z
11
1*
II
i
19
6
3
T
14
*
7
3
t
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10
1
15
2
29
1
S3
2
1
1
3ft
1
4
»
3*3
JUL
4
4
I*
7
3
T
23
11
13
1
4ft
12
t
15
4
1
17
•
7
2
11
T
1
30
27
1
60
3
32
3
5
4
1
41*
AUC
1
7
30
7
2
S
1
23
11
11
2
41
1
2
4
I
•
12
2
4
5
11
If
9
2
SI
29
i
1
9
1
Z
340
SEP OCT
4
I
25 1
1
1
2
I
2
IS
17
a
2
7
1
I
1
10 2
4
1
4 2
t
1 3
1
7 2
16 7
1
11 T
42 24
3
1
17
3
1
2
237 II»
NO*
2
2
»
3
1
3
2
9
5
1
1
3
1
ft
1
3
2
13
1
3
•
1
1
3
1
a*
PEC
1
4
I
3
4
2
a
i
i
4
2
1
3
1
1
1
1
2
12
2
13
3
12
I
1
Z
1
*0
TOTAL
21
i
24
121
ft
43
3
2
20
34
ID
9
id*
*0
tl
41
14
ll»
23
1O
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63
2ft
2
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17
30
Z
20
S9
J
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29
4
111
4
3ft
329
10
3
2
49
214
li
2
45
34
33
19
9
ZH5
—FISH KILL GROUPING
NUMBER OF FISH
KILLED BY SIZE GROUP
1 - 1 • 000
I, COO - 10,000
10,000 • 100,000
100,000 - 1,000.000
>«It 000,000
UNKNOWN
e«TUTAL
BY SUE --
TOTAL
REPORTS
606
467
170
33
13
866
3,155
REPORTED
NO . Oh REPORT!,
6D6
467
170
33
13
145
1,434
fISH KILLED
NO. OF HSU
IT7.9Z!»
l,62t,34->
5.427.632
8,461,743
77,496.156
1.605.000
94 f 794, 801
5-12
-------
MANAGER'S GUIDE
TO
STORET
CHAPTER
6
LAKE WATER QUALITY
-------
LAKE WATER QUALITY
Section 314(a) of the Federal Water Pollution Control Act
requires that each State identify its publicly owned freshwater
lakes and classify them according to eutrophic condition. In
addition, the Act calls for the development of plans and
feasibility studies for lake pollution control and restoration
projects. The most recent guidance for preparation of the
States' 305(b) assessments suggests that this responsibility be
fulfilled by incorporating the required information into a
chapter of that biennial report,
Much of the information specified in the guidance for development
of lake classification schemes and restoration feasibility
studies is directly available from STORET. Stations that sample
lake water quality are clearly identified in the Water Quality
File, and the data stored at those stations can be manipulated
using any appropriate analytical program.1
EXISTING WATER QUALITY
Among other things, the guidance for development of lake
classifications suggests that the preliminary inventory include a
"summary of available chemical and biological data demonstrating
the current water quality of the lake". Similarly, the
feasibility studies for lake restoration projects are to describe
the water quality problems involved, using historical data and
one year of current baseline data. The guidance recommends that
baseline data be used to describe: present trophic conditions;
the physical, chemical, and biological impact of important
tributaries; an assessment of nitrogen and phosphorus inflows and
outflows; and vertical profiles of temperature and dissolved
oxygen levels.
The optimal data analysis technique for assessment of lake water
quality depends upon the size of the lake involved and the number
of STORET stations located on or near the lake. If data are
available only at a very limited number of stations, the best
method may be to summarize data values from one individual
station or to aggregate data from all stations on the lake and
technique 6-1: Identification of Lake Stations
6-1
-------
summarize accordingly.2 If a large number of stations is involved
and a significant amount of data is available, contour or area-
shaded mapping techniques may.be used.3
; TABLE 6-1
TROPHIC INDEXES
INVESTIGATORS SINGLE PARAMETER MULTIPLE PARAMETER
Rodhe (1969) Organic Matter
Beeton and Edmondson Nutrients
(1972)
Carlson (1977) Secchi 'depth .
Michalski and X
Conroy (1972)
Uttormark and Wall X
(1975)
Brezonik and Shannon X
(1971)
EPA National Eutro- X
phication Survey
Where the current trophic condition of a lake is of concern/ that
aspect of water quality is traditionally described using one of a
number of available trophic indexes. (A sampling of trophic
indexes, including the parameters used, is listed in Table 6-1).
Although STORET has no capability for the calculation of such an
^Technique 3-3: Assessing Existing Conditions in Terms of
Standards Violations; Technique 3-5: Illustration of Historical
Trends Using Statistical Summaries; and Technique 3-6: Plotting
Trends Over Time.
'Technique 6-4: Using Contour Maps to Illustrate Lake Water
Quality and Technique 3-4? Generation of Area-Shaded Maps.
6-2
-------
index, it does provide the means of retrieving the necessary raw
data, in either hard copy or machine-readable formats.*
Guidance for development of lake classifications also recommends
inclusion of an indication of whether the lake was surveyed in
EPA's National Eutrophication Survey (NES}. Data collected as
part of the NES was stored in the Water Quality File and should
be readily available to all STORET users.9
The "impact of important tributaries" and "nitrogen and
phosphorus inflows and outflows" may be assessed using techniques
described in Chapter 4, Pollution Sources and Control Programs.
If enough data are available, probably the most useful type of
analysis would be a multiple station plot.*
Vertical temperature and dissolved oxygen measurements, essential
to the determination of whether a lake is stratified, also are
required as part of the State's 314(a) inventory. The
appropriate technique for this type of analysis depends on the
size of the lake, the number of stations, and the amount of data
available. For a relatively small lake, or one for which STORET
contains a limited amount of data, the most effective
illustration of vertical profiles is a regression analysis of
parameter values versus depth at a single STORET station.7 For a
larger lake, a series of contour maps using data collected at
different depth ranges could provide a more complete picture of
stratified conditions.8
EVALUATION OF CONTROL ALTERNATIVES
If analysis of lake water quality indicates a stressed condition
or a trend toward water quality degradation, the next step is an
evaluation of causative factors. As part of the inventory
required by Section 314(a), all major point and non-point source
'Technique 2-4: Retrieval of Raw Data; Technique 3-10:
Formatting STORET Data for Input into SAS (Statistical Analysis
System); and Technique 3-11: Output of STORET Data on Punched
Cards.
*Technique 6-2: Retrieval of National Eutrophication Survey
Data.
•Technique 4-1: Use of Multiple Station Plots to Assess Cause
and Effect.
t •
'Technique 6-3: Displaying Lake Stratification.
•Technique 6-4: Using Contour Maps to Illustrate Lake Water
Quality.
6-3
-------
loads must be identified, and the relative magnitude of each
quantified.'
To assess the relative merits of various control alternatives,
the analyst can attempt to define the controllable portion of the
waste load using STORET data on flow, water quality, and
effluents. Once the controllable portion has been defined,
appropriate data can be input into a mathematical model to
determine whether water quality would improve significantly after
implementation of the various control alternatives.
Mathematical models of varying levels of complexity are available
for the quantification of the relationship between waste inputs
and lake water quality. Relatively detailed models, which
include specification of phytoplankton, zooplankton, and the
nitrogen and phosphorus cycles, were pioneered by Chen (1970) and
DiToro, et al, (1971). These and similar models require
significant data collection efforts for calibration and have been
incorporated in planning studies where significant water resource
decisions are to be made (Thbinann, 1975 and Hydroscience, 1976b).
For preliminary screening, a simpler modeling framework is
probably more cost-effective. For reference purposes, Rechkow
(1979) provides a summary and review of available single-
compartment (total phosphorus) models, and Thomann (1977)
compares detailed models to loading plot models (starting with
the classical work of Vollenweider (1968)), and details the
assumptions of the simpler apprrach.
In the final analysis, the relative water quality improvements
expected from the different control alternatives also hav^ to be
placed in a cost-benefit context. As STORET capabilities for the
storage of cost information are limited, most of the data for
this final phase of analysis must be derived from other sources.
•Technique 4-2: Retrieval of In-plant Data; Technique 4-3;
Retrieval of Permit and Effluent Data; and Technique 4-5:
Location and Characterization of Municipal Dischargers.
6-4
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
6-1
IDENTIFICATION OF LAKE STATIONS
This technique screens STORET stations in the area of interest
and restricts retrieval to those located on lakes. It can be
especially useful as a preliminary step in the States' Clean
Lakes inventories.
TECHNIQUE:
Use the Water Quality File retrieval
program RET; extract stations labeled with
the station type LAKE; specify printing of
descriptive paragraphs and station
identification information only.
DATA REQUIREMENTS:
Enter appropriate station identification
keywords defining the geographic area of
interest.
OUTPUT:
Station header information and a
descriptive paragraph will be printed for
each lake station in the geographic area
specified.
DOCUMENTATION:
Part WQ, Chapter RET, Section 6 (RET
Program).
Part WQ, Appendix F (Station Type Codes).
NOTES:
If the analyst does not specify printing of
station identification information and
descriptive paragraphs only, raw parametric
data values also will be printed.
The information contained in the
descriptive paragraph is entirely up to the
individual who stores the station, and may
be extremely variable.
6-5
-------
EXAMPLE: This example shows the station
identification information and descriptive
paragraph stored under station 270003,
which is maintained by the State of
Michigan on Lake Superior.
270003
4$ 42 32.0 089 58 41.0 2
L SUPERIOR
2S053 GOGEBIC CO., Ml
IRONKOOD TWP, SEC 30 2213
OFF PORCUPINE MTN PARK
/TVPA/AMBNT/LAKE 21MICH
0003 FEET DEPTH CLASS 00
DESCRIPTION
LAKE SUPERIOR AT PORCUPINE MOUNTAIN STATE PARK (LAKE SUPERIOR
TRAIL), OFFSHORE OF MIDDLE OF ISLAND, GOGEBIC COUNTY, T50N, R45h,
SECTION 30, IHONWOOD TOWNSHIP
GREAT LAKES SHORELINE DATA ARE TAKEN AT THIS STATION. HISTORICAL
BACTERIA INFORMATION IS FOUND HEHC.
6-6
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
6-2
RETRIEVAL OF NATIONAL EUTROPHICATION SURVEY DATA
The identification of lakes that were studied as part of the
National Eutrophication Survey is required as part of the States'
Clean Lakes assessments. This technique can be used to determine
where National Eutrophication Survey stations are located in a
specified geographic area.
TECHNIQUE: Use the Water Quality File retrieval
program INDEX; retrieve stations stored
under the agency code 11EPALES.
DATA REQUIREMENTS: Enter appropriate station identification
keywords to define the geographic area of
interest.
OUTPUT: This technique retrieves station
identification information for each station
selected, including State name, State and
county codes, brief location description,
agency and station codes, basin codes,
latitude/longitude coordinates, river mile
indexes (if stored), and station type.
DOCUMENTATION: Part WQ, Chapter RET, Section 6.
NOTES: This program retrieves no parametric data*.
6-7
-------
EXAMPLE:
This is the first page of output from an
INDEX retrieval, which lists modified
station identification information for
stations in the State of Michigan that were
stored under agency code HEPALES.
STORET KETRIEVAL OA« 79/08/01
PAOS 1
BASIN CODE STORAGE DATE
STATE
ST/CO I LOCATION
STATION TYPE
USER CODE STATION
LAT/LONG
INDEX
SECONDARY STATIONS
MILESLV1 LV2
COUNTY
LV3 LV4
. IIAJ/HIN/TERM
LVS LVf IV? LVa IV» tVIO I.V11
MICHIGAN
MICHIGAN
26049 HOLLOW A V RESERVOIR
11EPALES 26A001
43 «? 1?,0 083 29 21.0
INDEX
26049 HOLLOWAY RESEPVOIt)
/TYPA/ANBNT/LAKE
11EPAIES 26A002
43 06 52.0 083 2? 32.0
INDEX
2608? HOLLOHM RESERVOIR
/TYPA/AMSST/LAK E
11EPALES 26A003
43 0? 16.0 083 26 12.0
INDEX
26 CARO RESERVOIR
/TYPA/AMBNT/LAXE
11EPALES 26A101
43 27 30.0 OB3 24 30.0
INDEX
26055 BQARDMAN HYDRO POND
/TYPA/AHBNT/LAK E
11EPALES 26A201
44 40 00.0 065 25 00.0
INDEX
26055 BOARDMAN HYDRO POND
/TYPA/AMBHT/LAS E
11EPALES 26A202
44 40 00.0 085 25 00.0
INDEX
26005 ALLEGAN LAKE
/TYPA/AHBNT/LAKE
11EPALES 260301
42 32 00.0 085 52 00.0
INDEX
BASIN
210491
SASIS
BASIN
08109J
6-8
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
6-3
DISPLAYING LAKE STRATIFICATION
This technique plots regressions of dissolved oxygen versus depth
and temperature versus depth at a single lake station to
illustrate lake stratification. It is a practical way of
illustrating vertical profiles in a small lake that has only a
few sampling sites.
TECHNIQUE:
Use the Water Quality File retrieval
program REG; request the calculation and
plotting of linear regressions of dissolved
oxygen versus depth and temperature versus
depth at a specified STORET station
(parameter versus parameter, or Type 2,
regressions).
DATA REQUIREMENTS:
Enter a single agency and station code
pair; parameter codes for depth, dissolved
oxygen, and temperature; and data selection
keywords to define time period of interest.
OUTPUT:
For each of the two regressions requested,
a statistical summary page and one to four
pages of graphic output are produced
showing the correlation between the two
parameters involved. The graphic output
consists of a scatter diagram (line printer
plot) on which single data points are
represented by X's, multiple data points by
alphabetic characters (A=2 points, B*3
points, etc.), and y-axis intercepts of the
regression line by asterisks (*).
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 7.
This technique could also be used to
display vertical profiles of other
parameters.
There are multiple parameter codes in
STORET for dissolved oxygen and
temperature, so it may be advisable to
6-9
-------
provide alternative codes in case data are
not stored in the units of choice.
EXAMPLE:
This example shows a plot of dissolved
oxygen (DO) vs. depth at a single sampling
site, using data collected from May 1978
through June 1979. By connecting the
asterisks on the left and right y-axes, the
analyst could show a direct negative
correlation between DO and depth,
indicating stratified conditions. Output
from program REG also includes a page of
summary statistics.
o.onn
r>r»
6-10
-------
WATER QUALITY DATA ANALYSIS
TECHNIQUE
6-4
USING CONTOUR MAPS TO ILLUSTRATE LAKE WATER QUALITY
For large lakes or bays where there are a significant number of
sampling sites, this technique can be a useful way of depicting
spatial variations in parameter values. Alternatively, a series
of contour maps can provide an effective visual representation of
trends over time or depth.
TECHNIQUE:
Use the Water Quality File retrieval
program MSP. Request the printing of a
contour map depicting variations over space
in mean parameter values.
DATA REQUIREMENTS!
Enter latitude/longitude coordinates to
define the approximate perimeter of the
lake in question, data selection keywords,
to define the time period of interest (if
other than the period of record), and a
single parameter code.
OUTPUT:
A contour map is produced, which
illustrates how concentrations of a single
parameter vary over space.
DOCUMENTATION:
NOTES:
Part WQ, Chapter RET, Section 7.
This technique is not as effective for
illustrating water quality in streams or
small lakes, because of the spatial
distribution of sampling sites.
It is possible to print a specified symbol
or data values on the map, or neither?
however, care must be exercised to provide
sufficient visual resolution.
6-11
-------
EXAMPLE:
This map shows the distribution of
chlorophyll in Saginaw Bay. Plus signs (+)
indicate the locations of sampling sites.
6-12
-------
MANAGER'S GUIDE
TO
STORET
APPENDIXES
-------
APPENDIX A
BIBLIOGRAPHY
BARR, A. J., GOODNIGHT, J. H., SALL, J. P., HELWIG, J. T. A
User's Guide to SAS 76. Raleigh, North Carolina: SAS
Institute, Inc., 1976, 329 pp.
BEETON, A. M., EDMONDSON, W. T. The Eutrophication Problem.
Journal of Fisheries Research Board of Canada 29:673-682,
1972.
BREZONIK, P. L., SHANNON, E. E. Trophic State of Lakes in North
Central Florida. Florida Water Resource Center, Publication
No. 13, 1971, 102 pp.
CARLSON, R. E. A Trophic State Index for Lakes. Limnology and
Oceanography 22(2):361-369, March 1977.
CHEN, C. W. Concepts and Utilities of Ecologic Model. Journal
of the Sanitary Engineering Division, American Society of
Civil Engineers 96:1085-1097, October 1970.
DITORO, D. M., O'CONNOR, D. J., THOMANN, R. V. A Dynamic Model
of the Phytoplankton Population in the Sacramento-San Joaquin
Delta. American Chemical Society, Advances in Chemistry No.
106:131-180, 1971.
HYDROSCIENCE, INC. Areawide Assessment Procedures Manual.
Cincinnati, Ohio: U.S. Environmental Protection Agency, EPA
Report No. 660/9-76-014, 1976a.
HYDROSCIENCE, INC. Assessments of the Effects of Nutrient
Loadings on Lake Ontario Using a Mathematical Model of the
Phytoplankton. Windsor, Ontario: International Joint
Commission, 1976b, 116 pp.
HYDROSCIENCE, INC. Simplified Mathematical Modeling of Water
Quality. U. S. Environmental Protection Agency, March 1971,
124 pp.
MICHALSKI, M. F., CONROY, N. Water Quality Evaluation - Lake
Alert Study. Ontario Ministry of the Environment Report,
1972, 23 pp.
RECHKOW, K. H. Quantitative Techniques for the Assessment of
Lake Quality. U. S. Environmental Protection Agency, Office
of Water Planning and Standards, EPA Report No. 440/5-79-015,
January 1979, 146 pp.
A-l
-------
RODHE, W. Crystallization of Eutrophication Concepts in Northern
Europe. In: Eutrophication: Causes, Consequences,
Correctives. Washington, D. C.: National Academy of
Sciences, Publication No. 1700, 1969, pp. 50-64,
TETRA TECH, INC. Rates, Constants and Kinetics Formulations in
Surface Water Quality Modeling. Athens, Georgia: U. S.
Environmental Protection Agency, Environmental Research
Laboratory, EPA Report No. 600/3-78-105, December 1978, 317
PP.
THOMANN, R. V. A Note on the Relationship Between Dynamic
Phytopiankton Models and Plots of Loading Rate, Nutrients and
Biomass. Limnology and Oceanography 22:370-373, 1977.
THOMANN, R. V., DITORO, D. M., WINFIELD, R. P., O'CONNOR, D. J.
Mathematical Modeling of Phytoplankton in Lake Ontario. Part
I: Model Development and Verification. U. S. Environmental
Protection Agency, EPA Report No. 660/3-75-005, 1975, 177
pp.
U. S. ENVIRONMENTAL PROTECTION AGENCY. Basic Water Monitoring
Program, 2nd edition. U. S. Environmental Protection Agency,
Standing Work Group on Water Monitoring, EPA Report No.
440/9-76-025, May 2, 1978, 51 pp.
U. S. ENVIRONMENTAL PROTECTION AGENCY. STORET User Handbooks The
Right Answers for STORET Users. U. S. Environmental
Protection Agency, Office of Water and Hazardous Materials,
n.d., 2 volumes.
UTTORMARK, P. D., WALL, J. P. Lake Classification for Water
Quality Management. Madison, Wisconsin: University of
Wisconsin Water Resources Center, 1975, 62 pp.
VOLLENWEIDER, R. A. Advances in Defining Critical Loading Levels
for Phosphorus in Lake Eutrophication. Memorie dell'Istituto
Italiano di Idrobiologie 33:53-83, 1976.
VOLLENWEIDER, R.A. Scientific Fundamentals of the Eutrophication
of Lakes and Flowing Water, with Particular Reference to
Nitrogen and Phosphorus as Factors in Eutrophication.
Technical Report to the Organization for Economic Cooperation
and Development, Paris, DAS/CSI/68.27, 1968, 182 pp.
A-2
-------
APPENDIX B
GLOSSARY
AGENCY CODE: a one- to eight-character alphanumeric code that
uniquely identifies an organization responsible for
collecting water quality data and entering it into STORET.
ALGAL BLOOM: a proliferation of living algae on the surface of
lakes, streams, or ponds, stimulated by nutrient enrichment.
AMBIENT MONITORING: the collection of uniform data on
representative parameters for the assessment of long-term
progress toward national water quality goals.
ARITHMETIC MEAN: the average obtained by dividing a sum by the
number of its addends. See also GEOMETRIC MEAN.
BACTERIA: single-celled microorganisms that lack chlorophyll.
Some bacteria are capable of causing human, animal, or plant
diseases; others are essential in pollution control because
they break down organic matter in the water.
basin: see RIVER BASIN.
BIOCHEMICAL OXYGEN DEMAND (BOD): a measure of the amount of
oxygen consumed in the biological processes that break down
organic matter in the water. Large amounts of organic wastes
use up large amounts of dissolved oxygen; thus, the greater
the degree of organic pollution, the greater the BOD.
BIOLOGICAL MONITORING: the determination of the effects on
aquatic life in receiving waters due to the discharge of
pollutants, by appropriate techniques and procedures and at
appropriate frequencies and locations.
CHEMICAL OXYGEN DEMAND (COD): a measure of the amount of oxygen
required to oxidize organic and oxidizable inorganic
compounds in water. The COD test, like the BOD test, is used
to determine the degree of pollution in an effluent.
CITY MASTER FILE: one of several independent files that make up
the STORET system. The City Master File contains a listing
of unique identification codes for most U.S. cities,
communities, water treatment facilities, and municipal sewage
facilities.
CLEAN WATER ACT OF 1977: Public Law 95-217, including amendments
to the Federal Water Pollution Control Act.
B-l
-------
COD: see CHEMICAL OXYGEN DEMAND.
COEFFICIENT OF VARIATION: the ratio of the measure of
variability to the average about which the variation occurs.
coliform bacteria: see FECAL COLIFORM BACTERIA.
COMMAND PROCEDURE: a prearranged set of computer instructions,
permanently stored in an on-line data set, that enables
STORET users to execute a frequently-used set of instructions
by simply referencing the appropriate procedure; most Water
Quality File retrievals utilize the command procedure RET.
COMMON LOGARITHM: the exponent expressing the power to which the
number 10 must be raised in order to produce a given number.
COMPLIANCE MONITORING: water quality sampling and analysis
conducted to check compliance of an NPDES permittee with
permit limitations. Compliance monitoring is usually
conducted annually and covers only those parameters that are
listed in the permit.
COMPOSITE SAMPLE: a combination of individual samples obtained
at intervals over a period of time (e.g., several grab
samples spanning a 24-hour period and placed in a single
container or a series of samples taken at equal distances
across a stream section). See also GRAB SAMPLE.
CONSERVATIVE PARAMETERS: substances that do not decay with time
or disappear from the water system by settling, adsorption,
or other means.
CRITERIA: the levels of pollutants that affect the suitability
of water for a given use. Generally, water use
classifications include: public water supply, recreation,
propagation of fish and other aquatic life, agricultural use,
and industrial use.
CRITICAL PERIOD: the time during which the adverse combination
of relevant parameters causes the greatest degradation in
water quality to occur, such as the warm-temperature, low-
flow summer period for dissolved oxygen.
DATA: records of observations and measurements of physical
facts, occurrences, and conditions, in written form.
DATA BASE: a collection of data used for information retrieval
and reporting, usually a collection of data sets.
DATA SELECTION KEYWORDS: STORET keywords that enable a user to
restrict the parametric data retrieved to specific
parameters, sampling dates, sampling depths, and sampling
conditions.
B-2
-------
DATA SET: a collection of data records that have a logical
relationship to one another, reside within a computer system,
and are accessible to users.
DESCRIPTIVE PARAGRAPH: up to 1080 alphanumeric characters that
provide textual information about the location and sampling
activities of a STORET station.
DISCHARGER MONITORING REPORT: a report filed by NPDES permittees
that describes the parametric loadings of a facility's
discharge for parameters listed in the permit, based on
effluent guidelines that define pounds of discharge per pound
of production for several parameters known to be discharged
by a given industrial category.
DISK: a stack of round, flat plates on which information is
magnetically stored, and which is mounted on a single spindle
and rotated past a set of read/write heads in such a fashion
that very rapid access is possible to any data location.
DISSOLVED OXYGEN (DO): the oxygen dissolved in water or sewage.
Adequate dissolved oxygen is necessary for the life of fish
and other aquatic organisms and for prevention of offensive
odors. Low dissolved oxygen concentrations often are due to
the point source discharge of wastewater with high BOD, the
result of Inadequate waste treatment.
DISSOLVED SOLIDS: the total amount of dissolved material,
organic and inorganic, contained in water or wastes.
Excessive dissolved solids make water unpalatable for
drinking and unsuitable for industrial uses.
EFFLUENT: a discharge of pollutants into the environment, either
partially or completely treated or in their natural state.
ENRICHMENT: the addition of nitrogen, phosphorus and carbon
compounds or other nutrients into a lake or other waterway
that greatly increases the growth potential for algae and
other aquatic plants.
EQUIVALENT LOAD: the product of the flow and the pollutant
concentration, usually expressed in pounds or kilograms per
day, which represents the mass of material discharged to a
body of water per unit of time.
EUTROPHIC LAKE: a lake rich in dissolved nutrients, often
characterized by large amounts of algae, low water
transparency, low dissolved oxygen, and often shallow and
weed-choked at the edges.
EUTROPHICATION: the normally slow aging process by which a lake
evolves into a bog or marsh and ultimately assumes a
completely terrestrial state and disappears. During
B-3
-------
eutrophication the lake becomes so rich in nutritive
compounds that algae and other microscopic plant life become
superabundant, thereby causing the lake eventually to fill up
with settled material.
FECAL COLIFORM BACTERIA: a group of organisms common to the
intestinal tracts of man and animals. The presence of fecal
coliform bacteria in water is an indicator of pollution and
of potentially dangerous bacterial and viral contamination.
FECAL STREPTOCOCCI: nonmotile, chiefly parasitic bacteria, often
pathogens, which normally inhabit the intestines of man and
animals. Fecal streptococci are an important indicator of
sanitary quality in natural waters.
FEDERAL WATER POLLUTION CONTROL ACT AMENDMENTS OF 1972: Public
Law 92-500, the Federal law that authorized the water quality
management program, as part of a comprehensive Federal
program to restore and maintain the chemical, physical, and
biological integrity of the Nation's waterways.
FILE: synonymous with DATA SET.
FISH KILL FILE: one of several independent files that make up
the STORET system. The Fish Kill File contains information
on pollution caused fish kills, as reported to EPA.
FLOW: the movement of water in a stream or river in the
direction of lower elevation, usually quantified in cubic
feet per second.
FLOW DATA FILE: one of several independent files that make up
the STORET system. The Flow Data File contains stream flow
data collected by the U.S. Geological Survey.
GAGING STATION: a location on a stream or conduit where
discharges are measured. The station usually has a recording
or other gage for measuring the elevation of the water
surface in the channel or conduit.
GEOMETRIC MEAN: the "nth" root of the product of "n" factors.
See also ARITHMETIC MEAN.
GRAB SAMPLE: an individual water quality sample collected at a
specific date and time. See also COMPOSITE SAMPLE.
HARD COPY: computer output, usually on paper, that can be read
bi a human without mechanical or electronic assistance.
HYDROLOGY: the branch of physical geography concerned with the
origin, distribution and properties of the waters of the
earth, on the surface of the land, in the soil and underlying
rocks, and in the atmosphere.
B-4
-------
INPUT: the transfer of information into a computer's main
memory, or the information so transferred.
INTENSIVE SURVEY: the frequent sampling of certain parameters at
representative points (including points of effluent
discharge) for a relatively short period of time to assess
water quality conditions, causes, effects, and cause-and-
effect relationships.
JOB: the major unit of work of a computer system. A job
consists of one or several related steps defined by a series
of job control language (JCL) statements.
JOB CONTROL LANGUAGE (JCL): a user-written computer control
language used to define a job and its requirements to the
computer system. JCL tells the computer who submitted the
job, what program to run, where to find the input, and where
to route the output. IBM JCL statements have slashes (//) in
columns 1 and 2 of each line.
KEYWORD: an alphabetic word, letter, or expression that defines
the information to be retrieved from the Water Quality File
and how that information is to be presented. Valid
combinations of keywords and their values make up STORET
retrieval requests.
LAND USE: the physical mode of utilization or conservation of a
given land area at a given point in time.
LINEAR REGRESSION: a measure of the best-fit straight-line
relationship between two variables, expressed as a first-
degree algebraic equation.
loading: see EQUIVALENT LOAD.
logarithm: see COMMON LOGARITHM.
MATHEMATICAL MODELING: essentially an analytical abstraction of
the real world, which incorporates only those phenomena that
are relevant to the problem under consideration. These
phenomena are defined using mathematical relationships, which
can be solved to depict past, present or future conditions.
mean: see ARITHMETIC MEAN; GEOMETRIC MEAN.
MEMBRANE FILTER (MF): a thin, semi-permeable material used to
separate matter from a solution as it passes through it; may
be used in determination of bacteria counts in water quality
samples.
modeling: see MATHEMATICAL MODELING.
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MONITORING: periodic or continuous determination of the amount
of pollutants or radioactive contamination present in the
environment. See also AMBIENT MONITORING; BIOLOGICAL
MONITORING; COMPLIANCE MONITORING; INTENSIVE SURVEY.
MO.T? PROBABLE NUMBER (MPN): the number of organisms per unit
volume that, in accordance with statistical theory, would be
more likely than any other number to yield the observed test
result with the greatest frequency.
MUNICIPAL WASTE INVENTORY FILE {245 FILE): one of several
independent files that make up the STORET system. The
Municipal Waste Inventory File contains information on
municipal waste sources and disposal systems.
NATIONAL POLLUTANT DISCHARGE ELIMINATION SYSTEM (NPDES): the
national discharger permitting system authorized under
Section 402 of P.L. 92-500, including any State permit
program that has been approved by the EPA Administrator.
NON-POINT SOURCE: generalized discharge of waste into a water
body that cannot be located as to a specific source,
including agricultural or silvicultural activities, mining,
construction, disposal of pollutants in wells or in
subsurface excavations, saltwater intrusion, or hydrologic
modifications.
NPDES: see NATIONAL POLLUTANT DISCHARGE ELIMINATION SYSTEM.
NUTRIENT: an element or compound essential as raw material for
organism growth and development, including carbon, nitrogen
and phosphorus.
OBSERVATION: a measurement, or sampling, of a single parameter
at a specific location or station, at a specific point in
time.
OLIGOTROPHIC LAKES: generally deep lakes having a limited supply
of nutrients, biologically relatively unproductive, and
characterized by high water transparency and high dissolved
oxygen content.
OUTFALL: the final length of pipe or the mouth of a sewer,
drain, or conduit where an effluent is discharged into
receiving waters.
OUTLIER: a statistical observation not homogeneous in value with
others in a sample.
OUTPUT: the transfer of data out of a computer system's main
memory, or the data so transferred.
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PARAMETER CODE: one of a set of standard five-digit codes used
in STORET to identify individual elements with which values
relating to water quality are associated.
PARAMETRIC DATA: Water Quality File information describing the
conditions under which a sample was taken (such as date,
time, and depth) as well as the results of the sample
analyses.
PERMIT: a legal document that establishes the limits of
allowable discharges into navigable waters. Permits are
granted to individual dischargers only after they show that
their effluents will not contaminate a waterway in excess of
established water quality standards, or will not lower its
existing water quality.
pH: the reciprocal of the logarithm of the hydrogen ion
concentration, in grams per liter of solution.
PHYTOPLANKTON: free-floating or weakly motile microscopic
plants, found in various quantities in natural waters.
POINT SOURCE: any discernible, confined and discrete conveyance,
including but not limited to, any pipe, ditch, channel,
tunnel, conduit, well, discrete fissure, container, rolling
stock, concentrated animal feeding operation, or vessel or *
other floating craft, from which pollutants are or may be
discharged, or from which there is or may be a thermal
discharge.
PROGRAM: a logically self-contained sequence of instructions
that can be executed by a computing system to perform a
specific task.
QUALITY ASSURANCE PROGRAM: a prescribed, systematic set of
precautions to be taken in the course of the monitoring and
sample analysis processes to ensure that samples are
collected, preserved, and analyzed according to approved
methodologies.
RAW DATA: actual sample values that have not been summarized or
manipulated in any way by STORET's various statistical
routines.
REACH: a stream segment extending from confluence to confluence
or from confluence to stream end, as defined by a special
hydrologic numbering scheme developed by EPA.
REACTIVE PARAMETERS: substances that decay or degrade in the
environment due to physical, chemical and/or biological
activity. v
regression: see LINEAR REGRESSION.
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RETRIEVAL: the process of extracting data from a data base in a ,
desired format.
RETRIEVAL REQUEST: a collection of keywords and values that
describes a specific request for information to be obtained
from a STORET data file. Each retrieval request constitutes
one computer job.
RIVER BASIN: the total area drained by a river and its
tributaries, usually' measured in square miles.
RIVER MILE INDEX: a numerical code that identifies the location
of a sampling station on a river system by defining its
distance from and relationship to the mouth of the river
system.
RUNOFF: the portion of rainfall, melted snow, or irrigation
water that flows across ground surface and eventually is
returned to streams. Runoff can pick up pollutants and carry
them to receiving waters.
SAMPLE: a representative part of a body of water collected for
subsequent analysis for the presence of pollutants. See also
COMPOSITE SAMPLE; GRAB SAMPLE.
SAS (STATISTICAL ANALYSIS SYSTEM): a commercially available
software package for data management and statistical
analysis; SAS combines statistical routines, plotting, data
manipulation and report-writing capabilities.
SCATTER DIAGRAM: a two-dimensional graph consisting of points
whose coordinates represent corresponding values of two
variables whose relationship is being studied.
SECCHI DEPTH: the depth below the water surface at which a white
20-cm diameter disk is no longer visible to a trained
observer; a method of estimating the depth to which light can
penetrate in a water body.
Section 208 plan: see WATER QUALITY MANAGEMENT PLAN.
SECTION 305(b) REPORT: an assessment of existing and projected
water quality conditions and progress toward national goals,
submitted by each State to the Congress, as mandated in
Section 305(b) of the Federal Water Pollution Control Act.
self-monitoring: see DISCHARGER MONITORING REPORT.
STANDARD: a plan for water quality management containing four
major elements: the use to be made of the water; criteria to
protect those uses; implementation and enforcement plans; and
an antidegradation statement to protect existing high quality
waters.
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STANDARD DEVIATION: the square root of the arithmetic average of
the square of the deviations from the mean in a frequency
distribution.
STANDARD ERROR: a measure of the variance to be expected in
making statistical estimates of an unknown parameter; equal
to the standard deviation of the original frequency
distribution divided by the square root of the sample size.
STATION: a specific location, or collection point, where water
quality data are sampled.
STATION CODE: a one- to fifteen-character alphanumeric code that
identifies a specific geographic location where STORET water
quality data are collected. Each sampling site defined to the
STORET system has a single primary station code, which is
unique within a given agency, and up to three secondary
station codes, or aliases.
STATION DATA: Water Quality File information that describes the
geographical location of a sampling site.
STATION HEADER: a brief, highly structured summary of station
location information, which is printed at the top of outputs
from many Water Quality File retrieval programs.
STATION IDENTIFICATION KEYWORD: one of a series of STORET
keywords that specify which water quality stations are to be
retrieved from the Water Quality File, including both station
selectors and station restrictors.
STATION TYPE CODE: one of a series of alphabetic codes depicting
the characteristics of a STORET station, such as where the
station monitors water (in the open sea, a lake, a well, or a
pipe).
STORET: the acronym used to identify the computerized data base
utility maintained by EPA for the STOrage and RETrieval of
data relating to the quality of the waterways within and
contiguous to the United States.
STRATIFICATION: a condition in which horizontal or vertical
layers of a body of water exhibit distinctive and different
characteristics, especially with temperature, chloride, and
dissolved oxygen. Adjacent layers are clearly delineated in
most cases.
SUSPENDED SOLIDS: solids that either float on the surface of or
are in suspension in water, wastewater, or other liquids, and
which are largely removable by laboratory filtering.
SYSTEM: a group of computer programs that interlock to perform
user-specified tasks.
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TAPE: a reel of magnetic tape on which information is stored.
TERMINAL: a keyboard device used for human to computer
intercommunication.
TOTAL SOLIDS: the sum of dissolved and undissolved constituents
in water or wastewater, usually stated in milligrams per
liter.
TROPHIC INDEX; a means of quantifying the degree of
eutrophication in a lake through a calculation based on one
or more parameters related to the growth of phytopiankton.
TURBIDITY: a cloudy condition in water due to the suspension of
silt or finely divided organic matter, which interferes with
the passage of light through water.
TURNAROUND TIME: the elapsed time between the submission of a
job to a computer system and the return of results.
WASTE LOAD ALLOCATION: the assignment of target loads to point
and non-point sources to achieve water quality standards in
the most effective manner.
WASTEWATER: water carrying wastes from homes, businesses, and
industries; a mixture of water and dissolved or suspended
solids.
WATER POLLUTION: the addition of sewage, industrial wastes, or
other harmful or objectionable material to water in
concentrations or in sufficient quantities to result in
measurable degradation of water quality.
water quality criteria: see CRITERIA.
WATER QUALITY FILE: one of several independent files that make
up the STORET system. The Water Quality File contains
physical and chemical parametric water quality data as well
as station information.
WATER QUALITY MANAGEMENT PLAN: a management document that
identifies the water quality problems of a State-approved
planning area or designated areawide planning area and sets
forth an effective management program U. alleviate those
problems and to achieve and preserve water quality for all
intended uses, in accordance with Section 208 of the Federal
Water Pollution Control Act.
WATER QUALITY MANAGEMENT PROGRAM: activities conducted on the
Federal, State, and local levels for the purpose of
evaluation, and planning for the control of, water quality in
the Nation's waterways; it encompasses activities mandated in
Sections 106, 208, 303{e), and 305(b) of the Federal Water
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Pollution Control Act, as amended, as well as related program
guidance.
water quality standard: see STANDARD.
WATER YEAR: a continuous 12-month period during which a complete
annual cycle occurs. The U.S. Geological Survey uses the
period from October 1 to September 30.
ZOOPLANKTON: passively floating or weakly swimming microscopic
animals found in natural waters.
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APPENDIX C
ADDITIONAL SOURCES OF INFORMATION
This appendix lists the many sources from which additional
information helpful to water quality management analysts can be
obtained. For ease of reference, these sources have been grouped
into three categories: Maps; Meteorological, Climatological, and
Air Quality Data; and Water-related Data.
MAPS
Several types of informative maps are available to aid the water
quality analyst. These maps, when used properly, can be
excellent sources of data and helpful in understanding and
identifying "the whole picture" of a project.
A. United States Geological Survey Maps.
The U.S. Geological Survey offers a wide variety of
topographical maps of all areas of the United States,
including:
1. Standard Topographical Maps, which depict roads, towns,
political boundaries, some land use and land cover
information, landmarks, and locations of U.S. Geological
Survey streamflow gages as well as topographic
information (available in quadrangles, usually at a
scale of 1:24,000).
2. Topographic County Maps, which are similar in content to
the standard topographical maps except that they are
drawn at a different scale (either 1:50,000 or
1:100,000) and are available on a county-by-county
basis. (This series of maps is available for certain
areas only.)
3. Base Maps, which depict only water bodies, principal
towns, and county boundaries on a state-by-state basis
(at a scale of 1:500,000).
4. State Hydrologic Unit Maps, which add the drainage basin
and sub-basin outlines to the base maps described above.
5. Land Use, Land Cover, and Associated Maps, which depict
land use and land cover, political unit, hydrologic
unit, census county subdivision, Federal land ownership,
or State land ownership (available at scales of
1:100,000 and 1:250,000 for certain areas only).
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6- State Water Resource Investigation Folders, which
contain large maps depicting stream-gaging stations,
observation wells, water quality sampling stations, and
areas in which current hydrologic investigations are
proceeding. Smaller maps depict other significant
hydrologic aspects.
Information regarding each of these map series for areas east
of the Mississippi River can be obtained from:
Branch of Distribution
U.S. Geological Survey
1200 South Eads Street
Arlington, Virginia 22202
Telephone: (703) 557-2751
For areas west of the Mississippi River, information can be
obtained from:
Branch of Distribution
U.S. Geological Survey
Box 25286, Federal Center
Denver, Colorado 80225
Telephone: (303) 234-3832
Maps may be purchased through the above outlets, through
certain Geological Survey offices (over the counter), or,
although usually at a higher price, through authorized local
map stores.
B. National Oceanic and Atmospheric Administration.
A variety of maps and charts are available from NOAA,
including:
1. Conventional Nautical Charts, which are available for
navigable bodies of water (including the Great Lakes) in
the United States. These maps depict water depth by use
of contour lines and sounding depths, locations of buoys
and markers, type of bottom sediments, navigation
hazards, and other specialized nautical information
(scales are usually between 1:5,000 and 1:80,000).
2. Small Craft Charts, which are folding versions (of
varying detail) of the Conventional Nautical Charts
designed to be used in boats.
3. Bathymetric Maps, which depict water depth by using
color tint.
4- Tidal Current Charts and Tables, Tidal Current Diagrams,
and Tide Tables, which include daily current
predictions, current velocities, duration of slack tide
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periods, tidal height at any time, and other useful
information for estuarine and marine waters.
5- Special Issue Charts, Special Maps, and Projections.
Purchase information, including five free nautical chart
catalogs and a list of authorized nautical chart agencies,
can be obtained from:
Distribution Division, C44
National Ocean Survey
Riverdale, Maryland 20840
Telephone: (301) 436-6990
C. Defense Mapping Agency.
The Defense Mapping Agency Hydrographic/Topographic Center is
a good source of topographic and nautical maps on a worldwide
basis. These maps display a wide variety of information at
scales ranging from world-wide to local.
1. U.S. Army Corps of_ Engineers Navigation Charts, which
depict the channel centerline, centerline distances,
navigation hazards, and other information for many
navigable waterways in the United States, are available
through this agency.
2. Many specialized charts are also available, including,
among others:
Great Circle Sailing Charts
Loran A and C Plotting Charts, and
Aeronautical Charts.
A catalog of available charts and purchase information can be
obtained from:
DM0ADS, Attn: DDCP
6500 Brookes Lane
Washington, D.C. 20315
Telephone: (202) 227-2495
METEOROLOGICAL. CLIMATOLOGICAL. AND AIR QUALITY DATA
A. National Oceanic and Atmospheric Adm1n i s t r a t i on.
The best source of long-term rainfall data in the United
States is the National Weather Service. Data can be obtained
from the National Climatic Center either on tape files or
through published daily and hourly summaries. Data are
available for most first-order stations.
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Detailed climatological data can also be obtained for
stations in each State from the National Climatic Center.
These data are published in two forms:
1. Climatoloqical Data monthly summaries (State-wide),
which include daily temperature extremes and
precipitation at every station within a State as well as
daily evaporation, wind, soil temperature, snowfall, and
other supplemental information, where available, and
2. Local Climatoloqical Data monthly summaries (individual
stations), which include hourly precipitation
information and 3-hour temperature, cloud cover, cloud
ceiling, wind, visibility, dew point and relative
humidity information for individual first-order
stations.
Meteorological and climatological data can be obtained by
contacting:
U.S. Department of Commerce
National Climatic Center
NOAA Environmental Data Service
Federal Building
Asheville, North Carolina 28801
Telephone: (704) 258-2850
Yearly summaries are also available for first-order stations,
and special climatological data, such as solar radiation, are
available for certain select stations.
B. Environmental Protection Agency.
EPA provides a good source of air quality information through
its Storage and Retrieval of Aerometric Data system, SAROAD,
which is a centralized data bank containing ambient air
quality sampling data collected nation-wide. These data can
be important in estimating atmospheric rainout, fallout, and
washout loads to water bodies and groundwater. Information
regarding SAROAD can be obtained from:
Surveillance and Analysis Division
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Telephone: (919) 629-5491
WATER-RELATED DATA
A. U.S. Geological Survey.
Four of the water quality data sources provided by USGS are
described below.
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1. The WATer Data STOraqe and REtrieval System, WATSTORE.
contains data collected from stream-gaging stations,
lakes and reservoirs, surface water quality sampling
stations, water temperature stations, sediment stations,
water level observation wells, and ground water quality
wells. Data can be obtained through the USGS Water
Resources Division's 46 district offices, which are
usually located in the State capitals, or from the
National Center at the address below:
Chief Hydrologist
U.S. Geological Survey
437 National Center
Reston, Virginia 22092
Telephone; (703) 860-7000
2. Publications of Water Resources Data provide surface
water quantity information (Part 1} and water quality
information (Part 2) indexed by water year and State.
Much of the information contained in WATSTORE is also
contained in these publications, which can be acquired
through:
U.S. Department of Commerce
National Technical Information Service
Springfield, Virginia 22161
Telephone: (703) 557-4650
3. For specific inquiries, the USGS Water Resources
Division's 46 district offices, often located in the
State capital, can be the best source of information,
especially if only a limited amount of data or recently-
collected (unpublished) data are desired (e.g., flow
records for one river during one year).
4. Information is also available from the USGS NAtional
Water Data Exchange. NAWDEX. NAWDEX is not intended to
provide access to individual data records, although
access to WATSTORE and STORET data is possible through
this system. Its primary function is to index the data
held by its 350 member organizations and participants to
provide a central index of information. Sources of
water data information can be identified through a
computerized Water Data Sources Directory, and the
sampling sites, periods of record, and type of data
available from each source can be determined through the
Master Water Data Index. The computer searches can be
performed at USGS Water Resources Division district
offices and at certain member organization locations.
Information regarding NAWDEX can be obtained from:
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National Water Data Exchange
. . U.S. Geological Survey
421 National Center
Reston, Virginia 22092
Telephone: (703) 860-6031
B. National Oceanic and Atmospheric Administration.
NOAA has many useful sources of water data. Its three major
sources are described below.
1. The National Oceanoqraphic Data Center, NODC, houses the
world's largest usable collection of marine data. Some
estuarine and coastal data are available as well. This
information is available through:
National Oceanographic Data Center
National Oceanic and Atmospheric Administration
Washington, D.C. 20235
Telephone: (202) 634-7500
2. The National Geophysical and Solar-Terrestrial Data
Center, NGSDC. disseminates solid earth and marine
geophysical data as well as ionospheric, solar, and
other space environmental data. This information is
available through:
National Geophysical and Solar-Terrestrial
Data Center
National Oceanic and Atmospheric Administration
Boulder, Colorado 80302
Telephone: (303) 499-1000, Ext. 6215
3. NOAA also offers computerized data base location
retrieval services through its ENvironmental Data
Exchange (ENDEX) system. The Environmental Data Base
Directory, EDBD, is an ENDEX subsystem which enables
users to locate relevant data. An EDBD data file
description lists types of parameters and quantity of
data available, methods used to collect samples, data
formats, restrictions on data availability, when and
where the data were collected, who to contact for
additional information, and estimated cost of obtaining
the data. This subsystem is also integrated into the
previously described NAWDEX system. Further informatioi
is available through:
National Oceanographic Data Center
Data Index Branch, D782
2001 Wisconsin Avenue, N.W.
Washington, D.C. 20235
Telephone: (202) 634-7298
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C. U.S. Environmental Protection Agency.
An updated Environmental Systems Directory, which will
describe all available systems, will be available from:
Management Information and Data Systems Division
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 755-0984
While STORET is the primary EPA water quality data base,
other specialized, water-related data and information systems
are available through EPA, including the following:
1. The Model State Information System, MSIS. is used in
determining compliance with the National Interim Primary
Drinking Water Regulations. It is a decentralized
system used by several EPA regions and States.
Information pertaining to this system is available
through:
Office of Drinking Water
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 426-9805
2. The Technical Assistance Data System. TADS, is used to
reduce the effects of oil and hazardous materials spills
by providing on-line access to information on material
characteristics and emergency response procedures.
Information pertaining to this system is available
through:
Office of Water Program Operations
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 245-3045
3. The Contracts Information System. CIS, tracks the status
of procurement requests and provides summary information
on all agency procurements other than small purchases.
It contains a master file listing over 3,200 active
contracts and 12,000 modifications or other ancillary
records. Information pertaining to this system is
available through:
Procurements and Contract Management Division
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
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Telephone: (202) 557-7716
Tne Hazardous Wastes Data Management System/Underground
Injection Control, HWDMS/UIC, contains permit
information for facilities involved with disposal of
hazardous Wastes that are regulated by EPA. Information
pertaining to this system is available through:
Office of Solid Wastes - State Programs
and Resource Recovery Division
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 755-9150
The Surface Impoundment Assessment System, SIAS,
contains an inventory of the names and locations of
surface impoundments in the United States, with more
detailed information for certain locations. Information
pertaining to this system is available through:
Office of Drinking Water
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 426-9805
The Spill Prevention Control and Countermeasures system,
SPCC, contains descriptions of individual oil spills,
hazardous chemical spills, and tank ruptures as well as
facility inspection records. The data contained in this
system are not extensive, as many EPA regions keep
separate records and do not (and are not required to)
use this system. Information pertaining to this system
is available through:
Office of Water Program Operations
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 245-3045
The Chemicals .in Commerce Information System. CICIS. is
being established to handle data and information
pertaining to the implementation of the Toxic Substances
Control Act including health and environmental effects
studies, chemical activities within EPA, and a large
volume of confidential private business information.
Information pertaining to this system is available
through:
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Office of Toxic Substances - Chemical
Information Division
U.S. Environmental Protection Agency
401 M Street,-S.W.
Washington, D.C. 20460
Telephone: (202) 426-2447
8. The Construction Grants Management System, GICS, is a
management information system used to keep track,of
past, present, and projected EPA grants. Information
available through this system includes grant awards,
status of projects, financial breakdown of projects, and
more. Information pertaining to this system is
available through:
Grants Administration Division
PM 216
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 755-9251
9. The Federal Reporting Data System. FRDS, contains an
inventory of community and non-community public water
supplies as well as pertinent water quantity and quality
information. Much of the data contained in this system
comes from the decentralized MSIS system previously
discussed. Information pertaining to this system is
available through:
Office of Drinking Water
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 426-9805
10. The Permit Compliance System, PCS, contains an inventory
of NPDES permits and relevant information, including
facilities inspections, discharge monitoring activities,
compliance schedules, permit issuance and expiration
dates, and other scheduling information. Information
pertaining to this system is available through:
Office of Water Enforcement
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
Telephone: (202) 755-0994
11. The Establishment. Registration and Support System.
ERSS, contains confidential information pertaining to
the production and distribution of pesticides by
registered, pesticide-producing establishments.
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Information pertaining to this system is available
through: ; :! -
*. - ' - - - '" '
Pesticides and Toxic Substances Division
EN-342 ' ; •. '
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460 .
Telephone: (202) 755-0630
12. The National Eutrophication Survey system, NES. contains
detailed records of phytoplankton'species found in over
600 lakes during the National Eutrophication Survey.
Spring, summer, and fall data are available for each
lake. Associated physical and chemical conditions are
contained in STORET. Information pertaining to this
system is available through:
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
P.O. Box 15027
Las Vegas, Nevada 89114
Telephone: (702) 736-2969, ext. 327 .
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