MANAGER'S GUIDE

       TO STORET
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                     U.S.  Environmental Protection Agency

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Washington,  D.C.  20460

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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.

The 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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                        TABLE OF 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 GUIDEFITIN?              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 STORET 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 KILLS                                          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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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:
 DATA  ENTRY:
 STORAGE:
 RETRIEVAL
MEDIA:
FORMATS:
REVIEW:
                  Hard Copy         Cards
                         Tape
                                                                 Disk
                                       Microform
                                                                  Terminal
Maps
                                     Graphs       Statistics
                           FIGURE 1-1
                   FROM DATA TO INFORMATION
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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


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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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          WATER QUALITY

          MANAGEMENT

       PROGRAM GUIDANCE
                                        MANAGER S
                                        GUIDE TO

                                         STORET
           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
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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, B.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, B.C. ((800) 424-9067) can furnish you with the name
and telephone number of your representative.
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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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                        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.*

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.

 2Technique 2-2:  Identification of Stations  in a Specified Area
 and Technique  2-6: Plotting Locations of Monitoring Sites.
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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
 2-4:  Retrieval  of Raw Data; and Technique  2-5: Determination  of
 Sampling  Patterns over Time.
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            EFFECTIVE DISTANCE (MILES)


      10"'  10"*   10"'    10   I01     10*    I01
10'
          SECONDS

          10s   IOT
1 I

THERMAL
__



FLOATABLE3



BACTERIA




SUSPENDED SOLIDS



DISSOLVED OXYGEN
-
1

NUTRIENTS



TOXIC EFFECTS




DISSOLVED SOLIDS



                                                   FLOATABLES
      10 *   10 *  10"'   10   10'    10*
     I 5 FT I  (SOFT) (500FTI
             EFFECTIVE DISTANCE (MILES)
                                                        DISSOLVED OXYGEN
                                                            SUSPENDED SOLIDS
                                                        ACUTE TOXIC
                                                          EFFECT
                                                              T.I
                                                                      DISSOLVED SOLIDS
       DAY I  MONTH!  YEAR

        *FEK  SFSON    DECADE
             - LOCAL -
                         -REGION
                 SPACE SCALES
       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
 4Technique  2-7: Retrieval of  Intensive  Survey  Information.


                                2-6

-------
       discharge  of  pollutants".   Retrievals of  effluent and biological
        data  from  STORET  are discussed in Chapters 4 and 5 of this Guide,
       respectively.
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                                       2-7
I

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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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
Use the Water Quality File retrieval
program STA.


Enter the agency code for the agency of
interest.
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.
Part WQ, Chapter RET, Section 6.
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

-------
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 "COS" preceding
each station code indicate that these are
secondary station codes (S)  that are
available on-line (00).
21M1CH
DOS' AC00504.1
OOS AC00524.0
OOS AC00540.1
OOS AC00571.U
OOS AC00601.0
OOS AC00644.1
OOS AC00690.0
OOS AC00735.0
OOS AC00780.1
OOS AC00786.0
OOb ACOO/91.1
OOS ACOOU46.0
OOS ACQ0974.0
OOS AC01001.0
OOS AC01047.0
OOS AC01146.0
OOS AC01281.3
OOS AC01400.0
OOS AC01680.0
OOS AC01860.0
OOS AC01930.0
OOS AC02000.2
OOS AC02300.0
OOS AC02blG.2
OOS AC02680.3
OOS AC03395.0
OuS AC04260.2
OOS AC04990.3
OOS AC05660.0
OOS AC08659.1
OOS AC09600.3
OOb AC17260.1
OOS AC20044.0
OOS AUS002
OOS AUS008
OOS BA0010
OOS BA0017
OOS KAG023
COS BA0035
OOb BA0047
OOS BA0059
OOS BA0067
OOS BA0080
OOS BA0100
OOS BA0109
OOS BA0122
OOS BA0139
OOS BC0013
OOS B03820
OOS B04240
OOS CCCP001
OOS CPCN020
OOS DECC001
OOS DEDP001
OOS DETP001
OOS DEWS010
OOS DI0006
OOS DI0013

OOS
OOS
OOS
OOb
OOS
OOS
OOb
OOb
oos
oos
oos
oos
OOb
OOb
oos
oos
COb
OOb
OOb
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
OOb
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos

AC00505
AC00525
ACOOS40
ACOOS75
AC00602
AC00665
AC00695
AC00735
AC00781
AC00788
AC00825
AC00857
AC00975
AC01001
AC01062
AC01150
AC01281
AC01438
AC01722
AC01880
AC01930
AC02030
AC02400
AC02560
AC02680
AC04001
AC04320
AC05220
AC05660
AC08850
AC09711
AC17260
AC20044
AUS003
AUS009
BA0011
BA0018
BA0025
BAC03E
BA0046
BAOOGO
BA0069
BA0081
BA0102
BA0110
BA0123
BA0142
B01450
B03901
B04330
CCCP010
CPCS010
DECC010
DEDP010
DETP010
DIQ001
DI0007
DI0014

.0
.0
.2
.0
.0
.0
.0
.1
. 0
. 1
.0
.0
.1
.1
.0
.0
.4
.0
.0
.0
.1
.0
.0
.0
.4
. 0
.0
.0
.1
.0
.0
.2
.1


























OOb
OOb
COS
OOb
OOb
OOb
OOb
QOb
OOb
OOb
oos
oos
OOb
oos
oos
OOb
OCb
oos
oos
oos
oos
oos
OOb
OOb
oos
oos
ocs
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
COS
oos
oos
oos
oos
COS
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos

^coobos
AC00527
AC00545
AC00580
AC00617
AC00670
AC00695
/.CU0735
AC00781
/C00788
/C00830
ACOC857
AC00975
A C C 1 0 2 0
AC01075
ACC1200
AC013?0
AC016CC
AC01744
AC01900
AC01930
A:02030
A:0250C
A:02571
AC0286C
A.:U4150
ACC4320
AC05220
AC07730
AC08970
AC 10130
AC 17260
V 20044
ALS004
AIE010
BAOC13
BAOC19
BAOC27
BACC39
BA0052
BAOC62
BAOC70
BA0087
BA01C4
BA0113
BA0124
BAC143
B01460
B04000
B04340
CCCP020
Ck 'P001
DECP001
DERP001
DEWN001
D10002
DI 1)009
01(1015

.1
. 0
.0
.0
. 0
. 0
.1
.2
. 1
.2
.C
. 1
.2
.0
. P
.1
. 0
. C
.0
.0
.2
.1






.0
. c
.0
.3
. 2


























OOS
OOS
OOs
OOS
OOS
OOb
OPS
OOb
0 0 S
oos
oos
oos
oos
OOb
cos
oos
OOb
oos
oos
oos
ocs
oos
ocs
oos
oos
oos
COS
oos
oos
oos
oos
oos
PPS
ocs
oos
oos
COS
oos
oos
oos
ocs
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos
oos

ACQ05.06.0
ACOOE30.0
ACOO=j45.1
ACOC584.0
AC00629.U
ACOP670. 1
AC0059G.O
AC 00 /48.U
AC00785.C
AC00790.P
ACCC830.1
AC0089P .0
ACOC975. 3
AC01P20. 1
AC0109P.O
AC0127C. P
ACP1327.P
AC0161C.P
AC01775.0
AC01920.P
AC0195S.C.
AC02031.2
AC025PO. 1
AC02580.0
AC02P'in.l
AC04230.0
AC04 8(55.0
AC0537U.O
AC07730.1
ACG960P.O
AC101 31.0
AC17350.C
ALOC23
AUS005
BA0001
BA0014
BA0020
BA0029
BAC01C
BA0053
BACOG3
BA0072
BA0089
BA0105
BA0116
BA0130
BA0149
B0250C
B04C10
B043C.P
CCTE010
ChPPOlO
DECP002
DERP010
DEWN010
DI0003
DI0010
DI0016

OPS
POb
oos
ons
oos
oos
OCb
OPS
PPS
ocs
OPS
OCb
OOb
ocs
oos
ocs
ocs
oos
ocs
oos
ocs
0 C S
PCS
OPS
ocs
CPb
oos
oos
oos
COS
oos
oos
OOb
oos
POS
oos
oos
POS
oos
ocs
COS
oob
oos
oos
oos
oos
oos
oos
COS
oos
oos
oos
oos
oos
oos
oos
oos
oos

ACC05P8. i)
ACC053C. 1
AC00551 . 0
AC00600.0
AcooMq.o
ACOOh7P . /
ACOP72" . P
'\eop /5B.p
A C 0 C 7 ," 5 . J
ACOP 190. i
ACOOf- 3r> . P
ACOPy 1 2 . P
ACOOy94 .p
ACC1P2C.3
A C 0 1 1 0 P . P
AC01?l'l . 1
AC01330. f
AC( lP.P
AC01U00. J
AC 05 170.1
ACC7/30.i
ACOCf.no. 1
AC12EOP.P
AC 137 70. R
ALOP45
AUS006
BAOOB4
BAOP15
BA0021
BACC3C
hAl/041
HA005G
BAOO P 5
BAOR74
BA009"
BA01C ^
BA0117
BA013?
BA0150
B 0 2 7 4 n
PC4P40
B04370
CCTE02P
ChPP020
DECPOiQ
DERP020
DLl- N020
D I 0 0 0 4
DIOOU
DI0017

(U s
I/PS
PCS
PCS
POS
PPS
POS
POS
OPS
PPS
ORs
['PS
I OS
PPS
ccs
OPS
oos
POS
PPS
('PS
OR:.
PPS
ocs
PPS
PCS
CPS
OPS
P P E
( PS
LOS
COS
OPS
ORE
OPS
PPS
PPS
C 0 S
CCS
CPS
G C E
OCS
P P S
OPS
ocs
CPS
ocs
POS
ocs
ocs
oos
cos
oos
oos
POS
CCS
ocs
ocs
oos

AC( PL.1 3 .(
"CI Pblt .c
C"Pb. 1
APP2>irr . /

A C P ', 2 ' I .1
A C P 4 y 9 (, . ,'
AC( 5h5?.(
"ri)8h59 '
Arc'jbpp . .'
AC1712C.1
AC1?77(, . 1
MJ3PP1
AUSP07
D A f P P 5
BA( Plf.
r ;. P r ? 2
BAP033
p f ( P ^ r
,;f PPSS
B A 0 P f, 'i
PAPP77
BM'091
BAPK P
BA011!)
BAP1 35
bAOlM
B P 3  1 P
P ') ', ? 1 C
C C A P P 1 R
CPCMG10
UCTPI110
CE.CPP ^P
DEHP03C
n E '.\ b o p i
D I 0 C 0 5
D I 0 0 1 2
Dipoie
                              2-10

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                   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.
           ST/CO I    LOCATION
           STATION TYPE
           USER CODE   STATION
              LAT/LONG

                  INDEX
                                                    SASIN CODE
                                                                STORAGE DATF
 SECONDARY STATIONS
 1ILESLV1   LV2
 **AJ/MIN/TERM
LVS  LV6   LV7  LVP  LV9  LV1C  IU1
  MICHIGAN
  MICHIGAN
  MICHIGAN
  MICHIGAN
  MICHIGAN
  MICHIGAN
  MICHIGAN
           2b053 "IONTHEAL H. AT AURORA ST.
           /TYPA/AMBNT/STREAM
           21MICH      270C09       INOC.'S
           46 27 01.D DSC 10 42.0
                 INDEX
           26053 MONTREAL R. AT COUNTY RT,
           /TYPA/AMBNT/STREAr
           21MICH      2701)10       I'JOOI'9
           If, 26 23.5 090 1C 00.0
                 INDEX
            2(,053 HONTIttAL II. AT COUMTY RL .
            /TYPA/AMBMT/STHtAM
            21MICH      270011
            46 23 16.0 090 08 2 8 . {,
                  INDEX
            260S3 JACKSON CR. AT M-28 biilTCE
            /TYPA/AMBMT/STHtAM
            21MICH      270014
            4b 30 i/.O 089 52 bO.O
                  INDtX
            26053 MONTREAL RIVER NEAR I;?ON'\OOD,
            /TYPA/A^BNT/STREAM
            112WRD      04026100
            46 30 26.0 090 13 47.0
                  INDEX
                                     2-12

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:
NOTES:
Use the Water Quality File retrieval
program INVENT or INV120.
Enter station identification and data
selection keywords to define geographic
area and time period of interest.
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.
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. Forest Service (agency code 14AGNFS9).
The retrieval was limited to data collected
in 1978 and 1979.
/TYPA/AHBNT/STREAM
PARAMETER
00004
00010
00020
0002$
00036
00042
00043
0004*
0004%
00052
00 tl
0006%
00076
00080
OOOfil
00095
00300
0030]
00403
00410
00400
00610
00615
00625
00630
00650
00900
01355
STREAM
HATER
AIR
(ARONTRC
MIND
ALTITUDE
CLOUD
CLOUD
PKEC1P
RELATIVE
STREAM
STREAM
TURD
COLOR
AP COL CD
CNOUCTvr
00
00
LAB
T ALK
T DT A L N
NH3-N
N02-N
T01 KJfL
N021N03
T P04
TOT HARD
ICE
MIOTH
TEMP
TEMP
PRESSURE
DlR.FROf
FEET
TTPE
AMOUNT
TOT DAT
MJH10IM
FLOW,
STAGE
TRBIOM1R
PT-CO
PT-CO
AT 25C

SATUR
PH
CAC03
TOTAL
TOTAL
N
N-TOTAl
P04
CAC03
COVER
FEET
CENT
CENT
HM OF HC
NORTH-0
AB MSL
MHO CODE
HMO CODE
IN
PERCENT
1NST-CFS
FEET
MACH FTL)
UNITS
UNITS
MICROMHC
MC/L
PERCENT
SU
MC/L
MG/L
MC/L
MC/l
NC/L
MC/l
MG/L
SEVERITY
NUMBER
1C
15
15
15
15
15
15
15
I)
15
10
15
14
13
1
14
15
15
14
13
3
1
3
14
14
*
1
HE IN
30.2000
13.2667
14 .3000
29.9019
178.667
1332.67
3.53133
4.53333
.139231
59.7333
183.167
87.8200
1.74285
103.846
37.CCOO
77.8571
10.U67
9B.2465
7.27142
27.2461
.13000C
.002000
.886666
.096428
.067500
45.6500
t. 00000
070858 C90704089141 402010404141 PERCH CIV.
46 31 10.0 cfB 39 5.0 3
10.9MI .079DEC. FROM M28f,FH)6N J.
26013 MICHIGAN
LAKE SUPERIOR 22C800
STURGEON RIVER-PESCH KIVfPR
14AGNFS9 781227
0000 CtASS CO
VARIANCE
52 .01?!
67.0667
101 .636
.038225
9108.82
49.1428
5.98C96
8.55240
.055008
'62.640
10951.5
.544(43
.478037
1674.31

465.827
4.30814
28.3?14
-0"2961
69.2295
.002100

.573033
.004855
.CC0479
38.6237

STAM V
T .21194
8.18942
1 0.0815
. I9513
95.4401
7.01020
2.44560
2.92445
.234537
23.72CO
104.649
.737999
.691402
*0.91F4

21. '830
? .07561
5 .32179
.30*895
f .32043
.04*826

. 75f989
.Of9t81
.021894
6 .? J480

COEF VAR
.23PBC6
.H7293
. 704997
.CO 6538
.534180
,0052fO
.692151
.645099
1.68452
.39)099
.568849
.C084C4
-3t7C7
.394029

.277213
.?041>(>
.054168
.0419?]
. 305 380
.3525C6

.85 ^74H
.722(22
.324357
.136140

STAND ER
2.2fC62
2.11450
2 .6C302
.050481
24.6425
1 .81002
.631451
.7*5089
.065049
6 .12449
33.T930
. 19C551
. 184765
11.3487

5 . 76P30
.535919
1 .37408
.081487
? .30767
.026458

. 4 37048
.018623
. 005851
3.10740

MAX IfUM
44.0000
22.0000
27.0000-
30.1600
360.000
1150. OC
6.00000
9.00000
.730000
95.0000
445.500
88.9000
3.70000
195.000
37.000C
131 .000
13.5000
109.600
7.70000
39.6OOO
.180000
.002000
1 .61000
.27COOO
.111000
54.0000
6.00000
M1NJTOM
22 .. fOOO
.OOOOOO
, lOCIf 0?
29.3700
.00 COCO
1330.00
.000000
.ocoooc
.ocoooo
19.0000
79.0900
86. 1000
.900000
40.0000
37.0000
48.0000
8.10000
89.7000
6.80000
ll.'-OOO
.09OOOO
.002000
. 100000
.020000
.027000
40.0000
6.00000
BE c r 11
78/04/?
78/03/1^
78/C3/(f
7P/03/CI
78/03/Cf
7P/03/Of
78/03/CE
78/03/Cf
78/03/Cf
7P/03/(f
7P/04/J4
78/03/t f
76/03/lf
78/03/Cf
79/02/i 7
78/C3/CI
78/c3/(f
78/03/f f
78/C3/(f
7t/c/;
78/o*/r<,
79/02/^1
78/0* /( *
78/03/U
78/C3/Cf
78/04/74
78/03/Cf
END DMl
78/C9/26
79/04/11
79/C4/1I
79/04/11
79/04/11
79/04/11
79/04/11
79/04/11
79/C4/11
79/C4/11
78/C9/26
79/04/11
79/02/27
78/09/26
79/C2/27
79/C2/27
79/04/11
79/04/11
79/C2/27
79/C2/27
79/02/27
79/C2/27
79/C2/27
79/C2/27
79/C2/27
79/C2/27
78/03/08
                              2-14

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                   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:        Part WQ, Chapter RET, Section 6.


NOTES:                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.
010858 090"'04089141 402010404141 PERCH RIV.
46 31 10.0 088 39 45.0 3
lO.gMI.O^DEG. F
26013 MICHIGAN
LAKE SUPERIOR 220800
STURGEON RIVER-PERCH RIVERR
14AGNFS9
'8122'' DEPTH 0
/TYPA/ANBNT/STREAM
INITIAL DATE ''8/03/08 ^8/04/24 ^8/05/09
INITIAL TIME-DEPTH-BOTTOM 1100 0000 1230 0000 1105 0000
00004
00010
00011
00020
00025
00036
00042
00043
00044
00045
00052
00061
00065
O00'"6
00080
00095
00300
00301
00403
00410
00600
00610
00625
00630
00650
00900
01355
STREAM
WATER
MATER
AIR
BAROMTRC
WIND
ALTITUDE
CLOUD
CLOUD
PRECIP
RELATIVE
STREAM
STREAM
TURB
COLOR
CNDUCTVY
DO
DO
LAB
T ALK
TOTAL N
NH3-N
TOT KJEL
N02SN03
T P04
TOT HARD
ICE
WIDTH
TEMP
TEMP
TEMP
PRESSURE
DIR.FROM
FEET
TYPE
AMOUNT
TOT DAY
HUMIDITY
FLOW,
STAGE
TRBIDMTR
PT-CO
AT 25C

SATUR
PH
CAC03
N
TOTAL
N
N-TOTAL
P04
CACO3
COVER
FEET
CENT
FAHN
CENT
MM OF HG
NORTH-0
AB MSL
WMO CODE
WMO CODF
IN
PERCENT
INST-CFS
FEET
HACH FTU
UNITS
MICROMHO
MG/L
PERCENT
SU
MG/L
MG/L
MG/L
MG/L
MG/L
MG/L
MG/L
SEVERITY

0.0
32.0
10.0-
30
225
1350
1
1
0.00
' 19.0

Qi . 0
2 . i
40
131
13.5
95.9
-1 T




0. 2
0.03

6
INITIAL DATE '18/OVO'7
INITIAL TIHE-DEPTH-BOTTOH
00004
00010
00011
00020
00025
00036
00042
00043
00044
00045
00052
00061
00065
000^6
00080
00081
STREAM
WATER
WATER
AIR
BAROMTRC
WIND
ALTITUDE
CLOUD
CLOUD
PBECIP
RELATIVE
STREAM
STREAM
TURB
COLOR
AP COLOR
WIDTH
TEMP
TEMP
TEMP
PRESSURE
DIR.FROM
FEET
TYPE
AMOUNT
TOT DAY
HUMIDITY
FLOW,
STAGE
TRBIDMTR
PT-CO
PT-CO
(SAMPLE CONTINUED ON NEXT
FEET
CENT
FAHN
CENT
MM OF HG
NORTH-0
AB MSL
WMO CODE
WMO CODE
IN
PERCENT
INST-CFS
FEET
HACH FTU
UNITS
UNITS
PAGE)
1045 0000

21.0
69.8
23.0
30
200
1330
6
6
0.45
94.0

8"i . 5
1.6
H


44
6.0
42 8
10.0
30
90
1350
3
5

19.0
446
46.6
3 "
-15
48
2. 5
115 2
i 5
12



0 3
( .08
40

-8/0
1230
71
;2.o
n.6
;- o
30
180
1330
5
i
0 . i j
10.0
164
88.0
1.3
115


34
9.0
48 2
i 0
29
2">0
1330
5
9
V.\i
95.0
233
Q1 . ^
1 9
80
65
10.2
92.4
1.6
26
1.05 C
0. 180
0.950
0.0
0.06
54

V25 -"8/08/09
0000 1130 OUOO
25
19.0
66.2
19. C
30
360
1330
8
4
0.00
40.0
->9
86.1
0.9
104


8/05/24
1015 0000
22
22.0
"1 6
23.0
30
0
1330
1
1

33.0
146
9"1 5
1.8
85
12
8 . "
133.5
.5
35
1.68 C
0 120
1 610
C 1
0.06
42

'8/08/25
1000 0000

1.1 c
62.6
20.5
30
45
1330
3
9
0 00
84.0

88.9
1 . 3
195


B/06/02
1030 0000
40
14.0
5^ 2
11 0
30
2 ""0
1330
4
3
0.05
51 0
243
b8 5



9.3
94 . 4









'8/09/0''
1045 0000
26
20 0
68.0
20.0
30
180
1330
3
2
0.00
94.0
158
88. 2
1.4
120


8/06/02
1035 0000













1.6
160
63


6.9
21



0.1
0 . O"7


^8/09/26
0900 0000
26
13.0
55.4
16.0
30
180
1330
0
6
0.38
64.0
123
88.0
1 6
if,


^8/06/15
0955 0000
>b
14.0
5"1 , 2
16 0
io
1.10
1 3 10
2
2
0.0 )
bO 0
1 ?4
bS.O
1 6
i :. o
" 3
1 U . 8
109.6
i . 3
2 "



u.c
0.05


19/02/21
1UOO 0000

O.'J
32 0
8.0
30
180
1330
0
0
u. 00
38. D

88 . 3
I . b

31

18/06/2^
1230 OOOu
33
21.0
69. 6
24 0
30
2^0
1330
6

0 . U J
51 . u
125
8 1 . u
1. 4
96
8L
8 b
I02.i
i . 3
29



0. 1
O.L"7


"9/04/11
U83U UUUU

1 . I
33.8
J . 0
30
9U
133U
6
(3
0.00
48 0

b8. 5
0 8

4

                              2-16

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
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".
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.
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.
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  v/inter of
                     1976-1977 to  the fall of  1978.  A  yearly
                     summary  for 1977 also is  included.
  STORET RETRIEVAL DATE 79/08/27
  /TYPA/AM8NT/5TREAM
   DATE
   FROM   OF
    TO    DAY  FEET
         00010    000""6    000 li 5     00060
IME DEPTH /iATEF     TURB   CNDUC'_'VY   STREAM
         TEMP   TR3IOTTR   AT 2SC     PLOW
         CENT   tUCH FTU  IICRO'IHO    CF3
  76/12/21
  VARIABLE NUMBER
  77/03/20
  77/03/21
  VARIABLE NUM3ER
  77/06/20
  77/06/21
  VARIABLE NUMBER
  77/09/20
  77/09/21
  VARIABLE NUMBER
  77/12/20
  77/01/01
  YCAR    NUMBER
  16/01/UO
  77/12/21
  VARIABLE NUMBER
  "8/03/20
  7B/03/21
  VARIABLC NU.-18ER
  76/06/20
  78/06/21
  VARIABLE NUMBER
  7S/09/20
  78/09/21
  VARIABLE NUMBER
  78/12/20
         3.00000  3.00000   3.00000   3.UOOOO   3.0UOGO   3.00001,


         3.00000  3.00000   3-OOCoO   3.00000   3.CO000   3.UOOOO


         3.00000  3.00000   3  OOtOO   3.00000   3.000LU   3.000 CO


         3.00000  3.00000   3.00COO   3.00000   3.00000   3.000GU


         12.0000  12.0000   120COO   12.0000   12.0000   12.000C


         3.00000  3.00000   3.00000   3.0COOO   3.0000U   3. u U U 01.


         3.000'JO  3.00000   3.0UOOO   3.00000   3.00000   3.1000C


         2.00000  2.00000   2.00000   2.00000   2.00000   2 COOOO


         4.00000  4.00000   4.00000   1.00000   4.00000   4.00000
                                          2-18

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
Use the Water Quality File retrieval
program LOC; specify labeling of stations
with a cross-reference number.
Enter station identification keywords to
define the geographic area and/or agency of
interest.
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.
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.
                              2-20

-------
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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.
A:
EG
CO
DO
to
FO
GO
HO
13
JO
tii
LO
MC
NO
00
PO
t.
R^
SO
TO
uo
VG
kO
xo
YO
0
ABO
 CO
ADO
AEO
AFO
AGO
AHO
410
AJO
AKO
ALO
AMO
ANO
too
APO
AQO
ARC
ASO
ATO
AUO
AVO
AUO
AXO
Arc
BAD
BBO
BCO
BDO
BEO
BFO
BGC
BHO
610
21MICH
21MICH
21HICH
2iHiCH
21H1CH
21MICH
21NICH
21H1CH
21MICH
21H1CH
21MICH
21NICH
21HICH
21N1CH
21M1CH
21HICH
21HICH
21HICH
21H1CH
21NICH
21MICH
21 MICH
21HICN
21HICH
21MICH
21NICH
21MICH
21HICH
21MICH
21MICH
21MICH
21HICH
21H1CH
21MICH
21MICH
21HICH
21M1CH
21MICH
J1MICH
21HICH
21 MICH
ZlHICH
21MICH
21MICH
21HICH
21MICH
21H1CH
21MICH
21MICH
21MICH
J1M1CH
21HICH
21HICH
21HICH
21MICH
JlHICH
21M1CH
21MICH
21MICH
320051
730002
730003
73001B
73002*
730025
7300*8
730065
730071
730097
730098
730099
730100
730103
73010*
730105
73013*
7301*7
7301*6
7301*9
730150
730151
730152
730153
73015*
730155
730156
730157
730158
730160
730161
730 J 63
73016*
730165
730166
730167
730168
730186
730187
730188
730239
7302*0
7302*2
7302*3
7302**
7302*5
760011
760012
760080
760109
760123
7*012*
760125
760126
760127
760128
790001
790002
790003
*3
*3
*3
*3
*3
*3
*3
*3
*3
*3
*3
*3
*3
*3
*3
43
43
*3
*3
*3
43
43
*3
*3
43
*3
*3
43
43
43
43
*3
43
*3
43
*3
*3
*3
43
*3
*3
*3
43
43
43
43
43
*3
43
43
*3
43
43
43
43
43
43
43
43
41 15.0 082
27 17.5 OB3
28 32.5 063
19 37.5 083
21 5*.0 063
23 3*.0 08*
2* 56.0 06*
26 16.0 08*
19 48.0 063
19 5C.O 083
19 28.0 063
19 41.0 083
19 44.0 063
20 28.0 063
21 29.0 083
21 07.0 083
23 03.0 083
29 54.0 083
28 52.0 083
26 10.0 083
2* 08.0 083
21 33.0 064
24 39.0 083
2* 56.0 063
25 17.0 083
26 01.0 083
28 03.0 083
29 36.0 083
29 48.0 083
21 23.0 084
25 31.0 083
26 35.0 083
27 20.0 083
27 46.0 083
20 56.0 084
19 19.0 063
21 45.0 083
19 50.0 083
19 18.0 083
20 56.0 083
21 17.0 083
19 42.0 083
21 12.0 084
21 30.0 084
23 39.0 063
25 55.0 084
21 03.0 082
33 08.0 083
21 29.0 083
39 04.0 082
22 17.5 082
21 35.0 063
20 33.0 063
19 47. 0 063
19 **.0 083
19 45.0 083
36 06.0 063
3* 05.0 063
19 27.0 083
57 32.0
55 38.5
50 16.0
** 19. C
57 17. C
00 *0.0
02 *2.0
02 *9.0
*9 49.0
43 58. C
4* 27.0
45 27.0
46 36. G
51 52.0
52 57. C
54 55.0
56 31. C
53 55.0
5* 48.0
56 27. C
57 58.0
03 10.0
57 58. C
57 42.0
57 11.0
56 30.0
54 36.0
54 13.0
54 02.0
03 29.0
56 48.0
56 00.0
55 25.0
54 15.0
00 14.0
48 28.0
56 07.0
42 53.0
47 36.0
53 02.0
53 52.0
* 51.0
03 20.0
00 05.0
57 51.0
01 30.0
58 38.0
05 *2.0
01 37.0
56 15.0
58 *7.5
01 33.0
03 18.0
03 52.0
0* 02.5
0* 18.0
08 12.5
13 25.0
39 07.0
2
1
2
2
2
2
2
2
2
*
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
*
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
4
0
C
0
0
0
0
0
C
0
0
0
0
0
0
0
0
r
C
C
0
0
f
0
0
0
0
C
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
N BR CASS R AT STANBAUGH PO
SIGINIH RIVER AT 6TH AVENUE
CHEBDYCAN1NG CR AT PORTSMOUTH RD
MATER INTAKE FROM CASS RlVfR
CASS RIVER AT M-13 BRIOGF
T1TTABAKASSEE RIVER AT CENTER RD
TITTABAWASSEE R AT 6XATIOT RO.
TITTABAWASSEE R. AT STATF ROAD
CASS f AT DIXIE HWY BRIDGE
CASS F. AT FRANKENH. BOAT IAKCNG
CASS RIVER AT M-83.
CASS RIVER AT DFHMEL ROAD.
CASS PIVER AT S. BEYER Rt .
CASS RIVER AT 1-75 BRIDGE.
CASS RIVER AT BRIDGEPORT eplrC= .
CASS RIVER AT STUOGR ROAD.
TITABA. R NEAR SAGINtM R CONFL.
SAGINAH R AT LIGHT BUOY 58
SAC1NAW PIVEF Off 1-75 BRIDGF
SAGINAH R OFF DAVENPORT AVE FR
SAGINAK R OFF CENTER ST BRIDGE
SHIAUASSEE R AT S RIVER RD
SAGINAW RIVER AT RUST AVE BRIDGe
SAG1NAM RIVER AT COURT ST FRIDGE
SAGINAH R AT BRISTOL ST BRIDGE
SAGINAH R AT GENESSEE AVE BRIDGE
SAGINAM R - 0.75 HI ABOVE 1-75
SAGINAH R - 1.0 Ml BELPH 1-75
SAGINAH R AT LIGHT BUOYS 63-6>
SHIAHASSEE R. 1/4 MI. PHOU SHAN
SAGINAU R. AT PENN CENT. RR BRIO
SAGINAH R. OFF CARROLLTDN M
SAGINAH R. UPSTR. CARROLLTON BAR
SAG. R. OFF CHEV. NOD. *T
FLINT R-SPAULDIN6 DRAIN
CASS P. 3.5 MILES H. OF FRANKFNH
CASS RIVER AT SHERIDAN ROAD.
CASS R. AT RCDAHHER RO.
DEAD CREEK AT CURTIS RD.
CASS R. AT FAYETTE STREET
CASS R. DHNSTR BRIDGEPORT HHTP
CASS R. DHNSTR FRANKENHUTH HHTP
SHIAHASSEE R 100TO UPSTR FLINT K
FLINT RIVER AT FERGUSON CAYOU
SAGINAH RIVER AT HICKS PARK
TI7TAPAMASSFE R 3 SAGINAN THP PK
S.BR.CASS R. AT FRENCH LINE RF.
CASS RIVER AT M-53 BRIDGE
DUFF CREEK AT FRENCH LINE RD
S FK CASS R AT M-19 BRIDGE
DUFF CR. UPSTR SOUTH BR. CASS R.
DUFF CREEK AT HOOD ROAD
DUFF CREEK AT MAVVILLE ROAD
DUFF CREEK AT BOYNE ROAD
DUFF CR JUST UPSTR MARLETTE HHTP
DUFF CR.400YC UPST MARLETTE HHTP
N.BR. CASS R. AT M-81 BR.
CASS P. AT KURDS CORNER ROAD.
PERRY CREEK AT ORMES ROAD.
2tr,63
261*5
26145
261*5
261*5
215
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
2M*">
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
2tl5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*5
261*'
261*5
2fcl5
261*5
261*5
261*5
261*5
26151
26151
26151
26151
26151
26151
26151
26151
26151
26151
26157
26157
26157
HURON CO. ,
SAGINAM CO.
SAGINAH CC.
SAGINAH CO.
KlCHIGAf.
MICHIGAN
SAGINAH CD.
SAG] NAM CO.
SAGINAH CC.
SAGINAU CC.
SAGINAU CO.
SAGINAH CO.
SAGINAH CO.
SAGINAH CC.
SAGINAU Cf.
SAGINAU CC.
SAGINAU CC.
SAGINAH CO.
SAGINAU CC.
SAGINAU CC.
SAGINAH CC.
SAGINAH CO.
SAGINAH CC.
AGINAH CO.
SAGINAH CC.
SAGINAH CC.
SAGINAU CC.
SAGINAU CC.
SAGINAU CC.
SAGINAU CC.
SAGINAH C( .
SAGINAU CC.
SACINAH CC.
SAGINAU CC.
SAGINAU CC.
5AGINAH CD.
SAGINAU CC.
SAGINAH CO.
SAGINAU CC.
SAGINAU CC.
SAGINAU CD.
SAGINAU CC.
SAGINAU CC.
SAGINAU CO.
SAGINAH CO.
SAGINAH CO.
SANILAC CO.
SANILAC CO.
SANILAC CC.
SANILAC CO.
SANILAC CC.
SANILAC CO.
SANILAC CD.
SANILAC CD.
SANILAC CO.
SANILAC CO.
TUSCOLA CO.
TUSCOLA CD.
TUSCOLA CO.

HI
MI
MI
SACINAH
SAGINAH
MI
MI
MI
MI
HI
MI
MI
MI
MI
Ml
MI
MI
MI
MI
HI
MI
Ml
MI
MI
MI
HI
MI
HI
HI
Ml
HI
MI
HI
MI
HI
HI
HI
HI
HI
MI
MI
MI
HI
HI
MI
MI
HI
Ml
HI
MI
HI
MI
MI
MI
MI
MI
HI
HI
        2-21

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:


NOTES:
Use the Intensive Survey Directory File
retrieval procedure ISFIND.
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.
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.
Part WQ, Appendix G.
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

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:


NOTES:
Use the Water Quality File retrieval
program INVENT or INV120; request that
individual station summaries be suppressed,
Enter appropriate station identification
and data selection keywords to define the
geographic area and time period of
interest.
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.
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 Michigan over 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
8288
7830
12773
25453
31913
52073
58707
69899
79734
94247
138528
77327
63923
69375
55919
8706
871500

f OF SAMPLE
940
676
287
344
798
1130
1016
2166
3447
6135
5105
5625
6362
6726
7117
9579
5051
3485
4362
3047
455
73853
bTA
= 0
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 YRS
< . 5
7
4
0
1
0
13
0
40
61
32
22
24
51
101
328
360
245
216
135
98
1
1739
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0
6
2
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4
5
17
5
27
20
25
35
63
113
12
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42
5
405
> = 3
3
0
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73
65
280
75
334
177
98
56
192
114
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7.29421
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STAND ER
6.80016

230.818
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MAXIMUM
200.000
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MINIMUM
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1 .70000
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200.000
BEG DATE
71/07/13
74/12/12
69/08/12
20/06/25
74/05/50
74/05/50
74/05/50
20/06/25
74/02/06
71/07/01
66/04/15
20/06/^5
20/05/25
20/(>6/
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                                   MANAGER'S  GUIDE
                                         TO
                                       STORET
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                               EXISTING WATER QUALITY
                                       AND
                                       CHAPTER
                                          3
                                  HISTORICAL TRENDS

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                            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(b) reports,
conclusions are to be based on data collected over the previous 2
water years (e.g., the 1980 reports draw on the results of
monitoring conducted from October 1977 through September 1979).

In some cases, only critical period data are used, and that
period may be defined by specific dates and/or flow and water
temperature conditions.  STORET provides the analyst with the
capability of limiting a retrieval to data that fulfill any of
these types of criteria.  It should be mentioned, however, that
retrievals based on flow values will be effective only if data
are stored in STORET under the appropriate parameter codes.   If
flow data have not been collected at the STORET stations of
interest, it may be necessary to interpolate using USGS flow data
collected at nearby gaging stations.1

In other instances, critical conditions may be defined on the
basis of spatial criteria.  For example, worst-case evaluations
technique 3-1:  Transfer of USGS Flow Data to STORET Stations.


                               3-1

-------
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(b) reporting, guidance
suggests that existing and projected water quality conditions in
each stream segment be reported (as depicted in Table 3-1) using
numerical codes representative of the frequency of water quality
standards violations.2  For other applications statistical
summary or mapping techniques may be more appropriate.3  Where
data to be aggregated have been collected over a relatively
lengthy period of time, existing conditions may best be evaluated
using techniques normally reserved for trend analysis studies, as
discussed below.
HISTORICAL TRENDS

Water quality trend analysis can provide insights into the
magnitude of water quality improvement or impairment in a
specified area of concern and can indicate the impact of
pollution control programs on water quality.  Inferences also can
be drawn using this type of analysis as to the need, if any, for
additional pollution abatement programs.

Like assessments of existing conditions, trend analyses may be
conducted using all data available for a particular stream
segment or using only data collected at a specified critical time
or location.  Of primary concern here, however, is the
comparability of recent and historical data.  Because so many
parameter values are significantly affected by changes in flow,
water temperature, or other seasonal factors, the data should be
normalized with respect to these factors, if possible, before
further statistical manipulations are requested.  Table 3-2 lists
the 11 major parameter groups defined in the 305(b) guidance and
indicates whether or not their values are generally dependent on
flow, temperature, or season.
2Technique 3-3:  Assessing Existing Conditions  in Terms of
Standards Violations.

3Technique 3-5:  Illustration of Historical Trends Using
Statistical Summaries and Technique 3-4:  Generation of Area-
Shaded Maps, respectively.
                                3-2

-------

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                                                                   3-3

-------
It is relatively simple to normalize STORET 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
Flow Temperature
X 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
 4Technique 3-2:  Calculation of Equivalent Loads.


                               3-4

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to be performed by computer, STORET output can be obtained on
tape, disk, or punched cards.5

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
5Technique 3-10:  Formatting STORET Data for Input into SAS
(Statistical Analysis System) and Technique 3-11:  Output of
STORET Data on Punched Cards.

6Technique 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.

technique 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
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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION;



NOTES:
Use the Flow Data File retrieval program
FLOSTR.
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.
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.
Part FL, Chapter 3.
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

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
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.
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.
Equivalent loads for each parameter
specified will be tabulated, with or
without corresponding raw data values.
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/AMBNT/STREAM
510014
44 14 54.4 086 19 
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                   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:
DATA REQUIREMENTS;
OUTPUT:
DOCUMENTATION:



NOTES:
Use the Water Quality File retrieval
program STAND; specify the printing of a
violations summary.
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 will include station identification
information for each station retrieved and
a summary of standards violations for each
parameter specified.
Part WQ, Chapter RET, Section 6.
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

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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.
/NTRTMT/INTAKE/STREAM
STN 1. SUMMARY. 1
740022 R00220 INSCR1
42 59 1J.5 082 25 29.0 1
WATER INTAKE FROM ST. CLAIR R.
26147 MICHIGAN
CITY OF PORT HURON WTP 0615
ST. CLAIR RIVER BASIN
21MICH
0033 FEET DEPTH CLASS 00
SUMMARY OF VIOLATIONS ON SAMPLES COLLECTED FROM 77/10/17 TO 79/01/08




NO OF VALUES
MEAN
MEDIAN
NO OF VIOLS
PERCENT VIOL
MINIMUM VIOL
MEAN VIOL
MAXIMUM VISL
MIN CRITERIA
MAX CRITERIA
01045
IRON
FE,TOT
UC/L
6
153.2
60.0
1
17.
110. 0
160,0
160.0
500,0
71900 00410
MERCURY T ALK
HG, TOTAL CAC03
UC/L M/L
5 5
0,1100 12.3]
0.2000 71. iO
0 1)
0. 0,
0.0 0,0
0.0 0.0
0.0 0,0


00400
PH

su
6
8.250
1. JSO
f
JJ.
1.700
1,800
9.900
8,500
32730
PHENOLS
TOTAL
UG/L
(
1.667
l.SSO
4
67.
l.JOO
2.425
4.000
1.000
                               3-12

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:


NOTES:
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.
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.
For each plot requested, an area-shaded map
is produced, illustrating spatial
variations of a single function of a single
water quality parameter.
Part WQ, Chapter RET, Section 7.
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.
                              3-14

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION;



NOTES:
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.
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.
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.
Part WQ, Chapter RET, Section 6.
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.
                                         bin oiii
                                         44  14 54.4 UHfe
     DATL
     Ft, CM
     TO
TI It
 OF
DAY
DCPI't]

fEf.r
    74/ul/Ul
    YEAH   'JU I3EB
         M^A-g
         ST'MH D"V
    75/01/UU
    73/Ul/ul
    YI:M<   pu 13Ef
         irA'j
         ST\ .1: 0"v'
    76/Ul/oJ
    7D/Ul/ljl
                                                                 . 7
                                                                 ,,7
                                3-16

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                   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:
DATA REQUIREMENTS;
OUTPUT:
DOCUMENTATION:



NOTES:
Use the Water Quality File retrieval
program PLOT; request that loadings be
calculated before plotting.
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.
For each station and parameter specified, a
plot of loadings vs. time (in days) will be
printed.
Part WQ, Chapter RET, Section 6.
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.
      STORE!
       510014
       44 11 Sm 086  19 24.9 2
       MflNISTEE R RT NflPUE ST BROG; CITY OF Mfl
       26101  MfiNISTEE CO., MI
       MflJ SflSIN: LflKE MICHIGBN 081300
       MIN BflSIN: MflNISTEE RIVER
       21MICH
       0000 FEET  DEPTH  CLflSS 00
e>

x
o
\
CD
rn
o
o
cc
3-
a
a
      a     zoo
      TIME OFIYS
                 
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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
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.
Enter station identification keywords to
define the geographic area of interest, and
one parameter code.
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.
Part WQ, Chapter RET, Section 7.
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

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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

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
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.
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.
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.
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.
STORE T











3
1
6
1
6


F
E
C

C
0
I
I

M
F
M
-
F
C
8
R


/
1
0
0
H
L












SYSTEM OS/23/79


MILES 0
2.4OE*03
2
1.92E*03
4
5
1.44E03
7
9.66E*02
1
1
2
3
4
5
4.88E<02
7
8
9
2 0
1
2
3
4
5
6
7
8
9
3 X
1
2
3
1
5
6
7
8
9
4
1
2
3
4
5
fc
7
8
9
5
1.0001
MILES 0
RANCE

MULTIPLE STATION PLOT IHSP]
FROM 670814 TO 741113 PLOT WO. 3
8 16 24 32 40 IK 56 MILES
X 2.40E03
.?
1.92t03

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:


NOTES:
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.
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.
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.
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

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                      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.
      7.00UU +-
     C
     h
     L
     0 5.9250 X
     I
     D
     E
      4.050U +
     C
     L
2. 7u(JO + ----------- + ---- X ------ + ----- X ----- + ----------- 4-

  1975.      1976.      1977.      1978.      1979.

                        TIME IN YEARS
                                                         1980.
                               3-24

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:
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.
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.
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

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                   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 transfered 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:
DATA REQUIREMENTS;
OUTPUT:
DOCUMENTATION:



NOTES:
Use the Water Quality File retrieval
program PUNCH.
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.
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.
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

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 MANAGER'S GUIDE
       TO
     STORET
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     CHAPTER
        4
POLLUTION SOURCES
       AND
CONTROL PROGRAMS

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                            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.
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.3 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.

 2Technique 4-2:  Retrieval of  In-plant Data.

 3Technique 4-3:  Retrieval of  Permit and Effluent Data..

 4Technique 4-4:  Generation of Effluent Reports.
                                4-2

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                          TABLE 4-1
       WATER QUALITY PARAMETERS AND POLLUTION PROBLEMS
Parameters
Total dissolved solids
and chlorides
Temperature
Carbonaceous BOD &
COD, total carbon
Organic nitrogen
Ammon i a
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

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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 and 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-
point source assessments. Some meteorologic data are stored in
the Water Quality File under appropriate parameter codes and at
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.
sTechnique 4-5:  Location and Characterization of Municipal
Dischargers.

^Technique 4-6:  Retrieval of Data on Selected Communities or
Facilities.

7Technique 4-7:   Identification  of Stations  that  Sample  Weather
Data.

technique 4-6:  Retrieval of Data on Selected Communities or
Facilities.
                                4-4

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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

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o
z
0
CONCENTRAT
A

S'OTAL
lONCENTRATION


1 BACKGROUND VALUE
           RIVER MILE
         FIGURE 4-1
   CONSERVATIVE SUBSTANCES
o
H
O
O
o
                     TOTAL
                     CONCENTRATION
            RIVER MILE
          FIGURE 4-2
     REACTIVE SUBSTANCES
              4-6

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Parameters generally regarded as reactive include coliform
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:
                     C0  = CuQu  + W
                           Qu  + Qo,


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.
 10Technique 4-1:  Use of Multiple Station Plots to Assess Cause
 and Effect.

 ^Technique 4-3:  Retrieval of Permit and Effluent Data.
                               4-7

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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

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
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.
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.
One mileage- or station-linear plot of mean
concentrations will be produced for each
parameter requested.
Part WQ, Chapter RET, Section 7.
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

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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.
5TOKET SYSTEM  CB/23/79
                         KULTIPLI STATION F'lllT (MSP)
                          FBOh- 67061"! TD 7!>0?11                 PLIT NO. 1
      MILES  0      P     16      .'1     3?     
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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION;
NOTES:
Use the Water Quality File retrieval
program ALLPARM; extract all stations
labeled PIPE; request printing of raw data
and station descriptive paragraphs.
Enter appropriate station identification
and data selection keywords to define the
geographic area and time period of
interest.
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.
Part WQ, Chapter RET, Section 6 (ALLPARM
Program).

Part WQ, Appendix F  (Station Type Codes)
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
                              DFTWHTP02Q
                             42 16 35 .<> "P3 rb
                             DETROIT  MI
                             26163   MICHIGAN
                             GREAT LAKES-SI.LSfc
                             LAKE ERIF
                             115GLRES  770*1?
                              COOO FEFT DEPTH
                                                   DESCRIPTION

                              THIS DAT* !S FROM SELF MONITORING REPORT Film BY THF CITY [F rETsm

                              MHTD FOR NPDES PERMIT MIO122BC2. IT MAY NOT RFFLECT F 1N4L ,OFF ICHL i

                              REPORTS TO THE PERMIT ISSUING AGENCY,DUE TO THE IANGUAGE OF THE  PEFflT.

                              ITS PURPOSE IN STORET IS INFORMATIONAL ND STATISICAL. PIF1SE CPNTACT

                              R.M.BUCKLE1 AT 313-226-7811 FOR MORE INFORMATION. FS1. 4/1/77

                              THIS SAMPLING POINT REPRESENT UN ARITHMETIC COMPOSITE OF TyD IN=IUINS.
/TYPA/MIH/TREATO/OUTFL/PIPE
                OETNMTPC20
               42 16 35.4 063 C6 28.C
               DETROIT MI
               26163 MICHIGAN
               GREAT LAKES-ST.LAURENCE
               LAKE ERIE
               115GIRES 770413
                0000 CLASS '0
PARAMETER
C0310
C0400
C053C
005 35
00550
C0665
C0720
00916
C0927
00929
C0940
C1002
C1007
01027
C1032
01034
010.42
01051
01055
01067
C1092
C1105
31615
3273C
50050
71900
74010
BOO
PH
RESIDUE
RESIDUE
OIL-CRSE
PHOS-TDT
CYANIDE
CALCIUM
MGNSIUM
SOD I UM
CHLORIDE
ARSENIC
BARIUM
CADMIUM
CHROMIUM
CHROMIUM
COPPER
LEAD
MA NONE SE
NICKEL
ZINC
ALUMINUM
FEC COL I
PHENOLS
CONDUIT
MERCURY
IRON
5 DAY

TOT NFIT
VOl NFIT
TOT-SXLT

CN-TOT
CA-TDT
MS, TOT
NA.TOT
CL
AS, TOT
BA.TOT
CD. TOT
HEX-VAL
CR.TOT
CU.TOT
PB.TOT
MN
NI. TOTAL
2N.TOT
AL.TOT
HPNECHED
TOTAL
FLOU
HC. TOTAL
FE
XG/L
SU
MG/L
MG/L
MG/L
MG/L P
MG/L
MG/L
MG/L
MG/L
MG/L
UG/L
UG/L
UG/L
UG/L
JG/L
UG/L
UG/L
UG/L
UG/L
UG/L
UC/L
/100ML
UG/L
MGD
UG/L
MG/L
NUMBER
793
BOO
818
774
8C2
791
744
117
117
117
799
122
111
809
5
816
817
709
117
816
810
109
825
805
912
508
816
MEAN
69.5233
7.3033P
122.458
70.5543
26.1907
3.41779
.0(0345
59.3298
18.513?
114.650
165.972
7.24672
241.962
IB .6072
56.CCOO
236.846
265.716
121.400
283.162
387.696
957.469
1347.39
363.536
143.227
916.261
.442*14
13.8632
VARIANCE
564.565
.113054
2263.76
683.405
211.270
1.482P3
.004232
392.615
23.4678
7561 .65
9010.54
57.7600
8757.66
530.015
1730.00
11603.3
11295.8
7230.^5
13483.9
18129.2
185293
565670
1363255
9648.01
34299.2
.176609
38.5476
STAN DFV
23.7606
.336235
47.5792
26.1420
14.5351
I .21772
.065056
19.6145
4 .84642
P7.0738
94.9239
7.6C132
93.5834
23.0721
41.5933
10B.643
106.262
85.0326
116.120
134.645
43C.456
752.111
1167.59
98.2243
185.200
.420249
6 .20669
COEF VAR
.341764
.C4f 33P
.3PP534
.370523
.554914
.3562F7
I .07807
.33397?
.?617F3
.75P15r
.'7192'
1 .C.8C3
,36f 737
1 .22411
. 742 737
.4567Cf
.3999P--
.700431
.410064
.347295
.449577
."P200
3.? 11 7
.t6579f
.201666
.949665
.44785?
STAND ER
.643763
.011629
1 .66357
."39655
.513253
.C43297
.002385
1 .B318?
.44(U '2
3 .C4996
3.35P16
.668191
t .P6255
.6T9412
18.6(11
3.79662
3 .71P33
3 . 1 934 7
K .7353
4 .71351
1.1247
72 .0391
40.^501
3.4619'
6 .13260
.1 18646
.217347
MAXIMUM
173. '00
6.30000
48". 000
192. "00
1C9.10C
12.23'1C
1.02000
142. Ore
29.7"OC
700.000
978.000
34.0000
600.000
475. 'CO
100.000
620.000
690.000
14-0. 00
P80.000
1960.00
7200.00
5300.00
24000.0
8rO.OOC
1447.^0
6.60000
42.9000
MINIMUM
11.0000
6.50000
IB. COOO
7.00000
2.40000
.200000
.010000
14.5TOO
1.65000
54.5000
53.0000
1.00000
100.000
1.00000
10.0000
10.0000
40.0000
.9rcoc
120 .000
90.0000
120.000
500.000
23. COOO
4.5COOO
570.000
.200000
.500000
BE f DATE
75/C1/C1
75/Cl/f 1
75/Cl/( 1
T. /0 1 / f 2
75/01/11
75/01/C1
75/C1/C 1
75/01/21
75 /0 1 / 2 (
75/01/21
7*/01/Cl
75/05/C4
75/03/tl
75/C1/C1
75/03/^1
75/01/C 1
75/01/C1
75/C1/2I
7e /0 1 / 2 C
75/C1/C1
75/01/C]
75/05/C1
75/ri/C 1
75/01/C1
75/Cl/( 1
75/01/2(
75/01 /( 1
END DATF
77/06/28
77/C6/3C
77/06/30
77/06/3C
77/06/30
77/06/3C
77/C6/30
77/C6/27
77/C6/27
77/06/27
77/06/30
77/06/30
77/06/27
77/C6/30
77/02/25
77/06/30
77/C6/3C
77/C6/3C
77/06/27
77/06/30
77/06/30
77/06/26
77/06/30
77/06/30
77/06/30
77/06/3C
77/06/3L
                                          4-12

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
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.
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.
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.
Part EF, Chapter 3.
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

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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 (HC), low (LC), and
average (AC). Pipe number appears in the
depth column.
a'iUn'l KL'iklLVAL LATL 79/07/13
/ritA/INL/M'ttTMT/GbTFL/I-IPE


LA'lt
t i\ow
'10
To/ol/01
^_p ( O J _
7o/ 01/01
77/lo/oi
>-r (V)-


77/lu/ol
77/H/ol
C t- 1 v j -


77/li/ul
77/1^/ul
Cf (V)-


77/12/01
7b/ol/ol
Ci- ( V ) -


To/ol/Ul
7o/o2/ol
Ct- ( 0 ) -


7o/o2/0l
7o/o j/u 1
Ci- ( V ) -


7o/ o j/ol
77/lu/ol
ci- ( v ) -


77/lo/Ul

boulO 00300
'il.SL LEl-TH AATEK LC
Ct' i'c>.P
LAi fttl CEI-T f'C/L
to!4
HC 3 J. o lo . b

i-lol
AC o.7
LC b.4
nC Si. J

Hoi
AC 9.4
1 . b.S
hC 11.2

i-lol
AC lo.5
LC 9.3
IiC 11.2

tlol
AC 1 0 . 1
LC 9.b
HC lo.b

FlOl
AC 1.1 10.5
LC U . 0 10.4
[1C 3.3 lo.7

tiol
AC 7.1 s.2
LC 1.1 b.b
llC 12.1 9.6

i-102
AC
LC
HC


36 05 lU.u O1^ j7 4o.o 3
..ESifch.. tLEC'Ii.IC CO
3''0ol t,C
nECVG S'l^y - LII'ILL ALA. A.,Ct Cl,-GuIL .N i 1-
EFNC T51230
0999 FLE'l ^Ltitj 2L/3.J oo
Oo4oo 31blb iooSo
Irti t'EC CCLI CONLUIT
Mf>j-FCiJ^ FLOi\
SL /100ML i-lCC

T.9o 4o


7.1o
b.7o co
7. bo 12U


b. CO
D.3o 4o
T.3o 150


d.lo
1.0(1 bO
0.50 bO


7.10
fe.bu 50
T.90 bU



fa. 9U 20
7.30 00



b.7o IK
7.7o 40


7.40 O.o2b
7.10 0.0o5
tt. 20 0.050

                              4-14

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                   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:
DATA REQUIREMENTS;
OUTPUT:
DOCUMENTATION:


NOTES:
Use the STORET command procedure named
EFFRPT, with print option 3, to obtain the
maximum amount of detail.
Enter agency and facility (station) codes
for the discharger(s) in question.
Beginning and ending dates may be
specified, if required.
For each facility specified, all parametric
effluent data and permit conditions will be
tabulated by pipe number and by reporting
date within 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.
Part EF, Chapter 3.
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.
* STO*ET EFFLUENT
FACILITY NC0003301
DISCHARGE PIPE 10
REPORTING DATE:
"ARAMEIER
CODE
03313 BOD 1T
5 0T HG/L 3NR
03*03 PH 1T
SU 31R
00500 RESIDUE "IT
TOTAL NG/L 3H
03533 RESIDUE PNT
HI NFLT HG/L OUR
03525 TOT  H
771101
CONCENTRATION L3ADJNG
L3H  F /A S/T
CONO --  23.3 --  -- 1/7 CP
VLS 233.0 3B.3 725.3
COND 6.30 -- 9.03 - " -- 1/7 CP
VIS 4.00 7.*3 9.33





1 VI3L II3N(S)


1 V1U(M3N(S)


>. VIOLATION) S)


1 VI 3L 1 I1JN (S)

                               4-16

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:


NOTES:
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.
Enter appropriate codes to define retrieval
criteria and output format.
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.
Part MWIF, Chapter 3.
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

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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.
     LlrA/uP-uA'l         MbMlCltAL nASlt. tACILlTY DATA          STCRET  SYSTEM
                                  (tOt\.\AT vv)


     1.
            MUNICIPALITY:
            BASIN:   (2215  )     1                         "'uliblllOl
            ShbA:         COUNT}:  (053)  GOGEBIC
            STAT'L REGION NUHtiErc:        CONGRESSIONAL DISTRICT NUMBER:   11
     2. FACILITY DISCHARGE

            RECEIVING WAILK:  KALLANbEh CREEK 1C       BLACK  RIVER
            STATt, bISChAKGE t-EKKIT NUHBEK:
            MULril-Puli\T LlGCHAKGL:  NO
            IN'IEKa'lAlE:  NO
            UUTFALL TO GirEN V^AT'Lk t;OD^:  iNO
            LA'1/EG^b EISChAi^GE I'GIM:  4b2b.^3/U90U3uO
            LISTANCE GblFAEL FKGw ShCKE:
            i>EtTn OUTFALL SUBSUKFACE:  x
            bPA ENFOKGEMENl CONFERENCE:  YES (04o)
            Et-A GRANTS AvAKbtD:
      J. FACILITY i>ESCRli"TIOw

            EXISTING T'KEATKEiMT:  (54 )  SECONDARY-STANb. RATE TR FL
            SERVING COMMUNITY:  NO
            CENSUS POPULATION:       2,faU5   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 DAILY FLOW (MGD)
                 DESIGN:       .550   ACTUAL:      .670   % INDUSTRIAL:   X
            INFLUENT (MG/L)
                 DESIGN BOD:   000295 ACTUAL BOD:   000173
                 % INDUSTRIAL:  X    SUSPENDED SOLIDS:  X
            EFFLUENT (MG/L)
                 TREATED BOD:  000029   SUSPENDED SOLIDS:  00002,3
            % 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 lb, 1979


                                     4-18

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                   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:
DATA REQUIREMENTS;
OUTPUT:
DOCUMENTATION:



NOTES:
Use the job control language provided in
the STORET help data set named RETCMFJ,  and
specify geographic area of interest.
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 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.
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

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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.


Cl ITS COntHJNl It
029000001 ANVIL LOCATION
00
093000001 BESSEMR
**
**
00
00
47* 10000) 1RUNT UN Hlht
00
476000001 IRONWODt
00
0
476)00002 1RONWUOD THP
*
786000001 RAMSAY
**BESSEHER TUP
**
920600000 THDMASTON
*
96*000001 WAKkF IfclLJ
**
976*00000 UATERSMEE I
0
 *
TOTAL FOR STAU 16
TUTAL NUMBER RECORDS IN FILE
-- MICHICIN
C0 COUNTY

0*9 GOGEBJt

053 GOCbBIC


033 OOGE6IC
0*3 GOGEB I C

053 GOGEBIC


0*3 GCGEB1C
053 GOGEBIC
0*3 GOGEBIC
T
'
0*3 UJ&tB C

0*3 &0&EB1C

0*3 GOGte C



16
(26)
CONGR
D1STR SNSA
11

11

1 1



] 1


11
1 1
11


1 1

1)

11

1 1


79/08/21 PAtt 1
	 CENSUS 	 PSGP S BASIN LAI/ f
1960 1970 HI CA 0 NJ HN SU6 LUM C
2*0 2*0 10 22 15

3.304 2.80* 33 22 1*


6^ 1 *4* 2 1 22 1*


10.26* 6,711 * * 22 1*

'
22 1*
632 63*21 22 14
1 .1*8 1.066 32 22 15

3 I 22 15
X 100 22 1*

3.231 2.7!>7 33 22 1*

X *00 22 12

Bfc4 711 2 1 2212
62000
94000
628*4 
00300 *
62000
94 COO
62000
94000
94000
620OO
94000
62712 
00924 *
94000

62240 
9 3* *
62812 *
00000 *
62000
94000
63106 C
9*>*30 C
62842 *
9*5*4 
61606 C
91046 C
62000
94000
STUDY
CAlttORV
00002

C0002

OO012
OOO02


00002

00002
00002
C0002
COQ02

00002


00002



00002



*
0*
**
00
00
**
0*
**
*
**
*#
00
*
**
00
00
00
00
**
00
 *
00
*0
**
00
*0
0*
 0
**
00

0
L F G t N D **
0
STUDY CATEGORY: CODE RECORD NO. **
IPM1NT POSITIONS 12*-I321 *

INDUSTRIAL IHPLEHENTAI IUN.. 00004 0
hUNiciPAL IMPLEMENTATION... oooio *
fEDtRAL 1NSTALLA1IGN 	 00100 
BONO SALES 	 OOIOO 
^000-,'029 IFUSS1L) *
THEkMAL 	 OIOOO> *
CONSTRUCTION GRANTS 	 04000 **

0
LATITUDE/LONGITUDE: - FLAG FIELD (PRINT POSITION 123) 0

= (BLANK) - CtNlE UF CUUNTY LAT/IONC *
C * LAT/LONG OF CIT CHANGED BY HO UFFlCf *
6
00 
***0 00*0* 00* *** 0*0 PUPUl A 1 I UN S [ It G R OUP**0 0*000 0*00 00 C****
00 
*0 tMQA CODING: CONTRACT AWARDS CODE: 
0 I ; t'NOEK 4S9 0  UNUbfc *00 **
00 2 = bOO - 999 : *00 - 999 **
** = 1 ,000 -  949 " 1.000 - 2.499 **
*
0*
**
00
0*
ft*
*
00
00
00


* *>oo.ooo L LVER = 2*0,000 t OVER **
0
00
- (BLANK ) Cl Y ONLY **
1 - WATER DIST ICT *
3 = FIRE. SCHOOL, DISTRICTS, Etc. 0
90


/BOSD/ eORDULF-
/C/ CITY
/CUHM SERV OIST/ CUIMUNMY btKVicts LJISIKICI

/FMSC/ M t HlRKS SAN1UHY C1S1KK1
/G SI*/ LA SU11DN
/GtN STA/ Ct EkATING STATUN
/H/ HA Lt T
/H SCH/ HI H SCHUUl
/HOU5 AUTH/ HOUSJM, AUlfiUKJIT
/HQUS1KC OhT/ HDUSIM, UM 1 C
/IMP C I SI/ IMPUUNOtD DlblKlCI * tSVlUNhtMAL
/IND MTk AUTH/ INOtUKDiN) MATER AU1HUHIT C CtM>

/MSS/ hUNICICAL Stwfch SYSUM e UIVIMUN
/PUD/ PUBLIC UJ1LITY DlSlhJCl  79/(t/^l
STP/ SEMtk IREATF-tM PLANT
SHOP C1R/ Sh3HPlfc& CENTER
ST HtlSP/ ST* H HQSPI IAL
ST P / SI* 1 1 ( Af K
SUBD/ -tBD I VISIUN
I/ I DUN
THP/ 1LMNSMP
IB PK/ TkAILEfc PAhU
TRTPLT/ IKtlMtNIKlNI
UN 1C AREA/ UN NCLkPORAU AKEA
UT IL DIST/ UTI L IIY DI bTRICl
/V/ V III At-l
/H D/ WAIEk OIMklC I
/W1R MKS/ UAIER UClkKS
                              4-20

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:
NOTES:
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.
Enter station identification and data
selection keywords to define geographic
area and time period of interest.
For each station retrieved, output will
include a descriptive paragraph (if
stored), station identification
information, and raw data on all parameters
sampled.
Part WQ, Chapter RET, Section 6 (ALLPARM
Program).

Part WQ, Appendix F  (Station Type Codes).
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.
            /LND/PRECIP
                          CA0101
                         37 48 15.0 122 26 4i.O 2
                         SAN FRANCISCO BAKER ST
                         06075  CALIFCRNIA
                         CALIFORNIA          Mi ;
                         SAN FRANCISCO BAY RtGIliN
                         22CAC1TY 78C707
                          0999 FEET DEPTH UAS5 CO
                                             DESCRIPTION

                          D*TA FOR THI! 5TAT10K MERE ASSEMBLED AS PART OF E(A PROJECT 6<-30-t496,

                          FSTABUSHMEN7 OF AN URBAN RAINFAll-RUNOFF-CUALITY DATA BASE. I OK FURTHER

                          INFORMATION, *ft THE FINAL REPORT BY W.C.HUBER AND J.P.HtANtY, "URPAN

                          RAINFALL-RUNOFF-OUALITY DATA EASE', E.PA-6 CO/8 - 77-CC9 , JULY 19?7.
/LNO/PRtClP
PARAHE TER
00061
00093
00095
U0099
00101
C0102
10103
00104
00129
C0310
00340
OC400
00*10
005 30
00535
00545
00552
G0610
00620
31505
31615
7C507
STREAM
SOLIDS
CNDUCTVY
TOX ICITY
SOLIDS
SCUDS
SOLICS
SOLIDS
PR EC IP
BOD
COD
PH
T LK
RESIDUE
RESIDUE
RESIDUE
riL-GRSf
NH3-N
N03-N
TOT COL1
FC COL I
PHOS-T
FLOk,
ELCAT
AT 25C
96HRS
* ON
X Oh
* OK
J ON
INT LPCI
5 DAY
HI LEVEL

CAC03
TOT NFLT
VOL NFLT
SETTLBLE
TOT-HEXN
TOTAL
TOTAL
MPN COKF
MPNECMED
ORTHO
INST-CFS
MG/L
NICRDMHO
*
74U FILI
14U F1LT
5U FILT
.45U FIL
1N/HR
MG/L
MG/l
SU
MG/L
MG/L
MG/L
ML/L
MG/L
MG/L
MG/L
/100MI
/100ML
MC/L P
NUMB tft
41
43
47
5
47
47
33
31
274
17
46
16
32
47
47
47
42
47
47
V
33
45
MFN
13.3814
1.93255
734.636
96 .: 000
44 .6978
P7.2S72
6.60303
6.22258
.058167
29 .6470
122.565
6.97777
52.4437
86.680f
51 .9149
1.86595
11 .9285
1.4817^
6.48722
1 .COOOO
l.COOCO
.910661
CAC !01
37 48 15 .0 122 26 4^> .( ;
SS FRANCISCO BAKER T
Ofc075 CAL IFCRN1 A
CALIFORNIA !* 
SAN FRANCISCO PAY REGION
22CACITY 76C7C7
T999 CLASS CO
VARIANCE
184.330
6.91264
.110MCP
8 L>. CO 00
127C .21
832.11"
128. m
41.6824
.0074M
417.369
1190C.C
.068862
768.266
4854.30
1088. P2
2.5136C
354.768
.737944
16.2736
.000000
.000000
.467223
STAN DEV
13.576H
2.62919
332C .71
B .94427
35.6400
28.8533
11.31=5
t .4^619
.086316
20.4296
109. Cf 7
.262416
27.7176
69.6735
32.9973
1 .5P543
18.8353
,8>;OC;6
4 .1,3405
. "OCOOO
.OCCCOO
.683'-37
Cf'EF VR
1 .t!46(
1 .36C48
4.52019
.C93169
.797355
1 .C5730
I .7142S
1.C3754
1 .483S1
.689094
.690035
.L37607
.528522
.8C3793
,6356t3
.849664
1 .579^1
.'-79766
.(21846


.750595
STANf tR
2 .12. 34
.4C'94f
484 .37*
4 f IOC(
5 19863
4 2l86f
1 97. 47
1 I'j957
. ^C'215
4 .' ">9l
16. 841
.C61852
4 . ( 998 
l'.16?9
4 .81315
.231259
?.9' t35
12':-' 3
5P6427
orcooo
CCCOCG
K 1F96
V A XI HUM
t- 1 .8 :t
i .5^;i
23 'OC .0
1 0 .1 CO
1 ;.jOO
112.1 CO
5S.TCO
21.7 a
.78 (,(
61 .OCCC
626 .< 00
7.4 I r 0
152. -OC
34 . ., r 0
155. COO
5 .500<: :
1 1 - . 3 0
3,OC ,.0
14.9. uo
1 .OQOCC
1 . 00 Cu
^.3CCuO
MINIMUM
3.26U C
. lOCOCf
127.CC.
no. occ
1 .7^,000
.cocooo
.000000
.( 6.00C
.ooccou
5 .ncooc
i2.;coc
6.6uCCC
24.6000
2 j . ' : 00
15.0000
.4 JCOC 0
. 1 KOOC
.2-'TOO
1 .6COOC
1 .00000
1 .' COOC
. L h L 00 C
RFC DMt
69 A 4/( 
69/04/1 4
69/04/f
6 /!( /i
69/04/1
6/( 4/(
6 /( */C
6t-/(4/l
(n/C 4/(
69/C4/C
69/04/C
69/C4/
69/04/C
69/04/C
69/04/t
69/C4/1
f,9/( 4/C
69 /< 4 / i
69/C4/C 4
69 /C 4/1 4
6/( 4/' '
69/C4/C 4
L-MJ DAI:
6V/ll/t'>
69/U/J-:
69/11/C.'.
69/11/ ',
69/11/C'
69/11/ '>
69/ll/i,'>
69/11/'-'-
69/11/^.5
69/11A--
69/11/.',
69/11/15
69/11/ '
6S/ll/_'>
69/11/C5
69/11 /Lh
69/11/ -
69/U/ -)
69/ll/v.1J
69/11/ ',
69/11/L'
69/U/. 
                                      4-22

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MANAGER'S GUIDE
      TO
    STORET
    CHAPTER
       5
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                               BIOLOGICAL MONITORING
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                            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.
2Technique 5-2:  Using Bacterial Data to Assess  the  Source  of
Fecal Contamination.
                                5-2

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1,
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IL








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-------
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 STORET'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.
 3Technique  5-3: Retrieval of  Fish Kill Data.
                                5-4

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:
NOTES:
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.
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.
For each station retrieved, yearly
statistical summaries of the logs of the
data collected will be tabulated.
Part WQ, Chapter RET, Section 6.
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

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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.,  Ml
    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
22.3506
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

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
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.
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.
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.
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

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oxygen) and unionized ammonia (using values
for temperature, pH and total ammonia).
          5-8

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                   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

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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

LAKE Lk STREAM
COOSt KlVtk
WHISKEY CHOTE
SHASTA LJKF
COASTAL WATERS
LOS ANGELES HARB
LLIS ANGE I L S HARB
SANTA BARE FARBO
LOS ANOf LE S HAKB
LOS ANGELES HArfB
LOS ANGEUS HARB
LOS ANGf IES HARB
ANACPST 1 A RIVER
LAKF JESSL'P
WAH1AKA RESIRVJ1
M1LNER hESVk
SNAKf/MlLKEE ktS
RILEY CFEEK
SPOON KJVEF
CHUN CRE EK
ILLIM'IS HVtR
SANGAMON HVER
KISHWAUKEE SCE B
COTTUNWDOD 1. IVER
COTTONUI'O! t IVER
ARKANSAS RIVEK
LEVEl Ck V" fK CR
KILLS  lOOibuO OR 0V

NEAR OR IN
ANN ISTON
ELAINE

3 ,180 ,OQO
1 ,000,00'J
?, 000, 003
1 'JO, OOJ
?b'J,OJ]
?3'i,9'3
1 7b,b?3
ISt),*1)!
>,387,!>aj
lil.jil
22B ,617
3*0, OOJ
213, COT
36'-,OOU
115,00)


OPERATION
GTHtk INDUSTRIAL
P01SOSS
MINING
OTHER OPERATIONS
OTHER INDUSTRIAL
(jTHlk INDUSTRIAL
SEbtRAGE SYSIFfl
PFTROLEUM
PtlROLFUM
PETROLEUM
PETROLEUM
SEWERAGE SYSTEM
SEHEkAGE SYSIFM
UNKNTMN
OTHER INDUSTRIAL
UTHER 1NDUSIRIAI
CITHER OPERATIONS
MINING
FOOD PRODUCTS
PC1SONS
MIN ING
UMKNClMN
MANURE DRAINAGE
MANURE DRAINAGE
MANURE DRAINAGE
MANURE DRAINAGE
FISH KILLS REPORTED
RIVER OR LAKE CITY OR IDHN DATE
MM DO YY
****************************
ALABAMA
LAKE MARTIN
COOSA RIVER
SNAN CREEK
CAHABA RIVER
CAHABA RIVER
CAHABA RIVER
VLLY CR WARRIOR
HATCHeCHUBBEE CR
HATCHECHUBBEE CR
COTTONHOOD CREEK
MOORE S CREEK
TOMBICBEE RIVER
tOMBICBEE RIVER
TOMBICBEE RIVER
SPANISH RIVER
THREE MILE CREEK
THREE MILE CREEK
SHADES CREEK
TONBICBEE RIVER
NOLAMD CREEK
BIC CREEK
ALEXANDER CITY
ANNISTON
ATHENS
BRENT
CENTREV1LIE ALA
CENTREVILLE ALA
CONCORD
COIIONTON
COTTONTON
(ALL ION
LANCOALE
MCINTOSH
MCINTOSH
HCINTOSH ALA
MOBILE
MOBILE
MOBILE
MOUNTAIN BROOK
NAHEOLA
PRATTVILLE ALA
TUSCALOOSA
6 05 64
5 14 61
5 14 62
9-62
11 23 60
7 22 60
10 03 64
11 20 64
10 23 6
7 13 6
9 04 6
6 14 6
6 21 6
6 15 6
6 15 6
6 15 6
7 06 6
9 23 63
9-62
7 20 60
B 26 62
CAUSE 	 IYPE 	
CODE IGAME *NOM~GAHE
******** **********
50
28
31
2B
2B
90
31
2B
ZB
31
90
24
24
90
26
31
31
31
2B
11
31
90*
20*
-
5*
10*
10*
431
65*
60*
30*
53*
B*
9*
-
-
-
-
75*
-
5*
81
10*
40*
-
-
20*
20*
57*
35*
40*
70S
47*
92*
91*
-
100*
100*
100*
25*
-
15*

COMMERCIAL
FISH LOSS!*)
************
.
40*
-
95*
70*
70*
141
15*
15*
70*
-
62*
76*
100*
-
-
-
-
-
BO*
92*
ESTIMATED SEVERITY
FISH KILLED (1K2II3H4I
I***************************
300
2OO ,000
-
-
-
-
11,000
6.000
-
-
350
327
7.985
1,000
2, BOO
430
5,700
-
-
500

_
2
-
2
-
2
2
2
2
4
1
3
2
4
2
3
2
2
-
2

AREA 0 H
AFFECTED A R
**********
_
120H
-
-
-
1H
?M
7M
7M
2M
3H
6H
-
-
2H
-
-
3N
-
1M
5N
_ _
21 -
-
1 -
-
- 12
7 -
21 -
-
-
1 -
-
- 12
- 12
-
- 4
- 6
1 -
-
- 8

                              5-10

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       POLLUTION CAUSE CLUES  AS USED IN  THt
                  HSH KILL  REPORTS
CAUSE:
   10 AGRICULTURAL UPLKAT10NS
      11  PtSTICIOEi
      12  FEkllLUthS
      13  MANURE,  SILO, FLEDLOT  DRAINAGE, ETC.
   20  INDUSTRIAL LiPLRAllDNS
       21 MINING

       22 EOUC   MNDkFD PRODUCTS
       23 PAPtl*  f, ALLltl) PRODUCTS
       2
-------

----- NUMBER OF FISH KILL REPORTS   -
STATES
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELANARE
DISTRICT OF COLUMBIA
FLORIDA
GEORGIA
KAMA 1 1
IDAHO
ILLINOIS
INDIANA
IDNA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEfiRA SKA
NEVADA
NEN HAMPSHIRE
NEK JERSEY
NEN MEXICO
NEN YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKOTA
TEWNC S5EE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
NEST VIRGINIA
WISCONSIN
WYOMING
* TOTALS
JAN


4
2




1
1
1

5
1
1
3



1

1


5



4

2


1

1
6



I
13
2

1
2
2


64
FEB


2
5




1
1

2
2
3
2
2



1

1
3

7



2



1
3

3
3


1
7
1

1
1
2

1
61
MAR


i
i

i


i
3

1
2
1
1






1

3
1


2

2


4

]
16



15
1

3
>
1
1
1
73
APR



6
1
6
1


2


4
1
4

2

I
1
6


4
1


4
1
3
1

3


11
1


24

1
6
6
1
2
1
117
NAY
2

2
2

6


4
1
1
1
10
9
7
3



1
3


23


2
2
1
6
3

10

5
37
1


17
3

11
5
3
1

206
JUN
6


IB

13
2

3
5

2
11
14
11
2
15
6
3
7
14
5

7
3

1
10
1
15
7
2
29
1
8
53
2
1
1
36
1

4
7
4
t

353
JUL
4

4
1$

7


3
7


23
11
13
1
46
12

5
IS
4
1
17
8
2
11
7
1
3D
i

27
1
6
60
3


32
3

5
3
2
*
1
413
AUG
1

7
30

7


2
S
1

23
11
11
2
41
1
2
4
8
8

12
2

5
11

19
3

9
2
4
SI



3
25
2
1
9
2
S
1
2
340
SEP
4

1
25
1
1

2
2
2


19
17
8
2
7



1






7

16
5
1
11

3
42
3
1

9
17


3
1
7
2

237
OCT
2
2

15
1
1


2
1
1

1
9
2
3




2


2

3
i
2

7
3

T

1
24











IIS
NOV
2

2
5
3



1
3
2
1
9
5
1


1





3

1

6
1
3
2

2

2
13

1

3
8
1


1
3

1
B6
DEC


1
4

1



3
4
2

8

1
1
4
2
1

3
1

1
1
1

2

12


2

2
13



3
12


1

1
2
1
*0
TUTAL
21
2
24
128
6
43
3
2
20
34
10
9
105
90
61
41
14
lit
23
10
19
63
26
2
88
17
30
2
20
59
5
115
29
4
111
4
36
329
10
3
2
49
214
IS
2
45
34
33
19
9
2155

--FISH KILL GROUPING BY
NUMBER OF FISH
KILLED BY SIZE GROUP
1 - 1,000
l.COO - 10,000
10,000 - 100,000
100,000 - 1,000,000
>-l ,000,000
UNKNOWN
S4TU1AL

SUE 
IOTAL
REPORTS
606
467
170
33
13
866
3.155


KtPORUD
NO . Oh REPLRTb
606
467
170
33
13
115
1 ,
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MANAGER'S GUIDE
      TO
    STORET
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                               LAKE WATER QUALITY

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    CHAPTER
       6

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                            CHAPTER 6


                       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
 2Technique  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.

 3Technique  6-4:  Using Contour Maps to  Illustrate  Lake Water
 Quality and Technique 3-4- Generation of Area-Shaded  Maps.
                                6-2

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index, it does provide the means of retrieving the necessary raw
data, in either hard copy or machine-readable formats.4

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.5

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.6

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
4Technique 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.
5Technique 6-2.
Data.

'Technique 4-1:
and Effect.

7Technique 6-3:

technique 6-4:
Quality.
Retrieval of National Eutrophication Survey


Use of Multiple Station Plots to Assess Cause


Displaying Lake Stratification.

Using Contour Maps to Illustrate Lake Water
                               6-3

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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 (Thomann, 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 approach.

In the final analysis, the relative water quality improvements
expected from the different control alternatives also have 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

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                   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:
DATA REQUIREMENTS;
OUTPUT:
DOCUMENTATION:
NOTES:
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.
Enter appropriate station identification
keywords defining the geographic area of
interest.
Station header information and a
descriptive paragraph will be printed for
each lake station in the geographic area
specified.
Part WQ, Chapter RET, Section 6 (RET
Program).

Part WQ, Appendix F  (Station Type Codes).
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

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  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
                                                      45 42 32.0 089 58 41.0 2
                                                      L SUPERIOR
                                                      26053   GOGEBIC CO. ,  MI
                                                      IRONVvOOD TWP, SEC 30     2213
                                                      OFF PORCUPINE MTN PARK
/TYPA/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, T'SON, R45lv,

                   SECTION 30, IKON'rtOOD TOWNSHIP

                       GREAT LAKES SHORELINE DATA ARE TAKEN AT THIS STATION. HISTOFil'.'AL

                   BACTtRIA INFORMATION IS FOUND HEhD.
                                      6-6

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
                      Use the Water Quality File retrieval
                      program INDEX; retrieve stations stored
                      under the agency code 11EPALES.
                      Enter appropriate station identification
                      keywords to define the geographic area of
                      interest.
                      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.
                      Part WQ, Chapter RET, Section 6.
                      This program retrieves no parametric data'
                               6-7

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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 11EPALES.,
     STORET RETRIEVAL DATS 19/06/01
                                                                                 PAGE  1

                                                                  BASIN CODE STORAGE DATE
     STATE
               ST/CO t   LOCATION
               STATION TVPE
               USER CODE    STATION
                   LAT/LONG

                      INDEX
   SECONDARY STATIONS
   MILESLV1   LV2    LV3
COUNTY


  LV4
 lAJ/MIN/TERM
LV5  LV6  LV  LVB   LV9   LV10   LV11
               26049 HOLLOWAY RESERVOIR
               /TYPA/A1BNT/LAKE
               11EPALES     26A001
               43 Qi l-'.o 083 29 21.0
                      INDEX
               26049 HOLLOWAY RESERVOIR
               /TYPA/AM BNT/LAK E
               11EPALES     26*002
               43 06 52.0 083 2^ 32.0
                      INDEX
               2608"1 HOLLOHAY RESERVOIR
               /TYPA/AMBNT/LAKE
               11EPALES     26A003
               43 O"1 16.0 083 26 12.0
                      INDEX
               26   CARD RESERVOIR
               /TYPA/AMBNT/LAKE
               J1EPALES     26A101
               43 2^ 30.0 083 24 30.0
                      INDEX
               26055 BOARDHAN HYDRO POND
               /TYPA/AMBNT/LUKE
               11EPALES    26H201
               44 40 00.0 085 25 00.0
                      INDEX
               26055 BOARDMAN HYDRO POND
               /TYPA/AMBNT/LAKE
               11EPALES    26A202
               44 40 00.0 085 25 00.0
                      INDEX
               26005 ALLEGAN LAKE
               /TYPA/AMBNT/LAKE
               11EPALES     260301
               42 32 00.0 085 52 00.0
                      INDEX
                                            6-8

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:


NOTES:
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).
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.
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 (*).
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

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                      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.
                               6-10

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                   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:
DATA REQUIREMENTS:
OUTPUT:
DOCUMENTATION:



NOTES:
Use the Water Quality File retrieval
program MSP.  Request the printing of a
contour map depicting variations over space
in mean parameter values.
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.
A contour map is produced, which
illustrates how concentrations of a single
parameter vary over space.
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

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EXAMPLE:
This map shows the distribution of
chlorophyll in Saginaw Bay. Plus signs (+)
indicate the locations of sampling sites.
                               6-12

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MANAGER'S GUIDE
      TO
    STORET
  APPENDIXES

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                           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

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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
    Phytoplankton 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 Handbook: 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

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                           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

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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

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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

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    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
    by 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

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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.
                               B-5

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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.

MOST 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.
                                B-6

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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.

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 phytoplankton.

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 to 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.
V        water quality standard:  see STANDARD.
I         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.
I        ZOOPLANKTON:  passively floating or weakly swimming microscopic
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             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:

              DMOADS, Attn:  DDCP
              6500 Brookes Lane
              Washington, D.C.  20315
              Telephone:  (202) 227-2495


METEOROLOGICAL, CLIMATOLOGICAL, AND AIR QUALITY DATA

A.  National Oceanic and Atmospheric Administration.

    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.    Climatological 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 Climatological 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-ord>r 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 STOrage 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 4J3 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 information
         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

4.    The 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

5.    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

6.    The Spi11 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

7.   The Chemicals  ir\ 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, FEDS/ 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, B.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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                                       U.S. GDVraNMEOT PRINTING OFFICE : 1980 0 - 117-37

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