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Table of Contents
o Agenda
o Attendees
o EPA's Biolo
Barbara Lam
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                        WATER QUALITY DATA ASSESSMENT
                             SEMINAR/WORKSHOP

                               PROCEEDINGS

                          November 19-21, 1985

                             Dallas, Texas
Barbara Lamborne, US EPA, STORET User Assistance

Beneficial Use Assessments in Oklahoma
Maine Hutchison, Oklahoma Water Resources Board

Arkansas Stream Classification Project  (Ecoregion Study
Update)
John  Giese,  Arkansas  Department of Pollution   Control   &
Ecology

Statistical Techniques for the Ecological Analysis of  Water
Quality Data
Jim  Piatt  and  Don  Ditmore,   New  Mexico  Environmental
Improvement Division

An Introduction to North Carolina's Biomonitoring Program:
Benthic Macroinvertibrates
David   Penrose,   North  Carolina  Department  of  Natural
Resources and Commercial Development

Bayou Bon Idee Water Quality Monitoring Project
Louis R.  C. Johnson, Louisiana Department  of  Environmental
Quality

The Texas Water Commission Fish Kill  Reporting System
Patrick Roques, Texas Water Commission

AQUIRE: Aquatic Toxicity Information  Retrieval
Ambient Toxicity Surveys
Michael Bastian, US EPA Region 6

Water Quality Trends Analysis
    - Analysis Plan for Water Quality Trends
    - Decision Tree for Trend/Change  Analysis
    - Examples of Water Quality Trends  Analysis
Lisa Lavange, Statistician, Research  Triangle  Park
                HEADQUARTERS LIBRARY
                ENVIRONMENTAL PROTECTION AGENCY
                WASHINGTON, D.C. 20460

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                          1985 State/EPA Region  VI
                       Water Quality Data Assessment
                              Seminar/Workshop
                                   Agenda

                            November 19-21,  1985
 Location

 29th Floor
 US EPA Region VI
 Conference Room
 InterFirst Two Building
 1201 Elm Street
 Dallas, TX 75270
              Contacts

              Henry M. Holman  (214) 767-9781
              Mike Bastian (214)  767-9987
              David Parrish (214)  767-9093
              Carl Young (214)  767-9200
              Barry Nash (214)  767-9094
    DAY/TIME
SUBJECT/SPEAKER
 Tuesday, November 19

 8:30 a.m. Registration

 9:00 a.m. Opening Remarks
           Fred Leutner, Deputy Director,  Monitoring  & Data Support Division
           U.S. Environmental  Protection Agency

 9:05 a.m. EPA's Biological  Data File  (BIOSTORET)
           Barbara Lamborne
           U.S. Environmental  Protection Agency

 9:35 a.m. Beneficial Use Assessments
           Main Hutcheson and  Shon Simpson
           Oklahoma Water Resources Board

10:05 a.m. Coffee Break

10:30 a.m. Ecoregion Study Update
           John Giese
           Arkansas Department of Pollution  Control A Ecology

11:00 a.m. Managing Effluent Data and  Using  Color  Graphics
           Glen Saums and Don  Dittmore
           New Mexico Environmental Improvement  Division

11:30 a.m. Lunch

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                                                                                    I



1:15 p.m. NC's Biological Monitoring Program
          David Penrose                                                             —
          North Carolina Department of Natural  Resources                            I
            and Community Development                                               ™

1:45 p.m. Bayou Bon Idee RCWP Study                                                 •
          Louis Johnson                                                             V
          Louisiana Department of Environmental  Quality

2:15 p.m. Coffee Break                                                              g

2:45 p.m. Fish Kill Investigation
          Patrick Roques                                                            •
          Texas Water Commission                                                    •


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3:15 p.m. AQUIRE:  Aquatic Toxicity Information Retrieval
                   Mike Bastian
                   U.S. Environmental  Protection Agency
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Location
                         1985  State/EPA  Region VI
                      Water Quality  Data Assessment
                            Seminar/Workshop
                                  Agenda

                           November  19-21,  1986
29th Floor
US EPA Region VI
Conference Room
InterFirst Two Building
1201 Elm Street
Dallas, Texas 75270
                                                           Contacts

                                                           Henry M. Holman (214) 767-9781
                                                           Mike Bastian (214)  767-9987
                                                           David Parrish (214) 767-9093
                                                           Carl  Young (214)  767-9200
                                                           Barry Nash (214)  767-9094
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Day/Time
              Wednesday and Thursday

              November 20-21

              8:30 a'.m. - 11:30 a.m.
              1:00 p.m. - 4:00 p.m.



              1.  Data Management (SAS)

                  0  Dataset organization
                  0  Record types
                  0  Variable types
                  0  Dataset storage

              2.  Data Display
                                              Subject/Speaker
       Data listings
       Plots/graphs
       Summaries
                                  Topics
                                             Water Quality Trends Workshop

                                             Lisa Lavange, Statistician

                                             Research Triangle Institute
                                             Research Triangle Park, N.C.
3.  Problem Identification and Solution

    0  Outliers
    0  Missing Data
    0  Data below the limits of detection  and  trace quantities

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4.  Preliminary Analysis Issues
    o

    0  Tests for seasonality

    0  External events of interest
5.  Parametric Analyses
    0  Serial correlations
    0  Moving averages
       SAS ETS
                                                                                  I
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       Parametric versus non-parametric approaches                                I
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    0  Probability distributions and transformations                               «
    0  Two-sample t-test                                                           •
    0  Linear regression analysis                                                  *
    e  Conditional probabilities of water quality changes and compliance

6.  Non-parametric Tests for Trends and Changes                                    m

    0  Pair charts                                                                 ij|
    0  Wilcoxin-Mann-Whitney statistic                                             j|
    0  Kendall's tau
    0  Seasonal  Kendall's tau                                                      —
    0  Aligned rank sum test                                                       •
    0  Spearman's rho statistic                                                    ™
    0  Sen statistic
    0  Determining the "best" test                                                 •

7.  Presentation and Interpretation of Results

    0  Reporting p-values for multiple comparisons                                 I
    0  Statistical significance versus substantive significance                     *

8.  Furtner Topics - Time Series Analyses                                          •



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               ATTENDEES
 Name
DAVE BUZAN
PATRICK ROGUES
STEVE TWIDWELL
JOHN GIESE
MIKE SCHURTZ
BOB HANNAH
PETE ROMANOWSKY
DUGAN SABINS
DON DITMORE
ROCKY MCELVANEY
STEVE HOUBHTON
MAINE HUTCHISON
CHRISTINE ADAMS
JIM PENDERGAST
CRAIG WEEKS
KEN HOLLY
LEE BOHME
BILL LUTHANS
KEN  O'HARA
LOUIS JOHNSON
JACK R. DAVIS
LARRY CHAMPAGNE
JIM PIATT
FRED LEUTNER
CLIFF BAILEY
PHIL NORDSTROM
FRED WERKENTHIN
DAVID CARDIFF
BARBARA LAMBORNE
PAT BAUGH
EMELISE CORMIER
DICK DUERR
JUDITH SCHWIETERS
ROBIN KNOX
BILL JANACEK
DAVID PENROSE
CHUCK BENNETT
CHRIS PETERSEN
MIKE JANSKY
CARL TOWNSEND
JOE KORPICS
RO FREEFIELD
DAVID PARRISH
KAREN YOUNG
CHERYL OVERSTREET
JEANENE PECKHAM
TOM NYSTROM
BOB FORREST
BRUCE MCDONELL
MIKE BASTIAN
KIMBERLY EVERETT
LEN LENARD
ROBERT ORGAN
EPA REGION 6,
EPA REGION 6,
EPA REGION 6,
 Agency
TEXAS WATER COMMISSION
TEXAS WATER COMMISSION
TEXAS WATER COMMISSION
AR DEPT. OF POLLUTION CONTROL & ECOLOGY
LA DEPT. OF ENVIRONMENTAL QUALITY
LA DEPT. OF ENVIRONMENTAL QUALITY
LA DEPT. OF ENVIRONMENTAL QUALITY
LA DEPT. OF ENVIRONMENTAL QUALITY
NM ENVIRONMENTAL IMPROVEMENT DIVISION
OK STATE DEPT. OF HEALTH
OK STATE DEPT. OF HEALTH
OK WATER RESOURCES BOARD
LA DEPT. OF ENVIRONMENTAL QUALITY
EPA REGION 6, DALLAS
EPA REGION 6, DALLAS
              DALLAS
              DALLAS
              DALLAS
LA DEPT. OF ENVIRONMENTAL QUALITY
LA DEPT. OF ENVIRONMENTAL QUALITY
TEXAS WATER COMMISSION
EPA REGION 6, DALLAS
NM ENVIRONMENTAL IMPROVEMENT DIVISION
US EPA, OWRS, MDSD (WH-553)
US EPA, OWRS, MDSD 
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                               EPA's BIOLOGICAL DATA MANAGEMENT SYSTEM
I                            BARBARA LAMBORNE - STOKET USER ASSISTANCE
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               EPA TO DEVELOP BIOLOGICAL DATA MANAGEMENT SYSTEM





     STORET is the Environmental Protection Agency's (EPA) computerized



data base for the storage and retrieval of parametric data pertaining to



the.quality of water within the United States.  It is accessed by hundreds



of users nationwide and has been used primarily for the handling of



physical and chemical water quality data.  As the trend towards biological



monitoring of water quality increases, so does the need for a biology



oriented data management system.  EPA has proposed that a biological



system be implemented as a conponent of the STORET water quality file.



     In the mid 1970's, EPA's Cincinnati Lab developed a biological data



system designed primarily for field survey information.  This system,



known as BIOSTORET, consists of extensive site and sample descriptors,



as well as a hierarchical taxononic coding system.  Because the Agency



was not fully committed to biological monitoring, the system was never



fully funded, and therefore, was utilized by only a few states on a pilot



basis.



     In the past few years, however, there has been a renewed interest



in biological monitoring.  For this reason, in the Fall of 1984 two



biologists were hired by the STORET Users group to bring up the BIOSTORET



system as a component of the STORET water quality file.  A re-evaluation



of the system resulted in the decision by EPA's Office of Water that



although BIOSTORET was satisfactory for addressing the needs of biologists



in the mid 1970's, it was not state-of-the-art in terms of biological



monitoring or data processing.

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     The Office of Water determined that biological data management                    |


should address three major subject areas:  bioassay, tissue residue and                ^


field survey.  Because of the Agency's commitment to provide support of                ™


biological data management, these three areas will be covered in a biologi-            ft


cal data management system to be implemented within, and integrated with,


the STORET water quality file.  The intent of the overall system is to                 M


provide users with the ability to retrieve these different types of


biological data, in addition to the physical/chemical water quality data               £


currently residing in STORET, simultaneously.                                          •

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     It was recommended that we review the Complex Effluents Toxicity


Information System (CETIS) for handling bioassay data.  CETIS was designed             fl


at EPA's Duluth Laboratory for assembling the results of effluent toxicity


tests.  We decided that this systen effectively handles toxicity data and              •


that we would make CETIS a component of the biological data system.                    m


     Although STORET users have had the capability of storing tissue residue


data within the water quality file in the past, these procedures are cur-              m


rently being enhanced.  The proposed Tissue Residue File will include the


following information:  species names (scientific or common) or numbers,               |


anatomy, life cycle stage, number of individuals per sanple, lengths and               ^


weights, and predator/prey information.


     The field survey component of the biological data system has, by far,             •


the most unknowns.  This area of the biological sciences encompasses a


wide range of subject areas some of which vary in significance frcra region             g


to region.  Our objective is to design a system which is comprehensive on
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a national level, with access similar to that of the water quality file.



In order to meet this objective, a requirements study is being conducted



to ascertain the basic needs for a field survey data system.  Existing



systems designed to support field survey types of data were evaluated.



The individuals responsible for the design of these systems were contacted,



as were other potential users.  Preliminary requirements have been defined



and a system prototype is currently being developed.  A memo will be sent



to STORET users and potential users of the biological file explaining the



system design.  Comments and constructive criticisms will be welcomed.



After reviewing the user comments the system design will be finalized.



Upon implementation, the field survey file will be available to



all STORET users.



     The support of biological data management is long overdue.  Many



STORET users have been waiting for years for such support.  EPA Headquarters



has shown a commitment to support these data, and the STORET User group



is following through with that commitment by designing a biological system



that is representative of user needs.  If you have any questions on the



biological system, please contact Barbara Lanborne at (FTS)/202-382-7220,



or 800-424-9067 and ask for STORET.





January 1986.

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     BENEFICIAL USE ASSESSMENTS IN OKLAHOMA

MAINE HUTCHISON - OKLAHOMA WATER RESOURCES BOARD

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                        BENEFICIAL USE ASSESSMENT
Introduction

     All streams in every state have potential or actual beneficial uses.
If a state neglects to assign specific beneficial uses, the de facto use
may primarily be waste transport since enforcement to regulaTe" discharges
beyond technology based limits generally depends upon the designation of
uses in the state's water quality standards.  The trend in Oklahoma has
been to assign  beneficial uses to all State streams to minimize their de
facto use as open sewers.

     The philosophy embodied in the current revision of water quality
standards is that every stream in Oklahoma, with the exception of
ephemeral streams, can support at least a minimal fish and wildlife
propagation use (secondary warmwater fishery). secondary body contact
recreation (SBCR). agriculture, and aesthetics).  These are the
beneficial uses designated for all streams not specifically mentioned in
the Oklahoma Water Quality Standards.  Numerical criteria to protect
these uses generally apply at all times when the actual measured stream
flow is greater than 1 cfs.

     Most of the beneficial uses designated for Oklahoma streams, such as
public .and private water supply (PPWS) and primary body contact
recreation (FBCR), are well defined.  However, fish and wildlife
propagation, the use to which some of the most stringent criteria are
attached, is not easily defined.  In Oklahoma, this use is currently
subdivided into four categories: 1) trout. 2) smallmouth bass, 3) primary
warm water fishery (PWWF), and 4) secondary warm water fishery (SWWF).
Experience has shown that these categories are not adequate for Oklahoma
because of the extreme range of climatic conditions across the state.
For example, some sources estimate that runoff in eastern Oklahoma is
about 100 times that in the western part of the state.  In order to
account for these variations, the state must be divided into regions
which are, in some fashion, rational, and a protective fishery use
designated for each region.

                      ASSESSMENT OF BENEFICIAL USES

     Even though the State does not have adequate fisheries beneficial
use designations, the OWRB has plunged into the assignment of beneficial
uses for the State's streams.  It is hoped that the data collected during
the surveys required to assign stream specific beneficial uses eventually
will aid in the development of more realistic fish and wildlife
propagation beneficial uses.

     Oklahoma has taken a three-tiered approach to designating beneficial
uses for State streams.  This is necessary because the State does not
have the resources to perform the detailed survey suggested by EPA
guidance (reference) on every stream.  First, a literature survey is
performed to examine all available data on the stream.  Literature used
included U. S. Geological Survey flow and water chemistry data, Oklahoma

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                                                                                    I
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files, Storet files of chemical constituents and fish kills, Oklahoma
Water Resources Board and OSDH permit files, the State Water Quality                to
Management Plan, and the State 305b report.  Streams which currently                •
contain a primary warm water fishery or are capable of supporting a
primary warm water fishery are so designated in Appendix A (Beneficial
Uses of Various Stream Segments) of the Oklahoma Water Quality Standards.           •
Streams which could not be designated as FWWFs are designator for a                 ™
one-day physical surveys.

     In many cases, where the literature survey is insufficient to                  £
designate beneficial uses, a one day physical survey is sufficient.
Experienced surveyors can sometimes designate appropriate uses by                   -^
observing the stream and taking a few simple measurements.  These one-day           •
surveys will be the main focus of this discussion and we will get back to            -
them in a minute.

     Some streams are too complex to assign beneficial uses on the basis            JQ.
of a one-day survey.  In this case, an intensive survey, following EPA
Beneficial Use Assessment guidance, is required.  However, these surveys            |»
are expensive and time consuming.  A state must keep these surveys to a             •
minimum in order to use the limited resources available for
beneficial-use designation to their best advantage.                                 .

     One-day surveys are designed to assess a stream's gross capability             •
to support a primary warm water fishery (or in some cases a smallmouth
bass fishery), a public or private water supply, and primary body contact           J|
recreation.  The one-day survey is divided into two parts.  The first               |
part is a physical assessment of the stream.


uui.j.ug me pool. Duuuuei.  ACHUCH.AVC OJ.I.CD wcj.c «_4juoeu wu wtu BILCCUU i i«jiu
topographic maps based on probable access points and locations upstream
and downstream from discharges.  From these sites, sampling stations that           •
were representative of the substrates, flow characteristics, habitats.              '•
and riparian zones of each stream segment were chosen in the field.  Each
site included about 300 yards of stream.  Stream banks are assessed for             •
vegetative cover and bank stability.  Land use in the immediate vicinity            •
was  examined.  Severe erosion and high turbidity may result if land use
practices denude vegetative cover and create unstable banks.                        ^

     Physical habitat was evaluated at each site where a fisheries                  *
designation was required.  Dimensions of pools, riffles, and flat runs
were measured and the substrate was classified.  Quantity and quality of            fe
undercut banks were evaluated, and brush piles and log jams were                    £
recorded.  Flow is the most important characteristic measured, and is a
determining factor in PPWS. PBCR and PWWF.  Flow was measured on a cross-           ^
section at each site.                                                                I

     Chemical characteristics are important in determining the beneficial
uses designated for a stream.  However, only limited chemical evaluation             Ij
can be accomplished during a one-day survey.  The temperature, dissolved             •
oxygen, pH, and specific conductance were measured at each site with a
Hydrolab, and turbidity usually was measured with a spectrophotometer.                ft
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Specific conductance gives an indication of salinity, hardness, and
metallic ions and is important in the drinking water designation.  The
other parameters are important in fish and wildlife propagation.

     A set of worksheets was developed to record the data at every site
that was visited.  Beneficial uses can be designated on the basis of
these data sheets and photographic slides of the site in a majority of
streams studied.

     Physical and chemical data are not sufficient to designate
beneficial uses in some streams.  In these cases, biological surveys are
performed.  Biological surveys usually deal only with fishes, but benthic
organisms and periphyton also may be studied if the occasion demands it.
Fishes are collected with a minnow seines or an electrofishing device,
depending on the situation, for one-half hour.  This gives an estimate
of species richness and relative abundance.  Obvious abnormalites, such
as fin damage and external tumors, are recorded.  This information is
often adequate to determine the current fishery classification of the
stream.

     Data from about 240 sites on the 80 streams are now ready for
analysis.  Interpretation of the results for the PFWS and PBCR beneficial
uses is relatively straightforward.  If there is adequate flow and
acceptable water chemistry, PPWS is designated.  If the stream is
accessible,  sufficiently deep, and substrate or debris in the water does
not prohibit it, PBCR is designated.

     The fishery classification is somewhat more complicated.  Because
these classifications are ill-defined and may take on different meanings
across the State, much more subjectivity is introduced in determinining
the appropriate fisheries classification.  The suitability of the PWWF
designation will be determined using a pairwise comparison and
non-parametric two-way analysis of variance (reference).  This technique
was used successfully to determine beneficial uses during the 1982 Water
Quality Standards revision.

     Since the meaning of PWWF varies across the state, the 150 sites for
which a fishery use designation is required must be broken into small
groups which are capable of supporting similar fish communitites.  This
is accomplished by grouping the sites on the basis of flow
characteristics and substrate.  A separate pairwise comparison is made
for each group.  Parameters appropriate for determination of PWWF are
measured (habitat, stream morphology, etc.)  A value of 1 is assigned to
the better site and a value of 0 to the poorer site for each parameter
examined.  If two sites are considered equal for a parameter, 1/2 is
given to both.  The pairwise comparison is  completed by members of the
OWRB field teams.  The evaluators  discuss the sites on the basis of
personal knowledge.  Photographic slides provide information on physical
habitat.  Diverging opinions are apparent  and are mediated for the final
rankings.

     Results of the pair-wise comparisons are analyzed by use of an
analysis of variance.  The numerical scores resulting from the pair-wise
comparison are totaled and ranked.  The site with the highest total is

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                                                                                   1
ranked first.  Differences in site ranks are calculated for all possible           •
combinations of sites.  Differences between sites equal to or greater
than the 95% confidence level are considered to be significant.  When two
ranks are significantly different, the higher ranks interpreted as                 •
indicating a PWWF and; the lower rank indicates a SWWF.  Sites which are           •
not significantly different are handled on a case-by-case basis.
Experience has shown that this method  eliminates biases among field               m
personnel.  The results of the analyses are presented to a Technical               •
Committee (which evaluates technical matters in the standards revision)
at their subsequent meeting.                                                       ^

     I expect that the analyses will be complete in the next month or so           V
and the resulting beneficial uses will be submitted to the standards
revision process prior to the next round of public hearings. I consider
one day surveys to be a cost efficient way to assign beneficial uses to
streams.
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             ARKANSAS STREAM CLASSIFICATION PROJECT
                    (EcoREGioN STUDY UPDATE)

JOHN GIESE - ARKANSAS DEPARTMENT OF POLLUTION CONTROL & ECOLOGY

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                Stream Classification PT^CJj^ect





Our stream classification project has been ongoing for three

years now, and will be completed with next spring's field

work.  I feel that most of you are aware of the ecoregion

project in Arkansas, some may even be bored with hearing

about it, but for those that might not be aware of the

project, let me briefly go over the concepts involved.



Basically, it was realized that the Water Quality Standards

should recognize the inherent natural diversity of the

waters in our state.  The blanket standard approach which we

have used for many years is fast becoming an ineffective

method o£ managing our water resources.



The heart of the concept is the process of dividing the

state into distinct regions which possess ecological

similarities.  This is done by overlaying maps of land

surface forms, potential natural vegetation, soil types and

land use.  The result is a map showing these regional

similarities.  Notice also that these regions ignore

political boundaries (state lines, county lines, river

basins, etc.)



I thought you might enjoy looking at actual slides of the

streams which we measured every physical, chemical and

biological characteristic of.  I would also encourage

comment from personnel of our border states, where these
                                - 1 -

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ecoregions extend beyond Arkansas, about the visible
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characteristics.
Let's start with the Delta Region - this is an area of our              •

state in which it is extremely hard to locate

least-disturbed streams.                                                |


     Boat Gunwale        21 square miles                                m

     Second Creek        60 square miles

     Village Creek      190 square miles                                £

     Bayou DeView       460 square miles                                g


For purposes of this presentation, I have chosen to

summarize only the dissolved oxygen concentrations, but if              •

time were available I could summarize all the individual                m

chemical, physical and biological characteristics of these

streams.  Here is a slide showing the fishery community                 •

found in these sites.  Initial work using the Jaccard

dissimilarity index and Detrended Correspondence Analysis               j|

clearly shows distinct ecoregional differences.  Sites                  m

within a given ecoregion were much more likely to contain

similar fishes th'an sites in different ecoregions.  Also                •

patterns of dominant fish species, "signature species," were

identified in each ecoregion.  Now, to move on through the              J[

Regions, the Gulf Coastal:                                              2^


     Whitewater Creek    20 square miles

     Big Creek           50 square miles                                V

     Bayou Freeo        100 square miles
                      - 2 -
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     Derrieusseaux Cr.

     Hudgin Creek

     L'Aigle Creek

     Moro Creek
142 square miles

187 square miles


232 square miles

451 square miles
Here is a summary of the dissolved oxygen concentrations

Here is the type of fishery found in these streams.




The next region is the Arkansas River Valley:
     Mill Creek

     North Cadron


     Ten Mile Creek

     Dutch Creek

     Petit Jean

     Cadron Creek
 17 square miles

 21 square miles


 49 square miles

110 square miles

241 square miles

308 square miles
Here is a summary of the dissolved oxygen concentrations.


Here is the type of fishery found in these streams.




The next region is the Ouachita Mountains:



     Board Camp          19 square miles

     South Fork Ouachita 47 square miles

     Little Missouri     70 square miles

     Cossatot River     122 square miles

     Caddo River        291 square miles

     Saline River       361 square miles

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Here is a summary of the dissolved oxygen concentrations.

Here is the type of fishery found in these streams.




The next region is the Boston Mountains:
     Indian Creek

     Hurricane Creek

     Archey's Fork

     Illinois Bayou


     Lee Creek

     Mulberry Creek
 47 square miles

 50 square miles

107 square miles


125 square miles


168 square miles

373 square miles
Here is a summary of the dissolved oxygen concentrations,

Here is the type of fishery found in these streams.




And, finally, the Ozark Highlands Region:
     Spavinaw Creek

     Flint Creek


     Yocum Creek

     Long Creek

     War Eagle Creek


     Kings River
 18 square miles

 19 square miles

 55 square miles

184 square miles


310 square miles


527 square miles
Here is a summary of the dissolved oxygen concentrations

Here is the type of fishery found in these streams.
                      — 4 _
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To try and really bring this project into focus, I made the

next slide showing the Use Attainability Analysis portion of

the current Federal Register on WQS.  Keywords I would draw

your attention to would be: fishable/swimmable, less

stringent criteria, naturally-occurring pollutants, natural

low-flow, physical conditions.



To read from the Federal Regulation:

   A state must conduct a UAA whenever

        (A) designating uses less than fishable/swimmable

        (B) or proposing to remove a fishable/swimmable use

        (C) or adopt subcategories of uses which require less

            stringent criteria, if the state can demonstrate

            that attaining the use is not feasible because

                 (1) naturally-occurring pollutants

                 (2) natural low flow

                 (3) irretrievable man-induced conditions

                 (4) hydrologic modification (dams, diversions)

                 (5) physical conditions (substrate, cover, flow,

                     etc.)

                 (6) controls more stringent than those required

                     by Sections 301(b) and 306 of the Act would

                     result in substantial and widespread

                     economic and social impact
                        5 -

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Finally, I would end up with some slides showing regional
characteristic such as -
       differences

       -Sulfates, showing regional values and comparing to
       current WQS
       current WQS
                                                                       I
                                                                       I
-Hardness, as it relates to toxicity and showing regional       •
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-Chlorides, showing regional values and comparing to            •
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               STATISTICAL  TECHNIQUES  FOR  THE

                    ECOLOGICAL ANALYSIS

                   OP WATER QUALITY  DATA




                 Jim Piatt  and Don Ditmore




               Surface Water Quality Bureau

     New Mexico Environmental Improvement  Division

                       P.O. Box 968

                   Santa Fe, New Mexico


                        87504-0968
                          ABSTRACT

     Data from two adjoining high-elevation streams demonstrate

statistical differences in average chemical concentrations,

variability and distribution.  Plotting the data illustrated these

differences and indicated that sediment and nutrient enrichments

from both point- and non-point sources on one of the streams

obscure some of the normal, background seasonal trends.

Nonparametric correlations demonstrate that the relationships

among the variables are altered by these impacts.  Computer

techniques which were utilized are summarized.

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                                  INTRODUCTION

              The Red River is a small, high-elevation tributary of the Rio
 I       Grande located in north-central New Mexico.   Cabresto Creek is one
 ^       of two perennial tributaries of the Red River; average discharge
         of this stream is approximately one-half that of the Red River.
 fl       The geological and topological features of both drainages are
         essentially the same.
 |            The upper watersheds of both streams are covered by
 —       Spruce-Fir forests; Quaking Aspen is the primary associate.  The
 *       upper reaches of both streams contain reproducing populations of
 •       trout, and are protected by the most stringent standards currently
         in use in New Mexico (WQCC 85-1). The entire Red River watershed
 •       is one of the more heavily used recreational areas in the State of
         New Mexico.
 *            The watersheds of both streams have historically been
 •       affected by livestock grazing, logging and the building and
         maintenance of access roads.  Private lands along the upper Red
 •       River (above the village of Red River) have undergone rapid, and
         extensive, development of secondary homesites with the requisite
 •       developments of  roads, utilities, and on-site sewerage systems.
 •       Other impacts on the upper Red River relate to  recreational usages
         which include winter skiing at the Red River  Ski Area and
 •       year-round camping and fishing activities.
              Between 1971 and 1983 the middle portion of the  Red River
 9        {from the village of Red River downstream to  USGS station

I

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ponds at Molycorp mine.   The data for Cabresto Creek are from USGS
station 08266000, upstream of the village of Questa.
                                                                       I
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08264970} received an average of 0.30 mgd of poorly treated
effluent from the town of Red River.   Significant manipulations of      M
the streambanks have occurred above,  within, and immediately below
the town.  These disturbances have affected both the riparian           £
vegetation and streambank stability throughout the reach.                ^
     For the purposes of this comparison, the lowest reach of           ™
concern begins approximately 4 km downstream of Red River's WWTP        •
discharge at the Molybdenum Corporation of America (Molycorp)
mine.  The mine diverts approximately 6250 acre-feet per year of        I
ground and surface waters for mill operations.  The mill tailings
are then piped 14 km to holding ponds downstream of the Village of      m
Questa.  Streamside road and pipeline developments have resulted        •
in significant disturbances of the riparian vegetation.  The
exposed soils from these disturbances, coupled with slurry spills       •
from broken pipelines, have produced significant stream bottom
deposits.                                                               m
     This report summarizes the statistical and computer                •
techniques utilized to compare the Red River with Cabresto Creek,
and the results of those comparisons.  The data for the Red River       •
are from USGS station 08264970, located downstream of the tailing
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               STATISTICAL PROCEDURES AND RESULTS




     Data for the analysis was retrieved from the USEPA STORET

system and stored in a SAS (Statistical Analysis System) data

set.  The SAS PROC MEANS (SAS Institute Inc., 1982)  procedure

was utilized to determine if discernable differences existed

between the two streams.  Comparing Tables 1 and 2 demonstrates

that differences in both physical and chemical factors exist in

average values, variabilities and distributions.  It should be

noted that a significant number of variables exhibit distributions

markedly different from the normal distribution as demonstrated by

significant skewness, kurtosis or both.  This lack of bivariate

normality requires the use of nonparametric correlation analysis

(2ar, 1974).

     Plotting the data for turbidity, total nitrogen, and total

phosphorus on the date of collection was accomplished using the

SAS PROC GPLOT procedure (SAS Institute Inc., 1981).  The

effects of various sources on the Red River are clearly

illustrated.  Turbidity on Cabresto Creek (Figure 1) exhibits a

seasonal pattern with turbidity peaking with maximum flows in late

spring.  The Red River, conversely, shows no such seasonality; the

number of perturbations on this system has produced a situation

with, for this system, consistently elevated turbidities.  Total

nitrogen  (Figure 2) exhibits a different pattern with both the Red

River and Cabresto Creek demonstrating seasonal fluctuations.

This figure also illustrates the  increase in both the

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                                                                        I

concentrations and the variability of this nutrient due to the
combination of sources affecting the Red River.  Total phosphorus       •
{Figure 3) exhibits yet another 'pattern1  (or lack thereof).   This
variable lacks any definite association with season, or year,  on        I
either stream.                                                          ^
     Spearman correlation matrices were generated by the SAS PROC
CORR procedure and are shown for the Red River (Table 3) and            ft
Cabresto Creek (Table 4).  Comparing these tables demonstrates
that the combination of sources on the Red River has altered the        p
system to the extent that the relationships among the variables         m
have been significantly altered.                                        ™
     Instantaneous discharge is positively, and highly, correlated      •
with turbidity and suspended sediments on Cabresto Creek; yet,
there are no such relationships on the Red River.  Conversely,          I
total organic carbon increases with increasing discharge on the
Red River while there is no such correlation between these              •
variables on Cabresto Creek.  This difference occurs even when the      •
average concentrations of organic carbon are identical on the two
streams.                                                                I
     Phosphorus has been determined to be the limiting nutrient
for algal growth on these streams (NMEID, 1982).  Thus, the             •
changes in the quantities of phosphorus, and the relationships of       •
this variable to others  is of special significance.  The  'typical
or background1 relationship of total phosphorus with suspended          •
sediments, as demonstrated on Cabresto Creek,  is not present on
the Red River.  The continual input of phosphorus from Red River's      B
                                                                        I
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 I
         wastewater treatment plant, and the numerous non-point sources
 I       which  increase suspended sediments, destroy the significance of
 ^

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          this  relationship.  The correlations of total phosphorus with
          other chemical variables have also been altered by these impacts
          (cfs, total nitrogen and chloride).
                                    DISCUSSION
 •             An  organism  indigenous  to  an  ecosystem  is  the  product  of
 fl        evolutionary  adaptation  to the  entire  spectrum  of physical,
          chemical and  biological  variables  that together comprise  the
 •        environment.   In  a  system  in which perturbations of sub-lethal
          magnitude have occurred, the interactions  of the variables  will
 •  .      determine the organism's ability  to survive,  grow and reproduce.
 •        An aquatic organism stressed by a  toxic pollutant may become more
          susceptable to other factors such  as predation, oxygen
 •        deficiencies, temperature  extremes or  other  limiting factors.
          Such perturbations, on another  stream in north-central New  Mexico
 •        have been shown to  result  in changes in community  structure with
 •        less desirable, more tolerant species becoming  dominant (Jacobi
          and Smolka, 1984b. ) .
 •             Nonparametric  correlations allow a quantitative examination
          of the interactions which affect the biota.   Spearman's
          correlations lack some of the power of the parametric techniques;

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                                                                        I
yet, they are simple to interpret and avoid the limitations of the
more restrictive mathematical models of the parametric techniques.      •
As was demonstrated, correlations can provide illustrations of
station differences, and,  by varying the time periods of the data       m
analyzed, can provide indications of trends within the individual
stations.
                                                                        I
I
                    COMPUTER TECHNIQUES

     Data for these stations are stored in the USEPA STORET
system; a PGM=RET retrieval was performed to obtain the data of
interest, and to store it in the format required for statistical
processing.  The SAS statements define the data file, apply             |
mnemonic titles to the variables in place of their STORET codes,
and applies a name to the stations.  Remark codes are analyzed and      •
then an  'echo check' {PROC PRINT) of the data is performed.  The        •
program  for the retrieval is listed in Table 5.
     The SAS PROC MEANS procedure was used to obtain simple             I
descriptive statistics for the data at each station to aid in the
initial  evaluation.  This procedure can provide a number of             "
different analysis; for our purposes, sample size, mean, standard       •
deviation, minimum, maximum, skewness, and kurtosis were
requested.  The SAS statements which provided these statistics is       •
listed in Table 6.
     The initial descriptive statistics indicated that differences      •

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existed between the two stations.  The SAS PROC GPLOT procedure

was then used to display the data of interest;  first for the

individual stations, and then combining both stations on the same

plot.  It should be noted that SAS processes dates as the number

of days since January 1, 1960.  If dates are required on the

horizontal axis, the plotting routine must be provided with the

intervals to be plotted.  An example of this technique is shown in

the "HAXIS=" statement in the program listing in Table 7.

     When it became apparent that a number of individual variables

were quantitatively affected by point- and non-point sources, the

question of the effects of these perturbations on the

relationships among the variables was raised.  The SAS PROC CORR

procedure was selected as the simplest approach to provide this

information.  There are a number of options within this procedure,

including the choice of parametric or nonparametric correlations.

The program which produced the desired correlation matrices is

found  in Table 8.
                          REFERENCES




     Jacobi, Gerald  Z. and Larry R. Smolka,  1984a.  June  1984.

 Intensive  survey of  the Red River  in the vicinity of  the  Red  River

 and Questa wastewater  treatment facilities and  the Molycorp

 complex, Taos County,  New Mexico   January  25-27, 1984.

 Environmental Improvement Division, Surface  Water Quality Bureau.

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I
                                                                        I

Santa Fe, New Mexico.  EID/SWQ-84/1  29p.
     Jacob!, Gerald Z. and Larry R. Smolka,  1984b.   August 1984.        •
Winter survey (VI) of the Rio Hondo, Taos  County, New Mexico
March 8,9, and 22, 1984.   Environmental Improvement Division,           •
Surface Water Quality Bureau. Santa Fe,  New Mexico.  EID/SWQ-84/3
19p.
     NMEID.   February 1982.   Point source waste load allocation       •
for the Town of Red River.   New Mexico Environmental Improvement
Division, Water Pollution Control Bureau.    Santa Fe, New Mexico.       |
39p.                                                                    -
     SAS Institute Inc.,  1981.   SAS/graph user's guide 1981           ™
edition.  SAS Institute Inc.,  Box 8000, Gary, North Carolina           I
27511.  126 p.
     SAS Institute Inc.,  1982.  SAS user's guide: basics  1982         £
edition.   SAS Institute Inc.,  Box 8000,  Gary, North Carolina          _
27511.  923p.                                                           ™
     WQCC 85-1.   January 1985.   Water quality standards for           •
interstate and intrastate streams in New Mexico.    Water Quality
Control Commission.   Santa  Fe, New Mexico.  41p.                       I
     Zar, Jerrold. H.,  1974.   Biostatistical analysis.        -.
Prentice-Hall, Inc.   Englewood Cliffs, N.J.  620 p.                     »

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     Table 5.  Listing of STORET, SAS and JCL commands required
to retrieve the data, create and save the SAS data set and print a
copy of the data matrix.

PGM=RET,PURP=205/STA,MORE=SAS,A=112WRD,8=08266000,3=08264970,
P=10,P=61,P=76,P=95,P=300,P=400,P=600,P=665,P=680,P=940,P=945,
P=70301,P=80155,PRT=NO NOECHO,
SASPARMS=BEGIN,
DATA OUTFILE.DALLAS;
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INCLUDE (FCFREAD)
OPTIONS NOSOURCE? * SUPPRESSES SOURCE LISTING BELOW HERE ;               .
RENAME P1=TEMP P2=CFS P3=TURB P4=COND P5=DO P6=PH P7=TN                  •
P8=TP P9=TOC P10=CHLORIDE P11=SULFATE P12=TDS                            ™
P13=SUSPSED;YEAR=YEAR(DATE);
IF STATION ='112WRD   08266000       '  THEN STATION = 'CABRESTO CR.;'    •
IF STATION ='112WRD   08264970       '  THEN STATION = 'RED RIVER1;       •
IF Rl NE ''  THEN PI =
IF R2 NE 'l  THEN P2 =
IF R3 NE ''  THEN P3 =
IF R4 NE ''  THEN P4 =
IF R5 NE ''  THEN P5 =
IF R6 NS ''  THEN P6 =
IF R7 NE ''  THEN P7 =
IF R8 NE "'  THEN P8 =
IF R9 NE ''  THEN P9 =
IF RIO NE '' THEN P10 =  .;
IF Rll NE '' THEN Pll =  .;
IF R12 NE '' THEN P12 =  .;
IF R13 NE '' THEN P13 =  .;
DROP P14-P50 R14-R50 AGENCY BEGDATE BEGTIME ENDDATE DEPTH TIME SMK  UMK
ENDTIME TYPE CALC NUMBER USGSREMK MORE;
PROC PRINT;                                                               •
TITLE1 STORET DATA IN SAS FORMAT;.                                       •
STOPSAS,
./MXM      JOB  (A755STORP,MMXM),STORET,TIME=(,19},                       •
./             MSGLEVEL=(1,1),PRTY=4                                     |
**ROUTE PRINT HOLD
**JOBPARM LINES=10                                                       m
./OUTFILE DD DSN=MXMA755.SASDATA,DISP=OLD                                •
/*

     PGM=RET is  the STORET program that  retrieves  the data               I
from the database and produces  a file  on disk (MORE=SAS). The            •
resulting SAS formatted  output  file  (DATA OUTFILE.DALLAS;)  is
saved  in a SAS  data library for further  processing.  Note that           •
the parameter's  remark codes  are examined and,  if  present,  the           £
corresponding parameter  value is set to  missing.   This  insures
that no data are included  in  the statistical analysis that  are not       _
strictly quantitative in nature  (ie, remark codes  k="less than           •
value", and  , j="estimated").                                            *
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                                                                         I

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     Table 6.  SAS statements necessary for the production of the
descriptive statistics.

PROC SORT DATA=DATA.DALLAS;BY STATION;
PROC MEANS DATA=DATA.DALLAS N MEAN STD MIN MAX SKEWNESS KURTOSIS;
BY STATION;

     The dataset is sorted by station which allows this 'variable'
to be used in later procedures as a 'classifying' variable through
which the results are partitioned.  The PROC MEANS procedure
produces the descriptive statistics which were desired.
Rather than accepting the default statistics, the specific
statistics of interest are specified.

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                                                                        I



     Table 7.  SAS statements required to plot the data in the
desired format via the GPLOT procedure.

GOPTIONS CHARACTERS CELLS AXES DEVICE=TEK4662A SYMBOLS;                 I
SYMBOL1 C=GREEN V=PLUS;
SYMBOL2 C=RED V=DIAMOND;
TITLE1 Figure 1. Turbidity on the Red River and Cabresto Creek.;
PROC GPLOT UNIFORM DATA=DATA.DALLAS;
PLOT TURB*DATE=STATION / VAXIS=0.0 1.0 10.0 100.0 150.0
HAXIS='010CT78'D '010CT79'D 'OlOCTSO'D 'OlOCTSl'D '01OCT82'D
HMINOR=1;
LABEL TURB=TURBIDITY FTU;
TITLE1 Figure 2. Total nitrogen on the Red River and Cabresto Creek.;   •
PLOT TN*DATE=STATION / VAXIS=0.0 1.02.03.04.0;                       •
LABEL TN=TN mg/1;
TITLE1 Figure 3. Total phosphorus on the Red River and Cabresto Creek. ;• •
PLOT TP*DAT£=STATION / VAXIS=0.0 0.05 0.10 0.15 0.20 0.25 0.30;         •
LABEL TP=TP mg/1;


     The GPLOT procedure produces output that can be displayed          •
on a graphics device such as a terminal or on a plotter as is
indicated here (DEVICE=TEK4662A).  The variable to be  plotted is        •
identified (TURB) and, the vertical (VAXIS) and the horizontal          |
(HAXIS) are scaled as required.  Note that the program is given
the specific dates desired in the  "HAXIS=" statement.                   _
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     Table 8.  SAS statement necessary for the production of the
nonparametrie correlations.

     PROC CORE DATA=DATA.DALLAS SPEARMAN NOSIMPLE OUTS=DATA.DALLAS4;
     BY STATION;


     The PROC CORR procedure produced the correlation
coefficients of interest.  The SPEARMAN option was specified in
order to obtain only the rank-order correlations which were
desired; both the Pearson product-moment and Kendall's Tau-b are
also available. The NOSIMPLE option was specified to suppress the
calculation and printing of descriptive statistics which had been
produced by the previous procedure.  An output dataset
(OUTS=DATA.DALLAS4) was created to store the data which could then
be used if other SAS procedures should be desired.

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        AN INTRODUCTION TO NORTH CAROLINA'S


BIOMOM70RING PROGRAM:   BENTHIC MACROINVERTEBRATES
        DEPARTMENT  OF  NATURAL  RESOURCES  AND


               COMMUNITY DEVELOPMENT


      DIVISION OF  ENVIRONMENTAL MANAGEMENT


             TECHNICAL  SERVICES BRANCH

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                      LIST  OF  TABLES  AND  FIGURES


Table 1.    Taxa Richness of Benthic Macroinvertebr«te»  from Seven


Hydrogeologic Regions in North Carolina


Table 2.    Biological Classification  Criteria:  Total Taxa  Richness  (ST)


Table 3.    Biological Classification  Criteria:  Ephemeroptera  +


Plecoptera +  Trichoptera (SEPT)


Table 4.    Taxa Richness Values  Collected  at  "Overlap"  Sites,  Summer  1983
Figure 1.   Toxicity Testing by  Bioassay  and  Stream  Surveys  in  18


Compar i sons


Figure 2.   Statewide Water  Quality  Ratings


Figure 3.   Statewide Biological  Ratings

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                                   INTRODUCTION


      North  Carolina  is  divided  into  three major  physiographic  regions:   the


 coastal  plain,  piedmont  and mountains.   Each  of  these  regions  have  distinct ben-


 thic  insect  faunas.   The  coastal  plain  region, which extends  inland for  approxi-


 mately  125  miles  (Traver  1932),  is characterized  by  large  swamps  and  several nat-


 ural  lakes.   Rivers  form large,  broad  floodplains  vegetated by bottomland hard-


 woods or  cypress,  tupelo,  and  black  gum (Brigham  et  al.  1982).  The soils here


 are mainly  sandy  loam,  and there  are many darkwater  streams.   Benke et al. (1984)


 found that  submerged  wooden substrates  (or  snags)  at sites  in  blackwater rivers


 have  high production  estimates  for lotic ecosystems.


      From the western edge of  the coastal plain to the foothills  of  the  Blue


 Ridge Mountains  is the  piedmont  section  of  North Carolina.  Traver  (1932)


 described this  section  of  the  state as  having an elevation of  200-500  feet,


 extending westward to a  plateau  attaining an  elevation of  1200-1500 feet.  The


 soils in  this section of  the state are  susceptible to  the effects of weathering


 and,  in areas where the  soils are disturbed,  streams generally  run  very  turbid.


 This  section of North Carolina  is also  the most highly industrialized and urban-


 ized  section of the state  and many of our investigations are subject to  the


 effects of enrichment and/or toxicants.


      The mountainous  sections of  the state  are bordered on the  east  by the Blue


 Ridge escarpment and on  the west  by the Great Smoky Mountains.  Streams  in this


 section of the state generally are turbulent with higher concentrations  of dis-


 solved oxygen and  lower water temperatures  than streams  in other  sections of the


 state.  Many of our earlier investigations  in the mountain region (Penrose and


Lenat, 1979) dealt with the effects of  sedimentation to benthic macroinverte-


brates.   These observations illustrated that  if the sources of  sedimentation were


controlled,  the effects of sedimentation are generally short-lived.

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                                                                                          I
      In unstressed streams  in  all  regions  of  the  slate we would  expect  taxe rich-
 ness of benthic macroinvertebrates to  remain  high.   For  example,  taxa richness            |
 values for  major benthic  invertebrate  groups  remained high  from  reference sites           _
 in  several  distinct  hydrogeologic  regions  in  North  Carolina  (Table  1).  These
 hydrogeologic  regions,  or  zones, are roughly  aligned west to  east with  Zone A the         I
 most  western zone  and Zone  G  the most  eastern.  The  data  in Table 1 are from
 unstressed  locations, mostly  forested  watersheds, and are the results from only           •
 one  collection  during the month of  August.  The single exception  is from a sand-
 hills  stream in  March.  Taxa  richness  values  for Ephemeroptera and Plecoptera             •
 were  higher  in  Zones A, 6 and  C, reflecting lower water  temperatures and higher           M
 dissolved oxygen values for these  mountainous streams.  On  the other hand, taxa
 richness for other groups were higher  in other zones.  Specifically, Mollusca in          3
 limestone rich coastal plain streams (Zone G) and Odonata in Zones F and G.
     These diverse environmental conditions offer a  unique challenge to aquatic           m
 biologists in several ways.  First, a  sample col-lection protocol has to be devel-         m
 oped that is flexible enough  to permit  the biologist to collect  specimen from
many aquatic habitats (qualitative) and not have to  rely on data  from one habitat         •
 type (ie. artificial substrates, Surber).  Habitat specific collection  techniques
 are  limited.  For  example,  do  you  need to  know that  species X is present at a             |
 density of 2000/m2 or that  species  X is present and  abundant?  Some qualitative
aspects of benthic community structures, especially  taxa richness, are  directly
 related to water quality.   A second challenge is to  develop assessment  criteria           '•
 relating benthic data to water pollution.
                             COLLECTION METHODOLOGY                                       |
     Qualitative sampling has  been  employed for many years to collect benthic
macroinvertebrates.  Qualitative collections should  be a useful  tool for environ-
mental  assessment.  However,  there  is a need to determine if such qualitative             •
 sampling techniques will produce consistent and reliable results.  Much of the
                                                                                          I

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         following section has been taken  from N.C. Biological Series *106 (N.C. Depart-
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         merit  of  Natural  Resources and Community  Development,  1983).

              Biological  monitoring groups  often  ere  allocated  a  fixed amount  of  money  and

         personnel,  but  are assigned a variable work  load.   Therefore,  their  usefulness  is

         directly related to the  time it  takes  to conduct  a  survey.   A lab  which  utilizes
 I
         only quantitative sampling might produce excellent data on one investigation, but
         might  produce jLfi information on several  other,  equally  important,  investigations.

         Qualitative sampling permits a greater work  load.

              This sampling methodology requires  that  a  stream or  river  be  wadable.  High

         water  conditions may severely impair  sampling efficiency  by  making critical habi-

         tats  inaccessible.   An  important  decision  in  qualitative  sampling  is  when  not  to

         collect  samples.  Poor  data  is often  worse  than no  data,  as  an  underestimate of

         taxa  richness may lead  to  an incorrect assessment of  water  quality.

             Many labs utilize  a  "timed"  qualitative  sampling technique.   We  feel  it  is

•       better  to process a fixed  number  of  samples,  usually  10.   Different  collectors

         work at  very  different  speeds, in part due  to differences in the  level  of  experi-

•       ence.  Also,  the time necessary to collect  a  sample will  vary.  Collecting  in  a

         targe  river  takes much  more  time  than collecting in a temporary stream.

•           The  sampling technique  outlined  here usually takes 4-6  man hours,  i.e.  1  1/2

—       to 2 hours  with  three collectors.   A  sampling team  can  usually  do  3  to  4  stations

"       per day.   Although  more quantitative  sites  could be sampled  in  a day,  the  quali-

W       tative technique produces  an enormous savings in processing  time.   Quantitative

         sampling  is  usually limited  to a  single  habitat, and  may  underestimate  species

         richness.   Allan (1975) found that 12 Surber  samples  underestimated  total  species

         richness  (from a variety of  collection methods) by  approximately 32%.   Our  lab

•       has obtained  similar  results (unpublished data) in  comparing total taxa richness

fi       from qualitative sampling  with taxa  richness  from 3 Hester-Dendy samples.

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      it  is  critical  that each collection team include one trained biologist.  An


 expectation of what  should be found (and where) should guide the choice of sample


 locations.  Also, an untrained eye may not see many of the more cryptic insects.


 Sampling  Techniques


      AM  sampling equipment must be simple to use, durable and portable.  The use        I7


 of glass  and electrical equipment should be minimized.  All samples are field


 picked  in white plastic or enamel trays.                                                 *•


     Kick Net.  A kick net is an easily constructed and versatile sampling               __

                                                                                         I
device.   It consists of little more than  a piece of netting, or window screen,           ^


between 2 poles.   The net  is positioned upright  on the stream bed,  while the area        |jj


upstream  is physically disrupted.  Many investigators have found that this simple


 technique gives very consistent  results (Hornig and Pollard 1978, Armitage 1978).        "•


     If too coarse a mesh  is used for  the kick net, many animals will not  be


retained.  If  too fine a mesh is employed,  the net clogs easily and washout              I


becomes a problem.  We find that  a good compromise is the use of a double  layer           •


of flexible, nonmetalic door  screening.   A border  of durable cloth material is


used to reduce tears and weights are sewn into the bottom edge.                          •


     Two kicks ere_ taken from riffle areas.   The two samples should be collected


from areas of  differing current  speed.   In very  small steams, or in sandy  areas           |


 lacking riffles,  kicks should be taken  from root masses, "snags" or bank areas.           M


All types of benthic macroinvertebretes can be collected by this sampling  device.
but emphasis is placed on Ephemeroptera, Plecoptera and Trichoptera.                     M


     Sweep Net.   A long-handled triangular, or 0-frame, sweep net is another


versatile sampling device.  Samples (2-3) are taken by physically disrupting an


area and then vigorously sweeping through the disturbed area.  Sweeps are usually


taken from bank areas and macrophyte beds.   Bank samples are particularly impor-


tant for the collection of "edge" species which prefer low current environments.         •
                                                                                         I

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                                                                                         I

                                                                                         I


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         Look for Chironomini (red chironomids),  01igochaetes,  Odonata,  mobile cased Tri-
 I      choptera, Hemiptera. ? i ft I i s.  Crustacea and  certain Ephemeroptera.
 H           A sweep net also can be  used to sample  gravel riffle areas where stonecased
         Trichoptera may be abundant.
 •           Fine-Mesh Sampler.   Since the kick  and  sweep  nets utilize  a relatively
         coarse mesh size,  an alternate sampling  technique  was  devised  to sample  the
   I.
         smaller invertebrates (especially the Chironomidae).   The resulting  sampler is
 —      known in our lab as a "chironomid-getter".   A cylinder (approximate  diameter  7
 ™      cm) is cut from PVC pipe or a plastic bottle.  Fine  nitex mesh  is  attached to one
 •[      end with glue and  a ring clamp.   The exact  dimensions  are not  critical,  but the
         cylinder should fit inside  another container, usually  a one  quart  plastic con-
 •      tainer.   This device can be used  in a variety of ways.
 ^           The simplest  technique is to wash down  rocks  or  logs in a  large plastic
 •      basin  (or  bucket)  partially filled with  water.  Rocks  are selected which have
 •  "    visible growths of  periphyton,  Podostcmon or  moss.   Any large  participate mate-
         rial  (leaves, etc)  is washed  down and discarded.   A  single composite sample can
 •      be  made from several rocks  and/or logs.   The  material  remaining in the basin  is
         poured  through the  fine  mesh  sampler and the  water allowed to  drain  out  com-
 P      pletely.   The residue is quickly  preserved  in 95%  EtoH.  This  is accomplished by
 •      placing the fine mesh sampler into another container  (see above) which is half
         filled  with alcohol.  The sample  is allowed  to sit for several  minutes and then
 '•      backwashed into a  picking tray.   Note that  this method of field preservation
         requires only a small  amount  of alcohol,  and  it may  be reused many times.  We
 |      usually bring 2 or  3 of  the fine  mesh samplers, so that one  may be soaking while
 •      another  is being picked.
              Field preservation  makes small chironomids and  oligochaetes more visible,
 •      and easier  to pick  up with  forceps.  This technique  is also  good for Baetidae.
         HydropiiIidae and  other  grazers.   The "pour-and-preserve" technique  also can  be
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                                                                                         I
 used in conjunction with  other  sampling methods.  For example,  the elutriate from


 a  kick  or  sweep  sample  can  be possessed  in  this manner.   It  is  also used  in con-         I


 junction with  sand samples  (see below).


      Sand  Samples.  Sandy habitats often contain a very distinct  fauna, but              fl


 eitraction of  this fauna  by means of dredge-type sampling can be  very  tedious. To        m


 sample  sandy substrate, we  employ a  large bag constructed of  fine mesh (300


 microns) nitex netting.   It can be quickly  constructed from  a one meter square           '•


 piece of netting, folded  in half and sewn together on the opposite side and the


 bottom.  This bag is employed like a Surber sampler, but the  lack of a rigid             |


 frame allows for easy storage when folded.                                                M

                                                                                         I
     The bag is held (open) near the substrate,  and the sand  is vigorously dis-


 turbed.  The material  collected (a lot  of sand and a few organisms) is emptied           I


 into a  large plastic container half-filled with  water.  A "stir and pour" elu-


 trigtion technique is used  in conjunction with the fine mesh  sampler.  After             •


 field preservation,  look  for Chironomitfae (especially flheosmi 11 i a. Har n i sch i a


group, Polvped iI urn spp  ),  Oligochaeta,  Gomphidae and some Ephemeroptera.


     Leaf-Pack Samples.   Leaf-packs,  sticks and  small logs should be washed down;


a large bucket  sieve  is useful for this procedure.   Leaf-pack and small log


samples are particularly useful  in large sandy rivers.  In such habitats, many of


the species are confined to "snags"  (Benke  et  al.  1979,  Neuswanger et  al. 1982).


Look for "shredders",  especially Tipulidae,  Plecoptera and Trichoptera.                  •


     Visual Search.   Visual inspection  of large  rocks and logs, (the larger, the
shore (in negligible current) will  harbor  certain Ephemeroptera, and leaves near


the shore may be the primary habitat for  some Gastropoda.  In general,  look for
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better) often adds to the species list.   Certain tightly adhering organisms may


be collected only by this technique.   Decaying logs should be picked apart to             •


look for chironimids, and many taxa can  be found under loose bark.  Rock near the
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         attached cases of Trichoptera. Turbellaria,  Coleoptera, Odonata (especially on



 P?      large logs), Gastropoda. Hirudinea and Megatoptera.



 ^           Mussel species can be obtained by careful visual  inspection of the bottom.  A



 '      mussel search should be conducted if dead shells are evident along the shore;



 I      look for midden heaps resulting from the feeding of  muskrats and other verte-



         brates.   However, only live specimens should be added to the species list.   Dur-



 •      ing periods of receding water levels, many species will move to deeper water,



  .       leaving  a visible "track".  The bases of aquatic weeds (especially water  willow)



 •      may contain many mussel species and must be  searched by hand.   If possible, mus-



         sels should be identified in the field and returned  (alive) to the stream.



         Sample Processing:  Picking and Identification



 •           Field separation of invertebrates from  large amounts of detritus ("picking")



 £.       can be difficult.  However,  it is infinitely less tedious than quantitative pro-



 •      cessing  of samples in the lab.  This is not  a  trivial point.  The amount  of



 A     -monotony and boredom involved in a task will probably effect both the level of



         performance and,  eventually,  the turnover  of personnel.  Nobody likes to  pick



 •       samples!



              It  is a simple matter to add some simple  measures of abundance to this samp-



 P      ling scheme.   As  invertebrates are identified, we classify them as Abundant



         (>10), Common  (3-9) or  Rare  (1-2).   Good field notes can also be used to  assign



         relative abundance values.



 I                                  BIOCLASSIFICATION CRITERIA



              There ere several  different  ways to analyze qualitative data.  Almost  all



         procedures require precise taxonomy.



 M            Comparisons  of qualitative data from different  sites are only useful  if the



 *       taxonomy and collection techniques were similar.  Erman (1881) found poor agree-



 I       ment  between several  "baseline"  studies when identification and collection  meth-



         ods  were  not  consistent.




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                                                                                         I
      Analysis of fauna)  assemblages is one way to detect  water  quality  problems.



 nities.   The taxa associated with  organic  loading (and  tow dissolved  oxygen) are         ^

 well  known.   More recent  studies have  begun  to identify  taxa  associated with sed-        *

 •mentation  and toxic  stress  (Winner  et  al.  197S,  Bode and Simpson  1982). However.        •

 identification at,  or  near,  the  species  level  is  desirable for  many genera,

 including Pol voed i lutn. Cr icotoous .  Hvdroasvche. Ephemere I la.  jjtqnonema  and               •

 BaetIs.  Note  that  tolerant  species  are  present  in  all aquatic  habitats (see

 Hynes  1960).   However, these  species will  usually become  dominant  only  in pol-           •

 luted  systems.   Allowances must also be  made  for  stream  size, geographic varia-          •

 t ion and seasonaIi ty.

     The presence of rare or  endangered  species is  often  associated with good            •

water  quality.  Again, species level taxonomy  will  usually  be required  (Resh and          ^
Unzicker  1975).

     The  simplest method of analyses  is  the  tabulation of  species  richness.

Species richness  is the simplest measure of  "diversity", and  the only  one  that

fits a dictionary definition.  The association of good water  quality with  high

species (or taxa) richness  is  intuitively obvious even to  the  non-biologist.

Increasing levels of pollution gradually eliminate the more sensitive  species,            m

leading to lower and lower  species richness.
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     A primary motivation for quantitative studies has been the calculation of
diversity  indices, especially the Shannon-We iner  index (Wilhm  1970).  While this          B

index functions well under conditions of simple organic  loading,  it often pro-

duces confusing results for other kinds of stress (Hocutt 1975, Mason 1975,

Hughes 1978. Godfrey 1978, Oden  1979. Statzner 1981).  Taxa richness values ere

usually more easily related to water quality changes (Dills and Rogers 1974, Win-

ner 1975, Green 1977. Bournaud and Keck 1980).                                            •
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      Total  taxa  richness  for  unstressed  streams  and  rivers  is  relatively constant

 both  temporally  and  spatially  if  comparisons  are  limited  to  streams of  similar


 size  (Lenat  1963.  Patrick  1975).   Any  differences  that  occur are  predictable.


 Seasonally,  a  maximum is  likely  to occur  in  early  spring, as over wintering


 species  which  emerge in  spring overlap with  spring-hatching  species.  However,


 collections  from the French  Broad River  (Penrose et  al.  1963)  suggest that some


 seasonal  changes in  taxa  richness are  not  inherent,  but  reflect seasonal changes


 in water  qua!ity.


      Tables  2  and  3  list  taxa  richness ranges describing water quality  conditions


 for several  major  ecoregions  in  North  Carolina.  Table  2  is  for total taxa rich-


 ness  and  Table 3 is  for  taxa  richness  ranges  for intolerant  species groups (EPT =


 Ephemeroptera  +  Plecoptera +  Trichoptera).


     We have also  conducted  overlap  studies  from which  two teams  conduct surveys


 at a  similar location  (see Table  O.   These  studies  serve as a form of  field


 quality assurance.   Good agreement was obtained al all  three locations  at which

 surveys were conducted during  the  summer of  1983, particularly for SEPT values.


                             BIOMONITORING PROJECTS


     Several biomonitor ing projects  using qualitative collection methods for ben-


 thic macroinvertebrates  in North  Carolina  include trend monitoring (Benthic


Macroinvertebrate  Ambient Network, or  BMAN),  instream toxicity testing  and


 impaired  use assessment.  Each of  these monitoring projects will  be briefly dis-


cussed.


     The Benthic Macroinvertebrate Ambient Network (BMAN) consists of a subset


 (160 sites) of all statewide ambient water quality locations (349).  Benthic


mac rotnvertebrates are collections which are staggered  from selected sites each


year,   for example,  data from many locations are collected each year, particu-


 larly from sites monitoring  specific problem  industries or interstate water

quality.   However, data  is collected from most locations on a 2 or 3 year rota-

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                                                                                         I
 tion.  These  rotations allow us to conduct surveys on a much broader statewide
 scale without  losing much resolution between years.  The objectives of the BMAN           •
 network are 1) determine long term trends in water quality as reflected in taxe
 richness and  2) suggest specific locations for intensive investigations.                  V
     Results  of the BMAN collections (North Carolina Department  of Natural                ti
                                                                                         m
Resources and Community Development 1985a) are discussed on a basin by basin for-
mat and include notes on the presence of pollution tolerant taxa (species), wat-          •
ershed characteristics and suspected pollution sources.  In 1964,  seventy-five
                                                                                          I
locations were assigned bioclassifications based on taxa richness criteria (clas-
sification criteria for estuarine  locations were provisional).  These results are

Iisted below:
          B j Qc J ass i f Jcat ion     t of Locat ions     S of Total
          Excellent               6                8
          Good                    16               21
          Good/Fair               30               40
          Fair                    18               24

          Poor                    5                7
     Benthic  macroinvertebrate collections were made tor the first time during
1984 at 22 locations.  Data at 28  locations,  having two years of data, suggest a
positive trend in water quality at 4 stations and no observable trend at 24 sta-
tions.  No negative trends  in water quality were noted at stations having two             •

years of data.
     Data from 25 locations, from which data has been collected for three conse-
cutive years, suggest a positive  trend in water quality at 4 stations and no              •
observable trends at 21 stations.  No negative trends  in water quality were noted
at  stations having three years of data.                                                   I
     Data from 97 stations were collected during the 1985 BMAN program.
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              A second  monitoring  program has  been  established  to  supplement  toxicity
 •      testing of  municipal  or  industrial  effluents.  At  facilities  either  suspected or
 m      known to lie discharging  a toxic  effluent (by using  toxicity  screening  procedures)
 *      benthic macroinvertebrates are  collected above and  below  discharge points  to
 •      assess instream effects.   Changes in  benthic community  composition as  well as the
         development of a toxic  assemblage is  used  to assess  instream  effects.  Thirty
 P      such investigations have  been conducted.   Frequency  of  agreement  between the
I         first 18 investigations  are illustrated  in Figure  1.
              Twelve investigations (Column  A)  illustrate agreement acute  between effluent
 fl      toxicity predictions  using fathead  minnows, (Pimephales promelasl and  instream
         biological  effects.
 •           At four of  the eighteen  facilities  (Column B),  fathead minnow tests were
 m      negative even  during  the  100* effluent test.  However,  toxicity was  detected with
 *      the  benthos.   Since effluent  samples  were  taken above  disinfection,  the  results
 tt      may  indicate chlorine  toxicity.   In each of these  instances.  The more sensitive
         Ceri odaohnia chronic  tests were  positive (Column C).
 •           In  three  of  the  eighteen  tests bioassay results suggested  instream  toxicity
         but  it  was  not detected with  benthos  (Column D).  However, in two of the three
 9      tests,  poor upstream  water quality  masked  the instream  toxic  effects.  Benthic
         community  structure suggested  toxic conditions both  above and below  the  facility.
         In  the  other of  these  three facilities,  the effluent didn't  reach the  stream.
 •           A  final document  assesses  surface water quality statewide  by reviewing
         existing data  sources  (North  Carolina Department of  Natural Resources  and  Commu-
 •      nity  Development  1985b).   These  sources  include fishery investigations and water
 ^       quality  studies  in  addition to benthological studies.   The final  result  is a
         series  of water  basin maps with  streams  in each basin  color coded as to  the water
 •       quality.   In addition, differences  were noted between  biological  surveys and
         chemical  surveys.   Figures 2  and  3  illustrate results  of water  quality rating and
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                                                                                         I
biological ratings.  Large discrepancies occur between these ratings due largely

to the variable effects of sedimentation and/or complex organic compounds which          M

are not analyzed  for  in most chemical  indices and are accounted for  in biological

investigations.   This  report illustrates where there are specific point and non-         ™

point source pollution problems and provides a foundation for review and improve-        •

ment of the state's water quality management plan.
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                                 LIST OF  REFERENCES

 Allan.  J.D.   1975.   The  distributional ecology  and  diversity  of  benthic  insects
      in Cement  Creek,  Colorado.   Ecology 56:   1040-1053.

 Armitage,  P.O.   1976.  Downstream changes  in  the composition,  numbers  and  biomass
      of bottom  fauna in  the  Tees  below Cow Green Reservoir and  in  unregulated
      tributary  Maize Beck,  in  the first  five  years  after  impoundment.  Hydro-
      biologia 58;   145-156.

 Benke,  A.C.,  T.C. Van  Arsdall  Jr.,  and D.M. Gitlespie.   1964.   Invertebrate pro-
      ductivity  in a  subtropical  blackwater river:   The  importance  of Habitat and
      life  history.   Ecological Monographs.  54(1)25-63.

 Benke,  A.C.,  D.M. Gillespie, F.K.  Parrish, T.C. Van Arsdall,  Jr.,  R.J. Hunter and
      R.L.  Henry,  III.   1979.   Biological  basis  for  assessing  impacts of  channel
      modification:   invertebrate  production,  drift, and  fish  feeding in  a  sou-
      theastern  blackwater  river.   Georgia  Institute Technology,  Earch  Resources
      Center,  ERC 06-79.   187 pp.

 Bode, R.W. and  K.W.  Simpson.   1982.  Communities in large  lotic  systems:
      Impacted vs. unimpacted.  Abstract, Thirtieth Annual Meeting, North American
      Benthologieft I Society.

 Bournaud, M.  and G.  Keck.   1980.   Diversity specifique et  structure des  peuple-
      ments de macroinvertebr6s benthiques au  long d'un cours  d'eeu:  le  Furans
      (AinD.   Acta Oecologia/Oecologia Gener.   1:131-150.

 Brigham. A.R., W.U.  Brigham  and A.  Gnilka.  1982.  Aquatic insects and Oligo-
      chaetes of North  and  South Carolina.  Midwest Aquatic Enterprises.  Mahomet,
      I I I inois 61853.

 Oitls,  G. and D.T. Rogers, Jr.   1974.  Macroinvertebrate community structure as
      an  indicator of acid mine pollution.  Environ. Pollut. 6:239-262.

 Erman,  D.C.   1981.   Stream macroinvertebrate baseline surveys:   a  comparative
      analysis from the oil-shale  regions of Colorado, U.S.A.   Environmental Man-
      agement 5:  531-536.

Godfrey, J.J.   1978.  Diversity as  a measure of benthic macroinvertebrate  commu-
      nity response to water  pollution.   Hydrobiologia 57:  111-122.

 Green,  R.H.   1977.   Some methods  for hypothesis testing and analysts with  bio-
      logical monitoring  data.  Jn:  J. Cairns,  Jr., K.I. Dickson and G.F.  West-
      lake teds.).  Biological monitoring of water and effluent quality.  ASTM STP
      607, Amer.  Soc. Testing & Materials,  p. 200-211.

Harrell, R.C. and T.C. Dorris.  1966.  Stream order, morphemetry,  physico-
      chemical conditions and community structure of benthic macroinvertebrates in
      an  intermittent stream  system.  Amer. Midi. Nat. 80: 220-251.

Hocutt,  C.H.  1975.   Assessment of  a stressed macroinvertebrate  community.  Water
     Res. Bull.   11: 820-835.

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 Horntg,  C.E.  and  J.E.  Pollard.   1978.   Macroinvertebrate  sampling  techniques for
      streams  in  semi-arid  regions.   Comparison of  the Surber method with a
      unit-effort  traveling kick  method.   EPA-600/4-78-040  28 pp.

 Hughes,  B.O.   1978.   The  influence  of  factors other  than  pollution on  the value
      of  Shannon's diversity index  for  bcnthic macroinvertebrates  in streams.
      Water  Res.  12:  359-364.
 Hynes, K.B.N.   1960.   The  biology of polluted waters.  Univ. Toronto Press.
      202 pp.
 Lenat,  D.R.   1983.  Benthic macroinvertcbretes  of Cane Creek,
      comparison with other southeastern  streams.  Brimleyana
                               Nor th Carolina
                               9: 53-69.
and
Mason, W.T.,  Jr.   1975.  Chironomidae  (Diptera) as biological  indicators of water
     quality.  Jn;  Organisms and  biological communities as  indicators of envi-
     ronmental quality.  Ohio St.  Univ. p.  40-51.

Neuswanger, D.J., W.W. Taylor and  J.B. Reynolds.  1982.  Comparison of macroin-
     vertebrate herptobenthos and  haptobenthos  in a side channel and slough  in
     the Upper Mississippi River.  Freshwater  Invertebr. Biology.   1 (3) 13-24.

North Carolina Department of Natural Resources  and Community Development.  1983a.
     Qualitative sampling of benthic macroinvertebrates:  A  reliable, cost-
     effective, biomonitor ing technique.  Biological Series *108.   11 pp.

North Carolina Department of Natural Resources  and Community Development.  1985b.
     Benthic macroinvertebrate ambient network  (BMAN) data review,  1984.  107 pp.

North Carolina Department of Natural Resources  and Community Development.'  1985.
     Assessment of surface water quality  in North Carolina.  259 pp.
Oden, B.J.  1979.  The freshwater
     receiving thermal effluents.
   littoral  meiofauna in a South Carolina reservoir
    Freshwat.  Biol.   9:  291-304.
Patrick, R.  1975.  Chapter  15:  Stream Communities.  Jjj.: M.S. Cody end J.M.
     Diamond 
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Traver, J.H.  1932.  Mayflies of North Carolina.  Journal of  the Elisha Mitchell
     Scientific Society.  47(1) 85-161.
Wilhm. J.L.  1970.  Range of diversity index  in benthic macroiovertebrate popu-
     lations.  J. Water Poll. Control Fed.  42: R221.
Winner, R.W., M.W. Boesel and M.P. Parrel I.   1975.  Response  of the macroinver-
     tebrate fauna to a copper gradient  in an experimentally  polluted stream.
     Verh.  Internat.  Verein Limnol.  19:  2121-2127.

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Table  2.  Biological classification Criteria:  Total Taxa
         Richness (S ).


Excellent
Good
Good-Fair
Fair
Poor
Mountain
Rivers
>91
77-91
61-76
46-60
0-45
Piedmont
Rivers
>91
77-91
61-76
46-60
0-45
Coastal A1
Rivers
>84
68-83
52-67
36-51
0-35
Coastal B2
Rivers
?
>60
46-60
31-45
0-30
  Shallow,  fast-moving
2 Deep,  slow-moving,  criteria  provisional

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Table 3.  Biological Classification Criteria:
         Epheroeroptera + Plecoptera + Trichoptera  (S£pT)
 >EPT
Mountain    Piedmont
 Rivers      Rivers
                                    Coastal A1   Coastal  B'
Excellent
Good
Good-Fair
Fair
Poor
  32-41
  22-31
  12-21
   0-11
24-31
16-23
 8-15
 0-7
Rivers
 >27
 21-27
 14-20
  7-13
  0-6
  Shallow,  fast-moving
  Deep, slow-moving,  criteria 'provisional
Rivers
  >11
  9-11
  6-8
  3-5
  0-2
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Figure  1.   Toxicity Testing  by  Bioassay and Stream Surveys
            in 18 Comparisons.
           Toxicity as Detected by  Bioassay  and Stream Surveys

                              in 18 Comparisons



           Frequency
       16
       12  -
        8  ~
        4  -
A:  Flow-Thru S Strean Survey Agree

B.  Toxicity nissed by Flow-Thru

C.  Toxicity Hissed by Flow-Thru and Chronic

0.  Toxicity nissed fay Strean Survey
                              B
                                   Class

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Figure  2.   Statewide Water  Quality Indices
    STATEWIDE WATER QUALITY INDICES
          Fair 6.4%      Poor 5.8%
 Good-Fair 14.
        % of state's total stream mileage.

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Figure  3.   Statewide Biological  Ratings
          STATEWIDE BIOLOGICAL RATINGS
            Poor 6.7%     Excellent 4.07'
     Good  16.9%
                               -Fair 42.9%
       % of state's total stream mileage.
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 m                        BAYOU BUN IDEE WATER QUALITY MONITORING PROJECT
               Louis K. C. JOHNSON - LOUISIANA DEPARTMENT OF ENVIRONMENTAL QUALITY
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           BAYOU BONNE IDEE WATER QUALITY MONITORING PROJECT



                                   Presented by

                                Louis R. C. Johnson

                         Environmental Program Specialist V
INTRODUCTION
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                The  Bayou  Bonne  Idee  Monitoring Project is located  in  Northeast Louisiana
 •        completely within Morehouse Parish.  The project was originated to monitor the  water
           quality of Bayou Bonne Idee as the result of a Rural Clean Water Project. Funding for the
 I        water quality monitoring project is  from the Environmental  Protection Agency  under
 «        Section 208 of the Federal Water Pollution Control Act (PL 92-500).  The Rural  Clean
 *        Water Project was funded because Bayou Bonne Idee  was highly affected by agricultural
 I        runoff.
                Agricultural runoff  is the  leading nonpoint pollution  problem in Louisiana.  As the
 |        result of effective weed control programs, fall tillage, skip-row planting, and continuous
 —        plowing of turnrows and field  borders   -   turbidity, suspended solids, nutrients, and
 ™        agricultural chemicals had reduced the usability of the bayou.  Toxaphene levels in whole
 •        body samples of fish were recorded as high as 45 mg/1.
                The  RCWP originally encompassed 220,000 acres and three different watersheds.
 •        Because of funding problems, the RCWP was revised twice in  1983.  The first time, the
           area draining into Cypress Bayou was removed from  the project. The second reduction
 »        removed the Bayou Gallon drainage  leaving only 66,000 acres adjacent to  Bayou  Bonne
•         Idee in the project. Likewise, the water quality monitoring  sites were also relocated to be
*         only on Bayou Bonne Idee.
I

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     The land involved is Mississippi delta farmland with predominate crops being cotton,


soybeans, and rice.  According to the U.S. Soil Conservation Service, W,880 acres of

66,000 acres in the RCWP are considered critical erosion acres.  The goal of the RCWP is

to have some type of agricultural BMPs on 75 percent of the critical acres. The cost of

the implementation of the BMP's is approximately 3.9 million dollars.  Seventeen different

types of BMP's are being implemented on the agricultural lands within the RCWP.

     Bayou Bonne Idee is presently a slow moving  body of water dotted  with cypress

trees.  The bayou contains three dams with weirs and drawdown structures.  Behind each

dam and weir a lake is formed.  The major drainage area is at the upper end (north) of the

bayou.  For most of the length of the bayou the highest elevations are near the edge of

the bayou.  Bayou Bonne Idee discharges into Beouf River, a major waterway in Northeast

Louisiana.
PROJECT DESCRIPTION




     The water quality monitoring program consists of monthly sampling at four (4) sites

located on Bayou Bonne Idee and one (1) site located on a tributary to the bayou. Samples

of both water and sediment are taken monthly.  Flow measurements are taken at one of

the sampling sites monthly. Fish samples have been taken twice per year from each of

the three lakes formed by the weirs.

     Field  analyses  consist of dissolved oxygen, temperature, pH,  and conductivity

measurements at  mid stream.  Laboratory analyses consist of two types. Type one is  the

normal 25  parameter scan done as part of the DEQ/OWR/WPC  monthly  Water Quality

Program.  The second type consists of a scan  for the 26  pesticides found  in the  129

priority  pollutant  list.  These scans  were done on both water and sediment taken from

each sampling site. Field analyses and samples were taken by the  Department of
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Environmental Quality's  Office of Water Resources  Northeast Regional Staff.   The

laboratory analyses were done  by  the Department of Environmental Quality's Office  of

Water  Resources Laboratory on the LSI) campus in  Baton Rouge and  the  Northeast

Louisiana University  Soil Laboratory in Monroe,  Louisiana.  Data resulting from the

project is now stored  in the DEQ/OWR Vax 780 digital computer located in Baton Rouge.

This data, after review, will be transferred to the EPA STORET system.
DATA REVIEW
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                At the time of writing this report, 607 samples had been collected.  This represents
 •        30,350 different chemical analyses and rain measurements, of which only 36 are rain
           measurements.   This data represents  data collected from February 1982 thorugh May
 j§        1985. Even though there has been a large amount of chemical anlayses done, the period of
§           record is  small.  This  short record period makes trend  analyses difficult for showing
           whether the water quality in Bayou Bonne Idee is improving as a result of the RCWP.
 ft             Pesticide and water quality data is very scattered on plots.  Bar charts of the lower
           station versus the upper station for six (6) pesticides shows pesticides found more times at
 j|        the lower station in both water and sediment than at the upper station.  The pesticides
 —        found present in water and sediment  samples  most of the time are DDT, DDE, ODD,
 ™        toxaphene, aldrin, and  deldrin.   The highest concentrations of these are found in the
 ft        sediment samples.
     Turbidity at one station runs from 36 NTU to 3000 NTU.  Turbidity plots are high

one month and low the next. Review of plotted turbidity data for the upper end station

(127) versus the  lower end station (122) shows the concentrations higher at the lowest

station.  But, the turbidity data for station 128 contains very high results. This station is

located on a tributary just one quarter mile south of the northernmost station and seems

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                                                                                           I

                                                                                           I
to be the largest source of pollutants to the bayou.
     Rainfall has been a factor during this monitoring program. The years 1982 and  1983          •  .
were wet, while 1984 and 1985 were  somewhat dry.  No flow was recorded at the  flow              ||
measuring site during the monitoring period due to lack of rainfall on numerous occasions.          m

                                                                                           I
OTHER FACTORS AFFECTING THE PROJECT                                                •

     The level of the bayou when the  project began was low.  The dams were cut, and the          ||
bayou was only a channel. Approximately six months after the monitoring began, the cuts          ||
in the dams were filled, and the lakes began to fill.  At the end of the project, the lower
lake was lowered in order to build a new bridge.                                                •
     During the life of this project,  two people primarily responsible for the sampling
were injured on the job - both due to  back injury.  Because of their injuries, supervisory          £  II
personnel had to take over  the  sampling program.   A permanent replacement did not          ^
take over until the project had only four months remaining.                                      ™

                                                                                           I
SUMMARY
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     After reviewing the data and the scope of the RCWP, this water quality monitoring
project on Bayou Bonne Idee was too short.  In order to properly evaluate a RCWP of this          M
size and duration, the water quality monitoring program should last at least the length of
the RCWP, in this case, 15 years.                                                             •
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                                                  -
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                                                  FIGURE 3
                                            KONITORDG STATICS
                                              BAYOU BOWE IDEE
                                          RURAL CLEAN WATER PROJECT
                                         HOREHOUSE PARISH. LOUlSL*uNA
O - Monthly Water Quality

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 BAYOU BONNE IDEE
MONTH

Ja nuary
February
March
Apri 1
May
June
July
August
September
Octomber
November
December

January
February
March
April
May
June
July
September
Octomber
November
December

January
February
March
Apri 1
Kay
June
July
August
September
Octomber
November
December
WATER QUALITY MONITORING PROGRAM
 RAINFALL DATA

 ATTACHMENT SIX
    YEAR

    1982
    1982
    1982
    1982
    1982
    1982
    1982
    1982
    1982
    1982
    1982
    1982

    1983
    1983
    1983
    1983
    1983
    1983
    1983
    1983
    1983
    1983
    1983

    1984
    1984
    1984
    1984
    1984
    1984
    1984
    1984
    1984
    1984
    1984
    1984
 RAINFALL
 6,
 4,
 2,
 5,
 1,
 6,
 2,
 5
 3,
10,
 7
 2
10
 2
 6
11
 5
 1
 2
 1
 8
10

 3
 7
 9
 6
 3
 7
 1
 6
 2
 7
 5
34"
37"
67"
29"
88"
81"
46"
21"
00"
27"
36"
18.71
,00"
 16"
,35"
,05"
,19"
,01"
,67"
,14"
,04"
,33"
,15"

.88"
.93"
,21"
.22"
,00"
.97"
.70"
.09"
.09"
,77"
.73"
 1.68"

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    BAYOU  BONNE  IDEE  WATER  QUALITY MONITORING  PROJECT
           FISH  TISSUE  RESULTS AND STATISTICS
                          IN  PPB

                     ATTACHMENT FIVE
DATE SAMPLED

09/22/79
08/20/79
08/20/79
03/19/80
03/19/80
09/22/79
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
01/20/84
10/25/83
10/25/83
DDT
300.00
.65
1.07
2.35
.26
300.00
85.21
127.62
32.35
35.28
255.65
10.56
201.23
18.05
230.78
143.15
866.52
1016.83
.00
.00
ODD
.00
.68
.71
4.17
.56
.00
288.15
179.49
44.36
46.22
255.65
10.91
269.09
36.02
410.18
249.52
1398.30
1825.59
16.15
8.54
ODE
3321.00
5.20
3.32
13.26
1.50
3321.00
761.62
319.80
272.38
366.43
1115.38
101.70
546.92
344.56
470.53
821.92
4715.00
1726.61
95.47
50.48
TOXAPHENE
1439
   1
   2
   3

1439
.00
.58
.98
.27
.18
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
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                           TEXAS WATER CUIfllSSlUN FISH KILL REPURT1NG SYSTEM

                                PATRICK ROQUES - TEXAS WATER COMMISSION

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                         THE TEXAS HATER COMMISSION
                         FISH KILL REPORTING SYSTEM

                               November, 1985


One of the most visible results of poor water quality  and pollution are  fish
kills.    The  Texas  Water  Commission  and  the  Texas  Parks and Wildlife
Department share the responsibility of investigating and reporting fish  kill
events in Texas.   The immediate purpose of the investigation is to identify
and eliminate the causes of the kill.   This may involve  the elimination  of a
discharge, the clean-up of a spill or just advice to a farmer about avoiding
dissolved oxygen depletion in his stockpond.  We also  attempt to gather  data
that  will  be useful in understanding the causes of the kill and preventing
future occurrences.   Some of this information could be  used for litigation
such as the counts of dead fish and their monetary value, a chain-of-custody
water and tissue sample, and the names of witnesses.


Information  that  is  gathered during an investigation  is passed on  through
reports,  in particular through the reporting system   that  I  am going to
describe  below,   but  also  in discussions with appropriate parties in the
Commission and with those responsible for the  pollution.    The fish  kill
reporting  system  is  intended  to  record and adequately document all  fish
kills that occur in the State of Texas.  The data base currently records all
fish  kills  investigated  by TWC and TPWD since 1970.   Investigations  made
before 1970 were reported in  memos  and  stored  in   the  agency's   central
files.  Since 1970 a formated report has been used. Data recorded  includes:
  Date of Kill
  County
  River Basin
  Stream Name
  Number of Fish Killed
  Game or Nongame Types
  Cause of Kill
In  the  mid-1970's this data was put into an automated data base that could
produce summary reports.

Three years ago we enhanced the automated system,  adding the capability to
record more information and formalized the procedure  for field investigation
by our district personnel.  The new information includes:
  TWC segment
  Pollution source, i.e., industrial, railroad, oil and gas
  Water body type, i.e., pond, estuary
  Monetary value
  Areal extent of kill, i.e., river miles, acres
  Duration of the kill in days
  Latitude and Longitude
  Names of complaintants and investigator
  Day and time of investigation

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                           FISH KILL INVESTIGATION
The  reports that are entered into the automated file are generated by  field
personnel from the Texas Water Commission or Texas Parks and Wildlife.   The
first  notice  of  a fish kill is usually reported by a citizen to the  local
field office of one of our agencies.   Rapid  response  is  essential   to  a
successful   investigation   and  fish  kills  take  precedence  over   other
activities in the district offices such as  stream  monitoring  and routine
municipal  and  industrial  inspections.    The  polluting agents dissiplate
                                                              of  the   cause
                                                              of sight  or  be
                                                           rapidly,  tissue
                                                          characteristics  of
                                                          information can  be
                                                          most familiar with
                                                         related  fish  kill
events.    They may also be able to describe the chronology of events before
the investigator arrived at the site.
rapidly  or  conditions
impossible to document.
scavenged by predators.
                         may  change,   making  confirmation
                          Fish may wash downstream,   sink  out
                          In warm weather fish  decompose
samples loose traces of organic pollutants,  morphological
injury, and evidence of disease organisms.    Often  useful
gathered  by  interviewing  local   citizens.    They   are
typical conditions in the waterbody and  historical   or
When the field personnel make an investigation,  they are prepared  to,    1)
interview local observers, 2} collect biological  information such as number,
size and species killed,  3) make physicochemical  measurements in the field,
and 4) collect water samples and tissue samples for laboratory analysis.   We
have prepared the "Fish Kill Investigation  Guidelines"   and  a  "Fish Kill
Investigation Checklist" which follow,  as Appendices 1  and 2.   Information
is coded by the investigators onto a form that can  be  directly   keypunched
into the computer.  An example of the computer report follows, below.
                   NUMBER OF FISH KILLS REPORTED PER YEAR
The number of fish kills reported each year has been  fairly  constant   since
the  data base was computerized in 1970 until  four years  ago.    It  is  likely
that some of this increase has resulted from more effective reporting  rather
than an increase in the actual number of kills.  Many  factors  influence the
frequency of reporting.   Some kills are  not   observed  by  anyone.    Many
citizens  are  not concerned about localized fish kills perhaps because they
have accepted them as an isolated or natural event.   Even  when citizens are
concerned, they may not know which public officials to  contact.  In the past
few years public awareness of water quality problems  has   increased.    When
state personnel appear on television during an investigation,   the  public  is
informed of their role in reporting fish kills and pollution events.    Fish
kills are now more likely to be reported, particularly  in urban areas.
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                                Year       Number Reported

                                1971         74
                                1972         66
                                1973         67
                                1974         61
                                1975         52
                                1976         55
                                1977         52
                                1978         69
                                1979         44
                                1980         64
                                1981         43
                                1982        161
                                1983        139
                                1984        >61
                          NUMBER OF THE FISH KILLED
More than two-thirds of the fish kills record that  less  than  5000  fish  were
killed.   This is certainly a conservative estimate since  the number of dead
fish observed is less than the number killed.    Dead fish  are washed  away,
sink  to  the bottom and are taken by scavengers.    About  two percent of the
fish kill events resulted in the loss of more than  half  of the total  number
of fish reported killed.
                         Number Killed       Cumulative  %

                              < 5                14%
                              < 500              40%
                              < 5000             71%
                              < 1 million        97%

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                           NUMBER OF TAXA PER KILL
We have data for only the last two years.
                         Taxa Count
Cumulative %
1
2 or less
3 or less
10 or less
30%
44%
61%
97%
                            DURATION OF THE KILL
About 15%'of the kills have this information.
                           Number of Days       Percent

                           less than 1           4.4
                              1 to 2              48
                              2 to 3              20
                              3 to 4             9.4
                              4 or more           18
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                     RIVER BASIN WHERE THE  KILL  OCCURRED
One-third of the fish kills were reported from  the  Brazos  River  Basin  and
San Jacinto/Brazos Coastal  Basin.

                       Basin                             % of Reports

             Canadian River                                1.9
             Red River                                     1.8
             Sulphur River                                    .5
             Cypress Creek                                    .8
             Sabine River                                  3.0
             Neches River                                  3.0
             Neches-Trinity Coastal                         5.0
             Trinity River                                  7.8
             Trinity-San Jacinto Coastal                    3.8
             San Jacinto River                             7.6
             San Jacinto-Brazos Coastal                    17.7
             Brazos River                                 14.3
             Brazos-Colorado Coastal                        1.5
             Colorado River                                8.1
             Colorado-Lavaca Coastal                          .5
             Lavaca River                                    .4
             Lavaca-Guadalupe Coastal                         .6
             Guadalupe River                                 .7
             San .Antonio River                             7.7
             San Antonio-Nueces Coastal                     1.0
             Nueces River                                  1.5
             Nueces-Rio Grande Coastal      '                3.2
             Rio Grande                                    1.8
             Bays and Estuaries                            5.2
             Gulf of Mexico                                  .9
                               WATER BODY TYPE


About  59% of the fish kills were reported from streams and  24%  in ponds and
reservoirs.  The remaining 17% occurred in the estuaries and Gulf of Mexico.


                          AREAL EXTENT OF THE KILL
More  than  70%  of  the fish kills on streams  and  rivers were  restricted to
less than two river miles.   Most kills reported from  ponds and  open  water
were limited to less than ten acres in area.

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                            CAUSES OF FISH KILLS
Dissolved oxygen depletion is listed when the cause  is  known  for   about  90%
of  the  fish  kills  reported each year.   This cause  may be over estimated
since dissolved oxygen delpletion is often suspected based on descriptions
by  witnesses  when the investigation is made after  water quality  conditions
have returned to normal.   Dissolved oxygen depletion is  caused by microbial
respiration, often at night by dense populations of  algae that are supported
by nutrient-rich conditions.   Microbial oxidation of  inadequately  treated
wastewater that is illegally or accidently discharged demands oxygen  and may
exceed the capacity of a waterway to reaerate.   Since  the capacity of water
to  hold  oxygen  is  lower at warmer temperatures and  respiration rates are
higher, about two-thirds of the fish kills caused by low  oxygen occur during
the months of May through September.

Other causes reported include:
   Industrial discharges of toxic or high BOD substances
   Runoff from feedlots and industrial  sites
   Spills of toxic substances
   Pesticide drift or runoff
   Disease
   Excessive chlorine in STP effluent
   Fish culled from commercial fishing operations
   Cold weather
   Illegal fishing with rotenone or electroshock
In  recent  years  kills related to oil and gas exploration are more  common.
Pesticides  and  disease  organisms  are  specifically   identified in  more
reports.   Fish kills resulting from industrial discharges are reported less
often,   although  dissolved  oxygen  depletion  resulting from    municipal
discharges and by passes are still as common.


The Texas Water Commission Fish Kill Reporting System has  been  useful  for
documenting  fish  kill  events,  identifying areas  where water quality is  a
persistent treat to fish and wildlife,  and characterizing causes of  fish
kills.   Accuracy and completeness of the reports depends upon the skill and
cooperation of field personnel.   Fish kill investigations are of particular
interest to the stream monitoring personnel and investigations are conducted
with enthusiasm.  Their skill depends primarily on a good instruction manual
and  training.    We  are  continually  evaluating   the  information  that we
request,  and clarifying and updating instructions.   We  hold annual  stream
monitoring  workshops  that  have  proven  useful  in  motivating  the field
personnel,   communicating  procedural   matters  for making   and  reporting
investigations,    increasing   skills   of  biological  identification  and
measurement of field parameters.   Our objective is  to  continue to build   a
long  term  record  and  improve the system's usefulness  as a tool for water
quality management.
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                                      Appendix  1
                      Fish Kill Investigation Guidelines


  I.   Objectives of Fish Kill  Investigations

      A.   Identify and eliminate man-made sources  of  pollution  causing  fish  kills.
      B.   Determine the number of dead fish, size  distributions and  weights  so
          that the monetary replacement values  of  the fish  lost can  be  calculated,
      C.   Gather information that could be used to prevent  or  lessen the  impact
          of future kills.

 II.   Materials

      A,   References

          1.  Monetary Values of Freshwater Fish and  Fish-Kill  Counting Giiide-
              lines.American Fisheries Society Special  Publication No.  13.  1982.
          2.  Checklist of Texas Freshwater Fishes.   Clark  Hubbs.  Texas  Parks
              and Wildlife Department Technical Series No.  11  1982.
          3.  Taxonomic keys to fish identification.

              a.  How to Know the Freshwater Fishes.   Samuel Eddy.   Second or
                  third edition.
              b.  Key to the Estuarine and Marine  Fishes  of Texas.   Second
                  edition.Parker,  J. C., D. R. Moore and  B.  J. Galloway.   1976

      B.   TWC Fishkill Forms-0129A and 0129B
      C.   Fish Kill Investigation Checklist Form (TWC 0563)
      D.   Maps
      E.   Instruments for measuring  dissolved oxygen, temperature,  pH,
          conductivity, sec.chi disc  and chlorine residual.
      F.   Sample containers

          1.  Aluminum foil and plastic bags for tissues
          2.  Acetone-rinsed glass jars with teflon liners  for
              water, sediment and/or tissue samples.
          3.  Cubitainers

      G.   Scale and ruler for measuring fish weight and length
      H.   Jon boat, motor and dipnets may be useful  for some investigations.

III.   Fish Kill Investigation Procedures

      A.   Notification of fish kill  by telephone to district office

          1.  Person receiving phone call at district office should  try to obtain
              as much information as possible from the person  that is reporting
              the fish kill. Use the "Fish Kill Investigation  Checklist"  as  a
              guide to what types of questions  to ask.

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Appendix 1, con't
    B.   Once  notification  of  a fish  kill  has been received, the district office
        should  notify  the  Texas  Parks  and Wildlife Department Regional Chemist"
        that  serves the  area  where the kill occurred.        —-

    C.   The district office should investigate the fish kill as soon as
        reasonably possible after notifying TPWD.

        1.  The district representative  investigating the fish kill should take
           as  a minimum of the  following equipment:

           a.  "Fish  Kill Investigation  Checklist".
           b.  Map-preferably a USGS  1:24000 scale topographic map.
           c.  Instruments for  measuring dissolved oxygen, temperature, pH,
               conductivity, secchi disc and chlorine residual.
           d.  Sample containers:   Ice  chest, aluminum foil and  plastic bags
               for tissue, acetone-rinsed jars with teflon liners  and
               cubitainers.
           e.  Scales and ruler for measuring fish.
           f.  Monetary Values  of Freshwater Fish and Fish-Kill  Counting
               Guidelines^

        2.  The fish kill  investigator should collect as much information
           asked for  on the  "Fish Kill  Investigation Checklist"  as is
           possible.  The fish-kill counting guidelines in the Monetary Values
           of  Freshwater  Fish and Fish-Kill Counting Guidelines  should be
           followed in  obtaining a  count of all dead organisms including fish,
           birds, mammals, reptiles,  amphibians and significant
           macroinvertebrates.  Ensure  that the count is a valid random count
           conducted  in an objective  manner.  The manner in which  the count is
           actually conducted should  be  well-documented in the investigator's
           field notes.

        3.  Water quality  data and water  quality samples should be  collected
           from a minimum of one site and preferably three sites.

           a.  The site at which dead or dying fish are counted.
           b.  The source of pollution  causing the kill.
           c.  A control  point  upstream  or in an area unaffected by the cause
               of the fish kill.

           Water quality  samples could  be submitted using Stream Monitoring
           Program forms  or  Chain-of-Custody tags.  Chain-of-Custody tags
           should be  used whenever  there is chance that litigation will
           result from  the fish kill.

        4.  Tissue samples if collected  (usually a whole fish), should be
           wrapped in new, heavy-duty aluminum foil with the tissue next to
           the dull side  of  the foil.   Data collected from tissue  specimens
           should include weight of individual specimens, total  weight of
           sample, length of specimens,  species, sex and physical  condition of
           specimens.   Foil-wrapped samples should be placed on  ice in the
           field and  frozen  as  soon as  possible.
                                                                                        I

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Appendix 1, con't
            Data  collected  during  the  fish-kill  investigation  should  be
            submitted  to  the  TWC Stream Monitoring  Unit  within 30  days of  the
            district's receipt  of  water quality  analyses from  the  laboratory.
            The data from the site of  the  kill should  be reported  on  TWC forms
            0129A and  0129B {See attached  example).  Data from additional  sites
            should be  reported  on  regular  Stream Monitoring  forms.  A copy of
            completed  TWC forms 0129A  and  0129B  should be mailed to the TPWD
            Regional Chemist  that  serves the area where  the  kill occurred.

            All questions concerning fish-kill investigations  and  forms should
            be addressed  to the TWC Stream Monitoring  Unit.

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Appendix 2
Fish Kill Investigation Checklist
1, Investigator , Hate 	 Time 	 .
2_ Complainant Address
Phone -
TDWR District
3. County Segment (TPWD Region)
Rasin
4. Field conductivity less than 3000 /imhos/cm: Yes NO
5. Kill location:
6. Cause of kill: *
7. Pertinent Observations:
Appearance of dead or dying organisims:
Behavior of dying organisims:
Weather conditions:
Water appearance:
Comments of residents or fishermen:
8. What proposed action is there to prevent future kills?
9 . -Date kill began: Mnuu tnng ki|| has tasted' .
10. What was the source of the pollution that caused the kill?
11. Estimated number killed: (Check one)
<10 D 10.000 - 100,000 D
10-100 D 100,000-1,000,000 D
100 - 1000 D >1, 000,000 D
1000 • 10,000 D
Over
{Complete back of form)
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Appendix  2,  con't


12.   Species counts


     Species
Total counted dead
    Total estimated dead
13.  Total length of river (feet) or surface area (acres) in which you actually counted dead organisims:
14.   Water Quality Measurements


     Date	
.Time.
. Depth.
     Parameter
 Value
                                                                                                    TDWR-OS63

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AQUIRE:  AQUATIC TOXICITY INFORMATION RETRIEVAL

            AMBIENT TOXICITY SURVEYS

       MICHAEL BAST IAN - US EPA REGION 6

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                          Presentation Summaries
                             Michael  Bastian


AQUIRE:  Aquatic Information Retrieval Toxicity Data Base

The staff of the Analysis Section within the Environmental  Services
Division retrieves information from AQUIRE through a contract with Chemical
Information Systems, Inc.  The data base contains summaries of papers
about the toxicity of single chemicals to freshwater and salt water
plants and animals.  AQUIRE excludes studies about toxicity to birds,
bacteria, adult amphibians and mammals.  It does not contain information
about the toxicity of complex effluents, oils or mixtures.

Data can be retrieved which pertains to (1) acute toxicity  (2) chronic
toxicity (3) bioconcentration studies (4) field studies (5) algal  studies
and (6) sediment studies.  Papers entered into the system are reviewed
for the quality of the test methods and the amount of supporting information            •
such as chemical analysis of the sample.  The summaries can be retrieved                •
in several formats which correspond to increasing levels of detail.

There are approximately 2,000 chemical and 40,000 records in AQUIRE.
The data base is frequently updated.


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                         Ambient Toxicity Surveys

The Environmental Services Division in cooperation with the Water Division
has allocated some laboratory and personnel resources for ambient toxlcity
surveys.  The objective of these studies is to screen ambient waters for
toxlcity and, preferably, to bracket reaches of a receiving water where
toxidty occurs.

The studies are designed to follow the guidance 1n the Technical  Support
Document for Water Quality-based Toxics Control.  Ambient toxicity studies
are appropriate for low flow conditions and multiple discharge streams.
They provide a measure of the combined instream toxicity and persistence
of toxlcity from all sources.  It is recommended that ambient toxicity tests
be Interpreted by comparison with the results of effluent toxicity tests
from the nearby area.

Generally, chronic tests are the recommended endpoints for ambient studies.
The EPA Houston laboratory can conduct toxicity tests with fresh  and salt
water organisms (Table 1).  Samples may be grab, replicate grab or composite
collections.  If the receiving water characteristics are variable, replicate
grab or composite samples are recommended.

We ask that state agencies that request ambient studies consider  how the
results can be used.  It is important that state agencies commit  to
follow up studies to identify sources of toxicity if the initial  results
warrant'this action.

Table 1.  Aquatic Toxicity Test conducted at the EPA Houston Laboratory
Type
Chronic
Chronic
Chronl c
Acute
Acute
Acute
Organism
Ceriodaphm'a
Fathead minnow
Fathead minnow
Fathead minnow
Daphnl a
Mysid
Endpoint
reproduction
hatching
and
Survival
Survival
and
growth
LC50 •
LC50
LC50
Test-period
7 days
7 days
7 days
48h
48h
48h
Note:  The sheepshead minnow, the salt water cousin of the fathead,  is
being cultured at Houston and will be used for salt water chronic tests
In the near future.
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•                                   WATER QUALITY TRENDS ANALYSIS
"                                    - ANALYSIS PLAN FOR WATER QUALITY TRENDS
                                      - DECISION TREE FOR TREND/CHANGE ANALYSIS
•                                    - EXAMPLES OF WATER QUALITY TRENDS ANALYSIS
                           LISA LAVANGE - STATISTICIAN (RESEARCH TRINGLE PARK)
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ANALYSIS PLAN FOR WATER QUALITY TRENDS

1. Identify data for analysis. Data consist of:
a.
b.
c.

d.
e.
Station(s);
Parameter! s) - raw data or water quality indices;
Time periods - sampling units (e.g. months) and beginning and ending
dates of series being analyzed;
Auxin lary data - flow, depth precipitation, etc.; and
Episodic data - plant or dam construction, etc.
2. Print dataset and edit data.
a.
b.

c.
d.
e.
f.
Observations (rows) correspond to sampling units (time intervals).
Variables (columns) correspond to water quality parameters (or
indices) under investigation.
Verify missing data witn source.
Verify units for each variable.
Check obvious outliers with source.
Can use STORET Retrieval or SAS PROC PRINT.
3. Generate Summary Statistics.
a.


b.
c.
Include mean, median, quartiles, minimum value, maximum value,
number of observations with missing values, and number of observations
below the limit of detection.
Produce frequency distribution - bar chart, stem leaf, or box plot.
Can use SAS PROC UNIVARIATE or PROC CHART.
4. Decide how to handle missing data and data below limits of detection.
a.
b.
c.
Delete observations.
Substitute mean values assuming an appropriate distribution.
Substitute regression estimates assuming an appropriate model.
5. Plot data.
a.

b.
c.
d.
e.
f.
9-




Plot water quality variables versus time for entire period being
studied.
Add reference line (vertical) for external events.
Add reference line (horizontal) for median.
Plot runs with reference to the median (+,-).
Plot runs with reference to preceding values (+,-).
Plot water quality variables versus seasons (e.g. months, quarters).
Can use STORET or SAS PROC PLOT.





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6.  Assess seasonal!ty.

    a.  Examine plots (3.f.)
    b.  Subtract seasonal means from raw values and  plot  to  see  if cyclical
        patterns nave been removed.
    c.  Can use SAS PROC MEANS or PROC SUMMARY.

7.  Formulate hypotheses.

    a.  Is a trend expected?
    b.  Is a change expected?
    c.  What magnitude and direction are expected for trends/changes?
    d.  Are probabilities of stream standard violations to be estimated?
    e.  What families of probability distributions are likely to fit
        the data?

8.  Perform statistical analyses for each hypothesis.

    a.  Probabilities of violation/compliance:
        i)   Generate Q-plots.
        ii)  Conduct goodness of fit tests - skewness, Kolmogorov, and/or
             Chi-square tests.
        iii) Compute probabilities based on estimated distribution functions.
    b.  Trends and changes analysis - reference decision  tree.

9.  Present data and interpret results.

    a.  Resolve conflicting results.
    b.  Adjust probability levels for multiple  tests, if  necessary.
    c.  Append plots, data listings, and summary statistics  to aid reader  in
        interpretation of results.
    d.  Identify possible problems with analyses needing  further work.
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                 DECISION TREE FOR TREND/CHANGE ANALYSIS
                               Change Only?
          I

          yes
          I
          Seasonal
                                           no
                                         Seasonal?
                                        _J	
  yes
  regression
with seasonal
and change
variables
  I	
    no
    t-test
                                                     yes
                             regression with
                             seasonal,  trend,
                               and change
                              variables
                                                      no
              regression with
                trend and
                  change
                variables
          I
   residuals normal
   & homogenous?
                                      residuals normal
                                      & homogenous?
  I
  yes
  I  .
 Stop
    no
 Rank Sum
test (aligned
If seasonal)
 I
 yes


Stop
                    Stop
No
                                                  Kendalls'  tau,
                                                  Spearman's rho,
                                                   or Sen  test
                                                  (adjusted  for
                                                  seasonal 1ty
                                                  If necessary)
                                                                      Stop

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•            EXAMPLES  FROM  THE  WATER  QUALITY  TRENDS  WORKSHOP
|       Example 1  Summary Statistics using SAS UNIVORIATE
         •Example £  SAS Plots for Seasonality and Trend Determination
         Example 3  SAS Plots of Deseasonalized Data
V       Example 4  SAS Regression Analysis
         Example 5  SAS Tests for Trend Analysis
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       Example  1    Summary  Statistics  using  SAS  UNIVARIATE
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