903R83011
           United States
           Environmental Protection
           Agency
Region 3
Sixth and Walnut Streets
Philadelphia, PA 19106
           CHESAPEAKE BAY:  A PROFILE
           OF ENVIRONMENTAL CHANGE
           xvEPA
        APPENDICES
        Region III Library
      Environmental Protection
TD
225
.C54C54
vol.2

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Regional Center for Environmental Informatio
            US EPA Region Ml
               1650 Arch St.
          Philadelphia, PA 19103

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     CHESAPEAKE BAY PROGRAM:
A PROFILE OF ENVIRONMENTAL CHANGE
           APPENDICES
                  September 1983

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                             PREFACE
This document includes the  four appendices to  the report Chesapeake Bay:
A Profile of Environmental  Change developed by the Environmental Protection
Agency's Chesapeake Bay Program.  The report and its appendices provide a
characterization of the Bay's water quality and resources.
                                   A-ii

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                                  CONTENTS


Preface	   A-Ii

Appendix A

    Figures	A_vi

    Tables	   A-vii

    Section

      1  The Chesapeake Bay Environment	A-l

      2  Segmentation	   £_2

      3  Data Collection and Summary of Statistical Analysis   	   A_Q

      4  The Northern  Bay in Historical Perspective	   £-18

      5  Individual  Research Projects ...  	  .  	   A-22

      6  Literature  Cited	•	A-24

Appendix B

    Figures	    B-ii

    Tables	    B-V

    Section

      1  Basin Features and Climatic Conditions  	  .  	    g_i

      2  Water and Sediment Quality Sampling Locations  	    B-10

      3  EPA Water Quality Criteria Violations in the Bay	    B-18

      4  The Derivation of Site-Specific Water Quality  Criteria
         for Eight Metals in Chesapeake Bay	    B-29

      5  Trends in Dissolved Oxygen	    B-32

      6  Methodology for Developing Metal Contamination Index;
         Tables of Metals Data	 .    B-61

      7  Levels of Heavy Metals in Oyster Tissue  from Virginia  	    B-76

      8  Current Conditions and Trends Data	 .    B-110

      9  Literature  Cited	    B-156
                                   A-iii

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Appendix C

    Figures	    C-±±

    Tables	    C-iii

    Section

      1  Life Cycles  of Major Species	    C-l

      2  Analysis  of  Oyster Habitat  	    C-32

      3  Sources and  Analysis of Fisheries Landing Data  	    C-37

      4  Analytical Approaches for Determining Trends in Fisheries  ...    c-53

      5  SAV Decline  and Geographic Analysis 	    C-55

      6  Literature Cited	         C-70

Appendix D

    Figures	    D-ii

    Tables	    D-iii

    Section

      1  Adapting  Water/Sediment Quality Data for Comparison
         to Resources	    D-l

      2  Statistical  Analysis of Submerged Aquatic Vegetation	    D-14

      3  Statistical  Analysis of Benthic Organisms 	    D-27

      4  Analysis  of  Finfish	    D-35

      5  Literature Cited	    D-50
                                    A-iv

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                            APPENDIX A







                            CONTENTS







Figures	       A-vi




Tables	       A-vii




Section




    1    The Chesapeake Bay Environment 	       A_l




    2    Segmentation	       A-2



    3    Data Collection and Summary of Statistical Analysis   	       ^_g




    4    The Northern  Bay in Historical Perspective 	       A-18




    5    Individual  Research Projects 	       A-22




    6    Literature  Cited 	       A-24
                                   A-v

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                               FIGURES





Figure !„  Chesapeake Bay Program segments used in data analysis
                                 A-vi

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                                  TABLES
Table 1.

Table 2.

Table 3.


Table 4.

Table 5.

Table 6.

Table 7.
Principal Segment Characteristics  .  .

Water and Sediment Quality  Data Bases
Summary of Data Tests  and  Statistical Analyses (Water and
Sediment Quality Data  Base)  	
Water and Sediment Quality Variables   	

Principal Commercial Fisheries  Species in Chesapeake Bay.

Living Resources Data Bases 	

Unusual Weather Conditions in Chesapeake Bay	
A-7

A-10


A-12

A-13

A-14

A-16

A-19
                                     A-vii

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                               SECTION 1
              THE CHESAPEAKE BAY ENVIRONMENT
    Many physical, chemical, and biological components make up the Bay
environment  and are connected in sometimes complex processes and
relationships.  To accurately interpret  the quality of the Bay's waters and
sediments, and the health of its major resources, several physical elements
and some important biological interactions had to be considered.
    These processes are numerous and will not be discussed in this volume.
To better understand these interactions, we suggest that the reader consult
any of the following publications:

         Chesapeake Bay:  Introduction to an Ecosystem (U.S. EPA 1982a);
         Chesapeake Bay Program Technical Studies:  A Synthesis
          (U.S. EPA 1982b);
         "The Biology of an Estuary" (Cronin et al. 1971);
         "A  Conceptual Ecological Model  for Chesapeake Bay" (Green 1978);
         Estuaries (Lauff 1967)
         The Chesapeake Bay in Maryland  - An Atlas of Natural Resources
          (Lippson 1973);
         "Estuarine Circulation Patterns" (Pritchard 1955);
         Chesapeake Bay Future Conditions Report
          (U.S. Army Corps of Engineers 1977);  and
         Beautiful Swimmers (Warner 1976).
                                   A-l

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                                  SECTION 2
                        SEGMENTATION CONCEPT
                      (adapted  from Klein, unpublished)
    The Bay is a  fluid  system with few obvious boundaries save perhaps the
sea surface and the  water-sediment interface.  Scientists, managers,  and
users of the Bay  are more  likely to see smooth variations from place to
place, rather than a system  composed of separable parts.  The person who
would partition the  Bay to aid in management is, therefore, faced with a
dilemma -- on the one hand,  fixed simple boundaries seem too rigid in a
fluid system, and, on the  other hand, time variable boundaries based on
intricate schemes violate  the criterion of simplicity.
    Because of this  dilemma, the Chesapeake Bay Program (CBP) planned to
divide the Bay into  regions, or segments, to assess and map past and
present conditions.   Segmentation can be used as an analytical tool that
recognizes the Bay as an interrelated ecosystem, composed of physically,
chemically, and biologically diverse areas.
    Using segmentation  to  look at water quality is not new.  Planning
agencies for the  Great  Lakes divided the lakes into zones with similar
nutrient and chlorophyll a_ levels to monitor eutrophication.  To locate
acceptable sites  for dumping treated sewage, planners segmented San
Francisco Bay into six  major areas according to flushing characteristics.
Under the Clean Water Act  of 1977, all streams in the United States are
segmented according  to  the water quality and assimilative capacities of the
stream (40 CFR131, U.S. Code of Federal Regulations Section 131).
    Ideally, the  segmentation approach would segment the Bay into areas
demonstrating like physical, chemical, and biological characteristics.
However, realizing that biotic communities result from abiotic regulators
such as nutrients and salinity, we simplified the approach by using
physical processes to segment the Bay into like classes.  To segment
Chesapeake Bay, we used circulation, salinity, and geomorphology.

BIOLOGICAL AND CHEMICAL CHARACTERIZATION OF SEGMENT BOUNDARIES

Main Bay

    The first segmentation boundary is between CB-1 and CB-2 and separates
Susquehanna Flats from  the upper Bay and lies in the region of maximum
penetration of sea salt at the head of the Bay (Figure 1).  Most freshwater
plankton are not  expected  to grow and flourish south of this region,
although some plankton  may be continually brought into the area by the
Susquehanna River.
    The second boundary between CB-2 and CB-3 demarcates the southern limit
of the turbidity  maximum,  a  region where suspended sediment causes light
limitation of phytoplankton  production most of the year.  This boundary
also coincides with  the long-term summer average for the 5 ppt salinity
contour — an important physiological parameter for oysters.
    The third boundary  at  the Bay Bridge, between CB-3 and CB-4, marks the
northern limit of deep  water anoxia in Chesapeake Bay and the 10 ppt
salinity contour.  In segment CB-4, water deeper than about 10 meters-'-
11 meter = 3.28 feet
                                     A-2

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                                                        Er-2
                        wr-3
                                                           Er-io
                                                         EE-3
Figure 1.   Chesapeake Bay Program segments used  in data analysis,
                                A-3

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usually experiences oxygen depletion  in summer  that may result in anoxia
and hydrogen sulfide production.   When anoxia occurs,  these deep waters are
toxic to fish, crabs,  shellfish,  and  other  demersal and benthic animals.
The anoxic layer is also rich in  nutrients  that may reach  the surface layer
by diffusion, mixing,  and vertical advection.   In  the  spring, the region
near the bridge is the site where phytoplankton and fish larvae traveling
in the deep layer from the Bay mouth  are brought to the surface by a
combination of physical processes.
    The fourth boundary, between  CB-4 and CB-5, a  transect located at Cove
Point, was established at a narrows;  below  this point, the Patuxent and
Potomac Rivers enter the main Bay. This segment is characterized by
salinities of 12 to 13 ppt in the long-term summer average and lies mid-way
in the area subject to summer anoxia.
    The fifth boundary, between CB-5  and CB-6-7, approximates the southern
limit of summer anoxic water and  the  18 ppt salinity contour.  Most of the
deeper areas of the Bay are found in  segment CB-5.  Segment CB-5, like
CB-4, experiences considerable nutrient enrichment during  the summer when
both phosphate and ammonium are released from suspended organic material
and bottom sediments.   This region also exhibits high  nitrite and nitrate
concentrations in the fall when the ammonium accumulated in summer is
oxidized by bacteria.   The southern boundary of CB-5 also  approximates the
region where the nitrate from the spring freshet becomes a critical
nutrient for the phytoplankton.
    The fifth boundary separates  the  lower  Bay  into three  regions with
different circulation patterns.  North of this  boundary, the Bay's density
stratification results in two distinct vertical layers.  The deep water
there moves in a net upstream flow, and the surface layer  flows
downstream.  Between this boundary and the  Bay  mouth,  the  density
distribution tends toward a cross-stream gradient  rather than vertical
one.  This results in net advective flows throughout the water column, on
the average to flow north in segment  CB-7 and south in CB-6 and CB-8.  This
pronounced horizontal gradient also exists  across  the  Bay mouth.  Thus,
plankton!c organisms and the larvae of catadromous fish are brought into
the Bay with the higher salinity  ocean water along the eastern side of the
lower Bay, until they become entrained into the lower  layer at segment CB-5
and are carried up the Bay to grow and mature.  Also,  the  high rates of
sand deposition in this segment are thought to  be  imported from the inner
shelf region at the ocean boundary.
    Eastern Shore embayments such as  Eastern Bay (EE-1), the sub-estuary of
the Choptank River (EE-2) , and Pocomoke and Tangier Sounds (EE-3) have
salinities similar to adjacent Bay waters and are  shallow  enough to permit
light penetration necessary for submerged aquatic  plant growth.  These
areas provide shelter for many invertebrates and small fish that contribute
to the Bay's natural richness.

Tributaries

    Boundaries have been shown across the mouths of the Bay's tributaries.
They serve to delineate the sources of freshwater, sediment, nutrients, and
phytoplankton seed populations that may grow to bloom  concentrations in the
main Bay.  Also along these boundaries, frontal zones  between tributary and
                                    A-4

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main Bay water tend to concentrate detrital matter  and nutrients,  making
them important mechanisms in the food chain of organisms  depending upon
circulation to bring them in contact with their food source.
    The major tributaries are also further divided  into three  segment
types: tidal fresh (TF), river-estuarine-transition zone  (RET),  and lower
sub-estuary (LE).   The tidal-fresh segments are biologically important as
spawning areas for anadromous arid semi-anadromous fish such as the alewife,
herring, shad, striped bass, white perch,  and  yellow perch.  There are also
freshwater species that are resident to these  areas such  as catfish,
minnows, and carp.  Also frequently encountered during the summer-time in
the tidal-fresh areas is the possible occurrence of blue-green algae
blooms.  The extent of these blooms is dependent upon nutrient supply,
retention time, and availability of light; however, these populations are
inhibited as they encounter the more saline waters  associated  with the
transition zone.
    The greatest concentration of suspended material occurs at the
interface of fresh and saline waters, and it approximates the  terminus of
density dependent estuarine circulation.   This phenomenon is typically
referred to as the maximum turbidity.  The significance of this  area lies
in its value as a sediment trap, entraining not only material  introduced
upstream but, additionally, material transported in the lower  layer from
downstream.  This mechanism also tends to concentrate any material
associated with the entrained sediment,  as evidence by the Kepone  incident
within the James River.  Kepone concentrations within the river  were
highest in the zone of maximum turbidity.
    The final segment type found within the major tributaries  is identified
as the lower sub-estuary segment.  This area extends from the  turbidity
maximum to the point where the tributary enters the main  Bay.  VJithin these
areas exist highly productive oyster bars.  Oyster  distribution, based upon
the Baylor bottom survey, shows heavy concentration of bars in the lower
sub-estuaries because of the favorable depth,  salinities, and  substrate.
In general, bars are located in depths of less than 11.5  m in  salinities
greater-than 7 to 8 ppt and on substrates that are  firm.  Seasonal
deficiencies in dissolved oxygen (DO) prevent  their establishment  in most
waters over 11.5 m deep; as a consequence, they are not found  within the
channel areas of these segments.

CONCLUSIONS

    The segmentation scheme as proposed,  using physical processes,  does in
general track with the major chemical and biological processes.  This will
be continually refined as data becomes available, allowing for
extrapolation of cause and effect relationships among segments of  similar
physical characteristics.
    The refinement as suggested above will enable sub-segmenting based upon
more segment-intensive data such as sedimentary structure because  many
benthic communities can only tolerate specific kinds of bottom materials.
A second refining criterion is depth.  Water column data  will  be
sub-segmented by depth into upper and lower layer.   The 10 meter depth
profile will distinguish between upper and lower layer sub-segments since
it is typically associated with the boundary between outward flowing upper
layer and landward flowing lower layer.
                                     A-5

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    The main quality being strived  for  in  this segmentation approach is
flexibility.  Depending upon the  problem being addressed, segments can be
collapsed to look at;  for instance,  an  entire tributary or can be refined
or sub-segmented to address a certain near-field  problem associated with a
particular power plant or sewage  treatment plant  outfall.  These diverse
areas, once identified and understood,  can be managed to maintain or
enhance their uses.

PRINCIPAL SEGMENT CHARACTERISTICS

    Some principal characteristics  selected for each of the segments are
shown in Table 1.
    Estuaries have a capacity to  assimilate waste before experiencing
significant ecological damage; this ability can vary dramatically from one
area to another.  To assess the water quality of  areas with similar
characteristics, the CBP divided  the Bay into regions, or segments, using
natural processes such as circulation and  salinity.  These 45 segments were
used as a framework to map and evaluate past and  present conditions of
Chesapeake Bay.
                                     A-6

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TABLE 1.  SEGMENTS OF CHESAPEAKE BAY AND THEIR PRINCIPAL SEGMENT
          CHARACTERISTICS
    Segment
                   Characteristics
Tidal-fresh reaches

Ches. Bay N. (CB-1)
Up. Patuxent (TF-1)
Up. Potomac (TF-2)
Up. Rapp. (TF-3)
Up. York (TF-4)
Up. James (TF-5)

Transition zones

Up. Bay (CB-2)
M. Patuxent (RET-1)
M. Potomac (RET-2)
M. Rapp. (RET-3)
M. York (RET-4)
M. James (RET-5)

Lower estuarine reaches

Up. C. Bay (CB-3)
L. Patuxent (LE-1)
L. Potomac (LE-2)
L. Rapp. (LE-3)
L. York (LE-4)

L. James (LE-5)
Sec. W. Trib. (WT-1-8)
E. S. Trib. (ET-1-10)
Lower Main Bay

Chesapeake Bay
  Lower Central
  (CB-4)
Chesapeake Bay
  South (CB-5)
o  dominated by freshwater inflow of the river system
o  spawning areas for anadromous and semi-anadromous
   fish
o  resident habitat for freshwater fish
o  dominated by freshwater plankton and aquatic
   vegetation
o  slight salinity (3 to 9 ppt,  mean)  influence
o  zones of maximum turbidity where suspended sediment
   causes light limitation of phytoplankton production
   most of the year
o  areas are valuable sediment traps,  concentrating
   material associated with sediments  including
   adsorbed toxic chemicals
o  upstream limit of deep water anoxia
o  moderate salinity (7 to 13 ppt,  mean)
o  two-layer, estuarine circulation driven primarily
   by freshwater inflow
   weaker estuarine circulation characterized by
   limited flow/flushing characteristics
   water quality controlled by the density structure
   of the main stem of the Bay at the tributary mouth
Chesapeake Bay
  General West (CB-6)
o  water deeper than 9.2 m usually experiences oxygen
   depletion in summer — can be toxic to fish,  crabs,
   shellfish,  and benthic animals
o  mean salinity of 9 to 14 ppt
o  rich in nutrients

o  influenced  by inflow from Potomac and  Patuxent  and
   rich in nutrients
o  mean salinity of 10 to 17 ppt
o  subject to  summer anoxia and contains  most  of the
   deeper Bay  waters

o  net southward flow
o  mean salinity of 14 to 21 ppt
                                  (continued)
                                     A-7

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TABLE 1.  (Continued)
	Segment	Characteristics	

Chesapeake Bay          o  net northward flow
  General East (CB-7)   o  mean salinity of 19 to 24 ppt

Chesapeake Bay          o  net southeastward flow
  Mouth (CB-8)          o  mean salinity of 19 to 23 ppt.

Embayments

E. Bay (EE-1)           o  have salinities similar to adjacent  Bay waters
L. Choptank (EE-2)      o  shallow enough to permit light  penetration for
Tangier Sound (EE-3)       submerged aquatic vegetation growth
Mobjack Bay (WE-4)      o  influenced strongly by wind patterns
                                      A-8

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                                    SECTION 3
            OBTAINING THE CHARACTERIZATION DATA SET
    After the CBP  defined  the segments of the Bay,  we were able to
characterize them  by determining water quality and  resource conditions for
each one.  To collect  the  appropriate physical and  chemical data bases to
use in characterization, a data information request was  distributed to CBP
staff and key investigators.  The spatial and temporal resolution, and
analytical method  were described for each variable.  These characterization
sheets of physical and chemical data were then compiled  and analyzed for
the nature and comparability of the field data.  To facilitate analysis,
the information was entered into a computer and displayed in a variety of
ways.  For example, the sources of data and variables sampled were
displayed by segment in a  table format and in histograms of sampling
frequency for specific variables across all segments. To supplement this
information, appropriate additional data bases were obtained to create the
CBP comprehensive  water and sediment quality data base.  The data base
continues to be updated and will be available to Bay researchers and
managers.  Table 2 summarizes the major data bases.
    Nutrient data  collected by the researchers funded through the Bay
Program were combined  with recent and historical data acquired from several
other agencies and institutes.  These data were subjected to intense
quality assurance  (QA) procedures to ensure that each represented the
collected information  and, furthermore, to ensure compatability with regard
to units of measurement so that the various data sets could be analyzed as
one.  The QA procedures applied to the data were a  combination of
graphical, statistical, and common-sense procedures.  The data were first
plotted using a representative symbol for each source to identify
measurement unit errors as well as obvious key punch and formatting
problems.  Following the correction of the problems identified in this
first-step, seasonal and annual means were plotted, again preserving the
source identity, to determine any compatibility problems that were not
identified earlier.  Next, the data were used to calculate means and
standard deviations.   Potential outliers, or points that are statistically
unexpected, were then  identified.  These potential  outliers were examined,
and researchers checked the source information as far back as possible for
clarification and  accuracy.  Those outlier points that could not be
explained were flagged for elimination in the analytical effort, but the
values still remain in the data base.  A final check examined the data
against limits established by the scientific researchers.  These limits
were based upon the location of the data within the Bay  as well as type of
data (e.g., water  column or bed sediment).  Once all the attempts to
justify these potential outliers were exhausted, those points exceeding
limits were flagged and eliminated from further analyses.  A summary of
data sets is shown in  Table 3.
    Because it was not possible to look at or use all the variables in all
the data sets, the Chesapeake Bay Program selected  a subset of physical and
chemical variables for extensive analysis based on  their role in the Bay
ecosystem (Table 4).
                                     A-9

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PHYSICAL AND CHEMICAL VARIABLES

    The distribution and stability of Bay environments depends on three
very important physical characteristics of the water -- temperature,
salinity,  and turbidity.  Temperature dramatically affects the rates  of
chemical and biochemical reaction within the water.  Salinity, the
concentration of dissolved salts in the water, also has an effect on  the
distribution and well-being of the various biological populations living in
the Bay.  Turbidity significantly affects plant life; too much suspended

TABLE 2.  WATER AND SEDIMENT QUALITY DATA BASES
Physical Variables/Nutrients
Agency
Temporal Coverage   Data Base Description
                                  Parameters
Chesapeake     1949-1980
Bay Institute
Virginia
Institute of
Marine Science
   1970-1980
Maryland
 'tfice of
Environment1  1973-1980
Program
Virginia St,'
Water Control
Board
   1964-19 b-i
Virginia
Bureau of
Shellfish Sanitation

Maryland       1968-1980
Department of
Health

EPA, Annapolis 1965-1979
Central Regional
Lab            1965-1970

EPA,           1980
Chesapeake Bay
Program        1977-1980
Bay, river, nutrient, AESOP,   Temperature,  salinity
Special, Model, Whaley-       D.O.,  pH,  Chi-a,
Carpenter, Pro-Con            nutrients

Slackwater                    Temp.,  sal.,  D.O.,
                              BOD,  Secchi,  Chi-a,
                              nutrients
   1966-1972    STORET/MD 106
                STORET/VA 106
                Maryland Shellfish
                Sampling Stations
                Main Bay

                Potomac

                CRIMP - Taft

                USGS, Fall Line
                              Temp.,  sal.,  D.O.

                              Temp.,  D.O.,  BOD,  pH,
                              Chl-a_,  nutrients

                              Temp.,  D.O.,  BOD,  pH,
                              turbidity, nutrients
                              Fecal coliforms
                              Fecal coliforms
                              Temp., conductivity,
                              D.O., BOD, Secchi,
                              Chl-a_, nutrients

                              Temp. Sal. ,  D.O.,
                              flow, nutrients,
                              Chl-a
                                   (continued)
                                    A-10

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TABLE 2.  (continued)
Agency
Temporal Coverage   Data Base Description
    Parameters
Toxic Substances

Maryland        1970-1981
Office of
Environmental   1971-1981
Programs

Virginia State  1970-1981
Water Control
Board

U.S. Environ-   1962-1981
mental Protection
Agency
Chesapeake
Bay Program
    1977-1981
                Haire - sediment

                Eisenberg - tissue
                Gilinsky - sediment and
                tissue, VA-106
                STORET
                water, tissue,  sediment
Heavy metals

Heavy metals,
PCB's, pesticides

Heavy metals,
organic compounds
Heavy metals,
pesticides, organics
Heavy metals
Helz - sediment
Nichols - sediment/water
National Bureau of Standards-
  sediment/water
U.S.G.S., sediment/water      Heavy metals,
Monsanto, sediment/water      Organics
Huggett, sedment/tissue       Organics
                                     A-ll

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TABLE 3.  SUMMARY OF DATA TESTS AND STATISTICAL ANALYSES  (WATER AND
          SEDIMENT QUALITY DATA BASE)


Data Tests

1.  Maps of station locations.   (Stations were keyed  to appropriate CBP
    segment, locations corrected if inaccurate, inappropriate stations
    deleted.)

2.  Spatial/temporal plots of observed data,  means, minimums, and maximums
    noted.  (Outliers were identified  and if  unrealistic  were eliminated.)

3.  Comparison of means of data bases  to determine bias in data base.
    (Problems with data base conversions or comparability of analytical
    techniques were noted and corrected.)

4.  Determination of duplication.   (Duplicate observations due to data base
    mergers were identified and deleted.)

Statistical Analyses

1.  Univariate statistics computed for corrected  data base by segment and
    appropriate temporal scale.  Maps  of "average" condition developed.

2.  Linear regressions over varying time windows  to determine historical
    trends.  Maps indicating trends over time developed.

3.  Log transformation of data, and non-parametric tests  were conducted
    when appropriate to more clearly discern  trends.

4.  Statistical correlations between variables utilized for interpretation
    (i.e., sediment size versus metal  concentrations;  salinity versus
    nutrient concentrations).
                                     A-12

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TABLE 4.  WATER AND SEDIMENT QUALITY VARIABLES
Physical/Chemical

freshwater flow
temperature
               wind
salinity
dissolved oxygen
pH
sediment size
turbidity (secchi
   disk)
     Nutrient

total phosphate
orthophosphate
   P04
total nitrogen
inorganic nitrogen
nitrate (NC>3)
nitrite (N02)
ammonium (Nlfy)
organic nitrogen
     Toxic

'total polynuclear
   aromatics  (PNAs)
 dieldrin
 terpenoid*
 DDT
 copper  (Cu)
 zinc (Zn)
 cobalt  (Co)
 nickel  (Ni)
 chromium  (Cr)
 lead (Pb)
 cadmium (Cd)
 mercury (Hg)
 Biological

chlorophyll a_
coliforms
*An unsaturated hydrocarbon occurring in  most  essential oils and oleoresins
 of plants.
material in the water can prevent essential light  from reaching  submerged
vegetation in the Bay, thus halting growth.  Very  turbid water can also
impair the feeding of organisms relying on sight,  and  prevent the setting
of oyster spat.
    Chemical variables such as DO, pH,  nutrients,  metals,  and organic
chemicals are important considerations  to characterization for they
influence productivity in the Bay and are useful overall water quality
indicators.  Dissolved oxygen is affected by temperature,  salinity,
circulation,  photosynthesis,  respiration,  and oxygen demand.  Low DO
radically affects the distribution of living organisms.  In water of low
salinity, unfavorable pH levels (those  below 5) can affect the spawning
habitats of anadromous fish and other organisms.
    Nutrients,  primarily nitrogen and phosphorus,  play a critical role in
the Bay's ecosystem;  they are the structural raw materials for the plant
life that in turn, forms the base of the food chain.   Inorganic  forms, such
as phosphate (P04), nitrate (N03), nitrite (N02),  and  ammonium
(NH4) are cycled through the ecosystem  via chemical and biological
processes.  Increasing urbanization and agricultural use of the  Bay
watershed, with the accompanying input  of nutrients from land runoff,
municipal sewage, and industrial effluent discharges can increase nutrient
levels above natural levels in certain  parts of the Bay.   The result is
often excessive algal growth.  Excessive algal blooms  can  cause  low oxygen
conditions due to night respiration of  the plants  or decay of the organic
plant material.
    Although certain metals are necessary for some organisms to  live, some
metals (inorganic chemicals) and organic chemicals are lethal to aquatic
organisms in particular quantities.   Lower levels  of contamination can
result in accumulation of toxic materials in tissues of fish and
                                     A-13

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shellfish.  Toxic materials can thus be transferred up  the  food  chain, even
to man, as evidenced by the mercury contamination of Minamata  Bay,  Japan.
Chronic effects can also impair reproduction,  change swimming  patterns and
growth.
    An assessment of fecal coliform levels was included in  the analysis  of
physical and chemical variables for characterization.  We included  fecal
coliform levels because these bacteria have been used traditionally to
assess water quality from a human health perspective.  Fecal coliform
levels are one of the criteria used in delineating areas closed  to
shellfishing.

ANALYSIS OF LIVING RESOURCE DATA

    For the characterization process, three criteria were used in the
selection of living resource variables:  economic importance,  ecological
importance, and availability of data.  For these reasons, analysis
concentrated on fisheries and submerged aquatic vegetation  (SAV).
    To identify trends in fisheries, commercial landings were  evaluated  for
sixteen commercially significant species (Table 5).  Trends in the  juvenile
indices for the major commercial species were  also assessed to obtain a
more objective assessment of abundance.  The juvenile index represents
annual abundance as the number of 0 age-class  fish of a given  species per
seine haul per river (or Bay area).  In addition, juvenile  indices  for
three non-commercial species (mummichog,  Fundulus heteroclitus;  Atlantic
silversides, Menidia menidia; and Bay anchovy, Anchoa mitchilli)
TABLE 5.  PRINCIPAL COMMERCIAL FISHERIES SPECIES  IN CHESAPEAKE  BAY
   Common Name             Scientific Name               Total  Landing
   	             	           (Ibs X 1000 for  1980)

Striped bass            Morone saxatilus                    2563.3
American oyster         Crassostrea virginica             21,958.1
White perch             Morone americana                    1101.9
Blueback herring!       Alosa aestivalis                    1369.1
Alewifel                Alosa pseudoharengus                1369.1
Menhaden                Brevoortia tyrannus              443,977.6
Croaker                 Micropogon undulatus                 622.1
Bluefish                Pomatomus saltatrix                 2791.2
Catfish                 Ictalurus sp.2265.7
Sea Trout               Cynocion regalis                    5113.6
Soft Clam               Mya arenaria                        1925.8
Blue Crab               Callinectes sapidus               58,956.5
Yellow Perch            Perca flavescens                      28.0
Spot                    Leiostomus xanthurus                1755.3
Shad                    Alosa sapidissima                    903.3
Hard Clam               Mercenaria mercenaria                570.7
^Combined in landing statistics as Alewife.
                                      A-14

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were analyzed.  An assessment of trends in these three non-commercial
estuarine spawners was intended to point out if the trends were influenced
by factors other than fishing pressure.  Atlantic silversides are heavy
users of SAV and could be expected to show effects of SAV loss.  Oyster
spat set data were analyzed to assess the reproductive potential of the
fishery and to provide a parallel with juvenile indices.   To obtain an
indication of the health of the oyster, condition index and
histopafhological data were analyzed.
    Data bases were selected according to their temporal and spatial
completeness (Table 6).  The historical records of the various fisheries
were obtained from statistical digests of the U.S. Fish and Wildlife
Service and the National Marine Fisheries Service, Fishery Statistics of
the United __Sta_te_s_.  The single exception is that the Maryland Department  of
Natural Resources' catch records were used for all finfish in Maryland
(except for the Potomac) for the period 1962 to 1980,  because these records
were more complete.  These landings wefe derived from reports submitted by
the commercial fishermen or from surveys taken of the fishermen and/or
market houses.  The harvest data are complicated by changes in collection
methods over the time period of report.
    One of the best sets of living resource data (Table 6) concerning
Chesapeake Bay is based on an estuarine fish recruitment  survey conducted
by Joseph B. Boone of the Maryland Department of Natural  Resources.   This
survey of young-of-the-year finfish has been continual and consistent in
technique since 1958 for four areas of the Bay including  the Nanticoke,
Choptank, and the Potomac Rivers,  and the head of the  Bay (Boone  1980).
    The density of annual oyster spat fall (set) is a  measure of  success  of
natural oyster reproduction and recruitment and may be an indicator of
water quality.  The Maryland Department of Natural Resources has  been
collecting information on the density of oyster spat set  in the Maryland
portion of Chesapeake Bay since 1939 (Meritt 1977; Davis  et al.  1981);  the
Virginia Institute of Marine Sciences (VIMS) has been  collecting  similar
information since 1946 (Haven et al. 1978).  The methodology of oyster
spat set data collection is described in more detail by Davis et  al.  (1981).
    VIMS researchers sampled oysters from 1955 to 1981 and developed  a
Condition Index that compares the meat of an oyster with  its theoretical
maximum size,  the volume of the shell cavity (Haven et al.  1981).   Research
in Maryland on oyster histopathology was obtained from the Maryland
Department of Natural Resources, Marine Animal Disease Laboratory in
Oxford, Maryland.  Shellfish,  including oysters and soft-shell  clams, were
analyzed for mortality,  twenty infectious and non-infectious diseases,  and
for physiological indicators such as general tissue quality,  shell
condition,  spawn cycle phases,  sex ratios,  size, and age.

Submerged Aquatic Vegetation

    Submerged aquatic vegetation is an important ecological resource  that
provides food and habitat to major fish species, and has  undergone  a
precipitous  decline in the past 10 to 15 years.   It was the subject  of  a
major Chesapeake Bay Program research effort (Orth and Moore 1982).
    Sparse data are available (Table 6)  on distribution and abundance of
SAV before 1970 (Orth and Moore 1982).   Since 1970,  annual  surveys of
vegetation have been taken by the  U.S.  Fish and Wildlife  Service Migratory
Bird and Habitat Research Laboratory (MBHRL).   In addition,  extensive
aerial surveys were made in 1978 (Orth  et al.  1979;  Anderson and Macomber
1980).
                                     A-15

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TABLE 6.  LIVING RESOURCES DATA BASES
Agency
Temporal Coverage
    Data Base
   Description
Units
NOAA, NMFS
USFWS

NOAA, NMFS
MD DNR
VA VIMS
MD DNR
VIMS
  1880-1981
  1962-1981
  1939-1981
  1946-1981
  1963-1981
  1955-1981
American        Scattered years
University      since 1936
(Anderson and
Macomber 1980)
U.S. FWS
EPA, VIMS,
A.U.

EPA, MDGS,
VIMS
CBL
  1971-1981
  1978-1979
  1980
  1970
Fisheries historical
landings (Bay-wide)

Fisheries landings by
basins (NOAA codes)

Oyster spat set on
natural cultch (MD)

Oyster spat set on
natural cultch (VA)

Oyster condition
index (MD)
Oyster condition
index (VA)
                     Historical SAV aerial
                     photographs
SAV Vegetation
Survey

SAV Aerial Survey
(Quads)

Bay Benthic Survey
Patapsco Benthic Survey
pounds
pounds
spat per
bushel

spat per
bushel

rating of
meat quality
poor to good

1) Index no.
 3.0 to  7.6
2) Yield of
 meats per
 bushel
3) Rating
 below
 average to
 above
 average

Vegetation
distribution
% vegetation
coverage

hectares of
vegetation/quad

biomass and
community
composition

biomass and
community
composition
                                 (continued)
                                      A-16

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TABLE 6.  (Continued)
                                           Data  Base
Agency        Temporal Coverage           Description           Units
VIMS            1973               Hampton Roads  Benthic       biomass and
                                   Survey                     community
                                                              composition

CBL             1978-1979          Calvert Cliffs             biomass and
                                   Benthic Survey             community
                                                              composition
                                    A-17

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                              SECTION 4
     THE NORTHERN BAY IN HISTORICAL PERSPECTIVE
    Contemporary environmental science in the Bay focuses  much  effort
toward explaining the  present condition of the system with some hope of
predicting the future.  To accomplish this goal,  it is helpful  to  examine
the past.  One important aspect of the Bay's ecology is that  continuous
human activity has been operating against a background of  natural  climatic
cycles, episodes, and  an occasional extreme event such as  a hurricane.  The
Bay ecosystem is dynamic, and our perspective of assimilative capacity can
benefit from examining the past with a view to the future.
    The time horizon begins at 1600, near the time of the  first permanent
settlement in Virginia at Jamestown.  In the context of extreme events,
which may shift the ecological "balance," it is instructive to  examine the
history of hurricanes  in the Bay.  Many people remember the impact of
Tropical Storm Agnes,  especially on the upper Bay, which occurred  in June
1972.  However, the "Great Hurricane" of 1933 probably resulted in
unidentified ecological impacts.  Also,  the period from 1877  to 1899 was
characterized by numerous severe hurricanes (Table 7).2
    Temperature is also a key ecological variable, and unusual  records
exist.  In June 1816,  ice and frost were recorded; July 1836  was noted to
be extremely cold.  Severe winter ice and freezing conditions were recorded
in 1780, 1784, 1899, and as recent as 1977.3  These extreme events,
operating against long-term trends in land-use activity, exemplify the
importance of defining spatial and temporal scales when making  ecological
assessments.
    It is equally instructive to recognize that major land "improvements"
such as farming were well along by the mid-1700's.  The effect  on  the
forested area shows a  consequent decrease, followed by a return to the
forests by the 1780's,  of much of the previously cleared land.   Much of
this land was devoted  to the production of tobacco and general
agriculture.  From about 1800 onwards, there is a clear and continual trend
in the conversion of forests into fields.
    Several towns exemplify the capacity of human intervention  into natural
erosional and sedimentological processes, principally through the  clearing
of land.  Joppatown, Maryland, founded 25.6 km^ northeast  of  Baltimore,
on the Gunpowder River, was created by the Maryland legislature in 1707
near the head of a wide, deep bay that afforded an excellent  harbor
(Gottschalk 1945).  By 1846, a hundred years after the town had reached its
peak development, an above-tidewater delta surface of about 2.4 km long had
formed.  By 1897, the  above tidewater deposits had filled  the entire
estuary opposite the old wharf;  as of the early 1940's, the  above-tide
deposits had isolated  the original town and left it land-locked
approximately 2.4 km from open water.  A similar story can be told for a
o
^Personal Communication:  "Climatic Events," William Cronin,  Chesapeake
 Research Consortium,  1983.
-^Personal Communication:  "Climatic Events," William Cronin,  Chesapeake
 Research Consortium,  1983.
41 km = 5/8 mile
                                    A-18

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TABLE 7.  UNUSUAL WEATHER CONDITIONS IN CHESAPEAKE BAY (COURTESY OF  WILLIAM
          CRONIN).
Year
    Major Weather Problem
1649
1667
1780
1784
1C u6
1812

1816
1821
1836
1877
1879
1881
1882
1886
1887
1894
1897
1899
1902
1920
1926
1928
1933

1936
1944
1954
1955
1960
1962
1967

1972
1977
1978
1979
1982
earliest historical record - hurricane
earliest published account - hurricane
severe freezing and ice conditions
severe freezing and ice conditions
severe hurricane
hurricane credited with saving Worcester County from
British attack in War of 1812
ice and frost in June
severe hurricane
extremely cold even in July
severe hurricane
severe hurricane
severe hurricane
severe hurricane
rare June-July hurricane
severe hurricane
severe hurricane
severe hurricane
extremely cold winter, hurricane
two tropical storms
severe hurricane in February
one of Maryland's severest tornados
severe hurricane
"The Great Hurricane of 1933" - greatest damage recorded
to that time.
severe hurricane
two hurricanes - both severe
Hurricane Hazel - severe
two severe hurricanes two weeks apart - Connie and  Diane
July gale Brenda and severe hurricane Donna
The "Great March Storm" was not classified as a hurricane
- it was called a long-lasting tropical storm - and did
some $250,000,000 damage from Florida to New England.
The most unusual hurricane of record - Doria with an
extremely erratic path.
Hurricane Agnes - up to 18 inches of rain flooded the
major tributaries with the Susquehanna averaging 15.5
times normal flow.  Sediment loads reached 1000 mg  L~l
- normally 10 mg L~l.  Soft clams and oysters suffered
heavy mortalities.  Total economic losses in Maryland and
Virginia totaled $42,741,900.
severe icing conditions in Bay
severe icing conditions in Bay
Hurricane David
coldest January on record
                                     A-19

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number of early commercial centers  around  the  Bay and  tidal tributaries,
including Port Tobacco, Maryland, on the Potomac River; Bladensburg,
Maryland, near Washington, DC;  and  the  upper tidal  Patuxent River.
    The metal supply to the Bay began to increase considerably about the
time of the Civil War, marking  the  early stages of  the Industrial
Revolution.  This knowledge provides a  background to possible exposures of
Bay organisms to these potentially  toxic materials.  Evidence suggests that
the metal load to the Bay peaked shortly after World War II.  Thus, one
might hypothesize that the benthic  communities in certain  regions of the
upper Bay have experienced higher than  natural exposure to some heavy
metals.
    Bottom sediment cores from  Furnace  Bay located  on  the  northern shore of
Susquehanna Flats provide good  insights into the history of submerged
aquatic vegetation and diatoms  (microscopic algae that leave behind a shell
formed from silica) (Brush and  Davis 1982). These  single-celled algae help
us make inferences about nutrient conditions at the time they were
deposited.  Apparently, at around 1720  the SAV species shifted dominance;
the formerly dominant waterweed and pondweed became sporadic, with wild
celery becoming abundant.  Changes  were noted  in the epiphytic algae that
grow on the leaves and stems of SAV. During this period of initial land
clearing, many diatoms became less  abundant, and a  few species disappeared
as the shallow waters became more turbid.   This was the first clear signal
that nutrient enrichment was probably occurring.  The  recent dramatic
decline of SAV is a phenomenon  whose magnitude in the  Bay  has no parallel
over the past 380 years.
    There is evidence that important changes have occurred in freshwater
runoff.  The peak flows in rivers have  increased by as much as 30 percent
during the last two hundred years  (Biggs 1981).  Additional evidence,
concerning changes in freshwater flow and  salinity, is provided by an
analysis of Foraminifera, 'a group of benthic shelled Protozoa, which have
representative species that are sensitive  to the salt  content of bottom
waters (Nichols 1982).  These changes are  believed  to  be related to
deforestation.  Climatic variables, such as those indicated by rainfall and
temperature records for Philadelphia beginning in 1738 (Landsberg and Yu
1968), do not correlate with the fresh-salt pattern, thus  providing
evidence that the relatively rapid  cycles  of fresh  and salt conditions are
likely the result of human intervention.
    Fisheries are of direct concern to  people, and  it  is noteworthy that
the first published records began in 1880.  Note that  the  harvest has
fluctuated over the period of record.  Marine  spawners have dominated the
record.  Anectodal information  suggests that the availability of various
fish species have changed over  time. For  example,  as  early as 1629,
Captain John Smith reported that the near-shore fishery was not so abundant
as in 1607 to 1608.
    From a research perspective, the earliest  nutrient data were taken in
the late 1930's by scientists working out  of the  Chesapeake Biological
Laboratory.  The laboratory, the oldest state-supported research facility
on the East Coast, was not founded  until 1925. Hydrographic work at the
Chesapeake Bay Insititue, The Johns Hopkins University,  only began about
1949, and the Virginia Fisheries Laboratory, now  the Virginia Institute of
Marine Science, first conducted work about 1940.  The  first comprehensive
nutrient survey in the northern Bay did not occur until  1964.  These
institutions represent the earliest major  research  focus on the Bay, but
                                     A-20

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this period of 30 to 50 years  is  brief  compared  to  the prior history of
change.  However, interest in  oysters stimulated early studies beginning in
the latter 1880 and 1890's (Brooks  1891).
    This brief summary leaves  an  indelible  impression.  The Bay has been
interacting in imperfect ways  with  natural  events,  hurricanes and cycles of
climatic change.  But more importantly,  human activity made some marked
impacts on the Bay by the mid-1700's; however, the  most significant impacts
were initiated in the mid-1800's  and reached high levels around World War
II.  The past 40 years have been  a  time of  new events for  the Bay — some
possibly not coded into the genetic memory  of the Bay species, including
man, and the accompanying chlorinated hydrocarbons  and excessive metal and
nutrient enrichment.  An observation of considerable importance is the
relatively short period of scientific research on the Bay  relative to the
period of impact by human activity.  Interdisciplinary work that focuses on
questions of interest to society  is of  very recent  origin.
                                      A-21

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                                 SECTION 5
                  INDIVIDUAL RESEARCH PROJECTS
NUTRIENTS

Governing  Chesapeake Waters:  A History of Water  Quality Controls on
    Chesapeake  Bay, 1607-1972

Historical Review of Water Quality and Climatic Data from Chesapeake Bay
    with Emphasis on Effect of Enrichment

Water Quality Monitoring of the Three Major Tributaries to the Chesapeake
    Bay

Ware River Intensive Watershed Study

Evaluation of Management Tools in the Occoquan Watershed

Effects of Specific Land Uses on Nonpoint Sources:  Pequea Creek Basin,
    1979-1980

Chesapeake Bay  Nutrient Dynamics

Patuxent River  Intensive Watershed Study

TOXIC SUBSTANCES

The Characterization of the Chesapeake Bay:  A Systematic Analysis of Toxic
    Trace  Elements

Fate, Transport, and Transformation of Toxics: Significance of Suspended
    Sediment and Fluid Mud

Dredging:   Implementation of Innovative Dredging  Techniques in the
    Chesapeake  Bay

Physical Characteristics and Sediment Budget for  Bottom Sediments in the
    Maryland Portion of Chesapeake Bay

Animal/Sediment Relationships

Chesapeake Bay  Sediment Trace Elements

The Biogenic Structure of Lower Chesapeake Bay Sediments

Interstitial Water Chemistry

Toxic Point Source Assessment of Industrial Discharges

Interpretation  of Toxic Substances in the Water Column
                                     A-22

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SUBMERGED AQUATIC VEGETATION

Distribution and Abundance of Submerged Aquatic  Vegetation in the
    Chesapeake Bay,  Virginia

Distribution of Submersed Vascular  Plants,  Chesapeake  Bay,  Maryland

Distribution and Abundance of Waterfowl and Submerged  Aquatic Vegetation  in
    Chesapeake Bay

The Biology and Propagation of Eelgrass,  2o_s_te_ra marijna,  in Chesapeake  Bay

Sediment Suspension  and Resuspension from Small  Cr.aft  Induced Turbulence

Interactive Studies  of Light, Epiphytes,  and Grazers

Changes in the Chesapeake Bay as Recorded in the Sediments

Propagation and Impact of Herbicides on Submerged Aquatic Vegetation

Functional Ecology of Submerged Aquatic. Vegetation

Submerged Aquatic Vegetation in Chesapeake  Bay - Its Role in the Bay
    Ecosystem and Factors Leading to Its  Decline

ENVIRONMENTAL MANAGEMENT

Review of Regional Water Quality Control

Evaluation of Institutional Arrangements
                                     A-23

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                                  SECTION 6

                            LITERATURE CITED

Anderson,  R.R.,  and R.T.  Macomber.   1980.  Distribution of Submerged
    Vascular Plants,  Chesapeake  Bay, Maryland.  Final Report.  U.S.
    Environmental Protection Agency's Chesapeake Bay Program.  Grant No.
    R805970.  117 pp.

Biggs,  R.B.   1981.   Freshwater Inflow to Estuaries, Short and Long Term
    Perspectives.  In:   Proceedings  of  the National Symposium on Freshwater
    Flow to  Estuaries.   R.D. Cross,  and D.L. Williams, eds.  FWS/OBS-81/04.
    Washington,  D.C.   11:305-321.

Boone,  J.G.   1980.   Estuarine Fish Recruitment Survey.  Maryland DNR Report
    F-27-R-6.

Brooks, W.K.  1891.   The Oyster.  The Johns  Hopkins University Press.
    (2nd ed. 1905)

Brush,  Grace S., and F.W. Davis.  1982.  Stratigraphic Evidence of Human
    Disturbance  in Chesapeake Bay Tributaries.  Draft Final Report.  U.S.
    Environmental Protection Agency, Chesapeake Bay Program, Annapolis, MD.

Cronin, L.E.,  and A.J.  Mansueti.  1971.  The Biology of an Estuary.  In:
    A Symposium  on the Biological Significance of Estuaries.  Sport Fishing
    Institute.  Washington,  DC.  pp. 13-39.

Davis,  Harold E., D.W.  Webster,  and  G.E. Krantz.  1981.  Maryland Oyster
    Spat Survey  Fall 1980.   Technical Report.  Maryland Sea Grant Publ. #
    UM-SG-TS—81-03.   22 pp.

Gottschalk,  L.C.  1945.  Effects of  Soil Erosion on Navigation in Upper
    Chesapeake Bay.   The Geographical Review.  35: 319-338.

Green,  Katherine A.   1978.   A Conceptual Ecological Model for Chesapeake
    Bay.  U.S. Fish and Wildlife Service.  SFWB 144807.

Haven,  D.S., W.J. Hargis, Jr., and P.C. Kendall.  1978.  The Oyster
    Industry of  Virginia:  It's  [sic] Status, Problems and Promise.
    S.R.A.M.S.O.E.  No.  168.   V.I.M.S.

Haven,  D.S. , W.J. Hargis, Jr., and P.C. Kendall.  1981.  The Oyster
    Industry of  Virginia:  It's  [sic] Status, Problems, and Promise.
    S.R.A.M.S.O.E.  No.  168.   V.I.M.S.

Klein,  C.J.   Unpublished.  Chesapeake Bay Program Segmentation Approach.
    Chesapeake Bay Program Working Paper.  September 1981.  21 pp.

Landsberg, H.E., and C.S. Yu.  1968.  Preliminary Reconstruction of a Long
    Time Series  of Climatic Data for the Eastern United States.  Inst. of
    Fluid Dynamics and Applied Math.  Tech.  Note BN-571, University of
    Maryland,  College Park,  Maryland.  30 pp.

Lauff,  G.H., Ed.  1967.  Estuaries.  AAAS, Publ. No. 83.  757 pp.
                                     A-24

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Lippson, A.J.  1973.  The Chesapeake Bay in Maryland.   An Atlas of Natural
    Resources.  The Johns Hopkins University Press.   55 pp.

Meritt, Donald W.  1977.  Oyster Spat Set on Natural Cultch  in the Maryland
    Portion of the Chesapeake Bay (1939-1975).   UMCEES  Special Report No.
    7.  Horn Point Environmental Laboratories,  Cambridge,  MD.

Nichols, Maynard, Richard Harris, Galen Thompson,  and Bruce  Nelson.  1982.
    Fate, Transport and Transformation of Toxic Substances:  Significance
    of Suspended Sediment and Fluid Mud.  EPA-R8060020102.   Chesapeake Bay
    Program, U.S. Environmental Protection Agency, Washington, D.C.  97 pp.

Orth, R.J., K.A. Moore, and H.H. Gordon.  1979.   Distribution and Abundance
    of Submerged Aquatic Vegetation in the Lower Chesapeake  Bay, Virginia.
    EPA-R8059 51010.  U.S. Environmental Protection  Agency's Chesapeake Bay
    Program, Annapolis, MD.  199 pp.

Orth, R.J., and K.A. Moore.  1982.   Distribution and Abundance of Submerged
    Aquatic Vegetation in the Chesapeake Bay:   A Scientific  Summary.  In:
    Chesapeake Bay Program Technical Studies:   A Synthesis.  E.G.
    Macalaster, D.A. Barker, and M.E. Kasper,  eds.   U.S.  EPA, Washington,
    DC.  pp. 381-427.

Pritchard, D.W.  1955.  Estuarine Circulation Patterns.   Proc. Am. Soc.
    Civil Engrs.  81:717-1 to 717-11.

U.S. Army Corps of Engineers, Baltimore District.  1977.   Chesapeake Bay
    Future Conditions Report.  Baltimore, MD.

U.S. EPA.  1982a.  Chesapeake Bay:   Introduction to  an  Ecosystem.
    Washington, DC.  33 pp.

U.S. EPA.  1982b.  Chesapeake Bay Program Technical  Studies: A Synthesis.
    E.G. Macalaster, D.A. Barker, and M.E. Kasper, eds.   U.S. Environmental
    Protection Agency, Washington,  DC.   635 pp.

Warner, William W.  1976.  Beautiful Swimmers:   Watermen,  Crabs and the
    Chesapeake Bay.  Little, Brown and Co; rpt.   New York.   Penquin Books,
    1982.  304 pp.
                                     A-25

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APPENDIX B





 CONTENTS
Figures
Tables .
Section
1
2
3
4
5
6
7
8
9.


Basin Features and Climatic Conditions 	

EPA Water Quality Criteria Violations in the Bay 	
The Derivation of Site-Specific Water Quality Criteria

Methodology for Developing Metal Contamination Index;
Tables of Metals Data 	
Levels of Heavy Metals in Oyster Tissue from Virginia . . .
Literature Cited 	
B-ii
B~v
BT
Bi n
- 1 U
B1 R
R 9 Q
B"3 9
J /
B£ 1
B-76
Bi 1 n
V.- 1 S£

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                              FIGURES





Figure 1.   Long-term air temperature (in fahrenheit) (50 degrees F = 10
Figure 2.

Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
Figure 8.

Figure 9.

Figure 10.
Figure 11.

Figure 12.

Figure 13.
Figure 14.
Figure 15.
Figure 16.
Figure 17.
Figure 18.
Figure 19.

Figure 20.

Figure 21.
Figure 22.
Figure 23.
Average seasonal air temperatures (in fahrenheit) in
Baltimore, Maryland (50 degrees F = 10 degrees centigrade).
Chesapeake Bay drainage basin 	
Freshwater discharge for major rivers 	
Chesapeake Bay water quality sampling stations 	
Fecal coliform sampling stations 	

Chesapeake Bay toxic compound sampling stations for the
water column 	
Sampling stations for toxic bottom sediments in
Chesapeake Bay 	
Chesapeake Bay stations for sampling shellfish tissue . . .
Dissolved metals violations of the EPA water quality criteria
in Chesapeake Bay before 1971 to 1975 	
Dissolved metals violations of the EPA water quality criteria
in Chesapeake Bay after 1975 	
Volume of water with summer DO = 0.5 ml L~l 	
Susquehanna River spring flow, deviation from 31-year mean.
Monthly mean river flow of Harrisburg at Conowingo 	

Comparisons between salinity and DO profiles 	
Comparisons between salinity and DO profiles 	
Relation between salinity increase and DO decrease in two
springs with similar flows 	
Oxygen decrease per unit salinity increase at stations 848E
and 845F in July 1949 to 1980 	
Concentration of DO across the halocline 	

Average annual total phosphorus for segment CB-1 	
r>-£
B-3
B-5
B-6
B-ll
B-12
B-13

B-14

B-15
B-16

B-19

B-20
B-33
B-35
B-36
B-37
B-38
B-39

B-41

B-43
B-44
B-45
B-46
                                    B-ii

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Figure 24.  Annual trends in chlorophyll &> total nitrogen,  and total
            phosphorus in CB-2 .....................      B-47

Figure 25.  Average annual secchi for segment CB-2 ...........      B-48

Figure 26.  Average annual total phosphorus for segment CB-3 ......      B-49

Figure 27.  Average annual total phosphorus for segment CB-4 ......      B-50

Figure 28.  Population in the upper Chesapeake - lower Susquehanna
            region ...........................      B-51

Figure 29.  Land use in the upper Chesapeake - lower  Susquehanna
            region ...........................      B-53

Figure 30.  Fertilizer consumption in Pennsylvania ...........      B-54

Figure 31 (a).  Amount of bottom surface area at each depth
                from 0 to 40 m .....................      B-55

          (b) .  Depth versus surface area of bottom  ..........      B-56

Figure 32.  Short-term variations in fluorescence and dissolved oxygen
             from 1800 to 1820 hr, 5 June 1968,  upper Chesapeake Bay  .  .      B-59

Figure 33.  Cove Point 02 (ml L"1) in 1961 .............          B-60

Figure 34.  Location of ^lOp^ an(j meta]_ profile cores  ........        B-62

Figure 35 (a).  Aluminum concentration as a function  of the  Si/Al
                weight ratio ......................      B-65

          (b) .  Silicon concentration as a function of the Si/Al
                weight ratio ......................      B-65
Figure 36.  Silicon -aluminum weight ratio distribution  in
            dated cores from Chesapeake Bay  ..............      B-66

Figure 37 (a).  Chromium versus Si/Al in Chesapeake  Bay  sediments;  303
                hidden observations  ..................      B-70

       37 (b) .  Zinc versus Si/Al in Chesapeake Bay;  232 hidden
                observations ......................      B-70

Figure 38.  Zinc (Zn) and chromium (Cr)  concentrations  (ppm)  in
            Chesapeake Bay sediments ..................      B-71

Figure 39.  Degree of metal contamination in the Bay based on the
            Contamination Index (Cj) .................       B-75

Figure 40.  Total P spring averages, 1977 to 1980.   Data depth averaged
            and grouped by 7 1/2-minute  USGS quadrangles ........      B-148

Figure 41.  Total P summer averages, 1977 to 1980.   Data depth averaged
            and grouped by 7 1/2-minute  USGS quadrangles ........      B-149


                                     B-iii

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Figure 42.  Total nitrogen annual  average,  1977  to  1980.  Data depth
            averaged and grouped by  7  1/2-minute USGS quadrangles	B-150

Figure 43.  Total nitrogen spring  average,  1977  to  1980.  Data are depth
            averaged and group by  USGS 7  1/2-minute quadrangles	  B-151

Figure 44.  Total nitrogen summer  average,  1977  to  1980.  Data are depth
            averaged and grouped by  USGS  7  1/2-minute quadrangles  	  B-152

Figure 45.  Total chlorophyll annual average,  1977  to 1980.   Data are
            surface averaged and grouped  by USGS 7  1/2-minute quadrangles.  .  B-153

Figure 46.  Total chlorophyll spring average,  1977  to 1980.   Data are
            surface averaged and grouped  by USGS 7  1/2-minute quadrangles.  .  B-154

Figure 47.  Total chlorophyll summer average,  1977  to 1980.   Data are
            surface averaged and grouped  by USGS 7  1/2-minute quadrangles.  .  B-155
                                     B-.iv

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                                 TABLES

Table 1.   Volume, Surface Area,  and Average Depth of GBP Segments
           in the Main Bay	~ B-4

Table 2.   Volume, Surface Area,  and Average Depth of CBP Segments
           of the Western Shore Tributaries  	 B-7

Table .3.   Volume, Surface Area,  and Average Depth of CBP Segments
           of the Eastern Shore	B-8

Table 4.   Meteorological Data for  Baltimore, Maryland	g_9

Table 5.   U.S. EPA Water Quality Criteria	B-21

Table. 6.   Dissolved Metal Violations	B-22

Table 7.   "Calculated" Dissolved Metal Violations 	 B-23

Table 8.   Dissolved Metal Violations  	 B-26

Table 9.   Numerical Acute Water  Quality Criteria for Salt Water
           Ogranisms	B-30

Table 10.   Total Phosphorus Regeneration for CB 1-5 By Depth	B-57

Table 11 (a).  Analysis of a Quartz-Feldspar Biotite Gneiss and its
               Weathering Products  	 B-67

         (b).  General Calculations  of Gains and Losses of Chemical
               Elements During Weathering  	 B-67

         (c).  Si/Al Ratios Calculated from Table 11 (a)	B-68

Table 12.   Observed Ranges of Water  Quality Yields, Concentrations, and
           Background Ranges Simulated by Regression Models	B-68

Table 13.   Trace Metal Versus Si/Al  Relations	B-7 3

Table 14.   Contamination Factors  and Degrees of Contamination
           For  Surface Surface Sediments From the Patapsco and the
           Elizabeth Rivers	B-7 4

Table 15.   Levels of Chromium in  Oyster Tissue in Virginia	B-7 7

Table 16.   Levels of Cadmium in Oyster Tissue in Virginia	B-78

Table 17.   Levels of Copper in Oyster Tissue in Virginia	B-79

Table 18.   Levels of Zinc in Oyster  Tissue in Virginia	B-80

Table 19.   Mean Levels of Pesticides, Polychlorinated Biphenyls
           and  Metals in Oysters  in  Virginia	B-81

Table 20.   Mean Levels of Pesticides and Polychlorinated Biphenyls
           in Oysters in Maryland	B-82


                                     B-v

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Table 21.   Mean Levels  of  Metals in Oysters in Maryland	B-84

Table 22.   Concentrations  of  Dissolved Metals by CBP Segment 	 B-86

Table 23.   Concentrations  of  Particulate Metals by CBP Segment 	 B-88

Table 24.   Concentrations  of  Particulate Metals by CBP Segment 	 B-90

Table 25.   Bottom Sediment Concentration of Metals, Geometric Mean,
           Minimum,  and Maximum of Metals by Segment	B-92

Table 26.   Cf Mean,  Minimum,  and Maximum of Metals by Segment	B-93

Table 27.   Cj Mean,  Minimum,  and Maximum by Segment	B-94

Table 28.   Mean Concentrations  of Total Metal in CBP Segments	B-95

Table 29.   Bottom Sediment Geometric Mean, Minimum, and Maximum of
           Metals (Western Shore)   	 B-97

Table 30.   Cf Mean,  Minimum,  and Maximum of Metals (Western Shore) .  .  . f$_joo

Table 31.   Cj Mean,  Minimum,  and Maximum (Western Shore)	B-104

Table 32.   Bottom Sediment Geometric Mean, Minimum, and Maximum of
           Metals (Eastern Shore)	B-105

Table 33.   Cf Mean,  Minimum,  and Maximum of Metals (Eastern Shore) .  .  . B-107

Table 34.   Cj Mean,  Minimum,  and Maximum (Eastern Shore)	B-109

Table 35
    (a).  Summary Statistics  for Physical Means, Annual Data (1977)  .  . g_xil

    (b).  Summary Statistics  for Physical Means, Annual Data (1978)  .  . B_n2

    (c).  Summary Statistics  for Physical Means, Annual Data (1979)  .  . 3.^13

    (d) .  Summary Statistics  for Physical Means, Annual Data (1980)  .  . „_, -, ,

Table 36
    (a).  Summary Statistics  for Physical Means, Seasonal Data (1977).  . g_^] 5

    (b).  Summary Statistics  for Physical Means, Seasonal Data (1978).  . g_^^g

    (c).  Summary Statistics  for Physical Means, Seasonal Data (1979).  . B_j20

    (d).  Summary Statistics  for Physical Means, Seasonal Data (1980).  . -g-^22

Table 37
    (a).  Summary Statistics  for Nutrient Means, Annual Data (1977)  .  . g_i24
    (b).  Summary Statistics for Nutrient  Means,  Annual Data  (1978)
                                                                     '  ' B-125
                                      B-vi

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    (c).  Summary Statistics  for Nutrient Means, Annual Data (1979)  . .  B-126

    (d).  Summary Statistics  for Nutrient Means, Annual Data (1980)  . .  g_i27

Table 38
    (a).  Summary Statistics  for Nutrient Means, Seasonal Data (1977). .  B-128

    (b).  Summary Statistics  for Nutrient Means, Seasonal Data (1978). .  jj-131

    (c).  Summary Statistics  for Nutrient Means, Seasonal Data (1979). .  B-133

    (d).  Summary Statistics  for Nutrient Means, Seasonal Data (1980). .  5-135

Table 39.   Summary Statistics for  the CBP Nutrients Data Base for        B-137
           Selected Parameters 	

Table 40.   Summary of  Statistically Significant Annual Nutrient Trends .  5-138

Table 41 (a).   Summary of  Statistically Significant Seasonal Nutrient
               Trends  (Spring) 	  B-140

         (b).   Summary of  Statistically Significant Seasonal Nutrient
               Trends  (Summer)	B-142

         (c).   Summary of  Statistically Significant Seasonal Nutrient
               Trends  (Fall)	B-144

         (d).   Summary of  Statistically Significant Seasonal Nutrient
               Trends  (Winter)	B-146
                                     B-vii

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                                SECTION 1
          BASIN FEATURES AND CLIMATIC CONDITIONS
GEOGRAPHY

    Chesapeake  B?   is  the dturned river valley of the Susquehanna River.
It was former1  \ /roximately 10,000 years ago when melting glacial ice
resultei   . _»  sea  level  rise that submerged the Susquehanna  River Valley.
The B•:•/ is approximately 322 kilometers (km)1 long with 12,872 km of
shoreline and  a surface  area of about 11,391 km2 (2)  including its
tributaries.   The  volume, surface area, and average depth of the Chesapeake
Bay Program segments were computed using a planimeter and bathymetric chart
and are shown  in Tables  1 to 3.  On the basis of this analysis, the average
depth of the Bay and its tributaries is 6.63 meters (m)3. Eastern Shore
segments are the shallowest areas (3.68 m average depth), and the main Bay
segments CB-4  to CB-8  have the deepest average depths (10.92 m to 7.83 m).

CLIMATE

    Meteorologic conditions in the Chesapeake Basin influence the
hydrodynamics  of the Bay and drive its circulation.  Table 4 summarizes the
1980 air temperature,  precipitation, and general wind conditions in
Baltimore, MD,  compared  with the norm, means, and extremes from past
years.  The monthly average air temperatures ranged from -0.3°C4 in
February to 25.9°C in  August.  Precipitation varied from 17.78
millimeters (mm)5  in December to 13.87 centimeters (cm)6 in  March.
Winds throughout the year were generally from the northwest  or west.
    A longer-term  perspective on climate can be found by looking at the
1900 to 1980 air temperature records for representative areas in the basin
including Baltimore, MD, Washington, DC, and Harrisburg, PA  (Figure 1).   It
appears from visual observation that localized air temperatures in
Washington, DC,  at National Airport have increased slightly, perhaps
because of increased urbanization.  This trend does not appear in the
Harrisburg or  Baltimore  data, probably because their stations are located
outside of the  downtown, highly urbanized area.  Figure 2 shows that over
the period of  record,  average summer air temperatures range  in the 70's
(degrees Fahrenheit),  fall and spring temperatures in the 50's (degrees
Fahrenheit), and winter  temperatures in the 30's (degrees Fahrenheit).

FRESHWATER INFLOW

    The three major tributaries of the Bay system are the Susquehanna,
Potomac, and James Rivers.  Together these three rivers drain about 70


1 1 km = 5/8 mile
2 1 km2 = 0.386 mi2
3 1 m.= 3.3 ft
4 1 OG = 5/9(°F -  32)
5 1 mm = 0.04  in
6 1 cm = 0.39  in
                                     B-l

-------
                        ANNUAL  TREND
                            AIR TEMPERATURE
   70H
   60-
   50-
                                                         -• WASHINGTON
A  70-

R

T
E
M
P  60

R
A
T
U
R
E  SO
BALTIMORE
   60H
   50-
   40-
                                                           HARRISBURG
    1900   1910   1920   1930   1940   1950   I960   1970   1980   1981

                                 YEAR


Figure  1.  Long term air temperature (in fahrenheit) (50 degrees F = 10
          degrees centigrade).
                                 B-2

-------
   80-
   70-
   60-
  A
  I
  R
   50H
  T
  E
  M
  P
  E
  R
  A 40-J
  T
  U
  R
  E
    30-
    20
    10
                         ANNUAL TREND
                        SEASONAL AIR TEMPERATURE
                              BALTIMORE
                                                                SUMMER
                                                                WINTER
     1900   1910   1920   1930   1940   1950   1960   19/0   1980   1981

                                  YEAR
Figure  2.  Average seasonal air temperature  (in fahrenheit)  in Baltimore,
          Maryland (50 degrees F = 10 degrees centigrade).
                                   B-3

-------
percent of the approximately 64,000  square  mile Chesapeake Bay drainage
basin (Figure 3)  and account for  about  80 to  85 percent of the long-term
average freshwater discharge Bay-wide (Wolman 1968) .  The long-term,
average annual flows from 1950 to 1980  for  the Susquehanna, Potomac, and
James Rivers are  shown in Figure  4.  Pritchard (1967) notes that the
freshwater flow from the Susquehanna alone  significantly affects the
physical and chemical characteristics of the  Bay.   As a result of  this
influence, the Bay proper is moderately stratified  with surface waters less
saline than the bottom waters.  The  greatest  vertical difference in
salinity occurs in the riverine-estuarine transition area in the upper
section of the Bay.
TABLE 1.   VOLUME,  SURFACE AREA,  AND AVERAGE  DEPTH OF CBP SEGMENTS* IN THE
          MAIN BAY


CBP
SEGMENT SEGMENT

SUSQUEHANNA FLATS
TURKEY PT - ROBINS FT
ROBINS PT - SANDY PT
SANDY PT - COVE PT
COVE PT - WINDMILL PT
WINDMILL PT - NORTHEND PT
TANGIER ISLAND - BAY MOUTH
NORTH END PT - BAY MOUTH
TOTAL
CODE
CB-1
CB-2
CB-3
CB-4
CB-5
CB-6
CB-7
CB-8


VOLUME
(106m3)
175.41
712.62
2499.59
9388.88
16485.81
6965.74
11701.70
3122.38
51052.13

SURFACE AREA
(106m2)
106.93
173.36
425.00
859.91
1748.47
756.85
1304.93
398.87
5774.32

AVER. DEPTH
(m)
1.64
4.11
5.88
10.92
9.43
9.20
8.97
7.83
8.84

*Total area and volume were calculated by summing values  given  for  each
 one-mile interval in Volumetric,  Areal,  and Tidal Statistics of  the
 Chesapeake Bay and Its Tributaries,  Cronin (1971).   For  those  segments and
 portions of segments having boundaries that did not correspond with
 Cronin's intervals, the area and  volume  were planimetered  from a
 bathymetric chart of Chesapeake Bay  (Goldsmith and  Sutton  1977).
                                      B-4

-------
              Major River Basins
    Legend
    	 State boundaries
   	 Rivers
       River basin boundaries
       Fall line
Susquehanna
      N
 Potomac
    James
    West
Chesapeake
                                              Patuxent
                                          Eastern Shore
                                     Norfolk
                 Rappahannock-York
              Figure 3.  Chesapeake Bay drainage basin.

                          B-5

-------
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-------
TABLE 2.  VOLUME, SURFACE AREA, AND AVERAGE DEPTH OF  CBP  SEGMENTS  OF  THE
          WESTERN SHORE TRIBUTARIES

SEGMENT
BUSH RIVER
GUNPOWDER RIVER
MIDDLE RIVER, SENECA
BACK RIVER
PATAPSCO RIVER
MAGOTHY RIVER
SEVERN RIVER
WEST RIVER
RHODE RIVER
SOUTH RIVER
PATUXENT RIVER
lower
middle
upper
POTOMAC RIVER
lower
middle
upper
RAPPAHANNOCK RIVER
lower
middle
upper
MOBJACK BAY -
YORK RIVER MOUTH
YORK RIVER
lower
middle
upper
JAMES RIVER
lower
middle
upper
TOTAL
CBP
SEGMENT
CODE
WT-1
WT-2
CREEK WT-3
WT-4
WT-5
WT-6
WT-7

WT-8


LE-1
RET-1
TF-1

LE-2
RET-2
TF-2

LE-3
RET-3
TF-3
WE-4


LE-4
RET-4
TF-4

LE-5
RET-5
TF-5

VOLUME
(I06m3)
60.50
74.86
47.21
34.55
467.40
89.85
130.03

122.55


521.29
34.02
4.34

5640.20
968.25
679.59

1339.17
254.23
214.97
1420.13


522.56
123.74
175.95

1769.00
308.54
429.44
15432.37
SURFACE AREA
(I06m2)
33.22
45.37
24.75
18.57
100.41
25.89
30.32

47.32


103.53
17.71
0.99

862.52
223.49
165.47

233.58
105.63
60.87
363.98


108.60
45.62
41.21

464.55
98.46
95.19
3317.25
AVER. DEPTH
(m)
1.82
1.65
1.91
1.86
4.65
3.47
4.29

2.59


5.04
1.92
4.38

6.54
4.33
4.11

5.73
2.41
3.53
3.90


4.81
2.71
4.27

3.81
3.13
4.51
4.65
                                       Bc-7

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TABLE 3.   VOLUME,  SURFACE AREA, AND  AVERAGE  DEPTH OF CBP SEGMENTS OF THE
          EASTERN  SHORE


SEGMENT

NORTHEAST RIVER
ELK RIVER
SASSAFRAS RIVER
CHESTER RIVER
EASTERN BAY
CHOP TANK RIVER
lower
upper
TANGIER SOUND
NANTICOKE RIVER
WICOMICO RIVER
MANOKIN RIVER
BIG ANNEMESSEX RIVER
POCOMOKE RIVER
CBP
SEGMENT
CODE
ET-1
ET-2
ET-3
ET-4
EE-1

EE-2
ET-5
EE-3
ET-6
ET-7
ET-8
ET-9
ET-10

VOLUME
(10 6m3)
18.80
106.84
168.31
533.36
1160.99

1194.96
457.99
3923.47
173.48
67.59
104.59
51.10
29.50

SURFACE AREA
(106»2)
15.79
47.22
36.51
147.06
258.84

348.24
99.67
1002.75
67.18
33.17
68.18
29.33
16.50

AVER. DEPTH
(m)
1.19
2.26
4.61
3.63
4.49

3.43
4.60
3.91
2.58
2.04
1.53
1.74
1.74
TOTAL
7990.98
2170.44
3.68

-------
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-------
                               SECTION 2

    WATER QUALITY AND SEDIMENT SAMPLING STATIONS


WATER QUALITY STATIONS

    The CBP water  quality data base contains sampling data  for  physical and
chemical constituents  in  Bay waters and tributaries from 1949 through 1981
at the sites indicated in Figure 5.
    Figure 6 indicates sites which were sampled at least once a month for
fecal coliforms  from 1976 to 1980 in Maryland.  The Patuxent River basin
has coverage from  1970 to 1980.  In Virginia, there are from 3  to 50
sampling stations  indicated in each of 98 shellfish growing areas.  Data
were available for 1974,  1975, and 1980.
    Bottom sediments were collected for the Bay Program during  the spring
and fall of 1979.   Analyses revealed over 300 organic compounds from
stations shown in  Figure  7.
    Samples from the water column were analyzed for organic compounds,
heavy metals, and  pesticides.  Samples were collected at stations shown in
Figure 8 from 1962 through 1981.  Figure 9 shows sediment sampling stations
for the same time  period.
    Shellfish tissue was  analyzed for heavy metals, organic compounds, and
pesticides.  Stations  sampled from 1962 through 1981 are shown  in Figure 10.
SPATIAL SAMPLING
    To provide a dynamic  picture of Bay-wide water quality  over the entire
period of record,  only those samples taken in representative stations were
selected for comparison.   Data from shallow, near-shore stations were not
used to calculate  regional averages, nor were samples taken in  deep
(> 10 m) channels.  Most of the samples used for analysis  were taken over
deeper waters associated  with the main-Bay channel.
    The greatest number of observations were present in the upper central
Bay, between Poole's Island and Cove Point.  In CB-3, sampling was
concentrated closer to the western and eastern shores where greater depths
coincide with two  ancient river beds.  Farther south, in CB-4,  the two
depressions converge in a deeper mid-Bay channel.  In this  segment, most
samples were collected mid-Bay over deeper water.  In the south Bay (CB-5),
most samples were  taken in the western half where the main  channel is
closer to the western  shore.  General Bay, CB-6, CB-7, and  Bay mouth CB-8
stations were generally distributed closer to the Eastern Shore in
proximity to deeper waters.

TEMPORAL COVERAGE

    For CBP segments where three or more stations were sampled  in any one
month, monthly water quality means were calculated.  Seasonal means were
calculated for segments with at least two of three monthly  means
available.  Annual means  were calculated for segments with  two  or more
seasonal means available  in the same year.
    The distribution of stations for which DO, TN, TP and Chi a_ data exists
varies over time.   Prior  to 1961, little data were available to calculate
annual and seasonal means for CBP segments.  Summer means were  calculated
for TP in the main Bay, the Bay mouth, and parts of the York and
                                     B-10

-------
Figure 5.   Chesapeake Bay water quality sampling station.
                               B-ll

-------
Figure ft.  Fecal coliform sampling
                               B- 12

-------
Figure 7.  Chesapeake Bay organic compound sampling stations.
                               B-.13

-------
Figure 8.  Chesapeake Bay toxic compound sampling  stations  for  the water
           column.
                                 B-14

-------
Figure 9.   Sampling  stations  for  toxic bottom sediments in Chesapeake Bay.
                                 B-15

-------
Figure 10.   Chesapeake Bay stations for sampling shellfish  tissue.
                                B-16

-------
Rappahannock Rivers.   Summer DO means  were  available  for  CB-5, and portions
of the York, Potomac,  and Patuxent  Rivers.   Annual  DO means were available
for CB-5 only.
    Summer and  annual  TP  means  during  1961  to  1965  were well distributed in
the upper Bay and all  of  the Potomac River,  Chester River, and Eastern
Bay.  Dissolved oxygen (DO)  was again  available  in  CB-5 only.
    More complete c verage exists for  the upper  Bay,  CB 1-3, from 1966 to
1970 for TP and, DO including the upper  Patuxent River, Potomac River,
Eastern Bay, sec adary western  tributaries,  and  a limited portion of  the
upper James. The first TN data became available for  the  same regions,
except hastem  Bay.
    During 1971 to 1975 coverage of the  main Bay extended down to the mouth
ui the Potomac  for TP  and TN.  Most secondary  western tributaries and the
upper Bay were  covered; however, sampling in major  tributaries was spotty.
No TP or TN means are  available for the  Patuxent or lower Potomac.  Eastern
tributaries were covered, including the  Wicomico and  Pocomoke Rivers.
Again, DO means were  limited, especially on an annual basis, to portions of
the upper Bay (CB-3) ,  upper  Potomac, York,  and lower  Rappahannock, York and
James Rivers.
    For 1976 through  1980,  summer TP and TN means are fairly complete as
far south as the Potomac  River  and  include  most  secondary tributaries.
Coverage includes all  major  tributaries, except  the mid- and lower
Rappahannock.  Data on summer DO, again, were  limited to the main Bay,
CB-3, Patuxent  River,  upper  and mid-Potomac, and lower York and James
Rivers.  Noticeably less  annual means  were  available  during 1976 to 1980,
indicating that seasonal  sampling was  not balanced  throughout those years.
                                     B-,17

-------
                                  SECTION 3

EPA WATER QUALITY CRITERIA APPLIED TO METALS IN THE BAY


     INTRODUCTION

         Heavy  metal concentrations that surpass the EPA water quality criteria
     are found  primarily in  the main Bay and western shore  tributaries.
     Monitoring data on toxic substances shows that the abundance of heavy
     metals appears to be related to the concentration of population centers.
     The highest water column metal concentrations in Maryland are in the
     Potomac River with zinc (Zn) in the fresh portion and  copper (Cu) in the
     estuarine, in Baltimore Harbor Cu, Zn, and in the main Bay  between the
     Gunpowder  River and Cove Point [Cu, cadmium (Cd), chromium  (Cr), Zn]
     (Figures 11 and 12).   In Virginia, the estuarine segments of the
     Rappahannock, York, and James Rivers contain levels of nickel (Ni) and Cu
     that exceed both acute and chronic criteria.   A similar pattern exists for
     the western half of the main Bay in Virginia.

     DERIVATION AND BASIS OF WATER QUALITY CRITERIA

         The EPA National Water Quality Criteria shown in Table  5 establish
     maximum constituent concentrations below which organisms, aquatic
     communities, water uses, and water quality are adequately protected.  The
     criteria are intended  to protect aquatic life from short-term (acute) and
     long-term  (chronic) effects (U.S. EPA Water Quality Criteria 1980).
         They are derived from laboratory data that, excluding endemic
     environments or species, are generally applicable to comparable field
     situations throughout North America.  The limits are intended to protect
     all the environments without being overly restrictive.  Although criteria
     are usually derived separately from freshwater and salt water environments,
     similar acute-chronic  ratios and bioaccumulation factors allow
     interchangeable criteria.
         Criteria, which are not intended to be overall limits,  are frequently
     used in the development of effluent standards.  Stanc'  rds establish a legal
     limit and  are designed  to consider environmental, social, economic, and
     other specific local conditions.

     USING THE WATER QUALITY CRITERIA

         The criteria, developed from measured effects under laboratory
     conditions, are based on toxicological "no effect" concentrations and
     reflect the soluble, biologically available fraction of the metal.
     Therefore, only those  fie d measurements reported as  "dissolved" can be
     properly compared to the criteria (Table 6).   The majority  of the data,
     reported as "total," cannot be compared in that form.   The  dissolved
     fraction of those field measurements (Kingston 1982) have been estimated by
     using equations developed by CBP researchers (Chapter  1).   The results of
     the "calculated dissolved" data are shown in Table 7.   These fractions are
     our best estimate of what is potentially available to  Bay biota.
         Both the "dissolved" and "calculated dissolved" data were compared to
     the appropriate salt water or freshwater criteria and  reported for both
                                         Brl8

-------
Figure 11.   Dissolved metals  violations  of EPA water quality criteria in
            Chesapeake Bay before  1971 to 1975.
                                   B-19

-------
Figure 12.  Dissolved metals violations of EPA water quality criteria in
            Chesapeake Bay after 1975.
                                   B-20

-------
chronic and acute toxicity (Tables 6,  7,  and 8).
    Chronic toxicity refers to behavioral or physiological  stresses  placed
upon the individual or reproductive failure within the species.  Although
toxicant levels may not be immediately harmful  for initial  generations  or
consumers, subsequent bioaccumulation  can create  irreversible  effects.
These criteria consider the metal's accumulation,  persistence, and effects
in aquatic systems.
    Acute toxicity, generally based on 48 to 96 hour  exposures,  refers  to
the lethal concentration for a specific percentage of test  organisms.
TABLE 5.  U.S. EPA WATER QUALITY CRITERIA (FROM U.S.  EPA 1980)
                              Aquatic  Life
Metal

 Cd
 Cr+3
 Cr+6
 Cu
 Pb
 Ni
 Zn
(a)
(b)
(c)
(d)
(e)
(f)
(8)
(h)
(i)
 exp
 exp
 exp
 exp
' exp
 exp
 exp
 exp
 exp
                Freshwater
Chronic
(a)
-
.29 u
5.6 u
(e)
(8)
47. u
Acute
(b)
(c)
21. u
(d)
(f)
(h)
(i)
(1.05 [In hardness]
(1.05 [In hardness]
(1.08 [In hardness]
(0.94 [In hardness]
(2.35 [In hardness]
(1.22 [In hardness]
(0.76 [In hardness]
(0.76 [In hardness]
(0.83 [In hardness]
                                           Salt water








8.52)*
3.73)
3.48)
.1.23)
9.48)
0.47)
1.06)
4.02)
1.95)
Chronic
4.5 u
18. u
4.0 u
7.1 u
58. u
Example: at CaCo3
50 m
.012 u
1.5 u
2200. u
12. u
.75 u
74. u
56. u
1100. u
180. u
Acute
59. u
1260. u
23-u
140. u
170. u
hardness of:
200 m
.051 u
6.3 u
9900. u
43. u
20. u
400. u
160. u
3100. u
570. u

        [In hardness]  - 8.52)
                                     B-21

-------
TABLE 6.  DISSOLVED METAL VIOLATIONS  (SOURCE:  VA 106)
Segment
  Metal
Observations
                                             Acute
        Violations
        %       Chronic
Potomac
TF-2
LE-2
Nickel
Nickel
     5
    13
 1
 3
20
23
 1
13
 20
100
Rappahannock
TF-3       Ni ckel
RET-3      Nickel
LE-3       Nickel
                   2
                   1
                  12
                      1
                      0
       50
                            42
            1
            1
           12
         50
         50
        100
York
TF-4
RET-4
LE-4
Nickel
Nickel
Nickel
     7
    10
    19
 3
 0
 9
43

47
 7
10
18
100
100
 95
James
RET-5
LE-5
Nickel
Nickel
     2
    75
 0
29
39
 2
75
100
100
Eastern Shore
ET-10
Nickel
                                               100
                                       B-22

-------
TABLE 7.  "CALCULATED" DISSOLVED METAL VIOLATIONS  (SOURCE:   MD 106, VA 106)

Segment

MAIN BAY
CB-2
CB-3






CB-4




CB-5





CB-7



CB-8



WESTERN
WT-2





WT-4





WT-5





Metal


Cadlmium
Lead
Nickel
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Lead
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Lead
Cadmium
Copper
Zinc
Lead
Cadmium
Copper
Zinc
SHORE
Lead
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Lead
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Lead
Nickel
Cadmium
Chromium (Cr3)


Observations


1
235
371
326
376
376
378
378
111
107
107
111
111
107
62
52
52
119
117
111
11
96
80
71
5
64
74

28
28
28
28
28
29
64
67
65
65
64
66
86
76
87
130
(continued)
B-23

Acute

1
0
0
1
0
6
8
1
0
0
0
5
0
0
0
0
0
4
0
0
0
11
0
0
0
13
0

0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0


Violations
% Chronic

100 1
0
1
1 1
22
2 22
2 47
1 17
12
0
0
5 30
0
1
10
1
1
3 73
2
1
11
11 96
3
1
5
20 64
1

0
0
0
0
0
0
0
0
1
1
0
2 2
1 7
0
1 7
4



%

100

1
1
6
6
12
4
11


27

1
16
2
2
61
2
1
100
100
4
1
100
100
1









2
2

3
8

8
3



-------
TABLE 7.  (continued)
Segment



WT-6






WT-7
WT-8
Patuxent
TF-1





Potomac
TF-2






RET-2





LE-2






Metal
Chromium (Cr6)
Copper
Zinc
Lead
Nickel
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Copper
Copper

Lead
Cadmium
Chromium (Cr3)
Chromium (Cr&)
Copper
Zinc

Lead
Nickel
Caamiuu;
Chromium (Cr3)
Chromium fl ")
Copper
Zinc
Lead
Cadmium
Chromium (Cr3)
Chromium (Cr&)
Copper
Zinc
Lead
Nickel
Cadmium
Chromium (Cr3)
Chromium (Cr&)
Copper
Zinc
Observations
130
86
95
10
8
10
8
8
10
10
29
10

274
274
274
274
275
275

37
28
37
34
34
32
37
15
97
90
90
92
96
5
2
63
51
51
121
174
Acute
1
7
4
0
0
0
0
0
0
0
1
0

0
1
0
0
3
1

0
0
0
0
0
0
0
0
6
0
2
0
0
0
0
4
0
0
13
0
Violations
% Chronic %
1
8
4







3



1


1
1









6

2




6


11

4
12
21
0
0
0
0
0
0
0
6
0

0
1
5
5
4
3

2
0
0
3
3
0
24
0
6
3
3
13
0
0
2
18
0
0
82
4
3
14
22







21



1
2
2
1
1

5


9
9

65

6
3
3
14


100
29


68
2
                                 (continued)
                                    B-24

-------
TABLE 7.  (continued)
Segment
Metal
Observations
                                             Acute
Violations
%       Chronic    %
Rappahannock
LE-3


York
LE-4


James
LE-5





WE-4


Cadmium
Copper
Zinc

Cadmium
Copper
Zinc

Lead
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Cadmium
Copper
Zinc
3
103
113

12
80
90

545
17
301
301
376
476
8
189
156
0
15
4

0
8
0

0
2
0
0
66
5
0
13
5

15
4


10



12


18
1

7
3
2
102
14

9
80
2

3
15
1
1
376
27
8
189
13
67
99
12

75
100
2

1
88
1
1
100
6
100
100
8
EASTERN SHORE

ET-2       Lead               27
           Cadmium            27
           Chromium (Cr3)     27
           Chromium (Cr6)     27
           Copper             27
           Zinc               27
ET-4       Lead               10
           Cadmium            10
           Chromium (Cr3)     10
           Chromium (Cr6)     10
           Copper             10
ET-5       Cadmium             1
EE-3       Lead                1
           Cadmium             4
           Chromium (Cr3)      1
           Chromium (Cr6)      1
           Copper             23
           Zinc                1
ET-10      Cadmium             1
           Copper             24
           Zinc               39
1
0
0
0
2
1
0
2
0
0
1
0
0
0
0
0
0
0
0
1
1
4



7
4

20


10








4
3
1
0
0
0
2
2
2
2
0
0
6
1
0
3
0
0
22
0
1
24
1
                                                             7
                                                             7
                                                            20
                                                            20
                                                            60
                                                           100

                                                            75
                                                            96

                                                           100
                                                           100
                                                             3
                                     B-25

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TABLE 8.  DISSOLVED METAL VIOLATIONS  (SOURCE:   N.B.S.  1980)

Segment
MAIN BAY
CB-1






CB-2






CB-3






CB-4
CB-5
CB-6
CB-7
CB-8
EE-1
EE-2
EE-3
WE-4
Metal

Lead
Nickel
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Lead
Nickel
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
Lead
Nickel
Cadmium
Chromium (Cr3)
Chromium (Cr6)
Copper
Zinc
7 metals
7 metals
7 metals
7 metals
7 metals
7 metals
7 metals
7 metals
7 metals
Observations

4
4
4
4
4
4
4
4
4
4
4
4
4
4
6
6
6
6
6
6
6
14
24
8
20
4
2
2
8
4
Violations
Acute % Chronic %

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
No violations
No violations
No violations
No violations
No violations
No violations
No violations
No violations
No violations

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












50
50
50




25
25
25




67
33
33











                                     B-26

-------
DATA SOURCES

    Ambient water quality monitoring data have  been gathered  by  the  States
bordering Chesapeake Bay and by the Chesapeake  Bay Program  itself.
    The Virginia State Water Control Board data base (Virginia 106)
contains data on dissolved nickel in the lower  Bay and  its  tributaries.
These data are shown in Table 6,  both as amounts and as percentages  of all
observations.
    "Total" metals have been collected and combined in  STORET, the EPA's
environmental data base, since the 1960's.  Data from both  VA 106 and MD
106 have been used to calculate the "dissolved"  phase and are shown  in
Table 7.
    Samples collected by the National Bureau of Standards (N.B.S.) are
shown in Table 8.  This 1982 research project (Kingston 1982) analyzed
dissolved metal concentrations in the main Bay  using neutron  activation
analysis.

RESULTS

    In addition to the main Bay,  areas most highly enriched with metals are
the Potomac River, Baltimore Harbor, the estuarine segments of the western
shore tributaries, and the Pocomoke Sound region.
    Throughout the main Bay, there are chronic  criteria violations for Cu
and, below Cove Point, chronic criteria violations for  Cd,  Cu, and Ni.
    The entire Potomac River is enriched — the tidal-fresh portion  by Zn
and the lower sections by Cu.   More than 10 percent of  the  Cu samples in
the lower-estuarine portion exceed acute criteria.
    The chronic criteria for Cu and Zn are exceeded more than 14 percent of
the time in Baltimore Harbor.   Twelve percent of the samples  from the
adjacent portion of Chesapeake Bay exceed chronic  criteria.
    The Rappahannock, York, and James Rivers in Virginia have been sampled
primarily in the lower estuarine portion.   Chronic criteria levels for Cu
and Ni are exceeded virtually 100 percent of the time in these rivers and
in Mobjack Bay.  In the lower James, the chronic criteria for Cd is
exceeded in 88 percent of the samples.  The acute  criteria  for Cu and Ni
are exceeded in 18 percent and 39 percent of the samples.
    Ninety-eight percent of the samples from the Pocomoke River and
Pocomoke Sound were above the chronic criteria  for Cu.  This  estuarine zone
is adjacent to Tangier Sound,  one of the sections  of Chesapeake Bay  least
impacted by anthropogenic activity.

CONCLUSIONS

    The EPA water quality criteria were developed  from  laboratory toxicity
tests based largely upon the ionic forms of the heavy metals, even though
metals in an estuarine environment may be in such  forms as  carbonates,
ligands, complexes, hydroxides, or adsorbed to  suspended organic and
mineral materials.  Although criteria used for  Chesapeake Bay are from
national values, it is possible that heavy metals  threaten  Chesapeake Bay
biota, especially in the western tributaries and the main Bay.  This
potential could be better evaluated if the extent  and duration of these
high concentrations were identified.
                                     B-27

-------
    Further analysis should consider  the applicability of national
standards to Chesapeake Bay, the  temporal and  spatial distribution of those
values exceeding the standards, and the usefulness of establishing
site-specific criteria for the Bay.   In Chapter  3, the implications of
water quality criteria for Bay organisms is  discussed further.

-------
                                 SECTION 4

THE DERIVATION OF SITE-SPECIFIC WATER QUALITY CRITERIA
              FOR EIGHT METALS IN CHESAPEAKE BAY


      The development of site-specific water quality criteria by the states
  will be possible under proposed changes by EPA to its current policy of
  presumptive applicability.  Currently, a state must adopt  the national
  water quality criterion for all water quality characteristics unless the
  state can justify a less stringent criterion [40 CFT Part  131, Section
  304(a)].
      The following site-specific salt water criteria developed by the CBP
  (using EPA's recalculation procedure) are similar to the more general
  national criteria.  Truely accurate site-specific criteria should be
  developed by conducting toxicity tests with resident species and site water
  (Parrish 1983).

  THE RECALCULATION PROCEDURE

      Site-specific water quality criteria for eight metals  [arsenic (As) ,
  cadmium (Cd), chromium (Cd), copper (Cu), lead (Pb), mercury (Hg), nickel
  (Ni), and zinc (Zn)] in Chesapeake Bay have been derived by using the
  recalculation procedure (Parrish 1983).  This procedure allows modification
  of  the national criteria acute toxicity data set by eliminating species or
  families not represented by species resident at a site.  It is meant "...
  to  compensate for any real difference between the sensitivity range of
  species represented in the national data set and species resident to the
  site.  The principal reason for potential differences is that the resident
  communities of a site may represent a more narrow mix of species because of
  natural environmental conditions (e.g., salinity, temperature, habitat, and
  other factors)" (U.S. EPA 1982a).
      On the basis of monitoring data that show excursions above national
  criteria for eight metals in the Bay, and on the basis of  the complexity of
  the Bay, this analysis considers eight metals and divides  the Bay into two
  sites based on salinity.  Site-specific criteria are derived for those
  areas where salinity is generally«<. 10 ppt and those where salinity is
  generally 2.10 ppt.
      It is limited to evaluation and derivation of criteria for salt water
  organisms in estuarine and marine environments.  In addition, a detailed
  analysis of the effects of the eight metals on all life stages (and
  therefore, susceptibilities) of test organisms has not been done.  Toxicity
  data considered here are those from EPA Criteria Documents; in many
  instances, these data include the results of toxicity tests with life
  stages other than adults.
      All organisms that occurred in Chesapeake Bay were assigned to the low
  (<'10 ppt) salinity site,  the high C>10 ppt) salinity site, or both
  (Lippson 1973,  Wass et al.  1972).
      Next,  by using the recalculation procedure detailed by U.S. EPA
  (1982b), site-specific acute water quality criteria were calculated for
  each metal for (a) Chesapeake Bay,  disregarding the organisms'  preferred
  salinity;  (b) Chesapeake Bay, low salinity; and (c) Chesapeake Bay, high
  salinity.   The results,  along with comparable national criteria, are shown
  in Table 9.
                                        B--29

-------
COMPARISON OF NATIONAL AND SITE-SPECIFIC CRITERIA
    Based on the recalculation procedure,  there is little difference
between the national water  quality  criteria  for eight metals and saltwater
organisms and the site-specific criteria for the same metals and organisms
indigenous to Chesapeake Bay (Table 9) .
    The criteria for five of the eight metals at the low-salinity site are
numerically lower than both the national criteria and the criteria for the
high-salinity site.   However, the differences are slight, usually less than

TABLE 9.  NUMERICAL ACUTE WATER DUALITY CRITERIA FOR SALT-WATER ORGANISMS
          (MICROGRAMS PER LITER; PARTS PER BILLION)

Metal
Arsenic
Cadmium
Chromium
Copper
Lead
Mercury
Nickel

Zinc

National
Criterion
242.3
55.2
2,343
6.78
434.3
3.848
201. a
137, b
174. a
173t>
Chesapeake Bay Criterion
Overall Low Salinity High Salinity
240.5
96.0
2,681
4.74
391.8
4.323
192
201
170
174
138.7
39.4
2,656
11.95
234.5
2.188
391
391
78
68
240.5
96.0
2,612
4.74
391.8
4.224
192
201
170
174

aBased on toxicity data for "Family Mean Acute Values."
       on toxicity data for "Species Mean Acute  Values."
a factor of two.  With the exception of  Cd,  there are  almost no differ-
ences between the national criteria and  the  criteria calculated for  the
high-salinity site.
    For all of the eight metals except one,  three of the  four most
sensitive families used to calculate the national criteria are indigenous
to Chesapeake Bay.  Thus, the similarity between the site-specific and the
national criteria is the result of similar data being  used in the
recalculation procedure.  Where dissimilarities occur,  they are caused by
using a lower total number of families and by the exclusion of sensitive
species not present in Chesapeake Bay.
    Based on extant data and current national guidelines, it appears  that a
water quality criterion derived for a metal  in salt water can be applied to
most estuarine or marine waters.
    This supports the hypothesis that if a metal is biologically available
to an aquatic organism of a particular physiological make-up, the effect of
the toxicant will be the same whether the organism is  indigenous to  Puget
Sound, the Gulf of Mexico, or Chesapeake Bay.  That is, if a family  of
animals that has a wide distribution and contains species sensitive  to a
toxicant is represented at a site, then  the  effect of  the toxicant will
likely be the same at a variety of sites. If such a relationship exists
for other kinds of chemicals and other specific salt water bodies (and it
                                      B.-30

-------
 appears that it does,  based on work with organisms  from Narragansett Bay,
Rhode Island, and Escambia Bay, Florida),'  the  derivation of
site-specific water quality criteria by the recalculation procedure may be
less appropriate than deriving the national criteria using all available
data over the range of  species sensitivity.

CONCLUSION

    To develop more meaningful and accurate site-specific water quality
criteria, it will be necessary to  use the more  expensive, time-consuming
procedures allowed by EPA where toxicity tests  are conducted with resident
species and site water.   Such tests will assure that the test organisms are
the same as or closely  representative of those  animals of local interest,
and that the effects of  water quality on the action  and availability of the
toxicant are taken into  account.
'Personal communication:   "Relative  Sensitivity of Indigenous Species to
 Toxicants," J. Gentile,  U.S.  EPA, Narragansett, D. Hansen, U.S. EPA, Gulf
 Breeze, 1983.
                                     B-31

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                                SECTION 5
                   TRENDS IN DISSOLVED  OXYGEN
    Dissolved  oxygen  is  of primary interest to water quality managers,
because it directly affects the well-being of aquatic life.   Sources  of
oxygen include diffusion through the surface from the atmosphere,
photosynthesis, and reduction of oxidized chemical species.   Oxygen is lost
from the water through respiration and oxidation of reduced  chemical
species.
    The oxygen concentration of estuarine water is influenced by  the
physical, biological, and chemical characteristics of the estuary.  The
saturation concentration for DO decreases with increasing salinity  (about
-0.05 mg L~l ppt~l) and  increasing temperature (about -0.2 mg L~l
°C~1).  So temporal and  spatial changes of DO concentrations would
occur in an estuary devoid of organic material as the salinity and
temperature of the system passed through annual cycles.
    Organic material  introduced into the system can serve as a source of
additional oxygen or  as  a sink for oxygen.  Photosynthesizing phytoplankton
and submerged  aquatic plants produce oxygen during daylight.  All
heterotrophic  organisms  consume oxygen, as do the plants at  night,  and
thus, become a sink for  it.  Biological oxygen consumption occurs both in
the water column and  in  the sediments.  Some chemical reactions,  occurring
primarily in sediments,  also consume oxygen.  The oxygen concentrations
measured in estuaries are the net result of these interacting factors.
    A distinct annual cycle in DO concentrations exists in Chesapeake Bay.
Low temperatures and  high mixing rates in winter maintain near-saturation
concentrations at all depths in the estuary.  In spring, freshwater input
from the Susquehanna  River reduces the mixing rate by increasing  density
stratification in the Bay, and warmer temperatures reduce oxygen  solubility
in the water.   The warmer temperatures may also stimulate organism
respiration.   As a result of these factors, the oxygen concentration
declines and may reach zero when consumption processes operate faster than
production and reaeration processes.  Regions of Chesapeake  Bay deeper  than
about 10 m have experienced low oxygen concentrations in summer for as  far
in the past as data were taken.  Cooling temperatures and increased wind
mixing begin  reaerating  the deep water in fall to complete the annual cycle.
    Because the DO cycle is a major annual feature in Chesapeake  Bay  with
significant water quality implications, it has been examined with as  much
detail as the  1950 to 1980 data allow.  The data considered  here  were all
collected by  investigators from the Chesapeake Bay Institute with Winkler
titration methodology.   These data were selected because of  fairly  uniform
precision and  accuracy over time, especially at low DO concentrations.
Oxygen electrode measurements were excluded from this analysis because  of
uncertainty in electrode response at low concentrations and  under reducing
conditions.
    The first  step in the analysis was to estimate the volume of  water
subjected to  low DO concentrations for eleven years between 1950  and  1980.
For purposes  of this  analysis, "low" is defined as 0.5 ml L~l (0.7  mg
L"-"-) or less.   At typical summer salinity and temperatures,  0.5 ml  L~l
represents approximately 10 percent of saturation.  The data are  presented
in Figure 13.   The trend is toward a greater volume of water with low DO
concentrations.  Comparing the two ends of  the graph, the volume  in July
1980 was about 15 times  the volume in July  1950.
                                     B-32

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  10-
   8-
  6-

-------
    The total volume of water that could become anoxic should be defined by
the bottom topography and halocline depth.  For the main portion of
Chesapeake Bay, the potential region  for anoxia extends from the channel of
the Patapsco River south to about Reedville, Virginia, near 37°45'N
latitude.  In this region the halocline is usually between 8 m and 14 m
deep.  In July 1980 nearly all of the potential volume contained low DO
water, most of it anoxic.  In 1977 and 1978, the low oxygen water was
present above the edge of the topographic depression.
    The second step in the analysis was to determine spring flows for each
of the years from 1950 to 1980.  This is important in terms of both the
effect on stratification in the Bay and the delivery of material that
contributes to the oxygen demand of the system.  Monthly average stream
flow of the Susquehanna River at Harrisburg for March, April, and May were
summed for each year.  The 31-year mean was formed, and the deviation from
the mean calculated for the spring of each year.  Figure 14 illustrates
deviation from mean spring flow. The Harrisburg data were used rather than
those from Conowingo Dam because the  Conowingo data were available only
back to 1968.  The flow at Conowingo  is about 10,000 to 20,000 cfs higher
than at Harrisburg with no discernible lag time in peak flows.
    Third, the years for which oxygen data exist were identified and are
indicated by large open circles in Figure 14.  Because 1950 and 1980 had
comparable spring flows and oxygen data, they were selected for more
detailed comparison.  Spring flow for 1957 was close to that for 1950 and
1980 so its oxygen data were also considered as necessary.
    Fourth, the annual flow records for 1950, 1957, and 1980 were graphed
and appear in Figure 15 along with the 1980 flow at Conowingo.  It was
hypothesized that these three years would exhibit similar stratification
patterns, so differences in DO concentrations could be attributed to other
factors.
    Next, review of the oxygen data revealed that many of the same stations
were visited in May 1950 and 1980, July 1950 and 1980, and September 1957
and 1980.  These stations shown in Figure 16 were selected for comparison.
    Because salinity has the major influence on water density in the
estuary, it is used here as an indicator of stratification.  Though
temperature also affects density, its influence Is small with respect to
salinity.  Figure 17 shows comparisons between salinity and DO profiles for
the periods cited above.  At station  848E on May 22, 1950, the salinity
stratification was slightly greater than on May 21, 1980 (Figure 17a) , but
the DO change was less (Figure 17b) in 1950 than 1980.  The temperature at
19 m was 10.9 °C in 1950 as opposed to 13.5 °C in 1980.  On July 18,
1950 (Figure 17c), the salinity was generally less than on July 28, 1980,
and the surface to bottom difference  was 7.4 ppt in 1950 versus 5.8 ppt in
1980.  Temperatures were 21 °C at 18  m in 1950 and 24.2 °C at 18 m in
1980.  In both years DO decreased with depth (Figure 17d) with minima of
0.13 mg L"1 in 1950 at 34 m and 0 mg  IT1 at 16 m in 1980.  On September
11, 1957, the salinity was similar to September 29, 1980 (Figure 17e), with
surface to bottom salinity changes of 5.9 ppt and 6.4 ppt respectively.
Temperatures at 18 m were 23.9 °C and 24.5 °C, respectively.  Dissolved
oxygen was generally lower in 1980 than in 1957.  The minima were 0.59 mg
L-l at 23 m in 1957, and 0 mg IT1 at  16 m in 1980.
    Two stations farther downstream  (818P and 804C) were likewise
examined.  On May 24, 1950 at station 818 (Figure 18a), the salinity was
similar to that of May 21, 1980. Surface to bottom differences were 8.3
ppt and 7.2 ppt, respectively.  Temperatures at 18 m were 12.3 °C and
14.6 °C, respectively.  Dissolved oxygen was generally lower (Figure 18b)
                                     B-34

-------
+80 r
                                                                              1980
     Figure  14.   Susquehanna River spring flow, deviation from 31 year mean.
                                        B-35

-------
C/5
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u
100




 90




 80




 70




 60




 50




 40




 30




 20




 10




  0

 January   February   March
       _ 1980
                                 April      May      June


                                              Month
July
August  September  October
      Figure 15.  Monthly mean river flow of Harrisburg at  Conowingo
                                                 B-36

-------
                                                             ^-r-

                                                             Chester River
                                                             ,-*
     10    20
^^MMIBMM^^^^H^^^^J

Nautical Miles



o  10  20


Kilometres
                                                                   Choptank
                                                                       River
                                                                     Nanticoke
                                                                         River
                                  -   •  ^\Xx,  V>
                               James River/  n  \W    ^/r>

                                                   I/-
Atlantic
 Ocean
              Figure 16.   Stations used  to sample for oxygen.
                                 77°
                                    B-37

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              Figure  18.   Comparisons between salinity and DO profiles.


                                             B-39

-------
 in 1980.   Minima of 2.1  mg L~l  occurred at 30 m in 1950 and 0 mg L"1
at 10 m in 1980.   On July 17,  1950  (Figure 18c), the salinity gradient at
station 804C was  similar  to that on July 31, 1980.  Surface to bottom
differences were  6.21 ppt and  6.61  ppt, respectively with temperatures at
18 m of 23.0 °C and 25.2  °C, respectively.  Dissolved oxygen was less
at all depths in  1980 (Figure  18d), with minima of 0.57 mg L"1 at 27 m in
1950 and 0 mg L"1 at 24 m in 1980.  Salinities at station 818P (Figure
I8e) were  somewhat different in  September 1957, and 1980; greater salinity
stratification existed on September 13, 1957, with a surface to bottom
difference of 6.26 ppt as opposed to  4.51 ppt for September 30, 1980.
Temperatures at 18 m were 23.7 oc in  1957 and 24.3 °C in 1980.  The DO
gradient was steeper in 1957 than in  1980 (Figure 18f), but measurements
were not made to  the bottom.   Minimum values were 1.47 mg L~l at 21 m in
1957 and 0.31 mg  L~l at 32 m in  1980.
    The salinity  graphs in Figures  17  and 18 generally are comparable for
the stations and  years selected.  This tends to confirm the hypothesis that
the years  1950, 1957, and 1980 have similar stratification patterns as well
as similar Susquehanna River flows.   Dissolved oxygen concentrations, below
the halocline, were generally  lower at all stations in 1980 than in the
previous years.  Temperatures  in 1980 were also slightly warmer, which
would reduce saturation concentrations, but do not account for the lower
concentrations that were  well  under-saturated.
    To view the data from another perspective, the volume of water subject
to low DO  concentrations  can be  estimated for July and August in eleven
years between 1950 and 1980.   For purposes of this analysis, "low" is
defined as 0.5 ml L~l (0.7 mg  IT1)  or less.  At typical summer salinity
and temperatures, 0.5 ml  L~l represents approximately 10 percent of
saturation.  The  data are presented in Figure 18.  The trend is toward a
greater volume of water with low DO concentrations.  Comparison of the two
ends of the graph show that the  volume in July 1980 was about 15 times the
volume in  July 1950.
    The total volume of water  that  could become anoxic should be defined by
the bottom topography and halocline depth.  For the main portion of
Chesapeake Bay, the potential  region  for anoxia extends from the channel of
the Patapsco River south  to about Reedville, Virginia, near 37°45'N
latitude.   In this region, the halocline is usually between 8 m and 14 m
deep.  In  July 1980 nearly all of the potential volume contained low DO
water, most of it anoxic.  In  1977  and 1978, the low oxygen water was
present above the edge of the  topographic depression.
    Although low DO concentrations  are a normal feature of the annual
cycle, oxygen was detectable at  all depths in 1950 and 1957.  Conversely,
oxygen was frequently absent from deep water in May, July, and September
1980.  One could hypothesize that the anoxic conditions observed in I9b0
resulted from the oxygen  demand  caused by greater organic material
concentrations in 1980 than in 1950 or 1957.  Unfortunately, there are
insufficient data on total nutrients, chlorophyll a_, or other indicators of
organic content for 1950  and 1957 to  test the hypothesis directly.
However, some indirect tests are possible.
    The first indirect test of the  hypothesis is provided by graphing the
change in salinity across the  halocline against the change in DO across the
same depth interval for stations between 904N and 804C in May 1950 and 1980
(Figure 19).
    The six data points for 1950 gave a regression line —  DO = 0.52,  S
ppt + 0.22 with r = 0.93.  The data,  except for station 904N, for May 1980
fall well off the regression line.  For an incremental salinity increase of
                                     B-40

-------
 -E  -12
  £

  O
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      0,4
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                                     - ADO = 0.52AS°/o0 +0.22

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                                                       O
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                                              1,6
                                                    O May 1950

                                                    • May 1980
20
                            24
Figure 19.   Relation between salinity  increase and  dissolved oxygen decrease

            in two springs with  similar  flows.
                                  B-41

-------
 about 0.4 ppt m 1,  the DO decrease  in May  1980 is about five times the
decrease in May 1950 and is independent  of  salinity stratification.  This
suggests a greater demand for oxygen below  the halocline in May 1980,
perhaps because of increased organic content of the deep water.
    A similar graph was developed  for all available data taken at stations
848E and 845F during July, 1949,  1950, 1957, 1959, 1961, 1962, 1969, 1970,
1977, 1979, and 1980 (Figure 20).  These data gave a regression line —  DO
= 0.55, S ppt + 0.22 with r = 0.87,  which is nearly identical to the line
developed with the May 1950 data.  This  similarity indicates that,
regardless of spring flows, by July  the  relative change in DO across the
halocline is primarily a function  of the salinity control on
stratification.  However, the absolute concentration of DO below the
halocline is a function of both the  stratification effect and the DO
concentration above the halocline.   The  data in Figure 21a-f indicate that
oxygen concentrations approach but do not reach zero when near surface
concentrations are greater than about 5  ml  L~l.  In the two other years
illustrated (Figure 21g, h), near  surface values are less than 5 ml L~l,
and anoxia was observed below the  halocline.
    There could be several explanations  for these observations.  First, the
time of day of the measurements was  not  uniform.  The oxygen concentration
in the upper layer should increase during daylight because of phytoplankton
photosynthesis and decrease at night from respiratory processes.  Second,
the organic content of the upper layer could be greater in 1977 and 1980,
exerting a proportionally larger oxygen  demand. Third, meteorological
events could have aerated the upper  layer before measurements were taken in
the years prior to 1977.  Fourth,  temperature could have influenced
respiratory rates in different ways  prior to 1977.  Fifth, the dominant
plankters could have been different, with different biomass specific
metabolic activities, in earlier and later  years.
    These are interesting possibilities, but let us return to the
hypothesis that anoxic conditions  result from greater organic matter
availability in recent years.  The second indirect test of the hypothesis
is provided by nitrogen and phosphorus concentrations in the fresh water
entering the Bay from the Susquehanna River.  The annual average nitrate
(Figure 22) and total phosphorus (Figure 23) concentrations have approxi-
mately doubled since the mid-1960's. If these nutrients reached the region
subject to summer anoxia, they could result in increased organic matter
production and/or oxygen demand.   In the region of the upper Bay from
Susquehanna Flats to Pooles Island,  total phosphorus, total nitrogen, and
chlorophyll a_ annual average concentrations have also increased (Figure
24);  Secchi depths have decreased  (Figure 25) since the mid-1960's.
Similarly, total phosphorus concentrations  between Pooles Island and the
Bay Bridge have increased (Figure  26).   Total phosphorus concentrations
have increased in the segment from the Bay  Bridge to the Patuxent River
(Figure 27).  These nutrient trends  do not  directly confirm the hypothesis,
but are consistent with it.
    By inspection, it is possible  to relate the observed nutrient
concentration changes in the upper Bay to man's activities on the
watershed.  One index of activity  is population changes.  Figure 28 shows
the population in the Susquehanna  River  drainage basin south of Sunbury,
PA, the eastern shore, and the western shore of the upper Bay, including
metropolitan Baltimore.  The population  increased by 40 percent between
1950 and 1980.  However, the nutrient concentrations approximately doubled
between the mid-1960's and 1980.   This suggests that population increase
alone does not account for all of  the nutrient increase.
                                     B-.42

-------
   o
   Q
        -1.4
        -1.2
        -1.0
         -.8
        -.6
        -.4
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r-0.87
0.4
0.8
1.2
    1.6
                                                           2.0
Figure 20.  Oxygen decrease per unit salinity  increase  at  stations 848E

            and 845F in July 1949 to 1980.
                                    B-43

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         Figure 21.   Concentration  of DO  across the halocline.

                                                  B-44

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  1.1-
  1 .0-
  0.9-
  0.8-
  0.7-
N  0.6-
I
r
R
A
T  0-S-
E
  0.4-
  0-3-
  0-2
   0-1
  0.0
            AVERAGE  ANNUAL NITRATE
               FOR  SEGMENT  CB-1
    19SO     19S5     1960
                                     D
1965

YEAR
1970     197S     1980
                    TOP LEVEL ONLY
     Figure 22.  Average annual nitrate for segment CB-1.
                          B-45

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      AVERAGE ANNUAL  TOTAL PHOSPHORUS
                FOR SEGMENT CB-1
  0-12-
  o.n-
  o.io-
  0-09-
  0.08-

T
0
T
A  0-07-
L

P
H
0  0-06
s
P
H
0
R  0-OS
U
S
  0.04-
  0-03-
  0.02-
   0-01
   0.00-
                                                D
                                          D
     1950    19S5     1960    1965     1970    1975

                           YEAR

                     TOP LEVEL ONLY
1980
   Figure 23.  Average annual total phosphorus for segment CB-1.

                            B-46

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                        ANNUAL TREND

              CBP Segment Designation "CB-2"
  0)
    25-]
  a
  o

  o


  O
                                                           M.5
                                                           -10
                                                                 CD

                                                                 cT


                                                                 2

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                                                                 (Q
                                                           -0.6
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    0.0-J
 Figure 24.
   1950    1955    1960   1965    1970    1975    1980


                   Year Sample Taken

       Annual trends in chlorophyll a^, total nitrogen and total

       phosphorus in CB-2.    B-47

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  0.50-.
      i
  0.87-
      H


      I

  0-84-
  0-78-
      -\
  0.75-
  0-72-
             AVERAGE ANNUAL  SECCHI
                FOR  SEGMENT  CB-2
S 0.69-1
E     3
c     1
C     "*
H 0-66-
1     1
  0-63-
  Q.60-
   0-S7-
                                              Q

   0.54-
   0-48-^
                                                 D  Q\
   0.45-1
     1950
19S5
1960
1965


YEAR
                1970
1975
1980
                      TOP LEVEL ONLY
       Figure 25.  Average annual secchi for segment CB-2.
                              B-48

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      AVERAGE ANNUAL  TOTAL PHOSPHORUS
                FOR SEGMENT CB-3
  0.16H
  0.15-
  0.13-
  0.12-
  0-11-
T
0
T
A  0-10-
L
H 0-09-
0
S
p
H 0-08-
0
R
U
S 0-07
  0.06-
   O.OS-
   0-04
   0-03
   0-02
     1950
             1955
         D
                    I960
1 965

YEAR
1 ' i ' '
1970
                                           1975
1980
                     TOP LEVEL ONLY
   Hgure 26.  Average annual total phosphorus for segment CB-3.

                           B-49

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      AVERAGE  ANNUAL TOTAL PHOSPHORUS
                FOR  SEGMENT CB-4
  0-17-


  0. 16-


  0.15-


  0.14-


  0.13-


  0.12-


  0.11-
T
0
T  0.10-
A
L
  0.09-
P
H
0  0.08-
S
P
H  0.07-
0
R
U  0.06-
S

  0.05-


  0-04-


  0.03


  0.02


  0.01
   0-00
     1950
1955
• ' • I ' '
 1960
' " i • '
1965

VEAR
1970
1975
                                                   1980
                      TOP LEVEL ONLY
    Figure 27.  Average annual total phosphorus for segment CB-4.

                            B-50

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    4,000,000
    3,000,000
o
=>   2,000,000
a
o
Q.
    1,000,000  -
                                                    Lower Susquehanna
                                                    Eastern Shore
                                                    Western Shore
Upper

Chesapeake
                  1950       1960      1970      1980

                                Year
Figure 28.  Population in upper Chesapeake - lower Susquehanna region.
                                      B-51

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    A second consideration is the  land-use  patterns in the lower
Susquehanna-upper Chesapeake region.   Figure  29 shows that the amount of
land in crops and pasture decreased,  forest remained about the same, and
other land uses increased.  Uses in  this  category include urban areas,
mines, quarries, marshes, and additional  non-agricultural activities.  The
increase in other land uses since  1950 produces the same trend as the
nutrient concentration changes, but  it is not quite the magnitude of the
nutrient changes.
    Another aspect concerns increased production on existing agricultural
land.  At present the only data available at  CBP is fertilizer consumption
for the entire state of Pennsylvania.   If we  assume that agricultural
practices are similar in the region  under consideration, then the trend for
fertilizer use in the lower Susquehanna and upper Chesapeake should be
similar to the Pennsylvania data trend.   Figure 30 shows that total
nitrogen applied has doubled since 1955,  and  the application of nitrogen
solutions increased by a factor of 135 in the same period.  Total P£05
consumption showed a decrease from 84,861 to  71,481 tons during the same
period.
    The patterns of man's activity on the watershed are consistent with the
observed nutrient concentration changes in  the upper Chesapeake Bay.
Population has increased, and non-agricultural land use has similarly
increased.  Although the acreage used for agriculture and pasture has
decreased, production has been sustained  by increased fertilization and by
growing three crops of some plants in two years rather than one crop per
year.  Because the use of nitrogen fertilizer has risen, the increased
nitrogen concentrations in the upper Bay  may  be linked to agricultural
activity.  However, since phosphorus fertilizer use has decreased, the
phosphorus increases in the Bay may  be due  to man's activity within the
"other" land-use category.
    There are two other aspects to the low  DO situation in the main portion
of the Bay:  habitat loss and chemical alterations.  When the Bay bottom is
covered by low DO waters, aerobic  benthic organisms lose their habitat, and
demersal forms are excluded from the deeper portions of the water column.
As the oxygen concentration approaches zero,  phosphorus release from the
sediments increases.  The purpose  of the  following discussion is to
estimate the changes in the affected sediment surface area as the oxycline
depth changes.
    Cronin and Mallonee's (1981) data on  the  dimensions of the Bay were
utilized to compute the bottom area  of the  Bay for segments CB 1-5 as a
function of depth.  Note that segment CB-3  was subdivided into CB-3a
(up-Bay from a line connecting Fort  Howard  and Swan Pt) and CB-3b (down-Bay
from that line).  That line represents the  upstream penetration of low DO
waters most of the time.  The data are graphically summarized in Figures
31a and 31b.  In Table 10, the bottom area  of the Bay below a given depth
is computed.  If the DO concentrations fall below the tolerance of benthic
or demersal organisms, then that much habitat will be lost.  For example,
if the depth of the oxycline is 14 m (Table 10), then about 120 x 10fem2
of bottom area in CB-4 (14 percent)  will  be below the oxycline.  If the
oxycline moves upward to 12 m, then  a total of 223 x I06m2 (26 percent)
will be below the oxycline.  Thus, for a  vertical movement of 2 m (from 14
m to 12 m) in the oxycline, 103 x  106m2 (^ percent) of additional
bottom in CB-4 will be covered with  low DO  water.
    An estimate of the phosphorus  liberated from the bottom sediments
covered with anoxic waters can be  be made by  utilizing regeneration rates
(Taft 1982) and the area of the bottom that is affected.  The data are also
                                      B-52

-------
       7x106
       6x1f>
        5x106
     CO
     CD
       4x106
        3x106
        2x106
        1 xlO6
                                                       Pasture
Other
                                                       Forest
                                                       Cropland
                    1950       1960      1970      1980
                                  Year
Figure 29.  Land use in the upper Chesapeake  -  lower Susquehanna region.
                                B-53

-------
  100,000 r

   80,000
in
o

0)  60,000
O)
o
c

ID


o
   40,000
   20,000
                                                                         Total N
                                                                         N Solutions
                1955       1960      1965       1970       1975       1980

                                         Year
     Figure 30.  Fertilizer consumption in Pennsylvania.
                                        B-54

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                      10   12   14   16   18   20   22   24   26   28  30   32  34   36
                                       Depth (mj
38
Figure 31a.   Amount of bottom surface area at each depth from 0 to 40m.
                                         B-55

-------
o
CM
O
o
o
o
o
OO
o
o
o
CO
O
CM
                                     jo oejD
             Figure 31b.   Depth vs  surface area of bottom.



                                       B-56

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TABLE 10.   TOTAL PHOSPHORUS REGENERATION FOR CB-1,2,3,4,5 BY DEPTH ("> 8m)

Segment
CB-1
CB-2
CB-3a
CB-3b
CB-4
CB-5
CB-2
CB-3a
CB-3b
CB-4
CB-5
CB-2
CB-3a
CB-4
CB-5
CB-4
CB-5
CB-4
CB-5
CB-4
CB-5
CB-4
CB-5
CB-4
CB-5
CB-4
CB-5
CB-4
CB-5
CB-4
CB-5
CB-4
Depth interval
(m)
8





10




12



14

18

22

26

30

34

38

42

46
Area
m2 x 106
0
22.66
26.02
89.92
585.60
1031.60
5.60
12.10
52.89
503.70
805.50
1.05
4.02
223
364
120
220
75
89
36
50
22
35
21
28
5
16
0.5
2.0
0.5
0.6
0.5
Potential P-release

2.10
3.76
3.76
2.59
4.15
2.10
3.76
3.76
2.59
4.15
2.10
3.76
2.59
4.15

















Total load

47.59
97.84
338.10
1516.70
4281.14
11.76
45.50
198.87
1304.58
3342.83
2.21
15.12
577.57
1510.60
310.80
913.00
194.25
369.35
93.24
207.50
56.98
145.25
54.39
116.20
12.95
66.40
1.30
8.30
1.30
2.49
2.03
                                      B-57

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 presented in Table 10.   As  an example, with an oxycline in CB-4 at 14 m,
310 kg P day~l are liberated;  if  the oxycline migrates to 12 m, 577 kg P
day~l are liberated.   The bottom  can serve as an important source of P,
and increases of this magnitude may be important to the nutrient dynamics
of the estuary (Taft  1982).
    Other investigators  have provided insight into the dynamic nature of
the oxycline.  Flemer and Biggs (1971) (Figure 32) found that variations of
1 m in the oxycline could occur on a time scale of minutes, presumably
because of internal waves.   Carpenter and Cargo (1957) proposed that
occassionally observed "crab wars" were caused by NW wind events with
durations of hours to days.   Cargo and Biggs  (1969) measured DO twice a
week for 3 years at a deep water  station in CB-4 and found wide variations
in, both DO concentration and the  depth of the oxycline on a time scale of
days to weeks (Figure 33).   Biggs (1967), in a study of Bay sediments in
CB-4, found evidence  of  long-term changes (years to decades) of the levels
of the oxycline.  The results of  these studies indicate that both
short-term and long-term fluctuations occur in DO concentrations and the
depth of the oxycline.  Even against the background of these fluctuations,
the temporal and spatial extent of anoxia observed in the late seventies
and early eighties is unprecedented in the historical period.

SUMMARY

    This section has  focussed on  changes in DO concentration in Chesapeake
Bay.  The volume of low oxygen water in the Bay during summer increased
markedly between 1950 and 1980.   Short- and long-term fluctuations have
been observed.  The relationship  between the salinity gradient and the DO
gradient has been established empirically.  Deviations from this
relationship, such as those  observed in May 1980, indicate the significance
of factors other than stratification that influence oxygen concentrations.
This relationship also draws attention to the importance of surface layer
oxygen concentrations in determining the flux rate to, and concentration
in, the lower layer.   Observations of increased nutrient concentrations and
turbidity in the northern reaches of Chesapeake Bay are consistent with the
notion that the different DO concentrations in 1950, 1957, and 1980 are
directly related to increased oxygen demand rather than to differences in
Susquehanna River flow effects on stratification.  Two of man's activities
on the watershed could contribute to the observed nutrient increases:
increased use of nitrogen fertilizer and a shift in land use toward
non-agricultural activities.
                                       B-58

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         1800
                      1805
                                   1810
                                                 1815
                                                              1820
Figure 32.  Short-term variations in fluorescence and dissolved oxygen
            from 1800 to 1820 hr, 5 June 1968, upper Chesapeake Bay.
            Legend:  long-dashed line = temp., short-dashed line =
            fluorescence, and solid line = dissolved oxygen (from
            Flemer and Biggs 1971).
                                     Br59

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                         O9 concentration (ml
co


0
   15
a
CD
a

   20
   30
                          Cargo and Biggs (1969)
                Figure  33.  Cove Point 02 (ml L !)  in 1961

-------
                              SECTION 6

         METHODOLOGY FOR DEVELOPING DEGREE
                   OF METAL CONTAMINATION

INTRODUCTION

    To assess trends for  the occurrence of metals in Chesapeake Bay,  one
can use sediment cores documenting changes over time.  A sediment  core,
analyzed for trace metals and with an established geochronology, can
estimate trace metal inputs,  assumming no diagenetic migration of  metals
through the length of the core.  Such an analysis must be conducted
carefully,  for the burrowing activities of benthic organisms  in aerobic
environments can disturb  the sedimentary record, create an "artificial"
210pb distribution, and influence trace metal patterns.
    The CBP conducted a core study of the Bay (Helz 1980) to  ascertain
historical trends in the  presence of metals.  These cores have been examined
for 210pb metal analyses  and degree of bioturbation (Figure 34).   If  one
assumes that 210pb ^s introduced uniformly to the Bay by atmospheric
processes,  then the depth-integrated 210pb concentrations for each core
will depend on the rate and  depth of biological mixing.  Rapid mixing to
great depths will yield a high total integrated 210pb concentration,
while slow mixing to only shallow sediment depths will yield  a low total
value.  The depth-integrated  210pj, concentrations from the cores of Helz
(1980) were plotted as a  function of sedimentation rate.   The depth-
integrated  values exhibit a  rough linear trend.   In the absence  of other
radiogenic  analyses to verify the 210pb sedimentation rates,  the
conservative interpretation  is to tentatively discard the 210pb  profiles
that exhibit high total integrated values (cores 6, 24, 55, 62,  63, 64, and
86).  Data on l-^'Cs are available from core 24 and show a broad  peak  that
is inconclusive in verifying  the 210pb chronology of that core.
    Cores 52, 99, and 102 are eliminated from consideration because the
      profiles near the surface of the cores show no decrease,  indicating
intense mixing of sediment to a depth equivalent to 50 years  of
deposition.  Although cores  14, 83, and 85 exhibit exponential
profiles, they are eliminated from further consideration because X-ray
analysis of box cores from these sites shows deep bioturbation,  and there
are frequent metal "spikes"  with depth in the cores.  Cores 4,  18, arid 60
exhibit exponential 2l°Pb profiles; have low 210Pb depth-integrated
concentrations;  exhibit lower, moderate bioturbation; show no metal spikes;
and have a  relatively uniform lithology.  In addition,  core 4 has  l-^'Cs
data that verify the 210pb sedimentation rate.  Some or all of the cores,
which have  been eliminated from consideration here, may in fact, possess
excellent 210 pb chronologies.  In the absence of confirming  radiogenic
data to verify the 210 pjj dates on the deleted cores, only cores 4, 18,
and 60 will be considered further.
    Several techniques have  been devised to estimate the  degree  of
contamination of sediments by metals. Turekian and Wedepohl (1961)
developed data on the average concentration of trace metals in various
sedimentary rocks.   Often contamination in modern sediments is identified
by the ratio of metal in  the  sample to metal in an average shale (or
sandstone); this ratio is termed the Wedephol ratio.  The problem  with this
technique is that there is no compelling evidence that  natural James  River
sediments,  for example, should have the same concentration of a particular
metal as the average of all of the earth's  shales.   Other investigators
                                      B-61

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Figure 34.   Location of  210Pb and metai profile cores (Helz 1980)
                               B-62

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 have chosen to normalize trace metal concentrations to some metal  present
in sediments in such high concentrations that it is unlikely that
anthropogenic sources could influence it to a significant  degree.
    The metal frequently chosen to ratio against is iron.   Unfortunately,
iron is relatively mobile after burial,  and significant quantities  can
migrate through sediment pore waters.  Still other investigators suggest
normalizing the metal content of sediment samples to the grain  size of  the
sediment.  There is usually a strong inverse correlation between sediment
size and metal content.  Grain size, though, is only a rough indicator  of
particle surface area, sediment organic content, and sediment mineralogy,
any or all of which are the probable cause of high metal concentration  in
fine sediments.
    Chesapeake Bay Program scientists have applied a different  approach to
the estimation of the degree of metal contamination in Chesapeake Bay
sediments.  By using pre-colonial Chesapeake sediments, we have avoided the
use of potentially mobile metals like iron; by measuring silicon and
aluminum, we have simultaneously accounted for sediment grain size,  and
mineralogy [sands are mostly quartz, silts, and clays (as  size  terms)]  may
be either quartz or clay minerals.

SCENARIO

    The sediments deposited in the Chesapeake are a mixture of  materials
derived from the rivers, shore erosion,  the organisms growing in the Bay,
the ocean, and the atmosphere.  The proportion of each component depends
principally on proximity to ocean and river sources, with  erosional,
biogenic, and atmospheric inputs contributing the strongest signals in
depositional areas where they are not overwhelmed by river or ocean
inputs.  Over time, the relative importance of different sources has
changed.
    Imagine the 66,045 knr Susquehanna River basin just prior to its
exploration by John Smith.  The watershed was probably 95  percent covered
by mature forests with a few clear areas that had recently been burned
over.  Biggs (1981) has estimated that the seasonal distribution of
freshwater discharge from the Susquehanna to the Chesapeake was different
then; springtime peak discharges may have been 30 percent  lower than at
present while summer and autumn low flows may have been 10 percent  higher.
This is because direct runoff as overland flow is much lower for forested
than for agricultural areas;  conversely, infiltration,  which contributes
water to the groundwater system, is higher under forest cover.
    In the mid-Atlantic region of the United States,  the principal  rock
weathering process is mineral hydrolysis.  Total hydrolysis, which  occurs
under intense, tropical, chemical,  weathering, produces a  forest soil
consisting of iron and aluminum hydroxides, and a solution rich in  silicon
which is carried away in the rivers.  In temperate regions,  where both
rainfall and mean temperature are lower, the intensity of  the hydrolysis
process is diminished.  Partial hydrolysis produces forest soil with a
principal residual clay mineral of kaolinite [Si205Al2(OH)£].   The
soil is rich in Fe, with a Si/Al ratio of approximately one,  and the
material carried by the rivers rich in Si (Table 11).
    As the forests of the Susquehanna watershed (and all of the other
watersheds of the Chesapeake) were cleared, direct runoff  increased.
Combined with increased erosion, this runoff caused higher sedimentation
rates in the Chesapeake by carrying more materials to the  Bay.  Lystrom et
al. (1978) have estimated background (natural) concentrations of materials
                                      B-63

-------
 in the Susquehanna disharge before  agricultural activity.  Particulate
sediment yield ranges between 7.4  and  104  tons km~2 with present land
use; prior to extensive agricultural activity, the range was from 5.7 to 29
tons km~2.  Table 12 illustrates the observed and simulated pristine
ranges for a number of water quality parameters in the  Susquehanna Basin.
The increased suspended sediment yields  from upland areas were comprised
principally of Al-rich soils that  had  accumulated under, and had been
protected by, the forest cover.  Thus, recently-deposited sediments of the
main Bay, near the Susquehanna, should be  more Al-rich  than those
down-bay.  Core sediments,  at a given  location, should  be Al-rich near the
surface and increasingly Si-rich with  depth  (age) in  those areas of the Bay
with a more or less constant, or small total contribution of Al and Si to
the sediments from shore erosion,  atmospheric, and biogenic sources.

SILICON-ALUMINUM RATIO

    In geochemistry, there  are relatively  few cases of  normal elemental
distribution; instead, the  distribution  in rocks, sediments, soils, and
waters most often approximates a lognormal function (Ahrens 1957).  Helz et
al. (1980) found that all elements analyzed  in their  Bay samples exhibited
an approximate lognormal frequency distribution.
    A plot of Helz et al. (1980),  Al and Si  data for  bulk sediments of the
Bay as a function of Si/Al  ratios, is  presented in Figure 35.  These bulk
samples range from silty clays to  sands.  Si/Al ratios  and mean weights for
average shale and average sandstone  (Turekian and Wedepohl 1961) are also
plotted.  There is a continuous size and composition  gradient between
shales and ^sandstones and,  given a lognormal distribution of elemental
abundance, one would expect a geochemical  gradient from shales to
sandstones; that is, we should be  able to  connect the shale and sandstone
points with a straight line on the figure.  For Al (Figure 35a), the
Chesapeake/bulk sediment data closely  approximate the continuum between
average shale and average sandstone, but for Si (Figure 35b), the relation
is poor.  Either Si is not  lognormally distributed in the Turekian and
Wedepohl shale data, with a significant  loss of Si occurring during the
interval between sedimentation and lithifiction, or the Susquehanna basin
is strongly enriched in Si.  Regardless  of the reason for the high Si
content of Chesapeake sediments, it  seems  apparent from the illustrations
that a continuous gradient  of Al content is  principally responsible for
changes in the Si/Al ratio.  Modern  Susquehanna bed sediment (Kelz core
SUS) and the average of over 3000  modern streams mud  samples (Keith et al.
1967) are also illustrated on Figure 35.  Both fall within the continuum of
Bay sediment values.
    Figure 36 illustrates the Si/Al  ratios for Helz cores 4, 18, and 60
plotted as a function of ^lOp^-derived age before the present.  Si/Al
ratios generally decrease toward the top (present) in each core, as is
predicted by the scenario of increasing  land clearance, surface erosion,
and delivery of Al-rich, fine materials  to the Bay from the Susquehanna
drainage basin.  Important natural and man-made events, and trends in the
Susquehanna drainage basin are presented on  the time  axis (data from Brush
and Davis" 1981).

METAL CONTENT AND SI/AL RATIOS

    The use of Si/Al weight ratios as  an independent  variable against which
to measure the concentration gradient  of trace metals relies on the
                                       B-64

-------



*
» *
* *
*
*
" It • * *
* . *
* » * *
» » * * *
» * » * »
s J* t » » „
S *«.. $
» * * *
* * * * * 1 .
•'iff
• •" "Ml H IK
•'•lip
1 « il f i t
* * » * » *


SKS3SSSS3!2°g
_ — »_^.P-^-_np.r.._

_JOO OU. t/> — _I»-«OOZ

- o
CJ
- 00
- w>


- *
o
-oi i
r
^
^
- o c
- CO 0
0
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CO

-------
1975
1950

1900
18GO
1700
1600
 1500
 1400
          n
          <  2
     D  -
     -  D

       J R
           R
          J -J
                                                   KM    SUSQUEHANNA^
                                CORE 18
                     60 —A
       D = Decline
       P = Peak
       R = Rise

      j	i    i	i_
                                        10

                                       Si/A I
                                                        15
Figure 36.   Silicon -aluminum weight  ratio distribution in
            cores from Chesapeake Bay (from Helz 1981).
                                                                   dated
                                       B-66

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TABLE lla.  ANALYSIS OF A QUARTZ-FELDSPAR BIOTITE GNEISS AND ITS  WEATHERING
            PRODUCTS (%).  COLUMN I REPRESENTS FRESH ROCK,  AND II,  III  AND
            IV REPRESENT GRADUALLY INCREASING DEGREES OF WEATHERING OF  THE
            MOTHER ROCK (FROM GOLDICH 1938)
Oxide
   Totals
100.07
                 II
99.71
                III
99.70
                                                                   IV
Si02
A1203
Fe203
FeO
MgO
CaO
Na20
K20
H20
Others
71.54
14.62
0.69
1.64
0.77
2.08
3.84
3.92
0.32
0.65
68.09
17.31
3.86
0.36
0.46
0.06
0.12
3.48
5.61
0.56
70.30
18.34
1.55
0.22
0.21
0.10
0.09
2.47
0.54
0.54
55.07
26.14
3.72
2.53
0.33
0.16
0.05
0.14
0.58
0.58
100.11
TABLE lib.  GENERAL CALCULATIONS OF GAINS AND LOSSES OF CHEMICAL  ELEMENTS
            DURING WEATHERING (%) FROM DATA GIVEN IN TABLE lla (FROM
            KRAUSKOPF 1967)
Oxide
           III
Si02
A1203
Fe203
FeO
MgO
CaO
Na20
K20
H20
Others
71.48
14.61
0.69
1.64
0.77
2.08
3.84
3.92
0.32
0.70
70.51
18.40
1.55
0.22
0.21
0.10
0.09
2.48
5.90
0.54
55.99
14.61
1.23
0.17
0.17
0.08
0.07
1.97
4.68
0.43
-15.49
0
+0.54
-1.47
-0.60
-2.00
-3.77
-1.95
+4.36
-0.27
-22
0
+78
-90
-78
-96
-98
-50
+1360
-39
Source:  Introduction to Geochemistry,  with permission of McGraw-Hill  Book
         Company.  Copyright 1967 by McGraw-Hill,  Inc.
                                      B-67

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TABLE lie.  Si/Al RATIOS CALCULATED FROM TABLE lla


Wt. % Si
Wt. % Al
Si/Al
I
33.4
4.7
7.1
II
31.3
5.6
5.6
III
32.2
5.9
5.5
IV
25.3
8.4
3.0

TABLE 12.  OBSERVED RANGES OF WATER QUALITY YIELDS,  CONCENTRATIONS,  AND
           BACKGROUND RANGES SIMULATED BY REGRESSION MODELS.   BACKGROUND
           RANGES ARE CALCULATED BY HOLDING CULTURALLY AFFECTED VARIABLES
           CONSTANT AT ZERO (MODIFIED FROM LYSTROM ET AL.  1978)

Water quality
characteristic
Sediment yield
(m tons Km" 2)
Sediment concentration
(mg L-l)
Dissolved solids yield
(m tons km"2)
Dissolved solids cone.
(mg L-l)
Av. Nitrogen cone.
(mg L-l)
N03 concentration
(mg L~l)
N03 yield
(m tons km~2)
Av. Phosphorus cone.
(mg L-l)
Phosphorus yield
(m tons km" 2)
P04 concentration
(mg L~l)
Observed Range Simulated Background Range
min. max. min. max.
7.4

13.3

11.7

29

.40

.15

.09

.02

.01

.01

104 5.7

295 13.1

108 5.9

282 17.4

1.59 .15

7.45 .13

3.1 .04

1.24 .01

.12 .01

.20 .00

29

102

12.6

29.6

.46

.69

.15

.14

.01

.01

                                       B-68

-------
 following assumptions:
o   There is a continuous gradient in Chesapeake sediments  from fine
    (Al-rich) to coarse (Si-rich) material.  Evidence for this  statement  is
    the plot of Si and Al in Figure 35 (a and b) .
o   Trace metals can be represented by a lognormal distribution.   Evidence
    for this statement for the earth's crust is provided by Ahrens (1954),
    for Chesapeake trace metals by Helz (1981), and for Susquehanna stream
    muds by Keith et al. 1967.
o   There is a continuous gradient of both trace metal and  Si/Al  ratios in
    Wedepohl shales and sandstones; that is, one can connect the  metal —
    Si/Al shale and the metal — Si/Al sandstone compositions with a
    straight line on a log plot.
o   There is no significant migration of metal during early diagenesis.
    For some metals, notably Mn and Co, there is strong evidence  that
    significant migration of metal from buried sediment towards surface
    sediments (causing surface enrichment) does occur.  For a few (notably
    Cu) , the data are conflicting, and for most (Zn,  Cr,  V,  Ti , Zr, Ni ,
    Pb) , the assumption is arguably valid.
Given the stated conditions, a model which separates estuarine  sediments
into three classes based on their metal content and their Si/Al ratios can
be developed.  These classes include:  impoverished (compared to  Wedepohl
ratios); enriched (compared to Wedepohl ratios); and enriched
(anthropogenic) (compared to pre-pollution sediments) .  To  evaluate a
sample in terms of the three metal components, the following information  is
required:  (1) Wedepohl shale and sandstone values  for Si, Al, and each
metal of interest; and (2) a statistically significant regression line for
log metal as a function of Si/Al for pre-pollution sediments.   Given that
information, one can construct a diagram for each  metal [Figure 37
illustrates the process with Cr (37a) and Zn (37b)j  in which all  samples
plot as impoverished, enriched naturally, or enriched anthropogenically .
    The equations for Wedepohl and Chesapeake lines are presented in Table
13a.  For each sample and each metal with an observed Si/Al  ratio,  one can
compute:

                     o   ~  Cp _   = Cf (contamination factor)
where:   CQ = surface sediment concentration and,  Cp  =  predicted
                concentration.

The predicted concentration of a metal  is  derived  from  the statistical
relation between the Si/Al ratio and  the log metal content of old,
pre-pollution sediments from the estuary.   Surface sediments whose observed
metal content is greater than the predicted value  are considered to be
contaminated.  One can consider the Cf  value to  be a  "percentage
exceedance."  When the observed metal concentration is  much less than the
predicted value, the Cf -^ 0;  when observed and predicted are the same, the
Cj = 0;  and when the observed exceeds the  predicted value, then Cf ]>-0.
The predicted Wedepohl metal concentration,  predicted Chesapeake
concentration, and the observed concentration for  cores 4 and 60 are
illustrated in Figure 38 for Cr and Zn.  Zinc contamination began in the last
quarter of the 19th century,  coincident with peak  land  clearance due to
timbering and agriculture as  well as  coal  mining in the Susquehanna drainage
basin.  Cr is illustrated as  a metal  that  shows  no historic enrichment in the
cores.  Brush (1981) has found a similar excursion of Zn concentration,
beginning in the early 18th century (pollen dated)  on the Susquehanna flats.
                                      B-69

-------
   1000
  E
  Q.
  n.
  O 100
     10
              CHESAPEAKE SAMPLES
              ANTHROPOGENICALLY
                    ENRICHED
                  CHESAPEAKE SAMPLES
                      IMPOVERISHED
                               8     10
                                  Si/AI
                             12    14    16    18
 Figure 37a.
Chromium vs. Si/AI in Chesapeake Bay sediments;  303 hidden
observations (Helz 1981).
   1000
    100
  c
  N
      10
                        CHESAPEAKE SAMPLES
                        ANTHROPOGENICALLY
                             ENRICHED
                                         CHESAPEAKE SAMPLES
                                         NATURALLY ENRICHED
                                           OVER WEDEPOHL
         CHESAPEAKE SAMPLES
            IMPOVERISHED
             -1	L_
8    10
  Si/AI
                             12    14    16
                                                           18
Figure 37b.  Zinc vs  Si/AI in Chesapeake Bay;  232  hidden observations
            (Helz 1981).
                                 B-70

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             CORE 4
          Zn (ppm)
            100
     1880 -
     r»8o-
     CORE 60
     Zn (ppm)
    100
                                                                         300
                                                        1880-
                                                        mo-
                                                        IWD-
                                                                    'OBSERVED
 1880-
          CORE4
          Cr (ppm)
            I ^OBSERVED
            I
            I
1880-
        CORE 60
        Cr (ppm)
            100
        i	i
             OBSERVED
Figure  38.   Zinc  (Zn)  and chromium (Cr)  concentrations  (ppm) in  Chesapeake
             Bay sediments.
                                         B-71

-------
CONTAMINATION INDEX
    The contamination index (Cj) for surface sediments by  metals  can  be
developed by combining data on the anthropogenic concentration of individual
contaminants and summing these contaminant factors (Cf).   The  Cf  value for
each metal is computed and all of the Cf values for a given sediment  sample
are summed to produce the index of contamination,  Cj;
                    Cf
CO-CP

Cr
                 n = 1            n = 1   ^p

The contamination index, Cj, for a large number of surface samples  from the
Patapsco and Elizabeth Rivers is presented in Table 14.   This method  of
characterizing estuarine sediments gives equal weight to all metals,
regardless of absolute abundance, and has no inherent ecological significance.
When this index is combined with bio-toxicity data (Chapter 3),  its
biological importance can be assessed.  Where individual metal CfTs exceed
1.0, they contain specific metal concentrations that exceed natural
Chesapeake sediments by 100 percent.  Most of the Patapsco samples  have
Cj's which exceed 10 (1000 percent).  These Cf's are based on the
correlation of Si/Al and metal content.  They should be  interpreted as
departures from the natural, deep metal concentration.  The correlation of
metals with Si/Al ratios should not be interpreted as causation, merely
covariance.  Controlling parameters for metal concentrations may well be
redox, pH, organic, or sulfur species present.
    Trace metal, Si, and Al data are frequently not available for the
majority of sediment analyses.  One cannot then apply the equations developed
in Table 13a to the majority of sediments.  As an alternate, one can  use the
predicted Wedepohl metal concentration at some representative Si/Al ratio for
estuarine sediments to estimate the contamination factor for each metal. The
Si/Al rdtio for Wedepohl shale (0.91) is considerably lower than the  lowest
Si/Al values found in surface sediments of the Bay and its tributaries
(geometric mean 4.4, max. 21, min. 1.8).  We have selected a Si/Al  ratio of
3.0 (2.55.D - below the mean) upon which to predict surface sediment  trace
metal concentrations and to compute contamination factors for each  metal
where no Si/Al data are available.  This selection minimizes the
contamination factor for sediment samples with Si/Al greater than 3,  and
maximizes the contamination factor for Si/Al less than 3.  Therefore, in
areas such as the Susquehanna Flats, which is very sandy, the contamination
factor is minimized, while in silty areas like the Northeast River  channel,
this factor is maximized.
    A computer search was conducted for all available surface sediment metals
data in the Chesapeake and its tributaries.  Predicted Chesapeake
concentrations (for Si/Al = 3) were used where significant and predicted
Wedepohl concentrations were used (for Si/Al = 3) when no Chesapeake  values
could be developed to calculate contamination factors for each metal.  The
sum of these individual factors; that is, the degree of  contamination, is
plotted in Figure 39.  This illustration represents our  best estimate, using
all available data, and of the potential metal contamination, from
anthropogenic sources, of the surface sediments of the Bay and its
tributaries.  No data exist near to shore, and large local increases  should
be expected close to outfalls.  These variations have not been indicated on
Figure 39.
                                       B-72

-------
TABLE 13.  TRACE METAL VERSUS Si/Al RELATIONS.  WEDEPOHL LINE FOUND BY
           DETERMINING EQUATION THAT FITS SHALE AND SANDSTONE AVERAGES.
           CHESAPEAKE LINE FOUND BY BEST FIT OF PRE-1700 HELZ CORE DATA
Metal
         Wedepohl line
      (shale - sandstone)
     Chesapeake Line
  (pre-industrial samples)
V
Cr
Ni
Zn
Cu
Co
Pb
Hg
As
Se
Cd
                a).  Wedepohl and Chesapeake Lines for Metals
log V = -.059 Si/Al + 2.16
log Cr = -.03 Si/Al + 1.98
log Ni = -.111 Si/Al + 1.93
log Zn = -.057 Si/Al + 2.03
log Cu = -.265 Si/Al + 1.89
log Co = -.129 Si/Al + 1.40
log Pb = -.030 Si/Al + 1.29
log Hg = -.132 Si/Al - .28
log As = -.284 Si/Al + 1.37
log Se = -.074 Si/Al - .15
log Cd = -.171 Si/Al - .36
log V = -.028 Si/Al + 2.15
log Cr = -.033 Si/Al + 2.04
log Ni = -.012 Si/Al + 1.60
log Zn = -.029 Si/Al + 2
Not significant
Not significant
log Pb = -.032 Si/Al + 1
No data
No data
No data
No data
13
33
              b).  Predicted Metal Concentration for Si/Al = 3,
           found by solving equations in above Table for Si/Al = 3
Metal     From Wedepohl Line

V         96 ppm
Cr        77
Ni        39
Zn        72
Cu        12
Co        10
Pb        16
Hg         0.2
As         3
Se         0.4
Cd         0.1
                                  From Chesapeake Line

                                  116 ppm
                                  87
                                  36
                                 110
                                  17
                                      B-73

-------
TABLE 14.   CONTAMINATION FACTORS  (Cf)  AND DEGREE  OF  CONTAMINATION  (Cj) FOR
           SURFACE SEDIMENTS FROM THE  PATAPSCO  (LETTER DESIGNATIONS) AND THE
           ELIZABETH RIVERS (NUMBER DESIGNATION)1


STAl
A
B
E
F
G
H
I
J
K
L
M
N
0
BH41
BH43
BH44
BH45
BH49
BH50
BH51
BH52
BH53
BH54
BH55
BH56
BH57
BH58
BH59
BH60
BH61
BH62
136
137
138
139
140
142
143
145
146
Cf 2
V
.471
.173
.647
1.76
.501
1.09
2.41
2.71
.931
1.05
.62
.199
.206
.160
.339
.559
.346
.947
.947
.284
.794
.709
.638
.565
.327
1.39
1.24
1.09
.68
.504
.504
-.128
.078
-.221
.225
.069
.101
.107
-.069
-.004
Cf
Cr
.323
.855
1.24
1.60
3.40
2.74
5.25
5.48
4.51
4.33
7.01
22.30
2.75
.579
1.05
1.47
1.34
2.21
2.45
.975
2.75
2.29
3.14
5.16
5.35
4.28
3.60
3.19
1.17
3.12
3.40
0.030
-.102
-.055
.063
.146
1.42
.396
-.205
.118
Cf
Ni
1.69
.630
.907
.879
1.20
.879
1.23
1.27
.916
1.36
1.33
1.06
1.72
.486
.750
.611
.542
.667
.972
.334
.919
.972
.969
1.03
.500
1.11
1.14
1.08
.441
1.08
1.17
-.261
-.130
-.314
-.029
-.105
.375
.021
.082
.098
Cf
Zn
2.84
2.28
6.22
3.82
3.18
4.63
6.81
7.64
5.10
6.74
4.75
6.69
4.15
2.37
3.46
4.10
3.90
3.86
4.42
1.81
4.49
4.13
4.64
4.78
2.83
6.68
5.27
3.31
1.67
3.02
2.92
-.375
.056
.104
3.77
11.38
5.07
1.46
1.89
8.39
Cf
Co3
4.67
5.17
6.14
4.00
3.89
4.89
3.60
3.89
2.43
6.83
6.83
2.00
1.00
7.00
6.71
6.67
6.00
2.71
3.71
1.33
2.12
2.75
1.75
2.00
1.14
2.00
1.50
1.50
.67
1.14
.86
2.60
1.00
-1.00
0.00
-1.00
9.00
2.00
-1.00
0.00
Cd4

10
9
15
12
12
14
19
21
14
20
21
32
10
11
12
13
12
10
13
5
11
11
11
14
10
16
13
10
5
9
9
2
1
-1
4
10
16
4
1
1

iData
2
Cf -




from Helz 1982.
c - c
o p

C
P









3C

4




o values
line
n

CT = •
I ^— -
n
B-74
computed
, log Co
= 6
.---•'*
*• c,.
f
= 1

from Wedepohl
= 0.129 Si/Ac + 1.30.







-------
    (C,)
          <4
          4-14
           No Data
Figure 39.  Degrees of metal  contamination in the Bay based on the
            contamination  index  (C ) .
                                       B-75

-------
                            SECTION 7

      LEVELS OF HEAVY METALS IN OYSTER TISSUE
              FROM MARYLAND AND VIRGINIA

    Tables 15 through 21  show levels of Cr,  Cd,  Cu, Zn, and other metals
and some pesticides found in  the tissue of oysters from Chesapeake  Bay
waters.  Data were collected  by the Virginia State Water Control Board
(VSWCB) and the Maryland  Department of Human Health and Hygiene and were
used in the CBP's assessment  of metals and pesticides in shellfish  and
finfish (Chapter 1).

EXPLANATION OF METAL TABLES

    The following tables  summarize metals data for Chesapeake Bay
segments.  The data are presented for Bay main stem, western shore, and
eastern shore tributaries.  For the Bay main stem, information is available
for dissolved and particulate metals in the  water column (Tables 22, 23,
and 24).  Mean, minimum,  and  maximum concentrations of eight metals in
sediments are shown in Table  25.  Bottom sediment contamination factors
(Cf and Cj) are presented in  Tables 26 and 27.
    Similar data are presented for other segments, except that no water
column data are available for any areas except four major western shore
tributaries (Table 28).  These tables include bed sediment concentrations
(Tables 29 and 32), contamination factors  (Tables 30 and 33), and Cj
(Tables 31 and 34) for western and eastern shore tributaries, respectively.

-------
TABLE 15.  LEVELS OF CHROMIUM (mg/kg)  IN OYSTER TISSUE IN VIRGINIA
           (SOURCE:   GILINSKY AND ROLAND 1983)
                              Mean     Minimum Value      Maximum Value     N
James River Area

Tidal Fresh Segment           -             -                              0
River Estuarine Transition    -             -                              0
Lower Estuary
      LE-5 upper             4.40          3.00             5.80           2
      LE-5 lower             4.00          4.00             4.00           2
Elizabeth River              3.5           3.50             3.50           2
Lynnhaven Bay                2.55          2.50             2.60           2
Back River                    -             -                              0
Mouth of Chesapeake Bay       -             -                 -             0
Total of James River         3.6           2.50             5.80           8

York River Area

River Estuarine Transition   3.75          2.50             5.00           2
Lower Estuary                3.40          3.0              3.80           2
Poquoson River                -             -                              0
Mobjack Bay                   -             -                 -             0
Total For York River         3.6           2.50             5.00           4

Rappahannock River

Tidal Fresh Segment           -                              -             0
River Estuarine Transition
      RET-3 upper             -                              -             0
      RET-3 lower            4.45          3.00             5.90           2
Lower Estuary
      LE-3 upper              -                              -             0
      LE-3 lower              -                              -             10
Total for Rappahannock
      River                  4.45          3.00             5.90           12
                                      B-77

-------
TABLE 16.   LEVELS OF CADMIUM (mg/kg) IN OYSTER TISSUE IN VIRGINIA
           (SOURCE:  GILINSKY AND ROLAND 1983)


James River Area
River Estuarine Transition
Lower Estuary
LE-5 upper
LE-5 lower
Elizabeth River
Lynnhaven Bay
Back River
Mouth of Chesapeake Bay
Total of James River
York River Area
River Estuarine Transition
Lower Estuary
Poquoson River
Mobjack Bay
Total For York River
Rappahannock River
River Estuarine Transition
RET-3 upper
RET-3 lower
Lower Estuary
LE-3 upper
, LE-3 lower
Total for Rappahannock
River
Mean

0.17

1.76
1.22
1.58
0.35
0.62
2.23
1.13

1.39
1.92
0.57
0.23
1.02


0.71
0.77

0.45
0.59

0.63
Minimum Value

0.10

0.10
0.20
0.10
0.18
0.11
1.20
0.10

0.52
0.15
0.21
0.01
0.01


0.05
0.32

0.11
0.11

0.05
Maximum Value

0.20

4.80
4.10
3.00
0.60
1.75
3.60
4.80

3.00
120.0
1.00
0.82
120.0


1.30
1.51

0.73
1.14

1.30
N

9

137
221
56
19
32
14
488

64
160
33
74
331


20
72

40
98

230

                                      B-78

-------
TABLE 17.  LEVELS OF COPPER (mg/kg)  IN OYSTER TISSUE IN VIRGINIA
           (SOURCE:  GILINSKY AND  ROLAND  1983)


James River Area
River Estuarine Transition
Lower Estuary
LE-5 upper
LE-5 lower
Elizabeth River
Lynnhaven Bay
Back River
Mouth of Chesapeake Bay
Total of James River
York River Area
River Estuarine Transition
Lower Estuary
Poquoson River
Mobjack Bay
Total For York River
Rappahannock River
River Estuarine Transition
RET-3 upper
RET- 3 lower
Lower Estuary
LE-3 upper
LE-3 lower
Total for Rappahannock
River
Mean

3.00

144.39
84.21
94.09
8.07
18.06
20.72
53.22

72.56
38.87
24.22
9.77
36.4


24.04
28.86

12.16
16.95

20.5
Minimum Value

2.5

2.2
3.00
3.40
4.4
6.60
14.00
2.2

15.1
2.9
13.6
1.2
1.2


1.8
1.4

2.1
1.8

1.4
Maximum Value

3.8

240.
272.0
243.00
16.0
40.7
36.0
272.0

137.0
491.0
44.0
75.0
491.0


48.0
65.0

21.9
55.1

65.0
N

9

137
225
56
20
32
14
493

61
168
33
74
336


20
70

40
104

234

                                     B-79

-------
TABLE 18.  LEVELS OF ZINC  (rag/kg)  IN  OYSTER TISSUE IN VIRGINIA
           (SOURCE:  GILINSKY AND ROLAND 1983)
                              Mean     Minimum Value     Maximum Value     N
James River Area

River Estuarine Transition     16            12               19            9
Lower Estuary
      LE-5 upper             1208            11             6000          130
      LE-5 lower              993            72             6546          227
Elizabeth River              3563          484            19900           54
Lynnhaven Bay                 405          235              600           20
Back River                    484          189              829           32
Mouth of Chesapeake Bay       563          435              740           13
Total of James River         1033            11            19900          476

York River Area

River Estuarine Transition    874          157             1550           61
Lower Estuary                 575          102             1550          158
Poquoson River                575          352              920           33
Mobjack Bay                   311            52              920           57
Total For York River          583.8         52             1550          309

Rappahannock River

River Estuarine Transition
      RET-3 upper             336            11              985           20
      RET-3 lower             439          123              895           72
Lower Estuary
      LB-3 upper              344          157              548           41
      LE-3 lower              425          175              973          107
Total for Rappahannock
      River                   386            11              985          240
                                      B-80

-------
TABLE 19.  MEAN LEVELS OF PESTICIDES, POLYCHLORINATED BIPHENYLS (PCB'S), AND
           METALS IN OYSTERS IN VIRGINIA (GILINSKY AND ROLAND 1983)

Oyster Tissue (ppm)
Geometric
Area Substance
James River DDT
DDE
ODD
PCB
Cd
Cu
Zn
York River DDT
DDE
ODD
PCB
Cd
Cr
Cu
Zn
Rappahannock River DDT
DDE
ODD
Cd
Cr
Cu
Zn
N
212
318
308
20
488
493
476
22
43
40
6
331
4
336
309
40
77
75
230
12
234
240
Mean
0.03
0.05
0.07
0.50
1.13
53.22
1033.00
0.01
0.01
0.01
0.23
1.02
3.6
36.4
583.8
0.01
0.01
0.01
0.63
4.45
20,5
386
Range
0.000
0.002
0.002
0.01
0.10
2.2
11
0.001
0.001
0.002
0.04
0.01
2.5
1.2
52
0.001
0.001
0.002
0.05
3.0
1.4
11
- 0.4
- 0.9
- 1.1
- 2.8
- 4.8
- 272
- 19900
- 0.04
- 0.09
- 0.03
- 0.40
- 120
- 5.00
- 491
- 1550
- 0.03
- 0.02
- 0.06
- 1.3
- 5.9
- 65.0
- 985

                                      B-81

-------
TABLE 20.   MEAN LEVELS OF PESTICIDES  AND POLYCHLORINATED  BIPHENYLS  (PCB'S)
           IN OYSTERS IN MARYLAND (EISENBERG AND TOPPING  1981)

Oyster Tissue (ppm)
Area
Tolchester-
Rockhall



West Chesapeake
(Balto. Harbor
to Rhode River)


Chester River




West Chesapeake




East Chesapeake
(Kent Island)



West Chesapeake
(Calvert Co. )



Eastern Bay and
Tributaries



Substance
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
N
4
4
4
4
4
36
36
36
36
36
12
12
12
12
12
7
7
7
7
7
2
2
2
2
2
3
3
3
3
3
91
91
91
91
91
Mean
0.013
0.013
0.003
0.004
0.001
0.015
0.015
0.003
0.003
0.002
0.009
0.010
0.002
0.003
0.002
0.008
0.006
0.002
0.002
0.001
0.020
0.011
0.004
0.006
0.003
0.008
0.008
0.002
0.002
0.002
0.011
0.013
0.002
0.003
0.003
Range
0.002
0.008
0.002
0.002
0.001
0.004
0.004
0.001
0.001
0.001
0.003
0.002
0.001
0.001
0.001
0.005
0.003
0.002
0.001
0.001
0.020
0.001
0.003
0.004
0.002
0.005
0.005
0.002
0.001
0.001
0.003
0.001
0.001
0.001
0.001
- 0.030
- 0.020
- 0.004
- 0.005
- 0.001
- 0.04
- 0.05
- 0.006
- 0.006
- 0.003
- 0.020
- 0.030
- 0.002
- 0.004
- 0.002
- 0.010
- 0.010
- 0.002
- 0.004
- 0.001
- 0.020
- 0.020
- 0.004
- 0.008
- 0.004
- 0.010
- 0.010
- 0.002
- 0.003
- 0.003
- 0.020
- 0.070
- 0.006
- 0.005
- 0.010
                                 (continued)
                                      B-82

-------
TABLE 20.  (continued)
Oyster Tissue (ppm)
Area
Patuxent River
and Confluence



East Chesapeake
(Choptank River)



West Chesapeake
(lower Potomac
River)


Upper Potomac
River



East Chesapeake
(Honga, Nanticoke
and Wicomico
Rivers, Fishing
Bay)
Tangier Sound


Tangier Sound
(Pocomoke River
Pocomoke Sound,
Big and Little
Annamessex Rivers)
Substance
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDD
DDE
Dieldrin
PCB
Chlordane
DDE
PCB
Chlordane
DDD
DDE
Dieldrin
N
23
23
23
23
23
76
76
76
76
76
16
16
16
16
16
23
23
23
23
23
40
40
40
40
40
3
3
3
40
40
40
40
40
Mean
0.011
0.009
0.002
0.002
0.002
0.007
0.010
0.002
0.003
0.002
0.008
0.009
0.002
0.002
0.002
0.013
0.013
0.003
0.003
0.002
0.005
0.007
0.002
0.002
0.002
0.004
0.004
0.002
0.004
0.006
0.002
0.002
0.002
Range
0.005
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.004
0.001
0.001
0.001
0.003
0.002
0.001
0.001
0.001
0.002
0.001
0.001
0.001
0.001
0.002
0.003
0.001
0.001
0.002
0.001
0.001
0.001
- 0.020
- 0.020
- 0.003
- 0.004
- 0.003
- 0.020
- 0.030
- 0.003
- 0.005
- 0.004
- 0.020
- 0.020
- 0.004
- 0.004
- 0.003
- 0.040
- 0.030
- 0.005
- 0.006
- 0.003
- 0.010
- 0.030
- 0.003
- 0.007
- 0.002
- 0.005
- 0.007
- 0.002
- 0.009
- 0.030
- 0.004
- 0.005
- 0.003

                                      B-83

-------
TABLE 21.   MEAN LEVELS  OF METALS  IN OYSTERS IN MARYLAND (EISENBERG AND
           TOPPING 1981)

Oyster Tissue (ppm)
Area
Upper Main Bay






Middle Main Bay






Patuxent River






Potomac -River






Lower Eastern Shore






Metal
As
Cd
Cr
Cu
Hg
Pb
Zn
As
Cd
Cr
Cu
Hg
Pb
Zn
As
Cd
Cr
Cu
Hg
Pb
Zn
As
Cd
Cr
Cu
Hg
Pb
Zn
As
Cd
Cr
Cu
Hg
Pb
Zn
N
38
58
55
58
58
54
58
69
118
104
118
118
105
118
40
91
90
91
91
89
91
27
40
40
40
40
38
40
35
50
44
50
50
43
50
Geometric Mean
0.006
2.10
0.18
58.79
0.01
0.03
1280.21
0.148
1.42
0.13
35.13
0.02
0.19
1178.59
0.13
2.20
0.08
57.86
0.02
0.007
932.04
0.70
0.73
0.03
16.82
0.02
0.00
575.22
0.04
0.81
0.21
27.53
0.04
0.02
1148.88
Range
0.00 - 0.16
0.28 - 5.72
0.00 - 1.80
6.79 - 274.73
0.003 - 0.04
0.00 - 0.40
18.70 - 2994.0
0.0 - 1.00
0.15 - 5.55
0.0 - 2.30
4.90 - 134.72
0.003 - 0.16
0.0 - 1.90
22.10 - 9434.00
0.0 - 0.68
0.07 - 7.80
0.0 - 2.40
0.81 - 2494.00
0.002 - 0.19
0.0 - 0.10
7.85 - 2416.00
0.00 - 1.20
0.16 - 2.21
0.00 - 1.00
4.17 - 36.10
0.002 - 0.23
0.00 - 0.00
72.20 - 1090.00
0.00 - 0.87
0.06 - 1.67
0.00 - 0.90
8.21 - 85.44
0.004 - 0.23
0.00 - 0.50
15.00 - 6025.00
                                 (continued)
                                      B-84

-------
TABLE 21.  (continued)
Oyster Tissue (ppm)
Area Metal
Upper Eastern Shore As
Cd
Cr
Cu
Hg
Pb
Zn
Middle Eastern Shore As
Cd
Cr
Cu
Hg
Pb
Zn
Western Tributaries As
Cd
Cr
Cu
Hg
Pb
Zn
N
97
129
129
129
129
127
129
61
108
103
108
108
101
108
11
25
19
25
24
21
25
Geometric Mean
0.08
1.23
0.14
28.37
0.01
0.04
802.61
0.08
1.14
0.20
30.57
0.02
0.06
886.86
0.00
1.24
0.01
36.98
0.08
0.02
835.03
Range
0.00
0.08
0.00
1.70
0.001
0.00
11.40
0.00
0.14
0.00
3.22
0.002
0.00
16.00
0.00
0.15
0.00
2.62
0.002
0.00
14.59
- 0.93
- 3.85
- 2.70
- 111.80
- 0.17
- 1.60
- 7998.00
- 0.82
- 2.42
- 2.40
- 78.70
- 0.05
- 1.40
- 7914.0
- 0.00
- 3.53
- 0.10
- 104.93
- 0.26
- 0.40
- 2204.50

                                     B-85

-------
TABLE 22.      CONCENTRATIONS  OF DISSOLVED METALS BY GBP SEGMENTS.
              NUMBER OF SAMPLES.   DATA FROM KINGSTON ET AL. 1982
N IS

Segment
CB-2, 3
Upper Bay
CB-4, 5
Mid-Bay
CB-6, 7, 8
Lower Bay
CB-1, 2, 3
Upper Bay
CB-4, 5
Mid-Bay
CB-6, 7, 8
Lower Bay
CB-1, 2, 3
Upper Bay
CB-4, 5
Mid-Bay
CB-6, 7, 8
Lower Bay

Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Dissolved
N
7
7
29
29
15
15
Dissolved
7
7
29
29
15
15
Dissolved
7
7
29
29
15
15
Cadmium, ug L 1
Mean
0.039
0.046
0.028
0.023
0.006
0.006
Chromium, ug L"~l
0.260
0.240
0.134
0.209
0.071
0.161
Cobalt, ug L"1
0.081
0.052
0.039
0.101
0.047
0.064

0.007
0.007
0.007
0.007
0.007
0.007
0.17
0.11
0.00
0.00
0.00
0.00
0.025
0.026
0.024
0.017
0.016
0.025
Range
- 0.101
- 0.086
- 0.087
- 0.022
- 0.034
- 0.040
- 0.41
- 0.40
- 0.74
- 1.68
- 0.14
- 0.92
- 0.156
- 0.082
- 0.210
- 0.556
- 0.098
- 0.144
                                 (continued)
                                      B-86

-------
TABLE 22.  (continued)
Segment
CB-1, 2, 3
Upper Bay
CB-4, 5
Central Bay
CB-6, 7, 8
Lower Bay
CB-1, 2, 3
Upper Bay
CB-4, 5
Central Bay
CB-6, 7, 8
Lower Bay
CB-1, 2, 3
Upper Bay
CB-4, 5
Central Bay
CB-6, 7, 8
Lower Bay
CB-1, 2, 3
Upper Bay
CB-4, 5
Central Bay
CB-6, 7, 8
Lower Bay
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
N
Dissolved Copper
7
7
29
29
15
15
Dissolved Lead,
7
7
29
29
15
15
Dissolved Nickel
7
7
29
29
15
15
Dissolved Zinc,
7
7
29
29
15
15
Mean
, ug L-l
1.01
0.95
0.28
0.17
0.55
0.35
ug L-l
0.14
0.12
0.11
0.09
0.09
0.17
, ug L-l
1.47
1.39
1.37
1.23
1.02
0.90
ug L-l
1.63
1.43
1.55
0.47
1.49
0.54
Range
0.37
0.43
0.08
0.08
0.08
0.17
0.00
0.00
0.00
0.00
0.00
0.00
0.85
0.92
0.56
0.82
0.78
0.55
0.00
0.00
0.00
0.00
0.00
0.00
- 1.64
- 1.48
- 1.14
- 0.57
- 1.80
- 1.14
- 0.51
- 0.40
- 0.88
- 0.52
- 0.41
- 1.59
- 2.59
- 1.65
- 2.30
- 1.99
- 1.32
- 1.25
- 8.09
- 5.52
-11.11
- 2.64
- 7.96
- 1.36
                                      B-S7

-------
TABLE 23.  CONCENTRATIONS  OF  PARTICULATE METALS BY GBP SEGMENT.  N IS THE
           NUMBER OF SAMPLES.   DATA FROM KINGSTON ET AL. 1982

Segment
CB-2, 3, and
ET-2,
CB-4,5
CB-6,7,8
CB-2, 3, and
ET-2
CB-4,5
CB-6,7,8

Surface
Bottom
Surface
Bottom
Surface
Surface
Bottom
Surface
Bottom
Surface
Bottom
Particulate
N
7
7
29
29
15
Particulate
7
7
29
29
15
15
Cadmium, ug L •*•
Mean
0.024
0.046
0.007
0.005
0.001
Chromium, ug L~l
3.03
3.28
0.17
0.29
0.37
0.57
Range
0.003 -
0.009 -
0.001 -
0.001 -
0.001 -
0.99 -
0.95 -
0.00 -
0.00 -
0.01 -
0.14 -
0.059
0.099
0.110
0.023
0.001
4.91
3.01
1.71
1.71
1.46
1.42
Particulate Cobalt, ug L~l
CB-2,3, ahd
ET-2
CB-4,5
CB-6,7,8
Surface
Bottom
Surface
Bottom
Surface
Bottom
7
7
29
29
15
15
1.097
1.234
0.058
0.091
0.080
0.168
0.381 -
0.391 -
0.021 -
0.017 -
0.029 -
0.061 -
2.365
2.365
0.329
0.442
0.329
1.049
Particulate Copper, ug L~l
CB-2, 3, and
ET-2
CB-4,5
CB-6,7,8
Surface
Bottom
Surface
Bottom
Surface
Bottom
7
7
29
29
15
15
1.13
1.40
0.03
0.09
0.11
0.28
0.32 -
0.95 -
0.00 -
0.00 -
0.00 -
0.00 -
2.34
3.34
0.44
0.42
0.74
2.82
                                  (continued)
                                      B-88

-------
TABLE 23. (continued)
Segment
N
Mean
Range
Particulate Lead, ug L~l
CB-2,3,and
ET-2
CB-4,5
CB-6,7,8
CB-2,3, and
ET-2
CB-4,5
CB-6,7,8
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
7
7
29
29
15
15
Particulate
7
7
29
29
15
15
2.42
3.70
0.18
0.33
0.22
0.26
Nickel, ug L"1
1.89
2.30
0.26
0.38
0.22
0.24
0.64
0.63
0.01
0.01
0.01
0.03
0.73
0.77
0.11
0.08
0.03
0.24
- 4.70
- 7.30
- 0.68
- 0.93
- 0.90
- 0.70
- 3.90
- 5.00
- 0.64
- 1.10
- 0.95
- 1.50
Particulate Zinc, ug L~l
CB-2,3, and
ET-2
CB-4,5
CB-6,7,8
Surface
Bottom
Surface
Bottom
Surface
Bottom
7
7
28
28
15
15
7.85
8.72
0.64
0.86
0.22
0.24
2.77
3.39
0.04
0.07
0.30
0.40
- 15.52
-14.0
- 2.36
-4.00
- 4.82
-14.9
                                     B-89

-------
TABLE 24.  CONCENTRATIONS OF PARTICULATE  METALS  BY  CBP  SEGMENT.  DATA FROM
           NICHOLS ET AL. 1981;   RANGE  IS  THE MINIMUM AND MAXIMUM VALUES FROM
           FIVE SURVEYS BETWEEN  MARCH-SEPTEMBER  1979, 1980.  N  IS NUMBER OF
           VALUES AVERAGED

Segment
CB-2, 3, and
ET-2
CB-4,5
Central Bay
CB-6,7,8
Lower Bay
CB-2, 3, and
ET-2
CB-4 , 5
Central Bay
CB-6,7,8
Lower Bay

Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Surface
Bottom
Particulate
N
20
20
25
25
45
45
Particulate
20
20
25
25
45
45
Cadmium, ug L~l
Mean
0.13
0.14
0.17
0.11
0.18
0.14
Copper, ug L~l
1.89
4.30
1.26
1.34
0.60
1.48
Range
0.004
0.013
0.004
0.004
0.02
0.01
0.19
0.73
0.23
0.80
0.13
0.29
- 1.80
- 1.80
- 1.20
- 0.74
- 0.32
- 0.85
- 4.30
-17.0
- 3.40
- 2.90
- 1.50
- 10.0
Particulate Lead, ug L~"l
CB-2, 3, and
ET-2
CB-4,5
Central Bay
CB-6,7,8
Lower Bay
Surface
Bottom
Surface
Bottom
Surface
Bottom
20
20
25
25
45
45
2.92
5.50
1.18
1.00
1.03
1.17
0.50
0.93
0.10
0.27
0.10
0.40
- 7.80
-15.0
- 2.20
-3.00
- 4.50
- 3.40
                                  (continued)
                                     B-90

-------
TABLE 24. (continued)
      Segment
                        Mean
                                      Range
CB-2, 3, and  Surface
ET-2          Bottom

CB-4,5        Surface
Central Bay   Bottom
CB-6,7,8
Lower Bay
Surface
Bottom
                           Partlculate Nickel,  ug L~l
20
20

25
25

45
45
1.80
6.21

0.89
1.28

1.44
1.70
0.16
0.58

0.06
0.12

0.06
0.07
                                                       7.10
                                                       34.0
                                                        ,10
                                                        ,30
- 2.70
-12.0
CB-2, 3, and  Surface
ET-2          Bottom

CB-4,5        Surface
Central Bay   Bottom
CB-6,7,8
Lower Bay
Surface
Bottom
                            Particulate Zinc,  ug L
20
20

25
25

45
45
12.4
23.8

 5.0
 6.9

 6.47
11.9
1.70
1.80

0.78
0.70

0.65
2.1
  30.0
  94.0

  17.0
  24.0

  28.0
  80.0
                                      B-91

-------
TABLE 25.  BOTTOM SEDIMENT CONCENTRATION OF METALS,  GEOMETRIC MEAN,
           MINIMUM, AND MAXIMUM,  OF METALS, IN ug g"1  (PPM)  BY  SEGMENT

Geometric Mean

Upper Bay
CB-1
CB-2
CB-3
Mid-Bay
CB-4
CB-5
Lower Bay
CB-6
CB-7
CB-8
Cd
2
1
I
2
1
2
1
2
1
1
4
Cr
39
21
35*
61
28
36
21
9
11
8
9
Cu
33
17
33
42
16
22
11
6
9
6
6
Pb
41
16
41
60
18
23
13
12
16
11
10
Ni
47
31
45
56
20
27
15
7
10
6
7
Zn
226
101
216
294
97
155
57
26
36
24
21
Hg
1

1

1

1
1
1
1
1
As
4
2*
4
5
6
6
4
4
4*
4*
4*
Minimum
Upper Bay
CB-1
CB-2
CB-3
Mid-Bay
CB-4
CB-5
Lower Bay
CB-6
CB-7
CB-8
0
0
0
0
0
0
0
0
0
0
0
4
7
13
4
1
4
1
.7
2
.7
2
0
0
4
2
0
0
0
.4
1
.4
1
6
6
10
8
1
0
1
1
2
1
2
11
11
12
19
0
2
0
.4
2
.4
1
26
45
41
26
0
11
0
1
8
1
3
0

0



0
0
0
0
0
.7
1.1
1
7
1
3
1
1
2
1
2
Maximum
Upper Bay
CB-1
CB-2
CB-3
Mid-Bay
CB-4
CB-5
Lower Bay
CB-6
CB-7
CB-8
2
2
2
2
4
2
4
3200
.4
.5
3200
159
51
50
159
120
120
58
37
31
37
37
182
95
56
182
64
64
40
36
36
10
27
190
53
72
190
108
79
108
49
49
49
39
150
71
81
150
70
70
40
37
37
21
25
1000
380
710
1000
400
570
240
260
260
31
132
.3

.3

.3

.3
.8
.8
.7
.3
11
1.3
6
11
15
7
15
11
5
11
4

* Fewer than 10 observations.
                                      B-92

-------
TABLE 26.  Cf MEAN, MINIMUM, AND MAXIMUM OF METALS BY SEGMENT



Upper Bay
CB-1
CB-2
CB-3
Mid-Bay
CB-4
CB-5
Lower Bay
CB-6
CB-7
CB-8

Upper Bay
CB-1
CB-2
CB-3
Mid-Bay
CB-4
CB-5
Lower Bay
CB-6
CB-7
CB-8

Upper Bay
CB-1
CB-2
CB-3
Mid-Bay
CB-4
CB-5
Lower Bay
CB-6
CB-7
CB-8

Cd
6
3
4
7
5
5
4
1548
-0.3
-0.4
4520

-1
-1
-1
-1
-1
-1
-1
-1
-1
— 1
-1

19
19
15
19
42
17
42
96,996
13
4
96,996

Cr
-0.5
-0.7
-0.6*
-0.2
-0.6
-0.4
-0.7
-1
-0.9
-0.8
-1

-1
-1
-0.9
-1
-1
-1
-1
-3
-1
-2
-3

0.8
-0.4
-0.4
0.8
0.4
0.4
-0.3
-0.6
-0.5
-0.6
-0.5
^f Mean
Cu
2
1
2
3
1
2
0.2
-0.6
-0.3
-0.7
-0.6
Minimum
-1
-1
-0.7
-0.9
-1
-1
-1
-2
-1
— 7
-2
Maximum
14
7
4
14
4
4
2
2
2
0.6
1

Pb
2
0.1
2
3
0.8
1
0.2
-0.2
0.2
-0.2
-0.4

-0.6
-0.6
-0.4
-0.5
-1
-1
-1
-2
-1
-1
-2

10
2
3
10
5
4
5
3
3
2
1

Ni
0.4
-0.05
0.3
0.7
-0.3
-0.1
-0.5
-0.8
-0.7
-0.9
-0.7

-0.7
-0.7
-0.7
-0.5
-1
-1
-1
-2
-1
-2
-1

3
1
1
3
1
1
0.1
0.03
0.03
0.4
-3

Zn
1
0.1
1
2
-0.5
1
-0.3
-1
-0.7
-0.9
-1

-0.8
-0.6
-0.6
-0.8
-2
-1
-2
-3
-3
-3
_o

8
2
5
8
4
4
1
1
1
-1
0.2

* Less than 10  observations.
                                     B-93

-------
TABLE 27.   Cj MEAN,  MINIMUM,  AND MAXIMUM BY  SEGMENT
               Mean        Minimum          Maximum
Upper Bay
CB-1
CB-2
CB-3
Mid-Bay
CB-4
CB-5
Lower Bay
CB-6
CB-7
CB-8
12.5
4.9
13.1*
19.2
6.2
9.0
2.5
-4
-3.9
-4.2
-3.9
-5
-5
-2.1
0.8
-6
-5.5
-6
-6
-5.6
-6
-6
49
31
22
49
46
26
46
7.2
- 0.5
2.6
7

* Less than 10 observations.
                                      B-94

-------
TABLE 28.   MEAN CONCENTRATIONS OF TOTAL METAL  IN CBP  SEGMENTS.   N  IS  THE
           NUMBER OF SAMPLES.   DATA FROM VIRGINIA STATE '106'  PROGRAM.  METAL
           CONTENT IN ug/L~l

Segment
POTOMAC
TF-2
RET-2
LE-2
TF-2
RET-2
LE-2
RAPPAHANNOCK
TF-3
RET-3
LE-3
TF-3
RET-3
LE-3
YORK
TF-4
RET-4
LE-4
WE-4
TF-4
RET-4
LE-4
WE-4
Mean

3.
13.
6.
11.

5.
9.
28.
13.

20.
22.
10.
10.
11.
18.
17.
17.

7
2
5
1

0
1
0
6

0
5
6
0
7
6
9
4
Range
Cadmium
1-10
Lead
1-90
3-10
2-60
Cadmium
.03-10
Lead
6-12
1-30
1-60
Cadmium
20-20
10-30
1-20
Lead
2-126
1-110
1-80
1-70
N

4
44
4
34

2
7
10
86

4
4
9
1
74
41
80
41
Mean Range N
Chromium
12.2 10-20 9
16.4 10-40 22
Nickel
20 10-30 2
Chromium
12.5 10-20 4
11.8 10-20 11
14.3 10-30 49
Nickel

Chromium
10.5 10-20 21
15.6 10-40 18
19.4 10-40 62
15.4 10-30 24
Nickel

Mean

19
24
38
25
22

16
24
29
44
59
54

14
20
28
30
37
37
25
60

.3
.7
.6
.0
.5

.0
.0
.0
.4
.0
.6

.8
.5
.9
.6
.2
.9
.7
.0
Range
Copper
10-50
10-70
Zinc
10-440
10-40
3-90
Copper
10-30
10-80
.03-80
Zinc
10-110
10-230
.02-470
Copper
10-30
10-40
10-90
10-60
Zinc
0-710
10-480
3-130
10-460
N

15
17
57
8
24

5
24
83
9
31
84

33
36
67
34
280
53
74
26
                                  (continued)
                                     B-95

-------
TABLE 28.  (continued)
Segment
JAMES
TF-5
RET-5
LE-5
Mean

10.0
10
151.
Range
Cadmium
10-10
9 1-1319
N
Mean
Range
N
Mean
Chromium
5
1
16
18.6
14.0
15.4
10-90
10-30
10-100
59
10
267
22.0
20.7
30.1
Range
Copper
10-110
10-50
10-200
N

61
15
330
                    Lead
Nickel
Zinc
    TF-5      24.3   1-735   114
    RET-5      9.7   3-20
    LE-5      13.4   0.6-140 487
                 86.8   10-1589 112
                 51.1   10-460   27
                 57.6   10-3399 423
                                      B-96

-------
TABLE 29.   BOTTOM SEDIMENT GEOMETRIC MEAN,  MINIMUM, AND MAXIMUM OF METALS
           ug g"1 (WESTERN SHORE)

Cd Cr

Western 3 253
Tributaries
WT-1
WT-2
WT-3
WT-4 5
WT-5 3 258
WT-6
WT-7
WT-8 1 * 66 *
Patuxent 1 * 24 *
TF-1
RET-1
LE-1 1 * 24 *

Western .2 0
Tributaries
WT-1
WT-2
WT-3
WT-4 2
WT-5 .2 0
WT-6
WT-7
WT-8 0.3
Patuxent 0.3 4
TF-1
RET-1
LE-1 0.1 4

Western 654 4756
Tributaries
WT-1
WT-2
WT-3
WT-4 5
WT-5 654 4756
WT-6
WT-7
WT-8 0.7
* Less than 10 observations.


Cu Pb
Geometric Mean
156 171


65
80*
156 382
174 161


17 * 12 *
16 * 17 *


16 * 17 *
Minimum
6 5


45
57
86 130
10 5


6
3 3


3 3
Maximum
2926 13890


96
110
230 640
2926 13890


123

(continued)
B-97
Ni Zn Hg

43 471 1*


58 277
75* 380*
681
42 493 1*


7 * 112 *
14 * 75 *


14 * 75 *

6 31 0


34 200
59 360
338
12 31 0


46
3 12


3 12

190 5500 0.4


73 360
92 400
936
190 5500 0.4


232



As

4*





4*








1





1








8





8







-------
TABLE 29.  (Continued)

Cd
Cr
Cu
Pb
Ni
Zn
Hg Ag
As
Maximum (continued)
Patuxent
TF-1
RET-1
LE-1
0.7


0.7
58


58
36


36
40


40
Geometric
Potomac
TF-2
RET-2
LE-2
Rappahannock
TF-3
RET-3
LE-3
York
TF-4
RET-4
LE-4
James
TF-5
RET-5
LE-5
1
2

1*
3*


3*
2*

4*
2*
3
3
1*
3
28
33
31*
19
21


21
28
58*
46
20
34
16
4*
38
25
29
28*
17
15


15
15
36*
29
11
6
20
27
26
36
44
28*
23
22


22*
25
42*
40
15
34
23
34
36
30


30
Mean
21
24
25*
15
20


20
13
23*
19
10
16
12
2*
18
210


210

202
211
325*
128
73


73
78
227*
172
59
188
118
149
217





1*
1*


1*



1
1*
1
1
1 2*
1
1
1 2





4
4


11*


11*
12*
8*
13*
10*
7
5
3*
8
Minimum
Potomac
TF-2
RET-2
LE-2
Rappahannock
TF-3
RET-3
LE-3
York
TF-4
RET-4
LE-4
James
TF-5
RET-5
LE-5
0
0

0
0.2


0.2
0.02

3.3
0.03
0
0.2
0
0
2
10
21
2
2


2
2
36
11
3
1
3
1
1
0
4
14
0
0.6


0.6
1
30
6
1
0.4
2
1 .
0.4
4
10
5
4
1


0.1
1
33
11
3
0.2
0.2
0.5
0.3
0
8
15
0
3


3
1
10
7
2
0.7
1
1
1
0
37
158
0
4


4
4
184
52
9
0.4
16
4
0.4
0
0


0.1



0.03
0.2
0.06
0.03
0 1
0.005
0
0 1
0
0


1


1
7

7
7
.2
.2
1
1
                                     (continued)
                                      B-98

-------
TABLE 29.  (Continued)


Potomac
TF-2
RET-2
LE-2
Rappahannock
TF-3
RET-3
LE-3
York
TF-4
RET-4
LE-4
James
TF-5
RET-5
LE-5
Cd

10
10

0.7
8


8
3

3.4
2
26
4
0.3
26
Cr

76
76
44
51
45


45
133
90
133
67
207
49
7
207
Cu

64
64
50
50
32


32
50
50
47
28
336
151
336
246
Pb
Maximum
450
450
107
59
75


0.3
88
50
88
38
563
72
53
563
Ni

67
48
36
67
30


30
36
36
30
29
54
54
4
45
Zn

1062
910
1062
894
148


148
327
313
327
207
7750
2000
393
7750
Hg

0.2
0.2


0.3



1.4
0.9
1.4
0.4
2.7
1
2
3
Ag As

8
8


15


15
19

19
13
2 42
16
4
42 42

* Less than 10 observations.

-------
TABLE 30.   Cf MEAN,  MINIMUM,  AND MAXIMUM OF METALS  (WESTERN SHORE)


Western
Tributaries
WT-1
WT-2
WT-3
WT-4
WT-5
WT-6
WT-7
WT-8
Patuxent
TF-1
RET-1
LE-1
Mob jack
WE -4
Cd Cr Cu
£f
62 5 24

5
6 *
42 12
64 5 27


5 * -0.6* 4 *
4 * -0.6* 0.8*


4 * -0.6* 0.8*
-0.2* -1 * 0.2
Pb Ni
Mean

18 0.2

0.6
1 *
23
19 0.1


-0.3* -0.8*
0.4* -0.5*


0.4* -0.5*
1 -1 *
Zn

5

2
2 *
5
6


-0.02*
0.1*


0.1*
-0.7
Minimum
Western
Tributaries
WT-1
WT-2
WT-3
WT-4
WT-5
WT-6
WT-7
WT-8
Patuxent
TF-1
RET-1
LE-1
Mob jack
WE -4

1 -1 -1

3

21 6
1 -1 -0.2



0.1 0.1 -0.8


0.1 -1 -0.8
-0.8 -1 -1

-0.7 -0.8

-0.1

7
-0.7 -0.7



-0.8 -0.9


-0.8 -1
-1 -1

-1

0.8

2
-0.7



-0.9


-0.9
-2
                                 (continued)
                                      B-100

-------
TABLE 30.  (Continued)
Cd

Western
Tributaries 6539
WT-1
WT-2
WT-3
WT-4 52
WT-5 6539
WT-6
WT-7
WT-8
Patuxent 6
TF-1
RET-1
LE-1 6
Mob jack
WE-4 1
Cr Cu Pb Ni Zn
Maximum

53 242 816 4 49

7. 12

18 37 8
54 243 816 4 49



-0.3 2 1 -0.2 0.9


-0.3 2 1.4 -0.2 0.9

-0.8 3 5 -0.7 -0.1
* Less than 10 observations.




                                 (continued)
                                      B-101

-------
TABLE 30.  (Continued)


Potomac
TF-2
RET-2
LE-2
Rappahannock
TF-3
RET-3.
LE-3
York
TF-4
RET-4
LE-4
James
TF-5
RET -5
LE-5
Cd

10
15 *

3 *
30 *


30
9.*

33.*
6.*
49.
18.
1.*
56.
Cr

-1
-1
-1 *
-0.9
-1 *


-1
-1
-1 *
-1 *
-2 *
-1 *
-2
-1 *
-1
Cu
£f_
2
3
2 *
1
0.8*


0.8
1
5 *
4 *
0.001*
4
2
2
5
Pb
Mean
3
5
2 *
0.8
1.*


1
2
4 *
5 *
0.1*
4
1
1
6
Ni

-0.5
-0.6
-0.5*
-0.5
-0.5*


-0.5
-1
-1 *
-1 *
-2 *
-1 *
— 1
-1 *
-1
Zn

3
3
4 *
2
-0.1*


-0.1
-0.05
3 *
2 *
-0.8*
5
1
0.7
9
Minimum
Potomac
TF-2
RET-2
LE-2
Rappahannock
TF-3
RET-3
LE-3
York
TF-4
RET-4
LE-4
James
TF-5
RET-5
LE-5
-2.
-2.

-1.
.8


0.8
-0.8


-0.7
-1
1
-1
-1
-2
-2
-1
-2
-2


-2
-2

-1
-2
-3
-3
-2
2.9
-1
-0.3
0.4
-1
-0.4


-0.4
-1

1
-1
-3
-2
-1.5
-2.7
-0.8
-0.2
-0.7
-0.7
-0.6


-0.6
-1

2
-1
— *?
-1
-1
-1.6
-2
-1
-0.6
-2
-1


-1
-3.

-2.
-3.
-3.
-3.
-2.
-2.6
-0.6
-0.7
0.4
-1
-0.8


-0.8
-2

0.4
*- 9
-3
-2
-2
-2.5
                                 (continued)
                                      B-102

-------
TABLE 30.  (Continued)


Potomac
TF-2
RET-2
LE-2
Rappahanno ck
TF-3
RET-3
LE-3
York
TF-4
RET-4
LE-4
James
TF-5
RET-5
LE-5
Cd

99
99

6
83


83
33


17
646
39
3
646
Cr

-0.4
-0.4
-0.6
-0.4
-0.5


-0.5
0.2

-2
-0.8
3
-1
-1
2.6


6
6
4
3
3


3
7

7
1
79
41
58
79
Cu Pb
Maximum
25
25
5
2
4


4
8

8
2
111
11
28
111
Ni

0.9
0.05
-0.4
0.9
-0.2


-0.2
-0.4

-0.8
-0.7
0.04
0.04
-1
-0.2
Zn

10
8
10
7
0.3


0.3
4

4
0.4
490
17
16
490

* Less than 10 observations.
                                      B-103

-------
TABLE 31.  Cx MEAN, MINIMUM, AND MAXIMUM (WESTERN SHORE)

Western
Tributaries
WT-1
WT-2
WT-3
WT-4
WT-5
WT~6
WT-7
WT-8
Patuxent
TF-1
RET-1
LE-1
Potomac
TF-2
RET-2
LE-2
Rap pahanno ck
TF-3
RET -3
LE-3
York
WE -4
RET-4
LE-4
James
TF-5
RET-5
LE-5
GI Mean

133




134


0.02*
4.1*


4.1*
10.4
15.3*

4,8*
31.0*

31.0*
7.5*
-4.3*
39.*
2.3*
69
12.3*
-4.2*
76
Minimum

0.02




7



-4


-4
-6
-0.8

-6
-2.4

-2.4
-5
-5
36
-5
-6
-0.2

-6
Maximum

6850




6850



10


10
32
32

16
79

79
42
-1
42
14
362
26

362

* Less than 10 observations.
                                       B-104

-------
TABLE 32.   BOTTOM SEDIMENT GEOMETRIC MEAN,  MINIMUM,  AND MAXIMUM OF  METALS
           (EASTERN SHORE)
— 	 — 	 ___ — __ — 	 „ 	 	 	 „ , .,
Cd
Cr

Cu Pb Ni :' i
Geometric Mean
Upper Eastern
Shore 2
ET-1 3 *
ET-2
ET-3
ET-4 2
Mid Eastern
Shore 2 *
EE-1 2 *
EE-2 1.*
ET-5

Upper Eastern
Shore 0.1
ET-1
ET-2
ET-3
ET-4 0.1
Mid- Eastern
Shore 0.5
EE-1 0.8
EE-2
ET-5

Upper Eastern
Shore 2
ET-1
ET-2
ET-3
ET-4 2
Mid-Eastern
Shore 1
EE-1 1
EE-2
ET-5

22
58 *


19

25 *
23 *
32 *



2



2

8
8




110



110

39
39



11
74


9

11
8
26



0



0

0
0




73



26

25
23



20 50 * 79
* 56.* 84 * 34.1 *


19 /•_.

* 13.* 15 * 123 *
* 22.* 9 * 124 *
* 3.* 24 * 121 -••

Minimum

.7 2 7



.72

2 8 50
6 50


Maximum

58 340



58 307

43 23 206
43 2 06


* Less than 10 observations.
                                 (continued)
                                      B-105

-------
TABLE 32.  (Continued)
                 Cd
         Cr
Lower Eastern
   Shore
ET-6
ET-7
ET-8
ET-9
ET-10
ET-11.
EE-3
1

1
Lower Eastern
   Shore
ET-6
ET-7
ET-8
ET-9
ET-10
ET-11
EE-3
10
1.1*   27 *
Lower Eastern
Shore 0.1
ET-6
ET-7 0.1
ET-8
ET-9
ET-10
ET-11
EE-3

1.5

2





          Cu      Pb

        Geometric Mean
          Ni
8

8
19

19
        13 *    17 *

            Minimum

         1       2

         1       2
                                   Maximum
5
5
20
20
29
29
88
88
54

52
                66 *



                 6

                 6
                                      330

                                      330
* Less than 10 observations.
                                      B-106

-------
TABLE 33.  Cf MEAN, MINIMUM, AND MAXIMUM OF METALS (EASTERN SHORE)



Upper Eastern
Shore
ET-1
ET-2
ET-3
ET-4
Mid-Eastern
Shore
EE-1
EE-2
ET-5

Upper Eastern
Shore
ET-1
ET-2
ET-3
ET-4
Mid-Eastern
Shore
EE-1
EE-2
ET-5

Upper Eastern
Shore
ET-1
ET-2
ET-3
ET-4
Mid- Eastern
Shore
EE-1
EE-2
ET-5
Cd


8
19 *


8

7 *
9
4 *



0.2



0.2

4
7




20



20

10
10


Cr Cu Pb
C_f Mean

-0.7 0.3 0.7
0.3* 5 * 2 *


-0.7 -0.03 0.6

-0.7* 0.4* 0.3*
-0.7 0.2 0.7
-0.6* 1 * -0.9*

Minimum

-1 -1 -0.9



-1 -1 -0.9

-1 -1 -0.9
-0.9 -1 -0.7


Maximum

0.3 5 2



0.3 1 2

-0.6 1 2
-0.6 0.9 2


Ni Zn


1 * 0.1
1 * 2 *


-0.03

-0.6* 0.3*
-0.8* 0.3
-0.4* 0.1*



-0.9



-1

-0.5
-0.5




2



2

0.9
0.9


* Less than 10 observations.
                                 (continued)
                                      B-107

-------
TABi.fi 33.  (Continued)
Cd Cr Cu Pb Ni
_C.f Mean
Lower Eastern
Shore 13 -0.9 -0.1 0.4 -1.*
ET-6
£1-7 5 -0.9 -0.2 0.5
ET-8
ET-9
ET-10
ET-11
EE--3 -0.9* -0.3* -0.06* -1.*
Minimum
Lower Eastern
Shore 0 -1 -1 -0.9
ET-6
ET-7 0 -1 -1 -0.9
ET-8
ET-9
ET-10
ET-11
EE-3
Maximum
Lower Eastern
Shore 49 -0.7 1 4
ET-6
ET-7 49 -0.8 1 4
ET O
J- O
ET-9
ET-10
ET-11
EE-3
Zn
-0.3
-0.3
-0.6*
-1
-1
2
0.6

* Less than 10 observations.
                                      B-108

-------
TABLE 34.  Cj MEAN, MINIMUM, AND MAXIMUM (EASTERN SHORE)
Upper Eastern
   Shore
ET-1
ET-2
ET-3
ET-4
Mid-Eastern
   Shore
EE-1
EE-2
ET-5
Lower Eastern
   Shore
ET-6
ET-7
ET-8
ET-9
ET-10
ET-11
EE-3
                        Mean
Minimum
                                                          Maximum
                       29.4*
                       29.4*
4.4*
6.1*
2.8*
                       -2.8*
                                           2.8
                    6.1
                       -2.8*
* Less than 10 observations
                                     B-109

-------
                                SECTION 8
                CURRENT CONDITIONS AND TRENDS
    The physical and chemical variables  described in this section  were used to
characterize  segments of Chesapeake Bay.  They include:  salinity,  temperature,
pH, turbidity, nutrients (forms of phosphorus and nitrogen),  dissolved oxygen
(DO),  chlorophyll a_.
    The data  are presented as a series of tables grouped by physical variables
and nutrient  variables.  Statistics for  each year's annual mean will be
presented for the years 1977 to 1980 (Table 35a-d); seasonal means for each
variable will then be shown, by year, for years 1977 to 1980 (Table 36a-d).
The same arrangement is followed for nutrients (Tables 37a~d and 38a~d).
    Summary of physical and nutrient means (depth-averaged) for current
conditions (1977 to 1980) are based on criterion requiring:
       2. 3 observations/segment for monthly mean;
       -? 2 monthly means/segment for seasonal mean;
       _T 2 seasonal means/segment for annual mean.
Monthly means, number of observations, standard deviation, minimum, and
maximum values are available for use in  hard copy at the CBP office,
Annapolis, MD; an example is shown in Table 39.  All of the above  variables
are also available for top (< 10 m) and  bottom ( ,>10 m) level in hard copy.
    Statistically significant trends over time in nutrients for each segment
are summarized in Table 40 (annual trends) and Table 41 (seasonal  trends).
Table 41 is further subdivided into 41a  (spring), 4lb (summer), 4lc (fall),
and 41d (winter).  An analysis of these  trends is included in Chapter 1,
Section 2. The actual distribution of nutrient data (grouped by 7 1/2 -
minute USGS quadrangles) is shown in Figures 40 through 47.
                                     B-110

-------
TABLE 35a.  SUMMARY  STATISTICS FOR PHYSICAL MEANS ANNUAL DATA
SEGMENT  YEAR
LEVEL  TEMP  SALIN
PH
SECCHI   JTD
CB-t
CB-2
CB-3
CB-3
CB-4
CB-4
CB-5
WT-2
fcT-5
WT-6
w/T-8
TF-1
TF-2
RET-2
LE-2
TF-J
TF-3
RET-3
LE-3
TF-4
RET-4
LE-4
LE-4
TF-5
R^T-5
\jt "b
LEJ-5
ET-2
ET-4
ET-5
ET-6
ET-7
ET-1'0
EE-1
EE-3
WE-4

1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977

T
T
T
B
T
B
T
T
T
T
T
T
T
T
T
T
B
T
T
T
T
T
8
T
T
T
B
T
T
T
T
T
T
T
T
T

16.2
17.5
18.7
16.7
17.9
14.8
17.1
17.9
15.9
19.3
19.7
18.6
20.7
20.6
20.1
22.6
22.8
21.7
21.1
25.0
22.2
21.4
21.0
24,6
23.8
22.9
20.9
20.3
19.0
19.6
21.9
20.1
21.0
18.9
20.1
21.2


1
6
12
11
17
13


7
9
2
0
6

2

8
15
3
10
18

2

18


5




10
10



.30
.78
.82
.61
.21
,52

t
.54
.41
.85
.51
.27

*38

131
.61
.84
.26
,79

^06

.26

t
.35

9


.72
.96
t

7.
7.
7.

?I
7.
7.
7.

f
7.
7.
7.
7.
6.

m
f
t
.
m
t
,

,
9
,
7.
7.
7.
7.
7.
6,
7.
7.


7
5
7

9
9
6
3


9
1
7
2
9












5
6
3
4
5
5
8
1



0
0

1




0


0


0

0
1
0
0
0

0

1












*55
.75

^81

t
%
,
.81

,
.61


.62

*65
,2b
,58
.59
.85

'.63
*
.06
«

f
m
t

,

9
,


17
12

4


11

7
6
48
31
1H
15












54
30
11
18
19
14

18



Us
.52

Il9

t
.27

".15
.57
.81
.67
.86
.23
•

f
9
t


m
t
t
t

.23
.88
.94
.90
,43
.77

^31


                                 B-lll

-------
TABLE 35b.  SUMMARY STATISTICS FOR PHYSICAL MEANS ANNUAL DATA
SEGMENT  YEAR
LEVEL  TEMP  5ALJN
PH
SECCHI   JTU
CB-1
CB-2
CB-3
CB-4
CB-4
CB-5
CB-5
WT-5
TF-1
RET-1
LE-1
TF-2
TF-2
RET-2
TF-3
TF-3
RET-3
LE-3
TF-4
RET-4
LE-4
LE-4
TF-5
TF-5
RET-5
LE-5

1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1976
1978

T
T
T
T
B
T
B
T
T
T
T
T
B
T
T
B
T
T
T
T
T
B
T
B
T
T

23.8
18.2
16.8
17.3
16.5
17.5
21.5
17,5
18.3
19.4
19.2
19.9
21.3
20.9
19.1
19.5
19.4
20.3
22.8
23.1
22.9
22.5
20,4
23.6
20.0
19.9


1
6
10
16
12
17
7
1
7
10
0
0
3


7

2
6

18



6


.56
.54
.76
.39
,42
,00
.13
.07
.69
.11
.21
.19
.23

.
.02

169
.91

167

t
t
.58

7
7
7
8
7
a
7
7
7
7
7
7
7
7













.9
.9
.8
.0
.3
.0
,6
.5
.0
.4
.8
.8
.8
,6

,
,
,
t
,
,
,
9
t
s
•


0
0
1




0


0

0
0

0

0
0


0





.59
.74
.81

t


.53


.56

I&0
.54

*50

Isi
.46


.56

f
•

6
25
13
4
3
2

9
23
24
5
12

17













.83
161
.76
.43
.27
.49

'.77
.74
.40
.72
.12

*90

B
9
t
f
9
t
t


9
•

                                  B-U2

-------
TABLE 35c.  SUMMARY  STATISTICS FOR PHYSICAL MEANS ANNUAL DATA
SEGMENT   YEAR
LEVEL  TEMP  SALIN
                                               PH
SECCHI   JTU
CB-1
CB-2
CB-3
CB-3
CB-4
CB-5
WT-b
TF-2
TF-2
RET-2
RET-2
RET-3
LE-3
LE-3
RET-4
LE-4
LE-4
TF-5
TF-5
RET-5
RET-5
LE-5
LE-5

1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979

T
T
T
B
T
T
T
T
B
T
B
T
T
B
T
T
B
T
B
T
B
T
B

20.5
17.8
14.9
16.5
18.5
16.3
14.7
17.4
16.0
17,6
15.0
20.3
22.0
19.8
21.6
21.8
21.6
16.9
21.5
17.0
20.2
17.4
20.5



7.
11.
9.
13.
5.
0.

l!

3l

.
5.
10.

,
,
0
•
7.




34
09
15
24
50
46

64

30


18
67





46



7
7

7
8
7
7
7
7
7

8











*
.7
.8

'.9
.2
.6
.6
.5
.6
.5
*
.0
«

.
,
,
,
,
,
.



0
0

1


0

0

0





0



0



152
.74

154

0
.48
.
.49
.
.44
.
.

.
.
.54
.
.
.
.57
•

9
22
11

4
4
12
22

25














.46
.28
.21

128
.24
.87
.16
*
.26
•

.
.
.
.
,
.
.
.
.
,
•

                                  B-113

-------
 TABLE 35d.   SUMMARY STATISTICS FOR PHYSICAL MEANS ANNUAL DATA
SEGMENT  YEAR           LEVEL TEMP  SALIN    PH    SECCH1  JTU
CB-1
CB-2
CB-3
CB-4
CB-4
CB-5
WT-4
WT-5
TF-i
LE-1
TF-2
TF-2
RET-2
LE-3
ET-4
1
1
1
1
1
1
1
1
1
1
1
980
9feO
980
980
980
980
980
980
980
980
980
1980
1
1
1
980
980
980
T
T
T
T
8
T
T
T
T
T
T
8
T
T
T
19.0
19.1
14.1
16.9
15.0
*
16.1
14.4
21.1
21.8
20.9
19.7
19,8
16.7
18.5

0.
10.
11.
17.

,
7.
4.
14.
0.
0.
2.

8*

80
16
31
74


01
86
52
14
11
93

26
7.6
7.7
7.6
8.0
7.5
*

7!7
7.3
7.7
7.3
7.2
7.5
7.7


0
1
1




0





1
*
.60
.17
.50

,
t

.17

9
m
9
9
.88
11
16
7
4
5
2

8


20

11


.87
.17
.71
.44
.44
.76

195


.78

152

•

                                  B-114

-------
 TABLE 36a.  SUMMARY STATISTICS FOR PHYSICAL MEANS SEASONAL DATA
SEGMENT   YEAR   SEASON LEVEL TEtfP   SALIM
PH
SECCHI   JTU
CB-1
CB-1
CB-1
CB-2
CB-2
CB-2
CB-3
CB-3
CB-3
CB-3
CB-3
C8-4
CB-4
CB-4
CB-4
CB-4
CB-4
CB-5
CB-5
CB-5
CB-5
WT-1
WT-2
WT-2
WT-4
WT-5
WT-5
W.T-5
WT-6
WT-6
WT-6
WT-7
WT-8
WT-8
WT-8
TF-1
TF-1
TF-1
RET-1
LE-1
LE-1
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
SPRING
SUMMER
FALL
SPRING
SUMMER
FALL
SPRING
SPRING
SUMMER
SUMMER
FALL
SPRING
SPRING
SUMMER
SUMMER
FALL
FALL
SPRING
SUMMER
FALL
FALL
SUMMER
SPRING
SUMMER
SPRING
SPRING
SUMMER
FALL
SPRING
SUMMER
FALL
SUMMER
SPRING
SUMMER
FALL
SPRING
SUMMER
FALL
FALL
FALL
FALL
T
T
T
T
T
T
T
B
T
B
T
T
B
T
B
T
B
T
T
T
B
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
B
T
T
8
8.7
25.9
14.0
13.0
25.8
13.6
14.1
10.3
25.2
23.0
16.7
12.8
6.0
25.0
20.9
16.0
17.5
8.8
23.9
18.5
18.4
23.1
12.7
23.0
13.3
13.4
24.0
10,3
14.7
24.1
19.2
23.8
11.6
25.2
22.2
13.8
24.2
17.8
18.7
18.9
15.7

Oll5
•

U53
1.07
3.99
11.82
7.63
13.81
8.72
8.50
15.98
12.22
1».03
14.11
17.63
12.14
14.12
14.30
16.50
.

oln
0.73


10.34
5.01
9.05
8.55
9.82
5.93
10.63
11.66

3^44
1.16
11.14
13.25
13.77
7.4
8.0
*
7.7
7.7
7.2
7.6
7.4
7.6
*
7.3
8.0
7.8
8.0
7.9
7.7

8.1
7.6
7.2
9
7.9
7.5
8.1
7.8
m
9


8.1

8.2
8.1
7.7
7.8
7.3
6.9

7.6
*
*

• *
7.09
0.54 18.31
0.58 12.89
0.52 21.14
0.66 15.08
* •
0.78 8.53
t .
0.80 13.95
1.35 5.17
, .
1.56 4.11
. f
2.51 3.28
3.41
t
. .
2.59
• *
.
13.54
9.00
88.33

'. 15)78
.
0.83 8.22
0.73 7.46
0.68 6.37
5.72
7.82
6.56
5.33
63.26
34.36
• *
0.78
t
• *
                             (continued)
                                 B-115

-------
TABLE 36a.  (Continued)

SEGMENT
TF-2
TF-2
TF-2
TF-2
RET-2
RET-2
RET-2
LE-2
Lt>2
TF-3
TF-3
TF-3
TF-3
TF-3
RET-3
RET-3
RET-3
LE-3
LE-3
LE-3
LE-3
TF-4
TF-4
RET-4
RET-4
RET-4
LE-4
LE-4
LE-4
LE-4
LE-4
LE-4
TF-5
TF-5
TF-5
RET-5
RET-5
LE-5
LE-5
LE-5
CT-2
ET-2
ET-3
YEAR
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
1977
SEASON LEVEL TE*P
SPRING
SPRING
SUMMER
FALL
SPRING
SUMMER
FALL
SPRING
SUMMER
SPRING
SPRING
SUMMER
SUMMER
FALL
SPRING
SUMMER
FALL
SPRING
SUMMER
SUMMER
FALL
SUMMER
FALL
SPRING
SUMMER
FALL
SPRING
SPRING
SUMMPJR
SUMMER
FALL
FALL
SUMMER
SUMMER
FALL
SUMMER
FALL
SUMMER
FALL
FALL
SPRING
SUMMER
SUMMER
T
B
T
T
T
T
T
T
T
T
B
T
B
T
T
T
T
T
T
B
T
T
T
T
T
T
T
B
T
B
T
B
T
B
T
T
T
T
T
B
T
T
T
16.7
18.2
26.5
18.8
18.0
25,1
18.8
15.6
24.5
19,5
19.7
26.7
25.9
21.5
18.6
25.0
21.4
17.1
25.0
25.2
21.2
26.6
23.4
16.9
26.4
23.2
15.1
14.7
25.9
24.8
23.2
23.4
28.8
28.1
20.4
27,5
20.0
26.0
19.8
19.5
15.0
25.6
26.7
SALIN PH
0.13 7.7
0.13
0.26 7.7
1.14
3.14 7.5
5.54 6.8
10.12
7.0
6.7
. .
1^34 '.
3l42 '
4.62
8.55
11.77
13.29
15.30
18^25 \
2.72
4.96
6,12
10.64 .
14.02
15.02
19^61 I
2U74 I
, .
1.42
2^70 I
• •
• •
17.48
19.03
23.58
7,4
0,97 7.5
0.24 8.1
SECCHI
0.56
0^59
0.68
0^50
•
*
*
0.53
Ol52
0^80
0.40
0.44
1.10
1.00
1.14
U63
0.53
0.62
0^54
0.64
0.71
Ol86
0^99
•
0.58
0^67
•
*
0.91
1.21
•
•
•
•
JTU
28.70
34^63
•
10.14
27.57
•
8.17
22.29
•
*
•
•
•
•
•
*
•
•
•
•
*
•
•
•
•
•
•
•
•
»
*
•
•
•
*
•
•
•
•
78.80
29.65
13.17
                                 (continued)
                                     B-116

-------
 TABLE 36a.  (Continued)
SEGMENT  YEAR  SEASON LEVEL TEMP  SALIN
                                 PH
                                SECCHI  JTU
 ET-4
 ET-4

 ET-5
 ET-5

 ET-6
 ET-6

 ET-7
 ET-7

 ET-10
 ET-10

 EE-1
 EE-1

 EE-3
 EE-3

 WE-4
 WE-4
 WE-4
 WE-4
1977
1977

1977
1977

1977
1977

1977
1977

1977
1977

1977
1977

1977
1977

1977
1977
1977
1977
SPRING
SUMMER

SPRING
SUMMER

SPRING
SUMMER

SPRING
SUMMER

SPRING
SUMMER

SPRING
SUMMER

SPRING
SUMMER

SPRING
SUMMER
FALL
FALL
T
T

T
T

T
T

T
T

T
T

T
T

T
T

T
T
T
B
11.9
26.1

13.9
25.2

16.9
26.9

14.7
25.4

16.4
25.5
11
26
,7
,0
13.7
26.4

14.9
25.6
23.0
22.7
     3.96
     6.74
     3^44
     2.47
     5.07
 6.06

 9.90
11.54

10.22
11.69
7.7
7.5

7.1
7,5

7.7
7.1

7.6
7.4

6.2
6.8

8.0
7.5

7.1
7,1
2.26
      26.06
      35.69

      11.77
      12.11

      21.59
      16.20

      27.08
      11.78

      16.60
      12.94
       4.92

      21.88
      14,73
                              B-117

-------
TABLE 36b.  SUMMARY STATISTICS FOR PHYSICAL MEANS SEASONAL DATA
SEGMENT  YEAR   SEASON  LEVEL TEMP  SALIN
                                               PH
SECCHI   JTU
CB-1
CB-1
CB-2
CB-2
CB-2
CB-3
CB-3
C8-3
CB-3
CB-4
CB-4
CB-4
C8-4
CB-4
CB-4
CB-5
CB-5
CB-5
CB-5
CB-5
WT-2
WT-5
WT-5
WT-5
TF-1
TF-1
TF-1
TF-1
RET-1
RET-1
RET-1
LE-1
LE-1
LE-1
LE-1
TF-2
TF-2
TF-2
TF-2
TF-2
RET-2
RET-2
RET-2
RET-2
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
SUMMER
FALL
SPRING
SUMMER
FALL
SPRING
SPRING
SUMMER
FALL
SPRING
SPRING
SUMMER
SUMMER
FALL
FALL
SPRING
SUMMER
SUMMER
FALL
FALL
SUMMER
SPRING
SUMMER
FALL
SPRING
SUMMER
SUMMER
FALL
SPRING
SUMMER
FALL
SPRING
SUMMER
SUMMER
FALL
SPRING
SPRING
SUMMER
SUMMER
FALL
SPRING
SPRING
SUMMER
FALL
T
T
T
T
T
T
B
T
T
T
B
T
B
T
B
T
T
B
T
B
T
T
T
T
T
T
B
T
T
T
T
T
T
B
T
T
B
T
B
B
r
B
T
T
25.6
21.9
7.1
26.2
21.4
7.6
5.2
24.4
18.3
6.5
5.1
24.5
21.7
22.5
22.7
9.1
24.4
23.0
19.1
20.0
26.4
10.5
23.5
18.6
14.9
24.5
25.3
15.5
14.7
25.4
18.1
13.4
24.7
23.4
19.4
13.9
17.9
26.6
26.9
19.0
16.8
16.4
26.4
19.6
0.14
•
9
0.52
2.59
3.57
11.01
5.35
10.69
9.52
14.54
9.18
15.18
13.59
19.45
10.03
10.91
14.55
16.31
19.44
t
9
4.51
9.74
0.71
0.89
9
1.62
4.97
6.55
11.56
7.58
8.91
10.95
13.83
0.12
9
0.20
0.15
0.23
1.26
9
2.35
6.09
7.4
8.4
7.6
7.9
8.2
7.9
7.6
7.6
7.9
7.9
7.2
8.0
7.1
8.1
7.5
7.9
8.1
7.5
8.0
7.7
8,3
9
7.5
7.4
6.9
7.0
7.0
7.1
7.6
7.3
7.4
8.0
7.6
7,1
7.7
7,7
*
7.8
7.5
8.0
7.3
7.2
7.6
7.9
9
•
0.50
0.67
*
0.48
*
0.84
0.91
2.29
9
1.62
•
1.53
•
•
1.83
•
•
*
9
9
9
9
0.54
0.43
9
0.62
9
0.50
•
•
•
•
•
0.51
9
0.58
•
•
0.40
9
0.55
0.84
6.51
7.15
36.75
13.79
26.28
22.26
9
8.87
10.15
6.99
9
3.21
3.20
3.10
3.33
2.59
2.59
9
2.28
2.59
•
9
8.84
10.70
26.43
26.08
38.63
18.70
20.95
20.42
31.82
5.80
8.59
9
2.78
13.79
9
12.05
9
•
24.93
9
18.43
10.35
                          (continued)
                                  B-118

-------
TABLE 36b.  (Continued)
SEGMENT   YEAR  SEASON  LEVEL TEMP   SALIN
PH
SECCHI   JTU
TF-3
TF-3
TF-3
TF-3
RET-3
RET-3
LE-3
LE-3
LE-3
TF-4
TF-4
RET-4
RET-4
LE-4
LE-4
LE-4
LE-4
TF-5
TF-5
TF-5
TF-5
TF-5
RET-5
RET-5
RET-5
RET-5
LE-5
LE-5
LE-5
LE-5
ET-5
ET-10
rfE-4

1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1978
1976
1978
1978

SUMMER
SUMMER
FALL
FALL
SUMMER
FALL
SUMMER
SUMMFR
FALL
SUMMER
FALL
SUMMER
FALL
SUMMER
SUMMER
FALL
FALL
SPRING
SUMMER
SUMMER
FALL
FALL
SPRING
SUMMER
SUMMER
FALL
SPRING
SUMMER
SUMMER
FALL
SUMMER
SPRING
SUMMER

T
B
T
B
T
T
T
B
T
T
T
T
T
T
B
T
B
T
T
B
T
B
T
T
B
T
T
T
B
T
T
T
T

25.4
26.0
12.8
13.0
25.1
13.6
24.6
24.4
15.9
24.1
21.5
25.1
21.1
25.2
24,3
20.5
20.7
15.3
28.1
27.1
17.8
20.1
14.8
27.1
27.3
18.2
14.6
26.9
20.9
18. 3
25.6
15.1
23.7

0.17
* *
* *

4.85
9.18
12.36
13.54
• •
1.97
3.40
5.86
7.95
16.08
18.78
* •
18.56
. .

t t
t ,
•
• t
* t
• *
• *
4.51
8.65

. *
7.0
. .
•

0.34
0 t
0.73
* •
0.31
0,68
1.15

* '
0.45
0.57
0,39
0.53
0.75
m
* •


0^47 '.

0^65 '.
•

9 9
t t
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* *
, ,
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                                B-119

-------
TABLE  36c.  SUMMARY  STATISTICS FOR PHYSICAL MEANS SEASONAL DATA
SEGMENT  YEAR   SEASON LEVEL TEMP   SALIN
                                              PH
SECCHI   JTU
CB-1
CB-1
CB-2
CB-2
CB-2
CB-3
CB-3
CB-3
CB-3
CB-3
CB-3
CB-4
CB-4
CB-4
CB-4
CB-5
CB-5
CB-5
CB-5
CB-5
CB-7
WT-5
WT-5
fcT-5
WT^S
TF-2
TF-2
Tf-2
TF-2
TF-2
TF-2
RET-2
RET-2
RET-2
RET-2
RET-2
TF-3
RET-3
RET-3
L£-3
LE-3
LE-3
TF-4
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
SUMMER
FALL
SPRING
SUMMER
FALL
SPRING
SPRING
SUMMER
SUMMER
FALL
WINTER
SPRING
SPRING
SUMMER
FALL
SPRING
SPRING
SUMMER
FALL
WINTER
SPRING
SPRING
SUMMER
FALL
WINTER
SPRING
SPRING
SUMMER
SUMMER
FALL
FALL
SPRING
SPRING
SUMMER
FALL
FALL
SUMMER
SUMMER
FALL
SUMMER
FALL
FALL
SUMMER
T
T
T
T
T
T
B
T
B
T
T
T
B
T
T
T
B
T
T
T
T
T
T
T
T
T
8
T
B
T
B
T
B
T
T
B
T
T
T
B
T
B
T
25.2
15.8
14.5
23.8
15.0
14.8
12.1
22.9
20.9
15.6
6.3
15.0
10.4
22.5
18.0
16.3
13.3
23.0
19.8
6.0
16.8
12.0
23.8
16.7
6.4
12.5
13.3
24.6
24.4
15.0
10.4
13.0
11.5
24.0
15.7
18.5
24.8
24.8
15.8
22.2

17*4
25.9
0


0*97
.
5.55
9.69
6.33
12.48
5.60
11.87
8.98
16.80
8.76
9.70
12.39
16.62
12.30
12.28
15.98
21.94
4.62
6.20
5.69
.
0.14

0*78
0.82

I
1.19
4.72
2.30
1.43

.
3.30
3.29


11.88
2.33

*
7.8
7.6

7.8
7.2
7.6

7*9
7.9
8.1
7.4
7.7

8.5
7.6
7.8
8.4
8.0
8.4
7.2
7.6
7.9

7.5
7.4
7.7
7.6
7.7
7.6
7.6
7.5
7.6
7.6
7,4
•

9
9
8.5

•
0.93
.

0*54
0.49
0.66
0
0.83

0*74
0
1.78

1.46
1.37



t
.
.

f
9
.
0.40
t
0.52

0*53

0.36

0*54
0.58

.
0.38
0.49

l'.62

0.36
9.47
9.45
28.06
19.59
19.18
15.62

8 ".92

9l09
*
4.31
4.75
3.96
4.57
2.75


5*72

*
11.08
20.43
7.10
*
24.28

22^89

19*31
*
35.25

20*64
19.90
*
0

0
0

0
•
                            (continued)
                                 B-120

-------
 TABLE 36c,   (Continued)
SEGMENT   YEAR   SEASON LEVEL  TEMP  SALIN
PH
SECCHI  JTU
RET -4
RET-4
Lfc-4
LE-4
LE-4
LE-4
TF-5
TF-5
TF-5
TF-5
TF-5
TF-5
TF-5
RET-5
RET-5
RET-5
RET-5
RET-5
RET-5
RET-b
LF.-5
LE-5
LE-5
LE-5
LE-5
LF.-5
ET-3
ET-5
WE-4
WE-4
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
SUMMER
FALL
SUMMER
SUMMER
FALL
FALL
SPRING
SPRING
SUMMER
SUMMER
FALL
FALL
WINTER
SPRING
SPRING
SUMMER
SUMMER
FALL
FALL
WINTER
SPRING
SUMMER
SUMMER
FALL
FALL
WINTER
SUMMER
SUMMER
SUMMER
SUrtKKR
T
T
T
B
T
B
T
B
T
B
T
B
T
T
B
T
8
I
B
T
T
T
B
T
B
T
T
T
T
8
25.0 6.35 . 0,34
18.1 4.00
24.3 9.31 . 0.66
24.2 18.30
19.2 12.02
18.9 ....
16.5 , . 0,48
19.1 ....
27.1 . . 0.70
25.7 ....
17.5 . . 0.63
19.7 ....
6.5 , . 0.36
16.6 ....
16.4 ....
26.1 ....
26.0 ....
18.2 ....
18.3 ....
7.2 ..
18.2 . . 0.45
25.7 8.44 . 0.64
22.8 ....
17.7 6.11 . 0.63
1 9 t i • t * •
7.8 7.84
24.1 ....
20.9 . 6.5 . 12.36
25.3 ....
24.9 ....
                                B-121

-------
 TABLE  36d.  SUMMARY  STATISTICS FOR PHYSICAL MEANS SEASONAL DATA
SEGMENT  YEAR   SEASON LEVEL TEMP   SALIN
PH
SECCHI   JTU
CB-l
CB-l
CB-2
CB-2
C6-3
CB-3
CB-3
CB-3
CB-3
CB-4
CB-4
CB-4
CB-4
CB-5
CB-5
CB-5
WT-4
WT-4
WT-4
WT-4
WT-b
WT-5
WT-5
WT-5
TF-1
TF-1
LE-1
LE-1
TF-2
TF-2
TF-2
TF-2
TF-2
RET-2
RET-2
RET-2
LE-3
LE-3
LE-3
TF-4
TF-5
RET-5
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
SPRING
SUMMER
SPRING
SUMMER
SPRING
SUMMER
SUMMER
FALL
WINTER
SPRING
SPRING
SUMMER
SUMMER
SPRING
SUMMER
SUMMER
SPRING
SUMMER
FALL
WINTER
SPRING
SUMMER
FALL
WINTER
SUMMER
FALL
SUMMER
FALL
SPRING
SPRING
SUMMER
SUMMER
FALL
SPRING
SUMMER
SUMMER
SPRING
SUMMER
FALL
SUMMER
SUMMER
SUMMER
T
T
T
T
T
T
B
T
T
T
B
T
B
T
T
B
T
T
T
T
T
T
T
T
T
T
T
T
T
R
T
B
T
T
T
B
T
T
T
T
T
T
13.7
24.3
14.3
23.8
11.4
23.4
21.4
16.7
5.0
11.7
9.5
22.0
20.5
23^4
21.8
10.2
26.6
21.4
6.0
13.1
24.2
15.2
5.1
25.6
16.6
25.9
17.6
14. B
14.1
26.5
25.3
21.5
14.5
25.0
24.2
11.6
24.6
13.9
26.2
27.2
25.9
oai
0.08
1.52
5.85
9.81
11.09
14.83
10.50
18.67
12.12
16.80
13^81
18.88
*
6.22
7.80
3.47
6.25
13.05
15.99
0.08
0.08
0.10
0.13
0.25
1.21
4.64
8.45
*
.
.
.
7.2
7.9
7.8
7.5
7,5
7.6
7.4
8.2
7.6
7.8
7.4
7l6
7.3
*
7.6
7.7
*
*
7.2
7.4
7.5
7.8
7.3
7.3
7.3
7.0
7.4
7.6
7.4
7.1
7.4
7.8
8,0
t
.
•
11.47
12.26
0.59 14.02
0.60 18.32
0,78 11.53
1.56 3.89
* •
1.44 5.93
8.04
1.55 2.95
2.84
3.10
2.42
• *
j •
11.43
6.47
* *
0.15
0.18
* •
23.19
Ol61 18^36
. .
12.55
0.63 10.48
• *
* •
.
0.43
0.52
                         (continued)
                                  B-122

-------
TABLE 36d.  (Continued)
SEGMENT  YEAR  SEASON LEVEL TEMP   SALIN    PH    SECCHI  JTU
 LE-5    1980  SUMMER   T   24,9   22.31    .      0.96
1980
1980
1980
1980
1980
SUMMER
SUMMER
SUMMER
FALL
SUMMER
T
B
T
T
T
24,9
22.4
26.0
11.0
25.6
22.31
26.18
6.82
9.70
3.05
•
•
7.5
•
7.0
 ET-4    1980  SUMMER   T   26.0    6.82   7.5     2.94  10.30
 ET-4    1980  FALL     T   11.0    9.70    .      0.82
 ET-5    1980  SUMMER   T   25.6    3.05   7.0      .     14.01
 WEr4    1980  SUMMER   T   25.2     ....
 WE-4    1980  SUMMER   B   22.4     ....
                              B-123

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-------
TABLE 40.   SUMMARY OF STATISTICALLY SIGNIFICANT ANNUAL NUTRIENT
           TRENDS DETERMINED BY PEARSON'S CORRELATION

Segment TP
CB-1 +
CB-2 +
CB-3 0
CB-4 0
CB-5 +
CB-6
CB-7
CB-8
WT-1
WT-2
WT-3
WT-4
WT-5 0
WT-6 0
WT-7
WT-8 0
TF-1 0
RET-1
LE-1
TF-2
RET-2 0
LE-2 0
TF-3
RET-3
LE-3 +
TF-4
RET-4 0
LE-4
TF-5 0
RET-5
LE-5 0
IFF TN N03
+ 0 +
+ 0 +
000
000
000






0
000
0

000
0 + +
+ 0
0 0
00 +
00 +
+ - 0

+ 0
0 0
0
000
0
0

- 0 0
N02
0
0
+
0
0







0
0

0
0
0
0
0
0
+

0
0
0
0

0

0
NH3
0
0
0
0
0







0
0

0
0
0

_
0
0

0
0
0
0

0
0
0
TKN
0
0
0
0
0







0


0
+
0

_
0
—

0
0
0
0

0

0
CHL-AU
0
+
+
+
0

+





0
+

0
0


0
0
0




0




                                  (continued)
                                        B-138

-------
TABLE 40.  (continued)
Segment    TP     IFF    TN     N03     N02     NH3      TKN     CHL-AU
ET-1
ET-2
ET-3
ET-4
ET-5
ET-6
ET-7
ET-8
ET-9
ET-10
EE-1
EE-2
EE-3
WE-4
0 0
0 +
0 0
0 +
0 +
0 0
0 0


0 0
0 0
0
+

_
0 + 0
0
+ + 0
000
0 + 0
- + 0


000
000


0
0
0
0
0
0
0
0


0
0


0
__
-
0
0 +
0 +
0
0


0 +
0




+
-
= increasing,
= decreasing,
0 = no trend
blank = limited
>
data,


                                       B-139

-------
TABLE 41a.  SUMMARY OF STATISTICALLY SIGNIFICANT  SEASONAL NUTRIENT
            TRENDS DETERMINED BY PEARSON'S  CORRELATION  - SPRING

Segment TP
CB-1 0
CB-2 0
CB-3 0
CB-4 0
CB-5 0
CB-6
CB-7
CB-8
WT-1
WT-2
WT-3
WT-4
WT-5 0
WT-6
WT-7
WT-8
TF-1 0
RET-1
LE-1
TF-2 0
RET- 2 0
LE-2 0
TF-3
RET-3
LE-3
TF-4
RET-4
LE-4
TF-5
RET-5
LE-5
IFF TN N03 N02 NH3 TKN CHL-AU
00 0
00 + 00 00
00000 0 +
00 + 00 0 +
00000 0 +

0 +




0




0


0 - + 0 00
00 + 0- 0 +
0 - 000




000



0 00
                                   (continued)
                                        B-140

-------
TABLE 41a.  (continued)
Segment    TP     IFF   TN     N03    N02    NH3     TKN    CHL-AU
ET-1
ET-2 0 0 - 0
ET-3 000 0
ET-4 00 +00
ET-5
ET-6
ET-7
ET-8
ET-9
ET-10
EE-1 00 000
EE-2
EE-3 0
WE-4


0
0






0




    + = increasing,           0 = no trend,
    - = decreasing,       blank = limited data,
                                        B-141

-------
TABLE 4lb.  SUMMARY OF STATISTICALLY SIGNIFICANT SEASONAL NUTRIENT
            TRENDS DETERMINED BY PEARSON'S CORRELATION - SUMMER

Segment IP
CB-1 0
CB-2 +
CB-3 0
CB-4 0
CB-5 0
CB-6
CB-7
CB-8
WT-1
WT-2
WT-3
WT-4
WT-5 0
WT-6
WT-7 0
WT-8 0
TF-1
RET-1
LE-1
TF-2 0
RET-2 0
LE-2 0
TF-3
RET-3
LE-3
TF-4
RET-4
LE-4
TF-5 0
RET-5
LE-5 0
IFF TN N03
0 0
00 +
000
000
0 0






0
0 0

0 0
0
0 + +

0 +
0-0
000
000


0

0

0-0

0
N02
0
+
0
0
0







0

0
0
0

0
0
0
0

0
0
+
0

-

_
NH3
+
0
0
0
0







-



0


0
0
0

0
0
0
0

0

0
TKN
0
0
0
0
0







0



0


-
0
0

0
0
0
0

0

0
CHL-AU
+
0
0
0
0







0

+
0
0


0
0
0









                             (continued)
                                        B-142

-------
TABLE 41b.  (continued)
Segment    TP     IFF   TN     NC>3    N02    NH3     TKN    CHL-AU
ET-1 000 0
ET-2 000000
ET-3 000 0
ET-4 00 00
ET-5 0+0000
ET-6
ET-7 000 0
ET-8
ET-9
ET-10
EE-1
EE-2
EE-3
WE -4
0
0
0
0 0
0

    + = increasing,          0 = no trend,
    - = decreasing,      blank = limited data,
                                        B-143

-------
TABLE 41c.  SUMMARY OF STATISTICALLY SIGNIFICANT SEASONAL NUTRIENT
            TRENDS DETERMINED BY PEARSON'S CORRELATION - FALL
Segment    TP     IFF   TN    N03    N02    NH3     TKN   CHL-AU
CB-1 00 +00
CB-2 0 + 0 + 0
CB-3 0+00 + 0
CB-4 000000
CB-5 + 000
CB-6
CB-7
CB-8
WT-1
WT-2
WT-3
WT-4 0
WT-5 00 0
WT-6
WT-7
WT-8 0 00
TF-1 0 +
RET-1
LE-1
TF-2 000000
RET-2 000000
LE-2(
TF-3
RET-3
LE-3 000
TF-4 + 0
RET-4
LE-4
TF-5 00 000
RET-5
LE-5 - 0
0 0
0 0
0 0
+
0
0 0
0 0
0
0
0
                                   (continued)
                                        B-144

-------
TABLE 41c.  (continued)
Segment    TP     IFF   TN     H03    N02    NH3     TKN    CHL-AU
ET-1
ET-2
ET-3
ET-4       00            +
ET-5
ET-6
ET-7
ET-8
ET-9
ET-10

EE-1              0
EE-2
EE-3
WE-4
    + = increasing,          0 = no trend,
    - = decreasing,      blank = limited data,
                                       B-145

-------
TABLE 4ld.  SUMMARY OF STATISTICALLY SIGNIFICANT SEASONAL NUTRIENT
            TRENDS DETERMINED BY PEARSON'S CORRELATION - WINTER
Segment
TP
IPF   TN    N03    N0£    NH3
                                         TKN   CUL-AU
CB-1
CB-2
CB-3
CB-4
CB-5
CB-6
CB-7
CB-8
0
0
0

0
0
0
0
+
WT-1
WT-2
WT-3
WT-4
WT-5
WT-6
WT-7
WT-8
 TF-1
RET-1
 LE-1

 TF-2
RET-2
 LE-2
0
0
0     0
0
              0       0
              0       0
 TF-3
RET-3
 LE-3

 TF-4
RET-4
 LE-4

 TF-5
RET-5
 LE-5
                                   (continued)
                                        B-146

-------
TABLE 41d.  (continued)
Segment    TP     IFF   TN     NC>3    NC>2    NH3     TKN    CHL-AU
ET-1
ET-2
ET-3
ET-4
ET-5
ET-6
ET-7
ET-8
ET-9
ET-10

EE-1
EE-2
EE-3
WE-4
    + = increasing,          0 = no trend,
    - = decreasing,      blank = limited data,
                                        B-147

-------
         TP<=0-042

         0-0420.245

         LIMITED DATA
Figure 40.  Total P  spring  averages,  1977 to 1980.  Data depth  averaged
            and grouped  by  7  1/2  minute USGS quadrangles.
                                        B-148

-------
      TP<=0-042

      0.0420.245

      LIMITED DATA
Figure 41.  Total P summer averages,  1977 to 1980.  Data depth averaged
            and grouped by 7 1/2 minute  USGS quadrangles.
                                       £-149

-------
      TN<=-3

      •3!-75

      LIMITED DATA
Figure 42.  Total nitrogen annual average,  1977  to  1980.   Data depth
            averaged and grouped by 7 1/2 minute USGS  quadrangles.
                                      B-150

-------
      LIMITED  DATA
Figure 43.  Total nitrogen spring average, 1977 to 1980.  Data are depth
            averaged and grouped by USGS 7% - minute quadrangles.
                                   B-151

-------
      TN>1 .75

      LIMITED  DATA
Figure 44.   Total nitrogen summer average, 1977 to 1980.  Data are depth
            averaged and grouped by USGS 71/2 minute quadrangles.
                                       B-152

-------
      CHL<=10

      1060

      LIMITED DATA
Figure 45.  Total chlorophyll annual average, 1977 to  1980.  Data are
            surface averaged and grouped by USGS 7 1/2 minute  quadrangles.
                                       B-153

-------
      CHL<=10

      1060

      LIMITED DATA
Figure 46.  Total chlorophyll spring average,  1977  to  1980.   Data are
            surface averaged and group by USGS 11/2 minute  quadrangles.
                                       B-154

-------
      CHL<=10

      1060

      LIMITED  DATA
Figure 47.  Total chlorophyll summer average, 1977 to 1980.  Data are
            surface averaged anb grouped by USGS 7 1/2 minute quadrangles
                                      B-155

-------
                              SECTION 9

                        LITERATURE CITED

Ahrens,  L.H.   1957.   The Lognormal Distribution of the Elements.
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Biggs, R.B.  1967.   The Sediments of Chesapeake Bay.  p. 235-280.  In:
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Biggs, R.B.  1981.   Freshwater Inflow to Estuaries, Short and Longterm
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Brush, G.S.,  and F.W.  Davis.  1981.  Stratigraphic Evidence of Human
    Disturbance in Chesapeake Bay Tributaries.  Draft Report to the U.S.
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    MD.   155  pp.

Cargo, D.G.,  and R.B.  Biggs.  1969.  Hydrographic Phenomena in the
    Chesapeake Bay.   Natural Res. Inst.  Univ. of Maryland.  Ref. #
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Carpenter, J.H., and D.G.  Cargo.  1957.  Oxygen Requirement and Mortalities
    of Blue Crabs.   Tech.  Report //  13.  Chesapeake Bay  Institute.  Johns
    Hopkins University.

Cronin,  W.B., and M.E. Mallonie.  1981.  Additional Statistics on the
    Dimensions of the Chesapeake Bay:  Areas  and Volumes of Segment
    Elements  per Meter of  Vertical  Thickness  Measured Relative to Bottom.
    Part 1.  Contract # R805959.  Final Report to U.S.  Environmental
    Protection Agency's Chesapeake  Bay Program.  Chesapeake Bay Institute
    Report # 22.  The Johns Hopkins  University.

Draxler, Roland R., and Jerome L. Heffler.  1981.  Workbook for Estimating
    the Climatology of Regional-Continental Scale Atmospheric Dispersion
    and Deposition over the United  States.  NOAA Technical Memorandum, ERL
    ARL-96.  Air Resources Laboratories.   Silver Spring, MD.

Eisenberg, M., and J.J. Topping.  1981.  Heavy Metal, Polychlorinated
    Biphenyls and Pesticide Levels  in Shellfish and Finfish from Maryland,
    1976 to 1980.  Office  of Environmental Programs, MD. State Dept. of
    Health and Mental Hygiene.   Baltimore, MD.  250 pp.

Flemer, D.A.  and R.B. Biggs.  1971.   Short Term Fluorescence and Dissolved
    Oxygen Relations in Chesapeake  Bay.   Ches. Sci. 12:45-47.

Gilinsky, E., and J.V. Roland.   1983.  A  Summary and Analysis of Metal and
    Pesticide Concentrations in  Shellfish  and Fish Tissues from Virginia
    Est-uarine Waters.  Virginia  State Water Control Board Publication.
    77 pp.

Goldich, S.S.  1938.  A Study in Rock Weathering.  J. Geology.  46: 17-58.
                                        B-156

-------
Goldsmith, V., and C.N. Sutton.  1977.  Bathymetry of Chesapeake Bay.
    Bathymetric Chart Series // 2.  Virginia Institute of Marine Science,
    Gloucester Point, VA.

Helz, G.R., S.A. Sinex, G.H. Setlock, and A.Y.  Cantillo.  1980.   Chesapeake
    Bay Sediment Trace Elements.  Research in Aquatic Geochemistry,
    Department of Chemistry, University of Maryland.  202 pp.

Helz, G.R., S.A. Sinex, G.H. Setlock, and A.Y.  Cantillo.  1981.   Chesapeake
    Bay Sediment Trace Elements.  Grant // 805954.   University  of Maryland.
    College Park, MD.  Final Report to the U.S. Environmental  Protection
    Agency's Chesapeake Bay Program.

Keith, M.L., E.F. Cruft, and E.G. Dahlberg.  1967.  Trace Metals in  Stream
    Sediment of Southwestern Pennsylvania.  Part I.  In:  Bulletin of  the
    Earth and Mineral Sciences Experiment Station.  The Pennsylvania State
    University.

Kingston, H.M., R.R. Greenberg, E.S. Beary, B.R. Hardas, F.R.  Moody, T.C.
    Rains, and W.S. Liggett.  1982.  The Characterization of the Chesapeake
    Bay:  A Systematic Analysis of Toxic Trace Elements.  Grant  No.  EPA
    79-D-X-0717.  Final Report to the U.S. Environmental Protection
    Agency's Chesapeake Bay Program.

Krauskopf, K.B.  1967.  Introduction to Geochemistry.  McGraw  Hill.
    New York.  721 pp.

Lippson, A.J.  1973.  The Chesapeake Bay in Maryland:  An Atlas  of Natural
    Resources.  Johns Hopkins University Press, Baltimore and  London.
    55 pp.

Lystrom, D.J., F.A. Rinella, D.A. Rickent, and  L.  Zimmermann.   1978.
    Multiple Regression Modelling Approach for Regional Water  Quality
    Management.  U.S. Environmental Protection Agency  600/7-78-1980.
    59 pp.

Nichols, M.N., R. Harris, and G. Thompson.  1981.   Significance  of
    Suspended Trace Metals and Fluid Mud in Chesapeake Bay.  U.S.  EPA
    R806002-01-1.  U.S. Environmental Protection Agency's Chesapeake Bay
    Program.  Annapolis, MD.  129 pp.

Parrish, R.  1983.  Report on the Derivation of Site-Specific  Water  Quality
    Criteria for Eight Metals in Chesapeake Bay.  Submitted  to U.S.  EPA
    Chesapeake Bay Program.  Annapolis, MD.  12 pp. + Appendices.

Pritchard, D.W.  1967.  What is an Estuary:  Physical Viewpoint.   In:
    Estuaries.  G.H.  Lauff, ed.  AAAS Publ. # 83.  Washington,  DC.

Sinex, S.A., and G.R. Helz.  1982.  Dynamics of Trace Element  Transport in
    a Rapidly Flushed, Industrialized Harbor.  (In manuscript, 26  pp.)

Taft, J.  1982.  Nutrient Processes in Chesapeake  Bay.   In:  Chesapeake Bay
    Program Technical Studies:   A Synthesis. E.G. Macalaster, D.A.  Barker,
    and M.E. Kasper, eds.  U.S. Environmental Protection Agency,
    Washington, DC.  pp. 103-149.
                                       3-157

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                             APPENDIX C






                              CONTENTS






Figures	   C-ii




Tables	   C-iii



Section




  1      Life Cycles of Major Species	   C-l




  2      Analysis of Oyster  Habitat  	   C-32




  3      Sources and Analysis of Fisheries Landing Data  	   C-37




  4      Analytical Approaches for Determining Trends in Fisheries  .  .   C-53




  5      SAV Decline and Geographic Analysis 	   C-55



  6      Literature Cited  	   C-70
                                    C-i

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                               FIGURES
Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure
1.

2.

3.

4.

5.

6.

7.

8.
Chesapeake Bay, Maryland oyster bars, and Virginia
Baylor bottoms 	
NOAA National Marine Fisheries Service Basins used in
resource data analysis 	
Distribution of submerged aquatic vegetation in
Chesapeake Bay, 1965 	
Area of submerged aquatic vegetation decline
between 1965 and 1970 	
Area of submerged aquatic vegetation decline
between 1970 and 1975 	
Area of submerged aquatic vegetation decline
between 1975 and 1980 	
Trends in SAV occurrence in six areas in the middle
Bay zone 	
United States Geological Survey topographic quad

C-36

C-48

C-56

C-57

C-58

C-59

C-60

           areas used for  aerial sampling of  submerged
           aquatic vegetation  	    C-61

Figure 9.   Percent of expected submerged aquatic vegetation occupied
           in 1978 for each sampling area	    C-62
                                    C-ii

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                                 TABLES
Table 1.   General Fishery Information

    (a).   Alosa aestivalis (Blueback Herring)   	     C-2
    (b).   Alosa pseudoharengus (Alewife)  	     C-3
    (c).   Alosa sapidissima (American Shad)	     C-4
    (d) .   Brevoortia tyrannus (Atlantic Menhaden)	     C-5
    (e) .   Callinectes sapidus (Blue Crab)	     C-6
    (f).   Crassostrea virginica (American  Oyster)	     C-7
    (g).   Cynoscion regalis (Weakfish)  	     C-8
    (h).   Cynoscion nebulosus (Spotted  Seatrout)  	     C_9
    (i).   Ictalurus catus (White Catfish)	     C-ll
    (j).   Ictalurus nebulosus (Brown Bullhead)  	     C-12
    (k) .   Ictalurus punctatus (Channel  Catfish)	     C-13
    (1).   Leiostomus xanthurus (Spot)	     C-14
    (m) .   Mercenaria mercenaria (Hard Clam)	     C-15
    (n).   Micropogonias undulatus (Atlantic  Croaker)  	     C-16
    (o) .   Morone americana (White Perch)	     C-17
    (p).   Morone saxatilis (Striped Bass)	     C-18
    (q).   Mya arenaria (Soft Shell Clam)  	     C-19
    (r) .   Perca flavescens (Yellow Perch)	     C-20
    (s).   Pomatomus saltatrix (Bluefish)  	     C-21

Table 2.   Environmental Conditions for  Spawning and  Development of
          Select Species

    (a).   Alosa pseudoharengus, Alosa sapidissima, Alosa  aestivalis.  .     C-22
    (b).   Brevoortia tyrannus, Ictalurus catus, Ictalurus
             nebulosus, Ictalurus punctatus	     C-23
    (c).   Cynoscion regalis, Morone americana, Morone  saxatilis.  .  .  .     C-24
    (d).   Perca flavescens, Leiostomus  xanthurus, Micropogonias
             undulatus , Pomatomus saltatrix	     C-25
    (e) .   Callinectes sapidus, Crassostrea virginica	     C-26
    (f) .   Mercenaria mercenaria, Mya arenaria	     C-27
Table 4.


    (a).

    (b).

    (c).

Table 5.

Table 6.
Table 3.   Ecology of Wetlands Found in the  Chesapeake  Bay Area
Ecology of Submerged Aquatic Vegetation Found in the
Chesapeake Bay Area

CeratophyHum demersum, Elodea canadensis,
   Valisneria americana  	
Myriophyllum spicatum, Potamogeton pectinatus,
   Potamogeton perfoliatus 	
Zannichellia palustris, Ruppia maritima,  Zostera marina
Acres of Public and Leased Oyster Grounds  .  .  .  ,

Acreage of Oyster Bars in Maryland by CBP Segment
                                                                C-28
C-29

C-30
C-31

C-33

C-34
                                    C-iii

-------
Table 7.  Baylor Grounds and Productive and Potentially Productive
          Baylor Ground Acreages in Virginia .............      C-35

Table 8.  NOAA Codes — Virginia ...................      C-38

Table 9.  NOAA Codes — Maryland ...................      C-41

Table 10. Virginia NOAA Codes Grouped by Basin ............      C-43

Table 11. Maryland NOAA Codes Grouped by Basin ............      C-45

Table 12. Aggregation of NOAA Water Codes into Regions and
          Associated CBP Segments  ..................      C~49
Table 13. Areas and Percentages of Total of Fisheries Basins .....      C-52

Table 14. Total SAV Observations for Each Segment, 1971 to 1981.  .  .  .      c~55

Table 15. Bay Segments Showing a Decline in the Percentage of
          Sites Vegetated (1971 to 1981) ...............      C~63

Table 16. Bay Segments Showing a Statistically Significant
          Decline in Diversity ....................      C

Table 17.  Rank of SAV Sampling Areas According to Percent
           of Expected Habitat ....................      C~64

Table 18.  Rank of CBP Segments According to Aggregated
           Sampling Areas  ......................      C~67

Table 19.  Comparison of Expected Habitat Ranking Results with Ranking
           of Maryland Segments According to USFWS MBHRL Data  ....      C-69
                                      C-iv

-------
                           SECTION 1







              LIFE CYCLES OF MAJOR SPECIES




                     GENERAL FISHERY INFORMATION




 ENVIRONMENTAL CONDITIONS FOR SPAWNING AND DEVELOPMENT OF SELECT SPECIES



         ECOLOGY OF WETLANDS FOUND IN THE CHEAPEAKE BAY AREA



ECOLOGY OF SUBMERGED AQUATIC VEGETATION FOUND IN THE CHESAPEAKE BAY AREA
                                 01

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-------
                              SECTION 2

                  ANALYSIS OF OYSTER HABITAT


MARYLAND DATA COLLECTION

    Maryland oyster  bars  are natural, ranging in size from one to 4,850
acres with a mean size  of 324 acres.  Most of these bars were designated  by
the Maryland Oyster  Survey (Yates 1913) at the conclusion of a six-year
survey of the bottoms.  The actual productivity of these bars has not  yet
been documented;  however,  it is known that proper substrate does exist in
most of these areas.  Since 1913, a limited number of bars were added  by
court order to deter private leasing; these bottoms were not surveyed.
    Using the data from Yates'  (1913) report and through personal
communication, Merritt  (1977) constructed oyster bar charts.  Merritt's
charts, the most  recent and comprehensive, were used to identify, locate,
and estimate unavailable  bar acreages.  The acreage values for most of
Merritt's bars were  taken from  the natural oyster bar charts prepared  in
1961 by the Coast and Geodetic  Survey for the Maryland Department of
Tidewater Fisheries, which were also based on Yates1 1913 survey.  Other
bar acreages were obtained from updated charts of natural oyster bars  and a
computer printout from  the Maryland Department of Natural Resources
Hydrographic Division.  Some of the bar acreages were obtained from the new
Maryland Bay Bottom  Survey (1980 to 1982).  Merritt's bars (1977) with
unavailable acreages were estimated from his charts.  Acreages of oyster
habitat are shown by fisheries  basin (Table 5) and CBP segment (Table  6).
    Where CBP segment boundaries cut across bars, a planimeter was used to
determine areas within  each segment.  All bars with available coordinates
in Yates' (1913)  survey were plotted on a CBP segmentation chart (Figure  1),

VIRGINIA DATA COLLECTION

    The Virginia  public oyster  grounds only delineate the boundaries of
naturally productive oyster beds (Haven et al. 1981).  These areas are
referred to as Baylor bottoms after James E. Baylor, who designated the
areas in 1894.  Baylor's  survey did not include an examination of the
bottom, nor was any  biological  data considered (Haven et al. 1981). Since
1894, 32,274 acres have been added by petition or by legislative action
(Haven et al. 1981). The Baylor bottoms cover most of Virginia's estuaries
(Figure 1).
    Haven et al.  (1981) surveyed these areas to determine the productivity
and potential productivity based on substrate and depth.  Bottoms comprised
of oyster rocks,  shell-mud or shell-sand at depths less than 7.6 m were
classed as productive or  potentially productive (for oysters).  They are
similar to the public bars in Maryland in that they both delineate areas
where salinity,  depth,  and substrate are adequate for oyster production.
The Baylor bottom acreages, productive or potentially productive acreages,
and coordinates for  Baylor bottoms were obtained from Haven et al. (1981)
(see Table 7).  Excluding the seaside eastern shore, all Baylor grounds
were plotted on a CBP segmentation chart.  Areas divided by a segment  line
were planimetered.  The productive and potentially productive areas were
represented by symbols  on Haven's  (1981) charts (1:20,000), which were also
planimetered where divided by a segmentation line.
                                     C-32

-------
TABLE 5.   ACRES  OF PUBLIC  AND LEASED OYSTER GROUNDS


Basin
+ Chesapeake Bay North
+ Chesapeake Bay Upper Central
+ Chester River
+ Eastern Bay
+ Choptank River
+ Chesapeake Bay Lower Central
Patuxent River
Honga River
Fishing Bay
Nanticoke River
Wicomico River
Chesapeake Bay South
Tangier Sound
Pocomoke Sound
Potomac River
Rappahannock River
Piankatank River
Chesapeake Bay General
Mob jack Bay
York River
Mattaponi River
Pamunkey River
Chicahominy River
James River
TOTAL
Public Oyster
Grounds
0
19,038
5,547
26,979
1,378
29,173
7,543
15,475
11,811
577
568
32,315
31,043
4,899
28,523
44,254
16,000
35,566
17,061
2,381
0
0
0
25,152
355,283
Leased
Grounds
21
0
0
212
454
778
1,119
1
333
190
1,268
0
889
4,303
9,389
19,022
328
20,170
1,516
26,729
0
0
0
13,260
99,982
Total

21
19,038
5,547
27,191
1,832
29,951
8,662
15,476
12,144
767
1,836
32,315
31,932
9,202
37,912
63,276
16,328
55,736
18,577
29,110
0
0
0
38,412
455,265

   +  These  acreages were taken from the new Maryland Bay Bottom Survey
      (1980  to  1982).
                                      C-33

-------
TABLE 6.  ACREAGE OF OYSTER BARS IN MARYLAND BY CBP  SEGMENT
Segment      Oyster Bar Acreage        Segment       Oyster  Bar  Acreage
CB-1
CB-2
CB-3
CB-4
CB-5
CB-6
CB-7
CB-8

WT-1
WT-2
WT-3
WT-4
WT-5
WT-6
WT-7
WT-8
LE-1
RET-1
TF-1
LE-2
RET-2
TF-2

46
26676
50695
32315







947

226
1049
1465
7322
214
7
25355
400


LE-3
RET-3
TF-3

ET-1
ET-2
ET-3
ET-4
EE-1
EE-2
EE-3
ET-5
ET-6
ET-7
















7948
22653
29329
94151
10314
577
568








                                      C-34

-------
TABLE 7.   BAYLOR GROUNDS AND PRODUCTIVE AND POTENTIALLY PRODUCTIVE  BAYLOR
          GROUND ACERAGES IN VIRGINA

Segment
CB-1
CB-2
CB-3
CB-4
CB-5
CB-6
CB-7
WT-1
WT-2
WT-3
WT-4
WT-5
WT-6
WT-7
WT-8
LE-1
RET-1
TF-1
LE-2
RET-2
TF-2
LE-3
RET-3
ET-1
ET-2
ET-3
ET-4
EE-1
EE-2
ET-5
EE-3
WE-4
LE-4
RET-4
TF-4
LE-5
RET-5
TF-5
ET-7
ET-8
ET-9
ET-10
Totals
Virginia Public
Oyster Ground
(Baylor's)




14477.4
17714.6
3374.3











2767.7


46878.0
4666.7







28118.4
17061.1
2210.8
170.1

25151.8






162590.9
Productive & Potentially
Productive Baylor Grounds
Baylor Bottoms Acreage




521.2
609.8
560.1











817.4


9476.2
2004.1







5397.8
1439.4
1048.6
8.5

16245.6






38.128.7
Percent Productive
or Potentially
Productive
Baylor's Acreage




3.6
3.4
16.6











29.5


20.2
42.9







19.2
8.4
47.4
5.0

64.6







                                      C-35

-------
Figure 1.    Chesapeake Bay,  Maryland  oyster  bars  (Yates  1913), and  Virginia
            Baylor bottoms (Haven 35  al.  1981).
                                    C-36

-------
                           SECTION 3
SOURCES AND ANALYSIS OF FISHERIES LANDING DATA
 DATA COLLECTION

     Historical  records of the fisheries were obtained  from Power (1958) and
 statistical digests of the U.S. Fish and Wildlife Service  and  the National
 Marine Fisheries  Service, Fishery Statistics of the United States.  The
 single exception  is that the Maryland Department of Natural Resources'
 catch records were used for all finfish in Maryland (except the Potomac)
 for the period  1962 to 1980 because these records were more complete.
     The landings  or harvest data used within this study  to depict trends
 were obtained from the files of the National Marine Fisheries  Service and
 Maryland's  Department of Natural Resources.  These landings were derived
 from reports submitted by commercial fishermen or from surveys taken of the
 fishermen and/or  market houses.  It should be recognized that  these
 landings do not constitute a statistically precise sampling method, but
 they are the only data that have been collected over a long period of time
 that can be used  to depict trends.  The validity of the  harvest data is
 further complicated by the changes in the collection method over the
 reported time period.  The longest record going back to  the late 1800's was
 originally  collected by the U.S. Department of Commerce  Bureau of Fisheries
 through a survey  of market houses and from reports from  the states that
 maintained  a data collection system.  These earlier reports collated the
 data as a state total (except for the Potomac River) instead of using a
 river system breakdown.  The more recent data collection system, and that
 used for data within this report by river system (1962 to  1980 data), was
 started by  the  State of Maryland in 1944 and is still  used to date.  The
 data for Virginia for the 1962 to 1980 time period was collected by the
 National Marine Fisheries Service (NMFS) until 1976.   Since that date, the
 Virginia Marine Resources Commission (VMRC) has gathered information.
     The major difference between the Maryland and Virginia system for
 Chesapeake  Bay  landings is that Maryland data is collected from mandatory
 monthly reports from the individual fishermen;  the Virginia data, formerly
 collected by NMFS and most recently by VMRC, is gathered through a
 volunteer survey  report from the market houses.  The exception to this
 system difference is for oysters.  Both states require mandatory reporting
 by the oystermen  because of the tax that is levied on  oysters.
     For individual river system reports within Chesapeake  Bay, the Potomac
 River has historically been reported separately.  Prior  to 1963, the
 Potomac River landings were compiled by NMFS from their  own data for the
 Virginia licensed fishermen and from Maryland State Department of Natural
 Resources for Maryland licensed fishermen.   Since 1963,  Potomac River
 landings have been compiled by the Potomac  River Fisheries Commission from
 mandatory monthly reports submitted to them by both Virginia and Maryland
 licensed fisherman fishing the Potomac.

 GEOGRAPHIC COMPARTMENTATION OF LANDINGS DATA

     Our basic unit of analysis was the NOAA water code (Tables 8 and 9).
 These codes  are grouped into basins (Tables 10 and 11).  The basins are
                                     C-37

-------
TABLE 8.  NOAA CODES — VIRGINIA
 0    Unknown (improper listing)
 1    Chincoteague Bay (62-75)  Back Bay (76-80)
 3    Chesapeake Bay General plus Tribs.  not numbered (62-75),  Back River
      (76-80)
 4    Great Wicomico River (62-75)
 5    James River (62-75), Bogue  Bay (76-80)
 7    Chicahominy River (62-75),  Bradford Bay (76-80)
 8    Mobjack Bay (62-75)
 9    York River (62-75), Burtons Bay (76-80)
11    Pamunkey River (62-75), Chesapeake  Bay Gen.  (76-80)
12    Piankatank River (62-75)
13    Mattaponi River (62-75),  Chickahominy River  (76-80)
15    Chincoteague Bay (76-80)
17    Coan River (76-80)
18    Cobb Bay (ocean)
19    Currioman Bay (77-80)
21    Corrotoman River (76-80)
23    Atlantic Ocean (62-75), East River  (76-80)
24    Atlantic Ocean
25    Elizabeth River (1977)
26    Rappahannock River (62-75)
27    Fleets Bay (76-80)
28    Potomac River (62-75)
29    Potomac River Tribs. (62-75), Great Wicomico River (76-80)
30    Misc. Tribs of Chesapeake Bay (62-75)
31    Hog Island Bay (76-80)
33    Back Bay (62-75), Horn Harbor (76-80)
37    James River Gen. (76-78)
39    Lafayette River (1977)
41    Little Wicomico River (76-80)
43    Lower Machodoc Creek (76-80)
45    Lynnhaven Bay (76-80)
47    Magothy Bay (76-80)
49    Mattaponi River (76-80)
50    Mattox Creek
51    Metomkin Bay (76-80)
53    Milford Haven (76-80)
55    Mobjack Bay (76-80)
57    Nansemond River (76-80)
59    Nomini Bay (76-80)
61    North River (76-80)
62    Unknown (Possibly James River)
63    Outlet Bay (77-78)
67    Pamunkey River (76-80)
69    Piankatank River (76-80)
70    Pocomoke River (76-78)
72    Pocomoke Sound (76-80)
                                 (continued)
                                      C-38

-------
TABLE 8.  (Continued)
 73   Poquoson River (76-80)
 74   Potomac Creek
 75   Potomac River gen. (76-80)
 76   Potomac River tribs (unclassified)  (76-80)
 77   Rappahannock River gen.  (76-80)
 78   Rosier Creek (Potomac)
 79   Severn River (76-80)
 81   South Bay (76-77)
 83   Swash Bay (1980)
 85   Upper Machodoc Creek (76-79)
 87   Ware River (76-80)
 89   Warwick River (76-79)
 91   Willoughby Bay (76-79)
 92   Winter Harbor
 93   Yeocomico River (76-80)
 95   York River Gen. (76-80)
 97   Unclassified Seaside Bays and Rivers (76-80)
 99   Unclassified Tributaries of Chesapeake Bay  (76-80)
111   Chesapeake Bay (Upper Western Section) (76-80)
117   Misprint (possibly 177  Rappahannock River)
137   James River (Lower Section) (76-80)
175   Potomac River (Lower Section) (76-80)
177   Rappahannock River (Lower Section)  (76-80)
195   York River (Lower Section)  (76-80)
211   Chesapeake Bay (Upper Eastern Section) (76-80)
237   James River (Central Section) (76-80)
275   Potomac River (Lower Central Secton) (76-80)
277   Rappahannock River (Central Section) (76-80)
295   York River (Central Section) (76-80)
311   phesapeake Bay (Lower Western Section) (76-80)
337   'James River (Upper Section) (76-80)
375   Potomac River (Upper Central Section) (1976)
377   Rappahannock River (Upper Section)  (76-80)
395   York River (Upper Section)  (76-80)
411   Chesapeake Bay (Lower Eastern Section) (76-80)
515   Atlantic Ocean
522   Atlantic Ocean
523   Atlantic Ocean
524   Atlantic Ocean
525   Atlantic Ocean
526   Atlantic Ocean
533   Atlantic Ocean
537   Atlantic Ocean
555   Atlantic Ocean
600   Atlantic Ocean
612   Atlantic Ocean
613   Atlantic Ocean
                                 (continued)
                                      C-39

-------
TABLE 8.  (Continued)
615   Atlantic Ocean
616   Atlantic Ocean
620   Atlantic Ocean
621   Atlantic Ocean
622   Atlantic Ocean
623   Atlantic Ocean
624   Atlantic Ocean
625   Atlantic Ocean
626   Atlantic Ocean
627   Atlantic Ocean
631   Atlantic Ocean
632   Atlantic Ocean
633   Atlantic Ocean
635   Atlantic Ocean
636   Atlantic Ocean
700   Atlantic Ocean
                                      C-AO

-------
TABLE 9.  NOAA CODES — MARYLAND
000   Totals
001   Assawoman Bay
003   Back River
005   Big Annamessex River
006   Blackwater River
007   Bohemia River
009   Bush River
Oil   Chesapeake Bay General - totals
013   Chesapeake Bay - North of Sassafras River
020   Chesapeake Bay - South of Cove Point
023   Chesapeake Bay - North of Sassafras River
025   Chesapeake Bay - North of Bridge, South of Sassafras River
027   "Chesapeake Bay - South of Bridge, North of Cove Point
029   Chesapeake Bay - South of Cove Point
031   Chester River
131   Chester River below Deep Point
231   Chester River above Deep Point
033   Chincoteague Bay
037   Choptank River
137   Choptank River Below Rt. 50 Bridge
237   Choptank River Above Rt. 50 Bridge
039   Eastern Bay
041   Elk River
043   Fishing Bay
045   Gunpowder River
046   Herring Bay
047   Honga River
048   Hoopers Strait
040   Isle of Wight Bay
049   Isle of Wight Bay
051   Little Annemessex River
053   Little Choptank River
055   Magothy River
057   Manokin River
059   Middle River
060   Miles River
062   Nanticoke River
162   Nanticoke River Below Long Point
262   Nanticoke River Above Long Point
064   Northeast River
066   Patapsco River
068   Patuxent River
168   Patuxent River Below Bridge at Benedict
268   Patuxent River Above Bridge at Benedict
 06   Patuxent River
070   Pocomoke River
072   Pocomoke Sound
                                 (continued)
                                      C-41

-------
TABLE 9.  (Continued)
 073   Potomac River
 173   Potomac River from Bay to Colton Point
 273   Potomac River Colton Point to Rt. 301 Bridge
 373   Potomac River Rt.  301 Bridge to Quantico
 473   Potomac River Quantico to Little Falls
 074   Potomac River
 174   Potomac River - Md. Tributaries to lower Potomac
 274   Potomac River - Md. Tributaries to lower central Potomac
 374   Potomac River - Md. Tributaries to upper central Potomac
 474   Potomac River - Md. Tributaries to upper Potomac
 076   St. Jerome Creek
 078   St. Mary's River
 080   Sassafras River
 082   Severn River
 084   Sinepuxent Bay
 086   Smith Creek
 088   South River
 089   Susquehanna Flats
 090   Susquehanna River
 092   Tangier Sound
 093   Transquaking River
 094   West River
 096   Wicomico River - Wicomico County
 099   Wye River
 012   Atlantic Ocean
 098   Atlantic Ocean
 375   Atlantic Ocean
 525   Atlantic Ocean
 537   Atlantic Ocean
 613   Atlantic Ocean
 614   Atlantic Ocean
 615   Atlantic Ocean
 616   Atlantic Ocean
 621   Atlantic Ocean
 622   Atlantic Ocean
 625   Atlantic Ocean
 626   Atlantic Ocean
 627   Atlantic Ocean
 631   Atlantic Ocean
 632   Atlantic Ocean
9000   Pacific Ocean
                                      C-42

-------
TABLE 10.   VIRGINIA NOAA CODES GROUPED BY  BASIN

Basin
Chincoteague Bay

James River









Great Wicomico

Chicahominy

Mobjack Bay

York River










Pamunkey River

Piankatank River

Mattaponi River

Rappahannock River





Potomac River




Year
1962-1975
1976-1980
1962-1975
1976-1980








1962-1975
1976-1980
1962-1975
1976-1980
1962-1975
1976-1980
1962-1975
1976-1980









1962-1975
1976-1980
1962-1975
1976-1980
1962-1975
1976-1980
1962-1975
1976-1980




1962-1975
1976-1980



NOAA Code
1
15
5
37
137
237
337
25
39
57
89
91
4
29
7
13
8
55
9
95
195
295
395
87
3
23
61
73
79
11
67
12
69
13
49
26
21
77
177
277
377
28
75
175
275
375
                                 (continued)
                                     C-43

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TABLE 10.  (Continued)
Basin
Potomac River Tributaries
Back Bay
Misc. Tributaries of Chesapeake Bay
Chesapeake Bay Gen.
Year
1962-1975
1976-1980
1962-1975
1976-1980
1962-1975
1976-1980
1962-1975
1976-1980
1962-1975
1976-1980
NOAA Code
29
50
74
76
17
19
43
59
78
85
93
33
1
23
5
7
9
18
24
31
47
51
63
81
83
97
515
30
99
41
45
53
3
111
211
311
411
11
27

-------
TABLE 11.  MARYLAND NOAA CODES GROUPED BY BASIN
Chester River (004)
        031
        131
        231

Eastern Bay (010)
        039
        060
        099
Choptank River (008)
      37
     137
     237

Fishing Bay (012)
     043
     093
     006
Chesapeake Bay North (014)
        007
        013
        041
        064
        080
        089
        090
        023
Chesapeake Bay - Upper Central (016)
     003
     009
     025
     045
     055
     059
     066
Chesapeake Bay - Lower Central (018)
    027        082
    046        088
    053        094

   Honga River (030)
        047
        048
Chesapeake Bay South (020)
     076
     029
     020

Nanticoke River (032)
     062
     162
     262
   Patuxent River (034)
     68        168
     69        268
Pocomoke River (036)
     070
                                 (continued)
                                     C-.45

-------
TABLE 11.  (Continued)
   Pocomoke Sound (038)
         072
Potomac River (040)
      73
      74
      78
      86
     173
     174
273
274
373
374
473
474
     Ocean (042)*
      1    614
     12    615
     23    616
     33    621
     40    622
     49    625
     84    626
     98    627
    375    631
    525    632
    537   9000 (Pacific Ocean)
    613

    Tangier Sound (046)
         005
         051
         057
         092
                Totals
                  0
                 11
   Wicomico River (048)
         096
* Note:  Ocean codes omitted from Chesapeake Bay landings analysis.
                                      C-A6

-------
shown in Figure 2.  In some cases,  NOAA codes  were  aggregated  into regions
(Table 12).  These regions can be related to Chesapeake  Bay segments but,
in most cases, the relationship is  not exact.   Use  of  NOAA water codes was
complicated by the fact that application of the codes  by NOAA was changed
during the period of record.  NOAA went through a change in its coding
system for the Virginia data in 1975.   Virginia data from  1962 to 1975 is
contained within the old coding system that lumped  an  entire river basin.
The new coding system divides rivers into more than one  unit.  The 1976 to
1980 landings are reported under this new coding system.   To have
consistent 1962 to 1980 landings, it was necessary  to  go back  to the old
codes by combining the new ones to  match the old system.   For  example,
under the old method, the Rappahannock River was considered as one basin;
under the new method, the Rappahannock is divided into four units.  In
addition, the codes do not remain consistent from year to  year for the same
area; i.e., code 1 from 1962 to 1975 represents landings for Chincoteague
Bay, but the same code for 1976 to  1980 shows  landings from Back Bay (see
Table 8).  The situation with Maryland data is not  the same because data
has been reported under the new system since 1962.  However, because we
wanted the Maryland data to be consistent with the  Virginia data, we used
the old system for reporting Maryland  data as  well.
    Chapter 2 reports fisheries landings in pounds  per acre by basin.  Each
of these basins was planimetered from  CBP computer  generated maps.  Table
13 shows the acreages of each basin and the percentage of  that basin when
compared to three larger areas:   western shore,  main Bay,  and eastern shore.
                                     C-47

-------
                                                      AY NORTH
                                                TAlfSEER SOUND
                                                   BAY GENERAL
Figure 2.   NOAA National Marine  Fisheries  Service  (NMFS)  basins used in
            resource data analysis.

-------
TABLE 12.  AGGREGATION OF NOAA WATER CODES INTO REGIONS AND ASSOCIATED
           CHESAPEAKE BAY PROGRAM SEGMENTS

Region
Upper Bay




Upper Eastern Shore






Western Tributaries








Mid-Eastern Shore






Patuxent M069
M068

Potomac


V28 (62-75)
V75 (76-80)









Segments
CB-1

CB-2

CB-3
ET-1
ET-2

ET-3
ET-4


WT-1
WT-2
WT-3
WT-4
WT-5
WT-6
WT-7
WT-8

EE-1


EE-2

ET-5

TF-2

RET-1 & LE-1
TF-2


RET-2


LE-2







NOAA Codes
MU90
M089
M013
M023
M025
M064
M041
MOO 7
M080
MO 31
M231
M131
MOO 9
M045
M059
MOO 3
M066
M055
M082
MO 88
M094
M039
MO 9 9
M060
M137
M053
M037
M237
M268

M168
M473
M474
V475
M373
M374
V375
M273
M274
V275
M173
M174
V175
M073
M074
                                 (continued)
                                     C-49

-------
TABLE 12.  (Continued)
Region Segments

















Lower Eastern Shore ET-6


ET-7
ET-8
ET-9
ET-10

EE-3






Mid-Bay CB-4

CB-5


Rappahannock TF-3
V077 (76-80)
V026 (62-75) RET-3

LE-3




NOAA Codes
M078
M086
V029 (62-75)
V076 (76-80)
V050
V074
V017
V019
V043
V059
V085
V078
V093
V004 (62-75)
V029 (76-80)
V076 (76-80)
V041
M062
Ml 6 2
M262
M096
M057
MOOS
M070
M072
MO 06
M093
M043
M047
M048
M092
MO 51
M027
M046
M076
V027 (76-80)
M020
V377 (part)

V377 (part)
V277 (part)
V277 (part)
V021
V177
V012 (62-75)
V069
                                  (continued)
                                       C-.50

-------
TABLE 12.  (Continued)
       Region
Segments
                                                       NOAA Codes
York
               V009 (62-75)
               V095 (76-80)
  TF-4



  RET-4

  LE-4

  WE-4
James
               V005 (62-75)
               V037 (76-80)
  TF-5
  RET-5
                                 LE-5
Lower Bay
               V003 (62-75)
               V030 (62-75)
  CB-6



  CB-7

  CB-8
V013 (62-75)
V049
V011 (62-75)
V067
V395
V295
V195
V008
V003
V073
V055
V079
V087
V061
V023
V337
V337
V007
V013
V237
V089
V057
V137
V025
V039
V091
V053
V033
V311
                                                                (62-75)
                                                                (76-80)
(part)
(part)
(62-75)
                                                                (76-80)
                                                                (part)
V211
V411
V311 (part)
V045
Vlll (76-80)
V099 (76-80)
                                       C-51

-------
TABLE 13.   AREAS AND PERCENTAGES  OF  TOTALS  OF  FISHERIES BASINS1
    Basin
    Area (acres)
  Sub-total
    Basin
  Sub-total
    Basin
  Sub-total
Total Area
      615,798
(22.9 % of total)

    Area (acres)
    1,565,766
(58.3 % of total)

    Area (acres)
      503,659
(18.7 % of total)

      2,685,223
Percent of Western Shore
Patuxent River
Potomac River
Rappahannock River
York River
James River
34,019
299,167
85,185
41,120
156,307
5.5
48.6
13.8
6.7
25.4
         100.0
    Percent of Main Bay
Chesapeake Bay
North
Upper Central
Lower Central
South
General

73,594
185,302
269,838
259,199
777,833

4.7
11.8
17.2
16.5
49.7
         100.0
Percent of Eastern Shore
Chester River
Eastern Bay
Choptank River
Honga River
Fishing Bay
Nanticoke River
Wicomico River
Tangier Sound
Pocomoke Sound
39,041
60,396
82,407
33,345
19,908
16,593
8,210
83,315
160,444
7.7
12.0
16.4
6.6
3.9
3.3
1.6
16.5
31.8
         100.0
1                    9
 One acre = 4048.58 vT
                                      C-52

-------
                                    SECTION 4

ANALYTICAL APPROACHES FOR DETERMINING TRENDS IN FISHERIES


         Treatments  of landings data include plotting  of  three-year moving
     averages, deviation  from the mean, cumulative deviation from the mean,
     comparison of means  by Student t and binomial probability tests, and
     correlation analysis.  Trends were determined by  inspection and verified by
     comparing pre~  and post-1970 means for the period of record (1962 to 1980).
         A number of caveats must be offered to those  who might wish to use
     fisheries landings data (as they are presently collected) to identify cause
     and effect relationships.  Among those considerations that complicate the
     definition of causal mechanisms and the ability to predict future
     variability in  fisheries are:  insufficient accuracy in measuring fish-
     stock abundance (landings data are not meant to measure abundance);  and the
     complexity of natural processes acting on fishery success, including
     natural and economic factors (Doubleday 1980). The  impact of these factors
     on the scientific ability to predict the dynamics of Chesapeake Bay fish
     stocks is elaborated upon in the following paragraphs.

     MEASUREMENT

         Even when using  scientifically collected estimates of fish biomass by
     acoustic and trawl surveys, resulting indices of  relative abundance
     typically have  + 50 percent margins of error unless  more than 100 sets
     (samples) are made at any given locale (Doubleday 1980).  Landings figures
     are not actual  landings, or a statistically precise  sampling of actual
     landings, but reflect reports and estimates made  by  individual fishermen.
     Such reports can easily be biased by poor individual record keeping and the
     fear of competition  from other fishermen or tax avoidance.  The Maryland
     Watermen's Association (1978, 1979) recently suggested that the Maryland
     commercial catch may be underestimated by as much as four to seven times
     when stocks are abundant and approximately equal  when stocks are low.  One
     final major complicating factor is that for some  species that are also
     sought by sportfishermen, the sports landings may equal or exceed
     commercial landings.  For example, it has been estimated that the sports
     catch of striped bass in Chesapeake Bay is equal  to  the commercial catch
     while the sport catch of bluefish is nearly 20 times the commercial catch
     (Williams et al.  1982).
         McHugh (1981)  states that "it is probably a conservative estimate that
     recreational fishermen took at least twice as much as commercial fishermen"
     in Delaware waters in the early 1970's.   It can be safely assumed that
     recreational fisheries are growing in the U.S.
         Finally Rothschild et al. (1981) and Bortone  (1982) discuss the need to
     normalize fisheries landing statistics using catch per unit effort to more
     accurately predict actual stock abundance.  Although both authors have
     attempted normalization procedures, Rothschild et al. (1981) state that the
     fishing effort  statistics in their present form are  "too crude for detailed
     analyses" and offer suggestions for improved catch per unit effort
     information.
                                          C-53

-------
COMPLEXITY

    As discussed in Chapter 2 of  this  publication,  climate and major
natural events create a number of interacting and sometimes conflicting
effects on the determination of year class  size.  Multiple hypotheses can
be put forward to explain observed events;  data are usually not complete
enough to select "the" single cause, if  one exists.
                                       C-54

-------
                             SECTION 5
         SAV DECLINE AND GEOGRAPHIC ANALYSIS
    Decline in SAV abundance has been documented  by Orth et al. (1982), and
is shown in Figures  3  through 7.
    A 650-station survey has been conducted annually by the Maryland
Department of Natural  Resources and the U.S. Fish and Wildlife Service
Migratory Bird and Habitat Research Laboratory.   Sampling stations were
distributed among GBP  segments as shown in Table  14.  Regression analyses
of results, showing  declines in percentage of sites vegetated and
diversity, are shown in Tables 15 and 16.

ASSESSMENT OF PRESENT CONDITION IN CHESAPEAKE BAY SEGMENTS

    Tables 17, 18, and 19 assess the present condition of SAV in Chesapeake
Bay segments.   Figure  8 displays the location of  quad areas used for areal
sampling of SAV; Figure 9 shows the percent of expected SAV occupied in
1978 for each sampling area.  A discussion of this information is found in
Chapter 2, Section 3.
TABLE 14.   TOTAL  SAV OBSERVATIONS FOR EACH SEGMENT, 1971 TO 1981.
           SAV ANNUAL SURVEY, MD DNR, AND U.S.  FWS  (MUNRO 1981)
               MARYLAND
Segment    Number of observations       Segment

 CB-1           317                     ET-7
 CB-2           118                     ET-8
 CB-3           277                     ET-9
 CB-4           522                     LE-1
 CB-5           559                    RET-1
 EE-1           461                     TF-1
 EE-2           635                     WT-1
 EE-3           1386                     WT-2
 ET-1            72                     WT-3
 ET-2           152                     WT-4
 ET-3           110                     WT-5
 ET-4           304                     WT-6
 ET-5           194                     WT-7
 ET-6           165                     WT-8

          TOTAL  number of observations   6,834
Number of observations

       110
       120
       129
       311
        99
        87
        50
        37
        77
        66
       209
        70
       120
        77
                                   C-55

-------
Figure 3.    Distribution of submerged  aquatic  vegetation  in Chesapeake Bay,
            1965 (after Orth et  al.  1982).
                                    C-56

-------
         Area of decline
Figure 4.    Area of submerged aquatic vegetation decline between 1965 and
            1970 (after Orth et al. 1982).   Loss of SAV during this period
            was concentrated in the upper and mid-Bay regions,  particularly
            the Patuxent River, lower Potomac River, and the Wicomico,
            Nanticoke,  and upper Choptank Rivers.
                                    C-57

-------
         Area of decline
Figure 5.    Area of submerged aquatic vegetation decline between 1970 and
            1975 (after Orth et al.  1982).   A major  loss of  remaining
            populations occurred during this period, largely because of
            runoff and sediment load acompanying Tropical Storm Agnes.
            Primarily affected were  the Susquehanna  Flats,  lower reaches of
            the Elk,  Sassafras, Back, Patapsco,  Choptank,  Rappahannock,
            Pocomoke, and York Rivers, and  the Honga River and Bloodworth
            Island areas.

-------
         Area of decline
Figure 6.    Area of submerged aquatic vegetation decline between 1975 and
            1980 (after Orth et al.  1982).   During this period,  remaining
            SAV beds in some areas showed further reduction and
            fragmentation;  major effects occurred in the Northern Neck,
            Eastern Bay,  lower Choptank,  and near Smith Island.

                                     C-59

-------
                                                         -A  BIG AND LITTLE ANNEMESSEX  RIVERS
                                                         -O  SMITH  ISLAND
                                                         -O  HONGA  RIVER
                                                         Hi  JAMES ISLAND  TO HONGA RIVER
                                                         -•  MANOKIN RIVER
                                                         -A  BLOODSWORTH ISLAND
0
 1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
        Figure  7.    Trends  in submerged aquatic vegetation occurrence in six areas
                    in the  middle Bay zone where SAV has markedly declined (data
                    from Kerwin et al.  1977;  unpublished data from Maryland's
                    Department of Natural Resources) (after Orth et al.  1982).
                                             G-60

-------
Figure 8.    United States Geological Survey  (USGS)  topographic  quad  areas
            used for aerial sampling of  SAV  (Orth et  al.  1979;  Anderson and
            Macomber 1980).
                                    061

-------
       0-2-5%

       2.5-6.3%

       6-3-15.8%

       15.8-39.8%

       39.8-100%
Figure 9.    Percent of expected submerged aquatic vegetation  occupied in
            1978 for each sampling area.
                                      O62

-------
TABLE 15.  BAY SEGMENTS SHOWING A DECLINE IN THE
           PERCENTAGE OF SITES VEGETATED (1971-1981),
           BY REGRESSION ANALYSIS*
       Segment	Level of Significance
CB-5
EE-1
EE-3
ET-5
ET-8
ET-9
WT-7
.01
.05
.01
.05
.05
.01
.05
Sum of all segments sampled          .01

  also   CB-1                        .10
         WT-6                        .10
Degression statistic:   % sites vegetated/time
TABLE 16.  BAY SEGMENTS SHOWING A STATISTICALLY
           SIGNIFICANT DECLINE IN DIVERSITY*
        Segment	Level of Significance
CB-5
EE-1
EE-2
EE-3
ET-5
ET-9
WT-6
WT-7
.01
.05
.05
.01
.05
.01
.01
.05
Sum of all segments sampled            .01

  also   CB-1                          .10
         ET-8                          .10
*By regression analysis of Shannon-Weaver Diversity
 ind'ex with time
                                    C-63

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TABLE 17.   RANK OF SAV  SAMPLING AREAS ACCORDING TO PERCENT OF EXPECTED HABITAT

Sampling
Area
(Fig. 9)




1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Potential
Habitat
(2 meter
contour)


acres
13134
4867
2973
3616
3712
8693
4338
5659
6939
3040
1803
2054
8057
5105
1861
1984
4330
3245
2812
138
8152
1198
3719
3624
7928
6674
5558
7017
5089
1659
2468
5767
6477
2487
1713
2852
1233
8254
7258
Expected
Habitat
(= 50 % of
potential)


acres
6567
2433.5
1486.5
1808
1856
4346.5
2169
2829.5
3469.5
1520
901.5
1027
4028.5
2552.5
930.5
992
2165
1622.5
1406
69
4076
599
1859.5
1812
3964
3337
2779
3508.5
2544.5
829.5
1234
2883.5
3238.5
1243.5
856.5
1426
616.5
4127
3629
Distribution
in 19781




acres
273
14
2
26
0
2
12
222
469
23
16
4
83
26
74
314
30
339
344
29
3100
96
37
67
1269
1215
152
1040
904
18
0
1181
1391
160
0
4
0
931
516
Distribution
in 1978 6
Expected 5
Habitat 4
% 3
2
1
4
1
0
1
0
0
1
8
14
1
2
0
2
1
8
32
1
21
24
42
76
16
2
4
32
36
5
30
36
2
0
41
43
13
0
0
0
22
14
Rank
0 -
= 2.6 -
= 6.4 -
= 15.9 -
= 39.9 -
= 76
5
6
6
6
6
6
6
4
4
6
6
6
6
6
4
3
6
3
3
2
2
3
6
5
3
3
5
3
3
6
6
2
2
4
6
6
6
3
4

2.5%
6.3%
15.8%
39.8%
75.9%
100%







































                                  (continued)
                                         C-64

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TABLE 17.  (continued)
Sampling
Area
(Fig. 9)




40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
7/3
74
75
76
77
78
79
Potential
Habitat
(2 meter
contour)


acres
3273
870
4322
1067
7134
188,2
296>3
21/2
5637
20'95
1358
no data
2426
2503
836
36.14
33162
16369
9265
10255
3261
4289
3266
3369
1283
2216
14427
1315
6703
3578
2365
10593
5965
7439
10300
10178
9931
11674
4388
2517
Expected
Habitat
(= 50 % of
potential)


acres
1636.5
435
2161
533.5
3567
941
1481.5
1086
2818.5
1047.5
679
—
1213
1251.5
418
1807
1681
8284.5
4632.5
5127.5
1630.5
2144.5
1633
1684.5
641.5
1108
7213.5
657.5
3351.5
1789
1182.5
5296.5
2982.5
3719.5
5150
3089
4965.5
58^37
2 17 4
1256.5
Distribution
in 19781




acres
121
0
0
69
480
34
2
7
0
0
6
no data
56
6
0
26
0
314
0
7
14
0
0
0
0
2
163
7
23
0
0
386
777
713
3666
1336
18
0
21
153
Distribution
in 1978 6 =
Expected 5 =
Habitat 4 =
% 3 =
2 =
1 =
7
0
0
13
13
4
0
0
0
0
1
--
5
0
0
1
0
4
0
0
0
0
0
0
0
0
2
1
1
0
0
7
26
19
71
26
0
0
1
12
Rank
0 - 2.5%
2.6 - 6.3%
6.4 - 15.8%
15.9 - 39.8%
39.9 - 75.9%
76 - 100%
4
6
6
4
4
5
6
6
6
6
6
-
5
6
6
6
6
5
6
6
6
6
6
6
6
6
6
6
6
6
6
4
3
3
2
3
6
6
6
4
                                     (continued)
                                        C-65

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TABLE 17.  (continued)
Sampling
Area
(Fig. 9)




80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
Potential
Habitat
(2 meter
contour)


acres
7944
7037
12362
8194
3983
7070
3629
8954
4956
7037
7386
3500
7499
7858
1279
7639
8580
3384
3853
2133
7355
8836
1037
12536
8862
7381
Expected
Habitat
(= 50 % of
potential)


acres
3972
3518.5
6181
4097
1991.5
3535
1814.5
4477
2478
3518.5
3693
1750
3749.5
3929
639.5
3819.5
4290
1692
1926.5
1066.5
3677.5
4418
518.5
6268
4431
3690.5
Distribution
in 19781




acres
570
1001
1193
199
13
329
457
993
26
147
985
633
158
1247
115
2015
2642
794
211
5
520
1277
143
106
539
0
Distribution
in 1978
Expected
Habitat
%


14
28
15
3
1
9
25
22
1
4
27
36
4
32
18
53
62
47
11
0
14
29
28
2
12
0
Rank
6 = 0 -
5 = 2.6 -
4 = 6.4 -
3 = 15.9 -
2 = 39.9 -
1 = 76 -
4
3
4
5
6
4
3
3
6
5
3
3
5
4
3
2
2
2
4
6
4
3
3
6
4
6

2.5%
6.3%
15.8%
39.8%
75.9%
100%



























^Data from Orth et al. 1979 and Anderson and Macomber 1980.
                                         066

-------
TABLE 18.  RANK OF GBP SEGMENTS ACCORDING TO AGGREGATED SAMPLING AREAS

Segment
ET-1
2
3
4
5
6
7
8
9
EE-1
2
3
CB-1
2
3
4
5
6
7
8
WT-1
2,
3
4
5
6
7
8
WE -4

Sampling Areas Included Rank
2
2,3
6,7
17,22,21,24,25
35,40,41
59
68
68
73
25,26,28,29
32,33,38,39
76,77,82,83,66,67,72,75,81
1,6
6,10
14,15,19,20,21
24,25,27,31,37,47,48,32
55,56,57,64,45,55,71,74,80,85
89,92
86,87,90,93,97,98,102,103
104
4,5
4,9
8
8
13,14
18,19
18,19
23,24,27
91,92,95,96,100,101

of Sampling Areas
respectively
6
6,6
6,6
6,2,3,5,3
6,4,6
6
6
6
3
3,3,3,3
2,2,3,4
6,6,4,5,6,6,3,3
5,6
6,6
6,4,3,2,2
5,3,5,6,6,6,6,2
6,6,5,6,5,6,3,2
5,5
3,3,3,4,2,4,3,6
4
6,6
6,5
6
4
6,6
3,3
3,3
6,5,5
3,5,2,2,4,3

Aggregated
Rank *
6
6
6
3
5
6
6
6
3
3
3
,3 4
6
6
4
5
,4,4 5
5
3
insuf f . data
6
6
6
4
6
3
3
5
4
                                   (continued)
                                     C-67

-------
TABLE 18.  (continued)
Segment
TF-ll
21
32
42
51
RET-ll
2
31
41
51
LE-1
2
3
4
5
Sampling Areas Included
36
—
—
—
—
45
42,43,44,50,51,78
—
—
—
46,54,44
51,78,52,53,79,60,62,63
84,88,89
99,100
105
Rank of Sampling Areas
respectively
6
6
6
6
6
6
6, 4, 4, 6,-, 6
6
6
6
6,4,6
-,6,5,6,4,6,6,6
6,6,5
6,4
6
Aggregated
Rank *
6
6
6
6
6
6
4
6
6
6
6
6
6
5
6

       lost before 1970;  "6"  ranking  applied  (Orth et al. 1982).
2Areas lost after 1970 (Orth  et  al. 1982).

 When a segment contained sampling  areas  having different ranks, areas having
 greater coverage of the  habitat were weighted more heavily in developing an
 aggregated ranking.
                                    068

-------
TABLE 19.   COMPARISON OF EXPECTED HABITAT RANKING  RESULTS WITH RANKING OF
           MARYLAND SEGMENTS ACCORDING TO USFWS MBHRL  DATA

Segment
Maximum %
1978 %
Sites Vegetated Sites Veg .
(year)l
CB-1
CB-2
CB-3
CB-4
CB-5
EE-I-
EE-2
EE-3
ET-1
ET-2
ET-3
ET-4
ET-5
ET-6
ET-7
ET-8
ET-9
LE-1
RET-1
TF-1
WT-1
WT-2
WT-3
WT-4
WT-5
Wt-6
WT-7
WT-8
52.38 (1971)
18.18 (1971)
15.38 (1980)
2.04 (1979)
58.7 (1971)
50.0 (1972)
73.68 (1976)
32.82 (1971)
14.29 (1979)
7.69 (1971)
30.0 (1971)
67.85 (1971)
29.41 (1971)
0
0
45.45 (1972)
83.33 (1971)
7.41 (1972)
11.11 (1978)
0
0
50.0 (1980)
42.86 (1977)
0
14.29 (1977)
57.14 (1971)
50.0 (1971)
14.29 (1976)
3.45
0
11.54
0
0
28.57
29.31
4.62
0
0
0
46.43
5.56
0
0
0
18.18
0
11.11
0
0
0
0
0
14.29
14.29
33.33
0
1978
max. %
7
0
75
0
0
57
40
14
0
0
0
68
19
0
0
0
21
0
100
0
0
0
0
0
100
25
66
0
Rank

6
6
3
6
6
3
4
5
6
6
6
3
5
6
6
6
4
6
2
6
6
6
6
6
2
4
3
6
Comparison with
Rank on Expected
Habitat Scale2
6
6
5
6
6
4
3,5
6,6,4
6
6
6
6,3
6
6
6
6
5
6
6
6
6
5
6
6
6
4
4
6

•"-Data from
2 0 - 2.
2.6 - 6.
6.4 - 15.
USFWS/MBHRL (1971-1980)
5 % = 6; 15.9 - 39.8
3 % = 5; 39.9 - 75
8 % = 4; 76 - 100

%«}
— J ,
7 = ? •
/o — /. ,
7 = 1
/o — -L .












                                   C-69

-------
                               SECTION 6

                         LITERATURE  CITED


Anderson,  R.R.,  and R.T.  Macomber.  1980.   Distribution of Submerged
    Vascular Plants,  Chesapeake Bay, Maryland.  Grant No. R805970.  Final
    Report to the U.S.  Environmental Protection Agency's Chesapeake Bay
    Program.  Annapolis,  MD.

Andrews,  J.D., D. Turgeon,  and  M. Hreha.   1968.  Removal of Pea Crabs From
    Live Oysters by Using Sevin.  Crassostrea virginica, Predation,
    Control, Crabs,  Pinnotheres, Sevin Pesticides.  Veliger. 11(2):141-143.

Andrews,  J.D., and J.L. Wood.   1967.  Oyster Mortality Studies in
    Virginia.   VI. History  and  Distribution in "Minchinia Nelson," A
    Pathogen of Oysters,  in Virginia.  Crassostrea virginica, Disease, MSX,
    Distribution Patterns,  Virginia, Chesapeake Bay.  Ches. Sci. 8:1-13.

Arnold, C.R.,  T.D. Williams, W.A. Fable, Jr., J.L. Lasswell, and W.H.
    Bailey.  1978.  Methods and Techniques for Spawning and Rearing Spotted
    Seatrout in the Laboratory. Proc. Annu. Conf. Southeast. Assoc. Game
    Fish Comm.  30:167-178.

Auld, A.H., and J.R.  Schubel.   1978.  Effects of Suspended Sediment on Fish
    Eggs and Larvae:   A Laboratory  Assessment.  Estuarine Coastal Mar. Sci,
    6:153-164.

Beaven, M., and J. Mihursky.  1980.  Food  and Feeding Habits of Larval
    Striped Bass:  An Analysis  of Laval Striped Bass Stomachs from 1976
    Potomac Estuary Collections.  Prepared by Chesapeake Biological
    Laboratory for Md.  Power Plant  Siting  Program, Dept. Natural Res.,
    Annapolis, Md. UMCEES Ref.  No.  79-45-CBL.  23 pp.

Bortone,  S .A.   1982.   Adjustment of Catch  (Landings) for Effort Among
    Chesapeake Bay Fisheries.   EPA  Technical Report.  In press.

Boynton,  W.R., T.T.  Polgar, and H.H. Zion.  1981.  Importance of Juvenile
    Striped Bass Food Habits in the Potomac Estuary.  Trans. Am. Fish. Soc.
    110:56-63.

Burbidge,  R.G.  1974.  Distribution, Growth, Selective Feeding, and Energy
    Transformations of  Young-of-the-Year Blueback Herring, Alosa aestivalis
    (Mitchill), in the  James River, Virginia.  Trans. Am. Fish. Soc.
    2:297-311.

Castagna,  M.,  and P.  Chanley.   1973.  Salinity Tolerance of Some Marine
    Bivalves from Inshore and Estuarine Environments in Virginia Waters on
    the Western Mid-Atlantic Coast.  Malacologia 12(l):47-96.

Chao, L.N., and J.A.  Musick.  1977.  Life  History, Feeding Habits, and
    Functional Morphology of Juvenile Sciaenid Fishes in the York River
    Estuary, Virginia.   Fish. Bull.  75(4):657-702.
                                   C-70

-------
Daiber, F.C.,  L.L.  Thornton,  K.A.  Bolster,  T.G.  Campbell,  O.W.  Crichton,
    G.L. Esposito,  D.R. Jones, J.M.  Tyrawski.   1976.   An Atlas  of
    Delaware's Wetlands and Estuarine Resources,  Technical Report  Number
    2.  College of  Marine Studies, University  of  Delaware, Newark,
    Delaware.   528  pp.

Davis, H.C.,  and A. Calabrese.  1964.   Combined  Effects of Temperature  and
    Salinity  on Development of Eggs  and Growth of Larvae of M.  mercenaria
    and C. virginica.  USFWS Fishery Bull.  63(3):643-655.

Domermuth, R.B., and R.J. Reed.  1980.   Food of  Juvenile American  Shad,
    Alosa sapidissima, Juvenile Blueback Herring, Alosa aestivalis,  and
    Pumpkinseed, Lepomis gibbosus, in the Connecticut River Below  Holyoke
    Dam, Massachusetts.  Estuaries.   3(1): 65-68.

Durbin, A.G.,  and E.G. Durbin.  1975.   Grazing Rates  of the Atlantic
    Menhaden  Brevoortia tyrannus as  a Function of Particle Size and
    Concentration.   Marine Biol.  33:265-277.

Doroshev, S.I.  1970.  Biological Features of  the Eggs, Larvae,  and  Young
    of the Striped  Bass, Roccus saxatilis (Walbaum),  in Connection with the
    Problem of Its  Acclimatization in the U.S.S.R.  J.  Ichthyol. 10:235-248.

Doubleday, W.G.  1980.  Coping with  Variability  in Fisheries.   FAO Fish.
    Rep. 236.   Report of the ACMRR Working Party on the Scientific Basis of
    Determining Management Measures.  149 pp.

Ellis, M.M.,  B.A. Westfall, O.K. Meyer, and W.S.  Platner.   1947.   Water
    Quality Studies of the Delaware  River with Reference to Shad
    Migration.  USFWS Spec. Sci. Rep.  No. 38.   19 pp.

Fable, W.A. ,  Jr., T.D. Williams, and C.R. Arnold.  1978.  Description of
    Reared Eggs and Young Larvae of  the Spotted  Seatrout Cynoscion
    'nebulosis.  Fish. Bull.  76:65-71.

Galtsoff, P.S.  1964.  The American  Oyster,  Crassostrea virginica, Gmelin.
    U.S. Fish & Wildlife Ser. Bull.  No. 64.  480  pp.

Gosselink, James.  1980.  Tidal Marshes - The  Boundary  Between  Land  and
    Ocean.  FWS/OBS/15:  U.S. Fish and  Wildlife  Service, Biological
    Services  Program.  13 pp.

Haven, D.S.  1957.   Distribution,  Growth, and  Availability of Juvenile
    Croaker,  Micropogon undulatus, in Virginia.   Ecology.   38(l):88-97.

Haven, D.S.,  and R. Morales-Alamo.  1970.  Filtration of Particles From
    Suspension by the American Oyster,  Crassostrea virginica.   Biol.  Bull.
    !39:(2):248-264.

Haven, D.S.,  W.J. Hargis, Jr., and P.C. Kendall.   1981.  The Oyster
    Industry  of Virginia:  It's [sic]  Status,  Problems, and Promise.
    S-.R.A.M.S.O.E.  NO. i68.  VIMS.
                                      C-71

-------
Hildebrand, S.F., and W.C.  Schroeder.   1928.   Fishes of Chesapeake Bay.
    Bull.  U.S. Bur. Fish.   43:244-247.

Hollis, E.H.  1952.  Variations in the Feeding Habits of the Striped  Bass
    Roccus saxatilis (Walbaum), in Chesapeake Bay.   Bull.  Bingham. Oceanog.
    Coll.  14:111-131.

Horwitz, Elinor Lander.  1978.   Our Nation's  Wetlands.   An Interagency Task
    Force Report.  U.S. Government Printing Office,  Washington,  B.C.  70 pp.

Hudson, L.L. , and J.D. Hardy, Jr.   1974.   Summary of the Biology of White
    Perch.  In:  Water Quality Criteria and the Biota of Chesapeake Bay.
    Prepared by the Chesapeake Research Consortium,  Inc. for U.S.  Army
    Corps. Eng., Baltimore District, Baltimore, MD.   CRC Publ.  No. 41.   pp.
    2-97.

Idyll, C.P., and W.E. Fahy.  1975.  Spotted Seatrout ... Shallow-Water
    Sport Fish.  Marine Resources  of the Atlantic Coast.  Leaflet Number
    13.  Atlantic States Marine Fisheries Commission.  Washington, DC.   4
    pp.

Joseph, E.B.  1972.  The Status of the Sciaenid Stocks of  the Middle
    Atlantic Coast.  Ches.  Sci.  13(2):87-100.

Jones, P.W., F.D. Martin, and J.D. Hardy, Jr.  1978.  Development of  Fishes
    of the Mid-Atlantic Bight.   An Atlas of Egg, Larval and Juvenile
    Stages.  Volume I.  Acipenseridae  through Ictaluridae.  U.S. Department
    of the Interior, Fish and Wildlife Service, Biological Services
    Program.  FWS/OBS-78/12.   366  pp.

June, F.C., and F.T. Carlson.  1971.  Food of Young  Atlantic Menhaden,
    Brevoortia tyrannus, in Relation to Metamorphosis.   Fish. Bull.
    68(3):493-512.

Kendall, A.W., Jr., and F.J.  Schwartz.  1968.  Lethal Temperature and
    Salinity Tolerances for White  Catfish, Ictalurus catus, from the
    Patuxent River, Maryland.  Ches. Sci.  9(2):103-108.

Kerwin, J.A., R.E. Munro, and W.A. Peterson.   1977.   Distribution and
    Abundance of Aquatic Vegetation in the Upper Chesapeake Bay, 1971 -
    1974.  In:  The Effects of Tropical Storm Agnes  on the Chesapeake Bay
    Estuarine System.  J. Davis, ed.  Chesapeake Research  Consortium, Inc.
    Publication No. 54.  The Johns Hopkins Univ. Press,  pp. 393-400.

Korringa, P.  1952.  Recent Advances in Oyster Biology.  The Quart. Rev.
    of Biol.  27:266-365.

Lippson, A.J., Ed.  1973.  The  Chesapeake Bay in Maryland:  An  Atlas  of
    Natural Resources.  The Johns  Hopkins Univ. Press,  Baltimore,  MD.
    55 pp.
                                    C-72

-------
 Lippson, A.J. , M.S. Haire, A.F. Holland, F. Jacobs, J. Jensen, R.L.
    Moran-Johnson, T.T. Polgar, and W.A. Richkus.  1979.  Environmental
    Atlas of  the Potomac Estuary.  Prepared by Martin Marietta Corp. for
    MD. Power Plant Siting Program, Dept. Natural Res., Annapolis, MD.
    279 pp.

 Lippson, R.L.  1971.  Blue Crab Study in Chesapeake Bay Maryland:   Ann.
    Progress Rept. Univ. of Maryland Natural Resources Institute,
    Chesapeake Biological Lab., Solomons,  MD.  Ref. No. 71-9.

 Loos, J.  1975.  Shore and Tributary Distribution of Ichthyoplankton and
    Juvenile Fish with a Study of Their Food Habits.  Prepared by  Acad. of
    Natural Sciences of Philadelphia, PA.  for MD. Power Plant  Siting
    Program, Dept. Natural Res., Annapolis, MD.

 Lorio, Wendell J., and William S. Ferret.   1980.  Biology and  Ecology of
    the Spotted Seatrout (Cynoscion Nebulosus Cuvier).  In:  Proceedings of
    the Red Drum and Seatrout Colloquium,  October 19-20, 1978.  pp. 7-13.

 Lucy, J.A.  1977.  The Reproductive Cycle of Mya arenaria L. and
    Distribution of Juvenile Clams in the Upper Portions of  the Nearshore
    Zone of the York River, Virginia.  M.S. Thesis, The College of William
    and Mary, Williamsburg, VA.  124 pp.

 Mansueti, R.J.  1961.   Movements, Reproduction, and Mortality  of the White
    Perch, Roccus americanus, in the Patuxent Estuary, Maryland.  Ches.
    Sci.  2(3-4):142-205.

 Matthiessen, G.C.  1960.  Observations on the Ecology of the Soft  Clam, Mya
    arenaria, in a Salt Pond.  Limnol. and Oceanog.  5:291:300.

 Md. Dept. Nat. Res.  1981.  Interstate Fisheries Management Plan for the
    Striped Bass of the Atlantic Coast from Maine to North Carolina.
    Contract to the Atlantic States Marine Fisheries Comm. Cooperative
    Agreement No. NA-8—FA-00017.  Nat. Mar. Fish. Serv.,  Gloucester,  MA.
    286 pp.

McHugh, J.L., and J.C.  Ginter.   1978.   Fisheries.  MESA New York Bight
    Atlas Monograph 16.  129 pp.

Meritt, Donald W.  1977.  Oyster Spat Set  on Natural Cultch in the Maryland
    Portion of the Chesapeake Bay (1939-1975).   UMCEES Special Report  No.
    7.  Horn Point Environmental Laboratories,  Cambridge,  MD.

Merrill, A.S., and H.S. Tubiash.  1970. Molluscan Resources of the
    Atlantic and Gulf  Coast of the U.S. Proc. Symposium on Mollusca Part
    III.  pp. 925-948.

Muncy, R.J.   1962.   Life History .of the Yellow Perch,  Perca flavescens, in
    Estuarine Waters of the Severn River,  a Tributary of  Chesapeake Bay,
    Maryland.   Ches.  Sci.   3(3):143-159.
                                   C-73

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Munro, R.  1981.   Data from the  Maryland  Dept.  of  Nat. Res. USFWS Annual
    Vegetation Survey, 1971 -  1981.   USFWS  Patuxent Wildl. Res. Ctr.
    Laurel,  MD.

Orth, Robert.  1976.   The Demise and  Recovery of Eelgrass, Zostera marina,
    in the Chesapeake Bay, Virginia.   Aquatic Botony.  2:141-159.

Orth, R.J.,  and  K.A.  Moore.  1981.  Submerged Aquatic Vegetation of the
    Chesapeake Bay:   Past, Present, and Future.  Trans. N. Amer. Wildl.
    Nat. Res.  46:271-283.

Orth, R.J.,  K.A.  Moore, and H.H. Gordon.  1979.  Distribution and Abundance
    of Submerged Aquatic Vegetation in the  Lower Chesapeake Bay, Virginia.
    600/8-79-029/SAV1.  Final  Report  to the U.S.  Environmental Protection
    Agency's Chesapeake Bay Program.   Annapolis, MD.

Orth, Robert J.,  Kenneth A. Moore, and Hayden H. Gordon.  1982.
    Distribution and  Abundance of Submerged Aquatic Vegetation in the Lower
    Chesapeake Bay, Virginia.  Grant  R805951010.   EPA Chesapeake Bay
    Program.  199 pp.

Peters, D.S., and M.A. Kjelson.   1975. Consumption and Utilization of Food
    by Various Postlarval and  Juvenile Fishes of North Carolina Estuaries.
    In:  Estuarine Research.   L.E. Cronin,  ed.  Academic Press, Inc., New
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Power, E.A.   1958.  Fishery Statistics of the United States 1956.  U.S.
    Dept. of Interior.  Fish and Wildlife Service  Statistical Digest 43.

Pristas, P.J. , and T.D. Willis.   1973. Menhaden Tagging and Recovery.
    Marine Fisheries  Review.   35(5-6):31-35.

Raney, E.C. , and W.H. Massmann.   1953. The Fishes of the Tidewater
    Section of the Pamunkey River.  J. Wash. Acad. Sci.  43(12):424-432.

Rothschild,  B.J., P.W. Jones,  and J.S. Wilson.  1981.  Trends in Chesapeake
    Bay Fisheries.  Trans. 46th  N.A.  Wildf. and Natr. Res. Conf. Wildlife
    Mgt. Inst.,  Washington, DC.   pp.  284-298.

Sandoz, M.,  and  R. Rogers.  1944. The Effect of Environmental Factors on
    Hatching, Moulting and Survival of Zoea Larvae of the Blue Crab,
    Collinectes  sapidus.  Rathbun. Ecology. 25:216-228.

Setzler, E.M., W.R.  Boynton, K.N. Wood, H.H. Zion, L. Lubbers, N.K.
    Mountford, P. Frere, L. Tucker, and J.A. Mihursky.  1980.  Synopsis of
    Biological Data on Striped Bass,  Morone saxatilis (Walbaum).  NOAA
    Tech. Report NMFS Circ. 433.  69  pp.

Shea, G.B.,  G.B. Mackiernan, L.C. Athanas,  and  D.F. Bleil.  1980.
    Chesapeake Bay Low Flow Study:  Biota Assessment.  Vol. III.  Western
    Eco-systems  Technology Phase I Final  Report to U.S. Army Corps. Eng. ,
    Baltimore District, Baltimore, MD. 202 pp.
                                    C-74

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Stevenson, J.C., and N.M. Confer.  1978.  Summary of Available Information
    of Chesapeake Bay Submerged Vegetation.  U.S. Fish and Wildlife
    Service.  FWS/OBS-78/66.

Stickney, R.R., G.L. Taylor, and D.B. White.   1975.   Food Habits of Five
    Species of Young Southeastern United States Estuarine Sciaenidae.
    Ches. Sci., 16:104-114.

Sulkin, S.D.  1975.   The Significance of Diet in the Growth and Development
    of Larvae of the Blue Crab, Callinectes sapidus  (Rathbun), Under
    Laboratory Conditions.   J.  Exp. Mar. Biol.  Ecol.  20:119-135.

Tabb, Durbin C.  1961.  A Contribution to the Biology of the Spotted
    Seatrout, Cynoscion nebulosus (Cuvier), of East-Central Florida.
    Florida State Board of Conservation, Technical Series.  35:1-23.

Thomas, D.L.  1971.   An Ecological Study of the Delaware River in the
    Vicinity of Artificial Island.  Part III.  The Early Life History  and
    Ecology of Six Species of Drum (Sciaenidae) in the Lower Delaware
    River, a Brackish-Tidal Estuary.  Progress Rep.  for January-December,
    1970.  Ichthyological Assoc. Bull. 3.  247 pp.

Ukeles, R.  1971.  Nutritional Requirements for Shellfish Culture.   In:
    Artificial Propagation of Commercially Valuable  Shellfish.  K.  Price
    and D. Maurer, eds.   University of Delaware.  212 pp.

U.S. Army Corps of Engineers, Baltimore District.  1973.  Existing
    Conditions Report.  Appendix C.  The Bay - Processes and Resources.
    Baltimore, MD.

Van Engel, W.A.  1958.  The Blue Crab and Its Fishery in Chesapeake Bay,
    Par£ I:  Reproduction,  Early Development, Growth, and Migration.  Comm.
    Fish. Review.  20(6):6-17.

Van Engel, W.A. , D.  Cargo,  and F. Wojecek.  1973. The Edible Blue  Crab  ~
    Abundant Crustacean.  Leaflet 15.  Marine Resources of the Atlantic
    Coast.  Atlantic States Marine Fisheries Commission.  Washington,  DC.

Wallace, D.E., R.W.  Hanks,  H.T. Pfitzenmeyer, and W.R. Welch.   1965.  The
    Soft Shell Clam ...  A Resource with Great Potential.  Marine Resources
    of the Atlantic  Coast Leaflet No. 3, Atlantic States Marine Fisheries
    Comm., Tallahassee,  FL.  4  pp.

Wallace, D.H.  1940.  Sexual Development of the Croaker, Micropogon
    undulatus, and Distribution of the Early Stages  in Chesapeake Bay.
    Trans. Am. Fish. Soc. 70:475-482.

Watermen's Association.   1978.   Survey of Commercial Finfishing Areas  in
    the Upper Chesapeake Bay 1976.  Maryland Dept. Natural Resources Report.

Watermen's Association.   1979.   Survey of Commercial Finfishing Areas  in
    the Upper Chesapeake Bay 1979.  Maryland  Dept. Natural Resources Report.
                                    C-75

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Wilk, S.J.  1978.  Biology and Ecology of the Weakfish,  Cynoscion regalis,
    Bloch and Schneider.  In:   Proceedings of the Colloquium on  the  Biology
    and Management of Red Drum and Seatrout.   Gulf States  Marine Fisheries
    Commission.

Williams, J.B.,  H.J.  Speir, S. Early,  and T.P.  Smith.   1982.   1979 Maryland
    Saltwaterfishing  Survey.   Tidewater Administration.  #  TA-CRD-82-1.
    100 pp.

Yates, C.C.  1913.  Summary of Survey of Oyster Bars  of  Maryland 1906-1912.
    1600 pp.

Yeo, R.R.  1965.  Yield of Propagules of Certain Aquatic Plants.  Weeds.
                                    C-76

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                             APPENDIX D


                              CONTENTS


Figures   ................................  D-ii

Tables  .................................  D-iii

Section

    1    Adapting Water /Sediment Quality Data for Comparison
        to Resources  .........................  T)-I
    2   'Statistical Analysis of Submerged Aquatic Vegetation ......  D-14

    3    Statistical Analysis of Benthic Organisms ...........  D-27

    4    Analysis  of Finfish ......................  D-35

    5    Literature Cited  .......................  D-50
                                   D-i

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Figure 1.

Figure 2.


Figure 3.
                      FIGURES

The toxicity index averaged over  Chesapeake Bay segments
D-9
Contour map of toxicity index values for surface sediment
of Chesapeake Bay	D-13

The diversity index  of benthic communities in the Patapsco and
Rhode Rivers	D-28
Figure 4.   Metal  contamination of the Patapsco River 	  D-31
Figure 5.


Figure 6.


Figure 7.

Figure 8.



Figure 9.

Figure 10.

Figure 11.

Figure 12.

Figure 13.

Figure 14.
Distribution of PNA, Benzo(a)Pyrene in channel sediments from
Baltimore Harbor and the Patapsco River 	
                                                             D-31
Density of Leptochierus  plumulosus in Patapsco and
Rhode Rivers	D-32
Bioassay of an amphipod against Patapsco River sediment

Three-dimensional plot of December temperature deviation
from long-term average temperatures, Potomac River flow in
April, and the juvenile striped bass abundance index  . .  .
D-34



D-40

D-42
Juvenile indices  for  striped bass in the Potomac River  .  .

Juvenile indices  for  striped bass in the Upper Bay	D-42

Juvenile indices  for  striped bass in the Choptank River .  .  .  D-42

Juvenile indices  for  striped bass in the Nanticoke River.  .  .  D-42

Juvenile indices  for  White  Perch in the Choptank River  .  .  .  D-45

Juvenile indices  for  White  Perch in the Nanticoke River .  .  .  D-46
                                      D-ii

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                                TABLES

Table 1.    Estimates of  Dissolved Metals  ................  D-3

Table 2.    Bay Segments  Grouped by  Salinity Based on Long- Term Average
           Values  ...........................  D-4

Table 3.    Hardness Values  for Representative Tidal- Fresh and
           Oligohaline Segments   ....................  p_4
Table 4.   Acute Heavy Metal  Values for Use on Tablulation of Frequency
           of Water Quality Criteria Violations
Table 5.   Chronic Heavy Metal  Values for Use on Tablulation of
           Frequency of  Water Quality Criteria Violations
Table 6.    Acute Criteria:   Levels of Each of Six Metals That May Not be
           Exceeded  at Any  Time   ....................  D-10

Table 7.    Ratio of  EPA  Criterion for the Most Toxic Metal to Each Other
           Metal ............................  D-10

Table 8.    Toxicity  Indices for  Different Spatial Segments of Chesapeake
           Bay and its Tributaries  ...................  D-12

Table 9.    Results of Correlation Analysis of Water Quality Variables
           Against Submerged Aquatic Vegetation   ............  D-15

Table 10.  Multivariate  Regressions of  SAV to Water Quality Variables
           Across Time by Segment ...................   T)-?l

Table 11.  Spearman-Rank Correlation Coefficient Results for SAV Against
           Water Quality Variables  ...................  D-26
Table 12.   Contamination Index, Toxicity Index, Annelid:Mollusc, and
           Annelid:Crustacean Ratios  for Reinharz (1981) Patapsco River
           Stations  	
                                                                         D-29
Table 13.   Diversity,  Redundancy,  and  Species Number for Patapsco and
           Rhode River Stations	D-30

Table 14.
    (a).    Result of Linear Regression Analysis of Juvenile Index
           against Air Temperature 	  D-36

    (b).    Relationship as  Represented by R Value and Determined by
           Correlation Analysis for Finfish Juvenile Index Versus Flow .  D-37

Table 15.   Potential Prediction Equations for Striped Bass Juvenile
           Indices as  Described by Multiple Regression  	  D-41
                                      D-iii

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Table 16.
Table 17.
Table 18.
Potential Prediction Equations  for White Perch Juvenile
Indices as Described by Multiple  Regression 	
                                                                         D-44
Ambient Water Quality Variables  that  Significantly Improve
the Linearity of the Residuals  from the  Potomac River
Prediction Equations for  Striped Bass Juvenile Indices.  . .  .  D-48

Ambient Water Quality Variables  that  Significantly Improve
the Linearity of the Residuals  from the  Potomac River
Prediction Equations for  White  Perch  Juvenile Indices  ....  D-49
                                       D-iv

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                             SECTION 1
  ADAPTING WATER/SEDIMENT QUALITY INFORMATION
          FOR COMPARISON TO  LIVING RESOURCES
    To facilitate comparison of toxicant levels  in  sediment or water
column, we modified  data presented in Chapter  1  to  increase their
biological applicability.  This adjustment was done through use of a
water quality survival  envelope and a toxicity index.

WATER QUALITY CRITERIA AND SURVIVAL ENVELOPE SCREEN

Methodology

    We determine tolerances of resource species  toward various toxic
substances from published information on bioassays  showing both acute
and sublethal effects.  A list was compiled of the  effects which
included LC5Q values (concentration of toxicant  that kills 50 percent
of the population),  LC^QO values (concentration  that kills 100 percent
of population), and  EC5y values (concentration causing a certain
effect, such as reduction in growth, in 50 percent  of the population),
for EPA priority pollutants, if sufficient toxicity information was
available.  (This list  is included in Kaumeyer and  Setzler-Hamilton
1982.)  Because different life stages of a species  may vary in
sensitivity to toxic materials, toxicity information was organized
into:  egg (or embryonic), larvae, juvenile, and where appropriate,
adult.
    These levels were compared to the published  EPA ambient water
quality criteria, both  24-hour or "chronic" values  (value should not be
exceeded as a 24-hour average) and "anytime" or  "acute" values
(concentration should not be exceeded at any time).  In the great
majority of cases, these EPA criteria were stricter than published
LC5Q values for various Bay species.  Where LC5Q values were lower
(i.e., the species was more sensitive), one-half the LC5Q value was
substituted.   These  values were used as threshold levels in screening
against measured water  column concentrations for each toxicant contained
in the CBP data file.
    Toxicants screened  include heavy metals, organic chemicals, and
total residual  chlorine.  Data for heavy metals  needed some
modification, as most had been recorded as "total metals," where the
value included  all forms (dissolved, particulate, and forms complexed to
suspended sediment).  In the environment only  the dissolved, or ionic,
fraction is usually  biologically available and thus potentially toxic,
at least to non-benthic species (U.S. EPA 1982a) .   The water quality
criteria are  based on "total recoverable metals;" under laboratory
bioassay conditions;  however,  these typically  represent inputs as salts
of metals and,  thus,  probably exist mainly in  the dissolved or ionic
fraction.
    Because national  criteria may be unnecessarily  stringent if applied
to total metal  measurements in waters where most of  the forms are
insoluble or  strongly bound to particulates, estimates of the dissolved
fractions were  derived from data collected in  the Bay mainstem by the

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National Bureau of Standards (Kingston et al.  1982).   In general, a
major fraction of cadmium (Cd),  copper (Cu),  and  nickel (Ni), exists as
dissolved, while the opposite holds  for zinc  (Zn),  lead (Pb), and
chromium (Cr).  In freshwater, (generally, the oligohaline  zone) some
forms show greater proportion in the particulate  fraction or  in the
region of the turbidity maximum (Table 1).
    Toxicity of metals varies with salinity,  pH,  hardness,  and natural
occurrence of chelating agents.   Bay segments were  grouped  by long-term
salinity average based on Stroup and Lynn (1963)(Table 2).  Freshwater
criteria were used for segments where long-term average salinities were
less than 0.5 percent (Stroup and Lynn 1963).  Oligohaline  segments,
where salinity may range between 0.5 and 5.0  ppt, but  which are riverine
in many of their chemical or physical features, were also screened using
freshwater criteria.  Also,  many of  their major biotic components are
more closely allied to freshwater than to high salinity areas (Shea et
al. 1980).  Saline criteria  were used for segments  where annual salinity
averages were greater than 5 ppt.
    To estimate water hardness (ppm CaC03) , which determines  the
actual freshwater criteria,  we calculated means of  hardness,  as well as
maximum and minimum values,  from the CBP data base  for freshwater and
brackish segments (Table 3).  Minimum hardness values  were  consistently
less than 50 ppm in freshwater areas.  For this reason, freshwater
criteria for 50 ppm hardness were used in these segments.   Brackish
segments showed hardness values ranging from  100  to greater than 2000;
freshwater criteria for 200 ppm hardness were used  in  these segments.
    Total metalrdissolved metal ratios were calculated for  "fresh,"
"brackish," and "saline" stations (based on previously discussed
salinity criteria) for Cd, Cu, Ni, Zn, Pb, and Cr.   Equations were
developed, based on mean total:dissolved ratios,  to estimate  dissolved
metals from "total" values (Table 1).  In data sets where only total
values were available, e.g., the Virginia and Maryland "106"  data, these
estimators were employed.
    It should be emphasized that these are only estimates,  not measured
values; thus the results of the criteria screen are suggestive of
problems, not definitive.
    For total residual chlorine, recommended  criteria  from  a  1983 draft
EPA document were employed.•*•  These guidelines were developed in a
manner similar to that for the Ambient Water  Quality Criteria
documents.  However, "instantaneous" concentrations (should never be
exceeded) and "chronic" values (should not be exceeded as a 30-day
average) were developed.  These are:
    Freshwater
           instantaneous     29.0 ug L~l
           30-day chronic     6.6 ug L~l
    Salt water
           instantaneous     25.0 ug L~l
           30-day chronic     5.7 ug L~l
    These values were screened against measured water  column  data from
the CBP data base.
 -^•Personal communication:  "Proposed Draft Water Quality Criteria for
  Total Residual Chlorine and Chlorine-Produced Oxidants," W.  Brungs,
  EPA-Naragansett, 1983.
                                      D-2

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TABLE 1.  ESTIMATES OF DISSOLVED METALS

[Where only "Total Metals:  values exist  (e.g., MD and VA  "106" data), the
following equations were used to estimate  "Dissolved Metals."  Letter
refers to segment group listed in Table  2.   (Source:  Kingston et al. 1982)]

Metal
Cadmium
0.5 ppt
1 - 5 ppt
5 ppt
Copper
0.5 ppt
and 1 - 5 ppt
5 ppt
Nickel
0.5 ppt
and 1-5 ppt
5 ppt
Zinc
0.5 ppt
1 - 5 ppt
5 ppt
Lead
0.5 ppt
1 ppt
Chromium
0.5 ppt
1 - 5 ppt
5 ppt

Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Diss
Equations
= 0.60 Total
= 0.73 Total
= 0.87 Total
= 0.32 Total
= 0.57 Total
= 0.35 Total
= 0.83 Total
= 0.30 Total
=0.15 Total
= 0.05 Total
= 0.04 Total
= 0.30 Total
= 0.07 Total
= 0.04 Total
= 0.02 Total
Group
Group A
Group B
Group C
Group A & B
Group C
Group A & B
Group C
Group A
Group B
Group C
Group A
Group B & C
Group A
Group B
Group C
                                       D-3

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TABLE 2.  BAY SEGMENTS GROUPED BY SALINITY BASED ON LONG-TERM AVERAGE
          VALUES FROM STROUP AND LYNN 1963
A.  Freshwater
                   0.5 ppt)
B.  Brackish
C.  Saline
              (0.5 - 5 ppt)
                     ppt)
TF-1,2,3,4,5
CB-1
ET-1,2,3
WT-1,2

CB-2,3
RET-1,2,3,4,5
ET-4
WT-3,4

CB-4,5,6,7,8
LE-1,2,3,4,5
EE-1,2,3
ET-5,6,7,8,9,10
WT-5,6,7,8
WE-4
TABLE 3.  HARDNESS VALUES (as ppm CaC03) FOR REPRESENTATIVE TIDAL-
          FRESH AND OLIGOHALINE SEGMENTS

Segment
CB-1
TF-1
TF-2
ET-1
ET-2
ET-3
WT-2
X
81.9
535.4*
74.1
56.0
145
81
73.1
Mln.
56
22
6
(single
52
49
58
Max.
121
2,430*
167
observation)
540
220
111

   * May represent an anomalous value.
                                      D-4

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Results

    For heavy metals, estimates of dissolved concentrations exceeded water
quality criteria in a number of areas.  Relative to the number of
observations, usually fewer than 10 percent were high enough to exceed
acute criteria (Table 4).  There are more violations of chronic criteria
(Table 5); this is particularly true for Cu and Zn (Chapter 1).  Most high
values occurred in the lower reaches of tributaries and in the upper and
mid-Bay.  High values of Cd, Cr, and Zn have been measured in some
tidal-fresh areas, such as the Potomac River and the Susquehanna Flats.
    Relatively few exceedences by organic chemical criteria were recorded
(Chapter 1).  This probably reflects paucity of observations and limits  of
methodologies employed for routine monitoring.   Those measured were
primarily pesticides and were recorded in tributaries.
    For total residual chlorine of 358 observations in (mainly) tidal-
fresh areas, 67 percent exceeded the draft criteria.  However, it should be
emphasized that methodologies employed in measuring chlorine in the  field
often were not accurate at low ambient concentrations; many of the recorded
values appeared to be limit-of-detection numbers.

Discussion
    Because each measurement in the CBP data base represents a single
observation, we have little feeling for the extent and duration of
exposures.  Similarly, variability in the field and laboratory
measurements leads to a certain "margin of error"  around the data upon
which criteria are based.  For example, differences of a factor of two in
similarly derived LC5Q numbers for a species would not be unexpected.^
Thus, the magnitude of the excursion above the  criterion (it exceeds the
criterion by 100 percent, or 200 percent,  for example) would perhaps be  a
more realistic assessment of potential damage.   This analysis is being
considered.
o
•^Personal Communication:   "Variability  in  LC5Q Responses of Organisms to
 Toxicants," W.  Brungs,  EPA-Naragansett, 1982.
                                     D-5

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TABLE 4.  ACUTE HEAVY METAL VALUES  FOR USE  IN  TABULATION OF  FREQUENCY
          OF WATER QUALITY CRITERIA VIOLATIONS,  IN  ug  L"1.   LETTER
          REFERS TO BAY SEGMENT GROUP

Metal
Cd
Cr+3
Cr+6
Cu
Ni
Pb
Hg
Zn
0.5 (A)
1.0*
2200.
21.0
12.0
1100.
79.
100.0*
Salinity (ppt)
0.5 - 5.0 (B)
6.3
9900.
21.0
43.0
3100.
400.
570.0
5.0 (C)
59.0
5150.
1260.
23.0
140.
334.0
3.7
170.0

     1/2 LC5Q value for striped bass larvae.
TABLE 5.  CHRONIC HEAVY METAL VALUES FOR USE IN TABLULATION OF  FREQUENCY OF
          WATER QUALITY CRITERIA VIOLATIONS, IN ug L'1.   LETTER REFERS  TO
          BAY SEGMENT GROUP

Metal
Cd
Cr+6
Cu
Ni
Pb
Hg
Zn
0.5 (A)
0.012
0.29
5.6
56.0
0.75
47.0
Salinity (ppt)
0.5 - 5.0 (B)
0.051
0.29
5.6
160.0
20.0
47.0
5.0 (C)
4.5
18.0
4.0
7.1
25.0*
0.025
58.0

  * No EPA value available.  Based on chronic toxicity to mysid  shrimp.

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A TOXICITY INDEX FOR METALS IN BED SEDIMENTS

Introduction
    A Contamination Index is presented in Chapter  1.   This  index estimates
the enrichment of a suite of heavy metals relative to  expected natural
concentrations in bed sediments:

                     i = 6                    i =  6
                    i = 1       Cp           i  =  1
Where Co = the surface sediment concentration of  a  given metal,
      Cp = the predicted concentration,  and
      Cf = the concentration factor.
    Calculation of the predicted concentration normalizes  for differences
in metal affinity for various sediment  grain sizes  and  organic content.
Thus the Cj is a dimensionless number only indirectly related to actual
concentration in the sediment .
    It is tempting to modify the index  so that it can better predict
potential biological impact of contaminated sediments.  However, it has not
always been easy to demonstrate direct  relationships  between the
concentration of toxicants in bed sediments and the effects on organisms.
Bioavailability of metals appears to  be related not only to gross
concentration, but to the forms in which they are present.  Their
availability also seems to depend on  geochemical  features  of the sediments
and of the species of organisms impacted (Ayling  1974,  Neff et al. 1978,
Ray et al. 1981) .  For these reasons, extensive sediment bioassay and
elutriate testing are needed to assess  the actual effects  of contaminants.
In addition, processes affecting bioavailability  require much further
study.  However, progress in this direction is only in  initial stages; we
are not ready, for example, to try to formulate "sediment  quality criteria"
analogous to the EPA Water Quality Criteria discussed above. 3
    Mindful of these many caveats, we have made an  initial attempt to make
the Cj more meaningful ecologically.  At this writing,  only
water-column-derived estimates of toxicity are available.  Making the
conceptual jump that metals most toxic  in the water column will prove most
toxic in bed sediments appears not unreasonable,  but  should, nevertheless,
be approached with some caution.  If  a  toxicity index,  weighted by relative
water-column toxicity, proves a better  predictor  of observed effects on
organisms than the non-weighted Cj, then we may be  heading in the right
direction.  (This is examined further in the section  on benthic organisms.)
Eventual availability of sediment-based  criteria  will allow us to refine
this index further.
^Personal Communication:   "Status of  Sediment Toxicity Information," W.
 Brungs, EPA-Naragansett,  1982.
                                      D-7

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    The toxicity index closely relates  to  the  contamination  index and is
defined as:
                        1=6       M
                        1=1       M.j_

where    Mj_ = the "acute"  anytime  EPA criterion  for  any  of  the metals,
but      HI is always the  criterion value  for  the most toxic of the  six
           metals.

    The "acute" anytime EPA criterion is  the concentration  of a material
that may not be exceeded in a given environment  at any time.  This value
may be different for different environments.   The criterion values are
calculated by standardized procedures using data from in-house EPA studies
and from published  scientific literature  (U.S. EPA 1982a) .
    EPA criterion values for each  of the  six metals  are  shown in Table 6;
the ratios of the value for the most toxic metal to  each of the other
metals appear in Table 1.   The toxicity index  was calculated for every
station where the Contamination Index was  calculated.  Each station  was
given an average salinity  value based upon its geographical location and
available salinity  data (Stroup and Lynn  1963) .   Because the toxicity of
metals is often greater in fresh water  than in salt  water,  we characterized
each station by its minimum salinity.  Bottom  salinities were used in every
case.  Freshwater stations were those with salinities less  than 0.5  ppt,
and these were assigned criterion  values  for freshwater  at  50 ppm
hardness.  Brackish stations were  those with salinities  between 0.5  and  5.0
ppm, and these were assigned criterion  values  for freshwater with a
hardness of 200 ppm.  Stations with salinities greater than 5.0 ppt  were
assigned criterion values for saltwater.   (See discussion in the section  on
Water Quality Criteria above.)

RESULTS AND DISCUSSIONS

    Much of the discussion in the  chapters of  this report is based on a
division of the Chesapeake Bay and its  tributaries into  spatial segments.
Accordingly, values for the toxicity index have  been analyzed in a similar
manner (Figure 1).   Not surprisingly, the  segment showing the highest mean
toxicity index is that encompassing the Patapsco River and  Baltimore
Harbor.  Clearly, this area is highly impacted by industrial activity and
has been characterized as highly polluted  with metals based on the
Contamination Index presented in Chapter  1.  Other segments with high mean
values for the toxicity index include the  lower  James River, the upper York
River up to the confluence of the  Mattaponi and  Pamunkey Rivers, and the
very upper reach of Chesapeake Bay near northeast Maryland. Somewhat less
contaminated are the main Bay adjacent  to Baltimore  and  the lower
Rappahannock River.  The main Bay  south of Baltimore and the entire  Potomac
River show little evidence of contamination with toxic metals; the main  Bay
south of the Rappahannock and the  entire  eastern shore south of the
Nanticoke River are more or less pristine  in terms of toxic metals.
    However, the analysis of metal pollution using mean  values for the
toxicity index in each segment can occasionally  lead to  incorrect
conclusions.  For example, the high nean  value for the toxicity index in
                                      D-i

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    i
0





0-5





5-15





15-40





>40
Figure 1.    The toxicity index (Tx) averaged over Chesapeake Bay segments.
                                     D-9

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TABLE 6.  ACUTE CRITERIA:  LEVELS OF EACH OF SIX METALS THAT MAY NOT BE
          EXCEEDED AT ANY TIME AS ESTABLISHED BY THE U.S. ENVIRONMENTAL
          PROTECTION AGENCY.  VALUES ARE TOTAL RECOVERABLE METAL IN ug L"1
Metal
                   0.5
                             Salinity (ppt)
                               0.5 x 5.0
                 5.0
Cadmium
Chromium (+3)
Copper
Lead
Ni eke 1
Zinc
1.5
2200.0
12.0
74.0
1100.0
180.0
6.3
9900.0
43.0
400.0
3100.0
570.0
59.0
5150.0*
23.0
344.0*
140.0
170.0

*No EPA criterion exists.  Value shown is 0.5 x
 species tested:   striped bass larvae.
                                                     for most sensitive
TABLE 7.  RATIO OF EPA CRITERION (ACUTE) FOR MOST THE TOXIC METAL TO EACH
          OTHER METAL
Metal
Cadmium
Chromium (+3)
Copper
Lead
Nickel
Zinc
                      0.5

                   1.0
                   6.8 x 10~4
                   1.2 x HT1
                   2.0 x 10-2
                   1.4 x 10~4
                   8.3 x 10-3
Salinity (ppt)
 0.5  x  5.0

 1.0
 6.4 x 10~4
 1.4 x 1Q-1
 1.8 x 10~2
 1.7 x 10"2
 9.4 x 10"2
                                                     5.0
x 10
    "3
3.9
4.5
1.0
6.7 x 10~2
1.6 x 1CT1
1.4 x 10"1
                                      D-10

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the lower James River is the result  of  extremely high values at a few
stations, while the majority of stations  in the area are relatively
uncontaminated with highly toxic metals (Table 8).  Therefore, an analysis
of the values for the toxicity index at individual  stations without regard
to segment boundaries provides a better perspective of  the problem.  A
contour map of toxicity indices using logarithmic intervals again shows a
high level of contamination in Baltimore  Harbor, but with the apparently
associated high indices in the adjacent main Bay, restricted largely to the
axis of the Bay (Figure 2).  Additionally,  the sediments in much of the
lower James River are relatively uncontaminated by  toxic metals; only those
sediments off Norfolk and near Portsmouth are highly contaminated.
Comparison of contour maps of Cj versus Tj  reveals  areas of similarity,
as would be expected.  In general, however,  the toxicity index map shows
more details of structure and variation within an area  than does the Cj
map.  Areas of greatest toxicity,  such  as Baltimore Harbor, an area
extending northward to the Susquehanna  Flats, the Northeast River, the
lower Rappahannock, upper York, and  the Elizabeth River, are also most
contaminated using the Cj.  In addition,  the lower  Patuxent River and
several smaller tributaries of the lower  James have high toxicity indices.
Moderately high values of the Tj occupy the central and upper Bay main
stem and lower reaches of most western  shore tributaries, except the James
River.  In general, this pattern follows  the distribution of finer
sediments in Chesapeake Bay, which is not unexpected, as heavy metals are
associated with the silt and clay fraction  of the substrate.
    Though a contour map based on logarithmic intervals allows a general
analysis of metal contamination of the  Bay's sediments, the toxicity index
at stations within a contour interval can vary greatly, especially within
the interval containing the highest  values.   Toxicity indices for stations
in Baltimore Harbor range from 3.2 to 2691.4 and reflect considerable
differences in the expected toxicity of the sediments.
                                      D-ll

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TABLE 8.  TOXICITY INDICES FOR DIFFERENT SPATIAL SEGMENTS OF CHESAPEAKE
          BAY AND ITS TRIBUTARIES.  INDEX IS BASED ON CONCENTRATION AND
          RELATIVE TOXICITY OF SIX METALS (Cd, Cr, Cu, Ni, Pb,  Zn) IN
          SEDIMENT SAMPLES.  (SEE FIGURE 1 FOR LOCATION OF SEGMENTS)

Segment

James LE-5
James RET-5
James TF-5
Lower Bay CB-6
Lower Bay CB-7
Lower Eastern Shore
Mid-Bay CB-4
Mid-Bay CB-5
Eastern Shore EE-1
Eastern Shore EE-2
Patuxent LE-1
Potomac LE-2
Rappahannock LE-3
Upper Bay CB-1
Upper Bay CB-2
Upper Bay CB-3
Upper Eastern Shore
Western Tributaries
York LE-4
York RET-4
York WE-4
Number of
Stations
31
1
3
10
28
EE-3 1
37
27
1
1
3
6
8
14
7
15
ET-1 I
WT-5 159
3
2
4
Mean

30.4
1.8
3.8
0.0
0.0
0.0
4.0
1.5
2.6
2.3
2.3
2.5
12.7
4.1
8.3
8.7
19.7
61.4
1.5
32.8
0.0
Standard
Deviation
39.8

2.7



3.6
4.2


2.9
2.8
12.7
5.6
4.4
6.5

218.4
4.0


Maximum
Value
131.4

1.5



11.2
18.1


4.7
6.5
31.9
19.9
15.6
21.2

2691.4
6.1
33.2

Minimum
Value
0.0

6.8



0.0
0.0


0.0
0.0
0.0
0.0
1.0
0.0

3.2
0.0
32.6


                                      D-12

-------
     NO   NO DATA
           0
-------
                                  SECTION 2

ANALYSES FOR COMPARING WATER QUALITY WITH SAV TRENDS


   CORRELATION ANALYSIS

      Because  SAV declines are hypothesized  to  be related to some water
   quality factors, certain variables  were tested  (by correlation analysis)
   against vegetation abundance in those Chesapeake Bay segments where
   sufficient data existed.  A parametric test  (Pearson's correlation
   coefficient) and a non-parametric test (Spearman's rho) were used.  The
   11-year data set from the Maryland  Department of Natural Resources and the
   USFWS  on SAV abundance was used as  an estimator of vegetation abundance.
   Among  the water quality variables screened were:  TN, nitrate, TP,
   dissolved inorganic phosphorus, chlorophyll a_,  turbidity, Secchi depth, DO,
   salinity, temperature, and pH.  There were compared to total percent
   vegetation using annual, spring, summer means,  and 95th percentile values
   for each variable in each segment.   Data were tested using direct
   comparison of a particular year's SAV data against water quality variables
   of that year (e.g., 1971 to 1971, 1972 to  1972).  In addition, under the
   hypothesis that growing conditions  of a previuous year might have a
   significant  effect on SAV success the next growing season, vegetation data
   were  tested  agains water quality variables for  the preceding year (e.g.,
   1971  SAV against 1970 variables).
      The results of the analyses are presented in Table 9.  Overall, the
   greatest number of significant correlations were found between SAV and
   nutrients; DO, pH, turbidity, and temperature also showed significant
   relationships.  Correlations were all negative  between SAV and the 95th
   percentile of TN, N03, the 95th percentile of N03, and the 95th
   percentinle  of IFF; the majority were negative  between TN and IFF.
   Correlations between TP and the 95th percentile were positive.  Chlorophyll
   a, DO, salinity, and temperature showed both  negative and positive
   correlations.  Turbidity usually correlated negatively with SAV, while
   Secchi depth showed mostly positive relationships.  The variable pH was
   always correlated positively with SAV, while  the 95th percentile showed
   consistent negative relationships.
      When assessed by region, the main Bay segments  (CB 1-5) demonstrated
   negative correlations with TN, N03, and IFF and positive correlations
   with  TP.   Turbidity (negative), salinity (positive), temperature
   (negative),  and pH (positive) were other major  variables showing
   correlations.  Overall, TN, N03, and the 95th percentile of N03 showed
   the most significant relationships.  Eastern Shore areas show the most
   significant  correlations with N03 (negative,  the  95th percentile TP
   (positive),  turbidity (mostly negative), DO (mixed), the 95th percentile of
   salinity (negative) and pH (positive).  Western Shore segments (including
   the Patuxent) have the fewest significant correlations, but the 95th
   percentile of IPF (negative), chlorophyll a_ (mixed) and DO (mostly
   positive)  can be noted.
      In general, these analyses simply show correspondence of trends in
   water quality and submerged vegetation.  They should not be taken as
   demonstrations of cause-and-effect.  However, most are consistent with  the
   hypothesis that increased nutrients and turbidity are linked to observed
   declines in  SAV.
                                        D-14

-------
TABLE 9.   RESULTS OF CORRELATION ANALYSES  OF  WATER  QUALITY  VARIABLES AGAINST
          SUBMERGED AQUATIC VEGETATION (SAV DATA FROM MARYLAND DNR AND THE
          U.S.  FWS 1971  to 1981).   MARYLAND ONLY.   P  = PEARSON'S CORRELATION
          S = SPEARMAN'S CORRELATION

Segment
CB-1











CB-2
CB-3







CB-4





CB-5












Analysis
P
S
S
P
P
S
P
S
P
S
P
S
P
P
S
P
P
S
P
S
P
S
P
S
P
S
P
P
P
S
S
P
P
P
S
S
S
S
P
P
Time
Period
annual
**
ft
annual lag
it
"
summer lag
ft
summer
ii
spring lag
11
annual
annual lag
11
tt
summer
"
summer lag
"
spring lag
annual lag
summer
11
s umme r 1 ag
"
spring lag
annual
n
••
"
ft
11
••
11
••
•f
11
annual lag

Water Quality
Variable
N03
TN
N03
temperature
N03
temperature
DO
95-salinity
N03
salinity
ph
PH
TN
N03
N03
IPF
95-TN
95-TN
95-TN
95-TN
IPF
turbid
95-pH
95-pH
95-pH
95-pH
TN
95-TN
95-TP
95-TN
95-salinity
95-DO
TN
TP
TN
DO
turbid
temperature
95-TN
95-TP
Correlation
Coefficient
-0.92
0.79
-0.84
-0.69
-0.90
-0.71
-0.85
0.62
-0.94
0.77
0.81
0.93
-0.71
-0.74
-0.76
-0.75
-0.81
-0.86
-0.81
-0.87
-0.62
-0.76
-0.67
0.69
0.66
-0.70
0.75
-0.87
0.64
-0.77
0.61
0.69
-0.83
0.72
-0.69
0.68
-0.84
0.62
-0.83
0.64
P ' F
0.005
0.006
0.004
0.03
0.03
0.03
0.004
0.05
0.02
0.04
0.05
0.001
0.02
0.015
0.01
0.01
0.005
0.001
0.004
0.001
0.05
0.03
0.04
0.02
0.04
0.03
0.03
0.002
0.03
0.01
0.04
0.02
0.005
0.02
0.04
0.03
0.04
0.05
0.006
0.03
n
9
10
9
10
5
9
9
10
5
7
6
6
10
10
10
10
10
10
10
10
10
8
10
10
10
10
8
9
11
9
11
10
9
10
9
10
6
10
9
11
                                  (continued)
                                     D-15

-------
TABLE 9.  (continued).
Time Water Quality
Segment Analysis Period Variable
S
S
S
P
S
P
CB-5 S
P
S
P
P
S
P
S
P
P
P
EE-1 P
P
S
S
P
P
P
EE-2 P
P
P
EE-3 P
P
P
S
S
P
P
P
P
S
S
S
S
P
S
••
••
"
spring
11
"
spring
summer
"
spring lag
"
"
summer lag
"
"
•*

annual lag
ii
"
"
"
spring
spring lag
annual
summer lag
annual lag
annual
"
*i
•*
••
annual lag
••
ft
••
**
it
"
11
spring

95-TN
95-DO
turbid
95-TN
95-salinity
TN
TN
95-TN
95-TN
95-TN
95-temperature
9 5-temperature
95-TN
95-TN
turbidity
Secchi
temperature
95-DO
PH
TP
salinity
salinity
95-TN
95-TN
N03
95-salinity
PH
95-turbid
turbid
pH
turbid
pH
N03
turbid
salinity
PH
N03
turbid
salinity
pH
95-Chl a
95-Chl a
Correlation
Coefficient
-0.78
0.61
-0.84
-0.77
0.64
-0.84
-0.74
-0.78
-0.83
-0.74
-0.86
-0.71
-0.77
-0.78
-0.73
0.95
-0.88
-0.70
0.75
-0.75
-0.65
-0.68
-0.99
-0.99
-0.94
-0.85
0.97
-0.91
-0.96
0.79
-0.94
0.79
-0.74
-0.96
0.76
0.89
-0.79
-0.94
0.76
0.93
0.91
0.90
P ' F
0.014
0.04
0.04
0.01
0.03
0.005
0.02
0.02
0.01
0.02
0.001
0.015
0.03
0.02
0.04
0.001
0.001
0.05
0.05
0.05
0.05
0.04
0.006
0.006
0.02
0.03
0.03
0.01
0.002
0.03
0.005
0.04
0.05
0.002
0.03
0.007
0.04
0.005
0.03
0.003
0.03
0.04
n
9
11
6
9
11
9
9
8
8
8
11
11
8
8
8
8
10
8
7
7
9
9
4
4
5
6
4
6
6
7
6
7
7
6
8
7
7
6
6
7
5
5
                                  (continued)
                                      D-16

-------
TABLE 9.  (continued) .
Segment Analysis
P
P
P
P
S
S
S
P
P
P
S
ET-4 P
P
S
S
P
P
S
P
S
P
P
S
S
P
S
S
P
P
S
P
P
P
S
S
P
P
S
P
P
P
P
Time Water Quality
Period Variable
spring lag 95-Chl a
summer turbid
DO
pH
turbid
DO
PH
summer lag N03
DO
temperature
DO
annual 95-N03
9 5-temperature
95-N03
9 5-temperature
DO
temperature
temperature
chl a_
turbid
annual lag 95-N03
9 5-temperature
95-temperature
9 5-temperature
95-DO
95-DO
temperature
spring 95-DO
95-salinity
95-DO
salinity
pH
DO
N03
spring lag 95-DO
95-salinity
95-DO
TN
IPF
N03
summer 95-TP
DO
Correlation
Coefficient
0.91
-0.94
0.95
0.88
-0.94
0.82
0.82
-0.96
0.95
-0.84
0.75
-0.62
0.62
-0.63
0.77
-0.83
0.77
0.80
0.99
0.70
-0.62
0.63
-0.63
0.77
-0.63
-0.63
0.64
-0.90
-0.73
-0.83
-0.76
-0.81
-0.83
-0.90
-0.75
-0.73
-0.88
-0.87
-0.98
-0.82
0.72
-0.77
P F
0.03
0.004
0.001
0.22
0.005
0.02
0.04
0.002
0.001
0.01
0.05
0.06
0.04
0.05
0.006
0.04
0.006
0.003
0.01
0.04
0.06
0.04
0.05
0.04
0.04
0.04
0.05
0.001
0.04
0.02
0.03
0.03
0.02
0.04
0.05
0.04
0.01
0.05
0.02
0.05
0.04
0.03
n
5
6
7
6
6
7
6
6
7
8
7
10
11
10
11
6
11
11
4
5
10
11
10
11
11
11
10
7
8
7
8
7
7
5
7
8
7
5
4
6
8
8
                                  (continued)
                                     D-17

-------
TABLE 9.  (continued).
Segment Analysis
S
S
P
S
S
S
P
P
S
P
ET-5 P
P
S
P
S
P
P
ET-5 P
P
S
S
P
P
S
S
S
P
S
P
P
S
S
P
P
S
S
S
LE-1 P
S
S
P
S
Time
Period
»
11
summer lag
••
"
ii
**
H
11
ii
annual
••
n
11
11
annual lag
11
annual lag
11
«t
"
spring
"
11
*f
11
spring lag
**
summer
11
summer lag
**
11
11
••
t?
••
annual
"
t?
annual lag

Water Quality
Variable
TP
temperature
95-TP
95-TP
N03
PH
TP
Secchi
Secchi
temperature
DO
temperature
temperature
turbid
DO
95-salinity
TP
chl a
turbid
chl a
IPF
95-salinity
N03
N03
IPF
turbid
salinity
PH
95-turbid
95-IPF
95-TP
95-turbid
DO
TN
TN
chl a
IPF ~
chl a
chl a
temperature
IPF
chl a
Correlation
Coefficient
0.72
0.33
0.81
0.66
-0.87
0.67
0.81
-0.91
0.95
-0.73
-0.74
0.71
0.70
0.74
-0.70
-0.61
0.74
-0.89
0.72
-0.81
0.69
-0.62
-0.74
-0.74
-0.74
-0.72
-0.72
0.91
0.79
-0.72
0.67
0.69
0.74
0.82
0.84
-0.84
0.81
0.95
0.89
-0.94
0.98
0.89
P F
0.04
0.25
0.01
0.05
0.05
0.05
0.01
0.05
0.05
0.02
0.01
0.01
0.02
0.02
0.02
0.05
0.02
0.003
0.04
0.01
0.05
0.05
0.06*
0.06*
0.06*
0.02
0.02
0.03
0.02
0.04
0.05
0.04
0.02
0.04
0.04
0.04
0.05
0.02
0.04
0.005
0.003
0.04
n
8
10
9
9
5
4
8
4
4
10
10
11
11
9
10
11
9
8
8
8
8
10
7
7
7
9
9
5
8
8
9
9
9
6
6
6
6
5
5
6
5
5
                                   (continued)
                                      D-18

-------
TABLE 9.  (continued).
Segment Analysis
P
P
S
P
S
WT-2 P
P
P
P
S
S
P
S
P
P
P
S
P
WT-3 P
P
S
P
WT-5 P
P
S
P
S
P
S
P
P
P
S
P
S
S
P
P
S
P
P
Time
Period
spring
summer
••
summer lag
**
annual
11
ti
annual lag
11
"
it
"
spring
spring lag
summer
11
summer lag
annual
annual lag
spring lag
summer lag
annual
ii
11
"
M
••
11
annual lag
**
"
"
spring
**
summer
tt
**
11
summer lag
tt
Water Quality
Variable
chl a
TP
chl a
DO
TP
95-IPF
IPF
PH
DO
DO
chl a
95-DO"
95-IPF
95-turbid
95-turbid
95-IPF
95-IPF
95-pH
95-turbid
95-TN
turbid
chl a
95-chl~a
95-Secchi
95-chl a
TN
TN
DO
DO
95-chl a_
salinity
Secchi
Secchi
DO
DO
95-N03
DO
salinity
DO
salinity
pH
Correlation
Coefficient
0.99
0.89
0.94
-0.89
0.99
-0.83
-0.79
-0.81
-0.89
-0.93
0.83
-0.79
-0.83
0.95
0.98
-0.90
-0.91
-0.89
-0.79
-0.87
-0.95
-0.78
-0.75
0.75
-0.77
-0.77
-0.85
0.65
0.72
-0.77
0.92
0.81
0.88
0.73
0.70
-0.86
0.77
-D.,67
0.70
-0.87
0.75
P F
0.01
0.04
0.02
0.04
0.01
0.02
0.03
0.05
0.01
0.003
0.04
0.03
0.02
0.05
0.01
0.01
0.01
0.04
0.06*
0.05
0.05
0.06*
0.04
0.04
0.02
0.04
0.01
0.04
0.02
0.02
0.03
0.05
0.02
0.02
0.02
0.03
0.02
0.05
0.05
0.003
0.05
n
3
3
5
5
4
7
7
6
7
7
6
7
7
4
4
6
6
5
6
5
4
6
8
7
8
7
7
10
10
8
5
6
6
10
10
6
8
9
8
9
7

*P = 0.06;  not  statistically  significant at the 95 percent level, but included
 here for possible  ecological significance.
                                      D-19

-------
Multiple Regressions Analysis

    To achieve better insight into the contribution  of water quality
variables to SAV abundance,  we used multivariate  regression analysis to
identify factors that best explained observed  vegetation  trends.  A
stepwise least-squares multiple regression procedure was  used, employing
the Statistical Analysis System (SAS)  package  (SAS Institute Inc., SAS
Circle, Box 8000, Cory, NC 27511).   A relatively  low level of confidence
was chosen for entry into the model (80 percent)  to  include all possible
predictor vectors in the initial screening process.  For  the first trials,
all of the previously listed water quality variables were included.
However, a low number of observations  of certain  variables (i.e., N  10) in
some segments necessitated their elimination before  regression equations
could be successfully derived.
    Results of the first analyses are  given in Table 10.  Again, there is
relatively little consistency from segment to  segment or  season to season
among the major independent  variables  in the equations.   It is not
unexpected that SAV responses should differ from  area to  area because
different SAV species are involved;  also areal trends in  water quality
vary.  In addition, the selection of variables can affect the outcome of
the analysis.
    As these analyses were,  by necessity, limited by the  11-year SAV data
base from the MD DNR and U.S. FWS, they are, at best, suggestive rather
than predictive.  With small data sets, it is  unlikely that any independent
variable beyond the first or second has predictive capability.^
    Therefore, these results should be viewed  with some caution, as they
are preliminary at best.  In addition to the above caveats, it is difficult
to identify or eliminate spurious correlations, or those  where a variable
represents a surrogate or analog of the actual (but  not tested) predictor.
Also, in some segments, paucity of water quality  leads to low degrees of
freedom, weakening the statistical validity of the resulting equation.
Upper Bay—
    In CB-1, 83 percent of SAV variability is  explained by negative
correlation with annual N03  concentrations,  thus  supporting the
hypothesis stating that nutrient enrichment adversely impacts rooted
vegetation.  Addition of the dissolved oxygen  variable, explains 84 percent
of SAV variability.  Summer  means of chlorophyll  a_ and dissolved oxygen
explain 78 percent of SAV variability;  these are  positive correlations.
Probably both SAV and phytoplankton are responding positively to the same
factor(s), possibly summer inflow or another non-tested variable.
    In CB-2, a less readily  explained  relationship exists:  92 percent of
SAV variability is explained by correlation with  annual N03 (negative) ,
and turbidity.  Using summer means only, 94 percent  of variability is
explained by total phosphorus and turbidity alone.   While a strong negative
correlation with N03 and total phosphorus,  again, tends to support the
nutrient and SAV hypothesis, the positive correlation with turbidity is
puzzling (however, see previous discussion of  linear regressions).
    In CB-3, 85 percent of SAV variability can be explained by a positive
correlation with annual total nitrogen and turbidity, a relationship not
expected and not readily explained.  Some complex process may be
^Personal communication:  "Interpreting Multiple Regression Analyses," R.
 Ulanowicz, Chesapeake Biological Laboratory,  1982.
                                      D-20

-------
TABLE 10.  MULTIVARIATE REGRESSIONS OF SAV TO WATER QUALITY VARIABLES,
           ACROSS TIME BY SEGMENT

Segment
CB-1



CB-2





CB-3

CB-4

CB-5


Time
Annual
Annual/
lagged
Summer
_Annual


Annual/
lagged
Summer
Summer
Spring/
lagged
Spring/
lagged
Summer/
lagged
Annual


Regression r2 n-r-F
1) SAV =
2) SAV =
SAV =

SAV =
1) SAV =
2) SAV =
3) SAV =
SAV =

1) SAV =
2) SAV =
1) SAV =
2) SAV =
2) SAV =
SAV =
SAV =
SAV =
1) SAV =
2) SAV =
3) SAV =
4) SAV =
62.3 - 58.9 (N03)
12.0 - 68.0 (N03) +
6.0 (DO)
34.9 + 4.4 (DO)

- 87.1 + .67 (CHL)
+ 11.8 (DO)
4.8 + 2.7 (TN) - 65.6 (TP)
•f .8 (Turbid) - 1.8 (DO)
6.7 + 4.3 (N03) -
68.9 (TP) + 0.9 (TURBID) - 2
6.1 - 63.2 (TP) +
0.8 (TURBID) - 1.6 (DO)
36.5 - 3.2 (DO) - 0.37 (CHL)

- 15.7 + 1.0 (TURBID)
- 12.2 - 32.4 (TP)
+ 1.0 (TURBID)
- 8.2 + 16.3 (TN)
- 18.1 + 19.1 (TN)
19.9 - 15.5 (N03)
21.2 - 11.4 (N03) - 0.2 (CHL)
- 1.4 + 9.0 (TP) +
0.02 (CHL) + 0.1 (TURBID)
8.4 - .5 (TN) - 6.5 (N03)
- .3 (CHL)
- 29.8 + 4 (DO)
6.7 - 13.2 (TN) +
2.5 (DO)
- 7.0 - 15.4 (TN) -
1.9 (SECCHI) + 3.2 (DO)
- 16.2 - 13.0 (TN)
- 16.4 (N03) - 1.5 (SECCHI) +
.82
.89
.43

.78
.99
.99
.2 (DO)
.99
.81

.87
.94
.74
.85
.71
.99
.99
.71
.85
.94
.98
4.4 (DO)
0.0016
0.004
0.08

0.0237
0.0018
0.006
0.004
0.04

0.0068
.0148
.0065
.0088
.0088
.0005
.0001
.0173
.0224
.0244
.0322
                                  (continued)
                                       D-21

-------
TABLE 10.  (continued)
Segment

EE-1


EE-3









ET-4





ET-5

WT-2





WT-3
WT-5



Time
Spring
Annual
Annual/
lagged
Spring



Spring/
lagged
Summer

Summer/
lagged
Annual

Spring/
lagged
Summer/
lagged
Summer/
lagged
Annual
Summer


Summer/
lagged
Annual
Annual




SAV =
SAV =
SAV =

1) SAV =
2) SAV =
3) SAV =

SAV =

SAV =

SAV =

SAV =

SAV =

SAV =

SAV =

SAV =
1) SAV =
2) SAV =

SAV =

SAV =
1) SAV =

2) SAV =

Regression
16.0 - 16.5 (TN) + (N03)
43.7 - 49.3 (N03)
4.6 + 12.6 (TURBID)
-245.25 (TP)
1.6 + 0.48 (CHL)
9.5 + 0.49 (CHL) - 13.9 (TN)
11.5 + 0.48 (CHL) -
12.0 (TN) - 53.9 (TP)
- 1.98 + 0.38 (CHL)

46.7 - 1.8 (TURBID)
- 19.1 (TN)
26.9 - 1.5 (TURBID)
- 156.4 (N03) + 0.68 (DO)
82.2 + 40.7 (TP)
-6.1 (DO)
93.4 - 63.4 (N03)

43.1 - 45.8 (N03) +
130.4 (TP) - 0.1 (CHL)
- 5.3 + 10.1 (DO)

64.6 - 4.7 (TURBID)
50.2 - 2.1 (TURBID)
12.5 + 19.3 (TN) -
1.4 (TURBID)
54.0 - 53.9 (N03)

31.0 - 139.1 (N03)
48.3 - 0.65 (CHL) -
13.6 (N03) - 6.9 (TN)
38.3 - 0.69 (CHL)
14.8 (N03)
r2
.92
.48
.93

.94
.99
.99

.85

.99

.99

.69

.98

.99

.68

.92
.92
.99

.99

.99
.96

.88

p
-------
TABLE 10.  (continued)
Segment    Time               Regression                  r2           p,.- p

          Annual/  1) SAV = - 9.13 + 40.97  (SECCHI)        .89          0.11
          lagged            - 10.92 (TN)

                   2) SAV = - 32.6 + 46.2 (SECCHI)         .69          0.08
          Summer      SAV = 7.3 - 0.4 (CHL) + 14.5         .99          0.001
                            (SECCHI)

WT-6      Annual/     SAV = - 30.6 + 53.5 (SECCHI)         .99          0.04
          lagged            + 6.6 (TN)
                                       D-23

-------
operating, or the results may represent  a  spurious  correlation or
autocorrelation.   Comparison with spring means of the  previous year
generates an equation with 84 percent  of SAV variability explained by
negative correlation with N03 and chlorophyll a_.  This latter
relationship is more comparable to equations for  CB-1  and  CB-2.
    No significant relationships were  found between annual water-quality-
variable means and SAV trends in CB-4.  Comparison  to  seasonal means of the
previous year produces two predictive  equations:  the  spring variables of
total phosphorus and turbidity (both positive) and  the summer variables of
nitrate and chlorophyll (both negative).  In  this segment, SAV may respond
positively to nutrient availability in the spring,  but negatively to the
summer loadings.
    In segment CB-5, 85 percent of SAV variability  is  explained by annual
total nitrogen (negative) and dissolved oxygen  (positive)  concentrations.
Comparison to spring means produces an equation which  explains 92 percent
of SAV variability by negative correlation with total  nitrogen and a
positive correlation with nitrate.
Eastern Shore—
    In segment EE-1, Eastern Bay, no significant  correlations were
identified using current annual or seasonal means.   Comparison of SAV
trends with annual water quality variable  means of  the preceding year
produces an equation which explains 93 percent of SAV  variability by
turbidity (positive) and total phosphorus  (negative).
    Segment EE-3, Honga River and Tangier  Sound,  had no correlations
identified with annual means.  Spring  means of  chlorophyll, both current
and preceding year, explain a major proportion of SAV  variability.  In the
summer, negative correlations with turbidity  and  total nitrogen produce an
equation explaining 99 percent of SAV  variation but significant only at the
93 percent level because of the low number of observations (p^0.07).
Water quality variables of the preceding summer entering into the
predictive equation are turbidity and  NC>3  (both negative), and dissolved
oxygen (positive).
    In segment ET-4 (Chester River), 69 percent of  SAV variability is
predicted by annual total phosphorus (positive) and dissolved oxygen
(negative).  Comparison with seasonal  variables of  the previous year shows
a negative correlation with nitrate for both  the  spring and summer;
however, relatively few observations were  available to produce these
equations.
    In ET-5 (Choptank River) , the only significant  relationship results
from a comparison of SAV to the summer variables  of the previous year; 68
percent of SAV variability is explained by dissolved oxygen alone.  This
relationship is difficult to explain,  although it may  represent a response
of SAV to some other factor for which  dissolved oxygen is  a surrogate.
Western Shore—
    Ninety-two percent of SAV variability  in  WT-2,  the Gunpowder River, can
be explained by a negative correlation with the annual means of turbidity
alone.  Comparison with summer means of the current year produces a
regression equation explaining 92 percent  of  SAV  variability by a negative
correlation with turbidity alone.  Addition of  total nitrogen and N03
increases goodness-xif-fit to 99 percent.  Comparison with  the spring means
of the preceding year produces an equation that explains 99 percent of the
observed SAV variation by a correlation with  total  nitrogen and nitrate.
Summer nitrate and total nitrogen concentrations  of the preceding year
                                      D-24

-------
explain 99 percent of SAV variability, as well.  However,  the small  number
of observations (n = 10) that were used to generate these  equations  is
reason for very cautious interpretation.
    In segment WT-3, the Middle River, the annual nitrate  concentrations
alone produce an equation explaining 99 percent of SAV variation.  No other
significant equations were produced.
    In the Patapsco River, WT-5, the annual nitrate and chlorophyll
concentrations account for 88 percent of the observed SAV  variability.  An
addition of total nitrogen increases goodness-of-fit to 99 percent.  All of
the correlations are negative.  Sixty-nine percent of SAV  variation  can be
predicted by an annual means of Secchi depth the preceding year  (for
example, when Secchi depth increases, so does SAV).  An addition of  total
nitrogen (negative) increases goodness-of-fit to 89 percent,  but decreases
significance to the (P<0.10) level.  The Summer means of chlorophyll and
Secchi depth can explain 99 percent of SAV variation.  In  this urbanized
estuary, these equations all relate SAV success to decreases  in  nutrients
and chlorophyll, and increases in Secchi depth.
    In segment WT-6, the Magothy River, 99 percent of SAV  variability can
be explained by Secchi depth and total nitrogen of the preceding year.

Summary of Multivariate Regressions

    In general, SAV responded negatively to nutrients, particularly  TN and
NC>3 concentrations.  The multivariable equations are suggestive, but not
conclusive.  It should be emphasized that none of these relationships are
intrinsically causative; SAV could be responding to a non-tested variable
co-occurring with the tested predictors.

Comparison of Segments

    The preceding linear and multiple regression analyses  serve  to identify
water quality factors that may be affecting SAV abundance  within each
segment.  To determine if any factor, or factors, could be acting
consistently on all segments, a nonparametric test, Spearman's rank-
correlation coefficient, was used.  Total percent vegetation  within  each
segment was compared with a number of water quality variables, including
TN, N03, NH3, TP,  DO, and chlorophyll a_.  Annual means, five-year
means, and maximums of various parameters were tested.  The Maryland DNR
and U.S. FWS SAV data from 22 Maryland Bay segments were used.   Results are
given in Table 11.
    Percent SAV was compared for possible positive or inverse relationships
with nutrients, chlorophyll a_, and dissolved oxygen.  Significant inverse
relationships were identified between percent SAV and mean annual TN of
both the current and preceding year (p^. 0.001).  In addition, if 5-year
means of SAV are compared to 5-year means of TN, they are  significant at
the 95 percent level.  There was no apparent relationship  between SAV and
annual N03, but a significant negative correlation was observed  between
SAV and N03 of the preceding year (p^C 0.025).  No significant
correlations were found between SAV and total phosphate.  When chlorophyll
£ levels (an indication of possible nutrient enrichment) are  compared to
submerged aquatic vegetation levels, a .significant correlation occurs with
maximum chlorophyll &_ of the preceding year.  In addition, the relationship
between SAV to mean annual chlorophyll a (of current year) is significant

-------
at the 90 percent level.
    In general, on a comparative segment  basis,  SAV  appears  to  respond
negatively to increased total nitrogen of both the current and  preceding
year.  This, as well as the negative relationship with N03 of the
preceding year, seems to support the results of  the  previous regression
analysis.  The negative response to maximum chlorophyll a_, an analog of
both nutrient loading and turbidity, also supports the SAV and  nutrient
enrichment hypothesis.
TABLE 11.  SPEARMAN-RANK CORRELATION COEFFICIENT RESULTS FOR SUBMERGED
           AQUATIC VEGETATION AGAINST WATER QUALITY VARIABLES.   rs  =
           CORRELATION COEFFICIENT,  ALPHA = LEVEL OF SIGNIFICANCE,  n = 22
  SAV
% SAV
  yr ~ % SAV
- x annual TN
- x annual TN of
  preceding year
- 5 yr ^ TN
0.70
0.70

0.41
                                                              alpha
0.001
0.001

0.05
% SAV
% SAV

% SAV
% SAV
5 yr ~% SAV
% SAV
% SAV
% SAV

% SAV
% SAV

% SAV
% SAV
% SAV

% SAV

% SAV
% SAV

% SAV
% SAV
- x annual N03
- x annual N03 of
preceding year
- x summer TN
- x maximum summer TN
- 5 yr ^ summer TN
+ x annual TP
- x annual TP
+ x annual TP of
preceding year
+ maximum annual TP
+ maximum annual TP of
preceding year
- x annual chl a
+ x annual chl a
- x annual chl a of
preceding year
+ x annual chl a of
preceding year
- annual maximum chl a
- annual max. chl a of
preceding year
+ annual maximum chl a
+ annual dissolved oxygen
0.08
0.43

0.11
-0.09
-0.11
0.10
0.08
0.03

0.08
0.06

0.30
0.16
0.19

0.13

0.37
0.25

0.20
0.37
N.S.
0.025

N.S.
N.S.
N.S.
N.S.
N.S.
N.S.

N.S.
N.S.

0.10
N.S.
N.S.

N.S.

0.05
N.S.

N.S.
N.S.

                                      D-26

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                                SECTION 3
        STATISTICAL ANALYSES OF BENTHIC  ORGANISMS

SHANNON-WEAVER DIVERSITY INDEX AND OTHER TESTS

Main Bay
    Use of the Shannon-Weaver diversity index
       ,
H   -  £
                                  TV
to compare the  bentic community with the contamination  of bed sediments by
metals (Cj,  Contamination Index) showed no apparent  relationships in the
main Bay.   Temporal  and spatial variability in H appeared to be related
more to estuarine  salinity gradient and sediment type than to the Cj.
    The ratio of annelids to molluscs and crustaceans has been cited as an
indication of environmental stress.  These ratios were  compared to both the
Cj and TI  using a  nonparametric procedure, the Spearman Rank
Correlation test.  However, no significant relationship could be
identified.   One difficulty is that benthic samples  for biological analysis
did not come from  the exact areas where toxic materials were sampled.
Innate variability of organism distribution would tend  to obscure
relationships in such cases.
    To avoid variability resulting from small scale  differences, the
annelid:mollusc ratios were compared from areas where the C^ was greater
than 4 and from areas where it was less than 4, using the Mann-Whitney U
test.  These differences were significant at about the  94 percent level.
Areas where the Cj was > 4 had, in general, annelidrmollusc ratios>15 (X
= 28; n =  6).  Areas where the Cj was<4 had ratios, in general,<;15 (X =
6.5; n = 13).

Patapsco River  and Elizabeth River

    The Patapsco River and Baltimore Harbor area was investigated by
Pfitzenmeyer in 1975 and by Reinharz in 1981.  This  tributary has been
subjected  to significant anthropogenic impact and could be expected to show
more effects on benthic communities than does the main  Bay.
    Within the  Patapsco, diversity (H) generally declines along the
gradient of increasing contamination of metals and organic chemicals (Bieri
et al. 1982b) (Figure 3, Table 12).  Only stations near the mouth of the
Patapsco retained  diversity comparable to that at the reference stations in
the Rhode  River.   A  group of stations in the inner estuary (PO 1,3,4 and 5)
shows low  diversities (H = 0.246 - 0.590) and high redundancy (dominance by
one or a few species)(Table 13).  They are dominated by polychaetes,
particularly Scolecolepides viridis.  Stations ?2 and Pg, also with low
diversities (H  =  0.678 to 0.838), are dominated by  polychaetes and
oligochaetes.  Two groups of stations in the mid-estuary (Pg  ^0  ll) anc*
(^6  7  13)  have diversity values ranging from 1.173 to 1.615, and are
dominated  by polychaetes, with a few molluscs (chiefly  Macoma balthica), as
well as some crustaceans.  Stations P^2 and 14 (H =  2.175 to 2.879)
have fauna dominated by a wide variety of polychaetes,  molluscs, and
crustaceans, similar to the Rhode River reference areas (H = 2.286 to
                                     D-27

-------
           Baltimore City

              P2
Patapsco
River
       Low diversity

       Moderate diversity

       High diversity
   Figure 3.   Diversity index (d) of  benthic communities  in the Patapsco and
               Rhode  Rivers (Reinharz  1981).

-------
 2.501).   Comparison of groups by Student-Neuman-Keuls test shows that all
 groups are  statistically different from one another.  However, the same
 procedure using  the Bonferroni (Dunn) test and Tukey's Studentized Range
 Test  ranks  groups 1 and 2 together, and 3 and 4 together.
 TABLE  12.  CONTAMINATION INDEX (Cr), TOXICITY INDEX (Tr), ANNELID .-MOLLUSC
           AND ANNELID:CRUSTACEAN RATIOS FOR REINHARZ 1981 PATAPSCO RIVER
           STATIONS
 Station
                                       Annelid rMollusc
Annelid:Crustacean
PO
1
2
3
4
5
6
7
8
9
10
11
12
13
14
55
20
131
164
58
39
41
36
85
130
35
42
21
97
11
26
13.8
-
100.4
8
40.8
15.3
21
40.8
46
12.3
17
8.3
17
—
23
15
-
51
11
37
2
3
5
29
33
30
3
14
4
_
-
-
253
1276
-
203
47
62
350
115
115
11
138
0.9

*X of at least two measurements,  except for Tj at  station P4
                 A comparison of  reduced-diversity areas with  both metal and
organic contamination of sediment in the Patapsco estuary  shows a  strong
visual correspondence (Figures 4 and 5).  Reinharz (1981)  found a  virtual
lack of salinity gradient in the estuary and (except for head branches of
the Patapsco) consistent silt-clay sediment  type.   Thus, the significant
differences in benthic diversity observed can best be explained by
pollution, and by other anthropogenic influences  (e.g., dredging).  Species
found in the most contaminated areas are opportunists, inhabiting  only the
upper layers of bed sediment.   Arthropods and molluscs become more
important in less-polluted regions of the estuary.  For example,
Leptocheirus plumulosus, a tube-dwelling amphipod, is an important member
of the benthic community in the Rhode River  reference area.  In the
Patapsco, Reinharz (1981) found this species in number only at P^2 an<3
P]_4,  the two least contaminated stations (Figure  6);  elsewhere within the
estuary, it was essentially absent.   This is similar to the observation of
Wolfe et al (1982), that the tube-dwelling amphipod  Ampelisca was  absent
from the impacted areas of the New York  Bight.
                                     D-29

-------
TABLE 13.  DIVERSITY, REDUNDANCY,  AND SPECIES NUMBER FOR PATAPSCO AND RHODE
           RIVER STATIONS.  GROUPS ARE ALL SIGNIFICANTLY DIFFERENT FROM ONE
           ANOTHER.

Station
po
pl
P3
P4
P5
P2
P9
P8
P10
pll
P6
P7
P13
P12
P14
Reference
Rl
R2
R3
H
0.330
0.561
0.343
0.590
0.246
0.838
0.678
1.173
1.296
1.193
1.615
1.416
1.400
2.879
2.715

2.286
2.348
2.501
r
0.864
0.831
0.906
0.783
0.893
0.491
0.731
0.630
0.634
0.676
0.523
0.603
0.549
0.307
0.312

0.420
0.369
0.366
N station group
1
8
8 1
6
4
3 2
5
8
10 3
11
9
10 4
8
16 5
14

15
13 6
15

P = Patapsco River stations,
R = Rhode River stations,
H = diversity,
r = redundancy,
N = number of species present.
                                      D-30

-------
   Baltimore
          Harbor
Baltimore County
       1  "r*
       kf
                 Anne Arundel County
       C,<50
Figure 4.   Metal contamination of the  Patapsco River  (data from Biees
           1982).
   Baltimore
          Harbor
Figure 5.   Distribution of PNA,  Benzo(a)Pyrene in channel sediments from
           Baltimore Harbor and  the  Patapsco River (Data from Bieri et al.
           1982).
                                    D-31

-------
           Baltimore City
             P2
    10-50 individuals

    >50 individuals m~2
Figure 6.   Density  of Leptochierus plumulosus  in Patapsco and Rhode Rivers
            (Reinharz  1981).

                                        D-32

-------
    Spearman rank correlation identified statistically significant
relationships between contamination of bed sediments and various  community
attributes.  Both the Contamination Index and the toxicity index  were
used.  When these variables were compared to community diversity,  the
relationship between H and the Tj was significant at the 98 percent
level.  The Contamination Index did not compare as well to changes in
diversity  (p — 0.08), indicating that the weighted toxicity index measures
potential  biological impact better than the Cj alone.
    Annelidrmollusc and annelid:crustacean ratios, based on numbers  of
individuals, were also compared to the Cj and Tj.   (These ratios  could
not be calculated for all stations, as some had no crustaceans  or
molluscs).  The relationship between the annelid:mollusc ratio  and the Cj
was not significant.  However, using the Tj,  the relationship was
significant at the 95 percent level.  In contrast, the annelid:crustacean
ratio showed a significant relationship with the Cj (pi 0.005),  but this
ratio'-s relationship with the Tj was not significant.  Only one TX
value could be calculated for station 4 (others were means of at  least 3
values), and it appeared anomalously low.  When this value was  omitted from
the calculation, the relationship became significant at the 92  percent
level.
    In the Elizabeth River,  trends were less distinct,  possibly because
there were smaller differences in contamination from site to site within
the river.   However, Schaffner and Diaz (1982)  identified a group of
stations characterized by shallow dwelling,  young  populations of  relatively
low diversity; these stations were considered "impacted" by high  levels of
toxicants in the bed sediments.
    The effect of sediment contamination on benthic organisms was further
explored using bioassay techniques.  Bioassays  were performed on  the
sediments in the Elizabeth and Patapsco Rivers  to  determine the effect of
sediments on survival rate of a burrowing amphipod (Rhepoxynius abronius)
(Swartz and DeBen, in prep.).  Statistical analysis indicated that
survivorship strongly correlates with the degree of contamination (Cj) as
well as the Cf for Ni and Zn, and approximates  an  exponential response to
dose (Figure 7).  An estimated LC5Q would be Cj =  15.   However, it
should be emphasized that this association does not necessarily imply
causation.   Unmeasured metals or organic materials co-associated with the
measured parameters may be contributing to,  or  actually causing,  the
observed mortality.
    This view is supported by the observation that Spearman rank
correlation of the annelid:mollusc and annelid:crustacean ratios with the
contamination factor (Cf) for both Zn and Ni  in the Patapsco showed  no
significant relationship.  Thus,  the relation between Cj and percent
survival cannot be used to identify specific  anthropogenic substances whose
control can result in improved survival.   However,  it does indicate  the
probable presence of one or  more toxic materials in the tested sediments.
                                     D-33

-------
            Bioassay of Amphipod against
               Patapsco River Sediment
               (As a Function of Nickel Enrichment)
  100 r
   75
O

E  50
co
   25
    0
                0
.500       1.00

   Ni (Cf)
1.50
2.00
 Figure 7.  Bioassay of an araphipod against Patapsco River sediment (as a
          function of nickel enrichment).
                              D-34

-------
                                 SECTION 4
                 STATISTICAL ANALYSES OF FINFISH
JUVENILE INDEX

     We  used young juvenile finfish collected in four  representative
 tributary areas of the Bay (Head of Bay,  Potomac River,  Choptank River, and
 Nanticoke River) to assess the impact of  various environmental variables on
 finfish.  The juvenile index is a better  indicator  of the abundance of fish
 stocks  than landings because it is influenced less  by fishing pressure and
 other factors.  Though not immune to uncertainty as an index of stock
 abundance (Polgar 1982), the juvenile index was correlated with
 environmental variables to elucidate possible factors that affect the
 recruitment of young fish into the harvestable population.
     It  should be noted that the age determined in the MD DNR juvenile index
 includes young-of-the-year or age 0 for alewife, bluefish, shad, striped
 bass, white perch, and yellow perch.  Year classes  may be mixed for
 anchovy, catfish, menhaden, mummichog,  silversides, spot, and weakfish.

 Linear  Regression Analysis

     Using linear regression analysis,  the juvenile  index was compared with
 freshwater inflow and air temperature in  the four tributaries.  Results are
 summarized in Table 14a.   In general,  species responded  positively to
 increases in flow and air temperature.  In the Northern  Bay, alewife
 responded negatively to February and March flows, which  may be related to
 water temperature.  The same may be true  for anchovy  and silversides.  In
 both the Potomac and the Nanticoke, striped bass responded negatively to
 increased April air temperatures.
     Although Table 14b indicates some subtle differences among species and
 among rivers basins as they relate to flow, the most  believable results are
 those represented by the combined basins  (aggregated  flows and aggregated
 juvenile indexes).  This approach shows that striped  bass responds
 positively to strong spring flows results,  which agrees  with Mihurskey et
 al.  (1981).  The marine spawners, bluefish, menhaden,  and spot are
 responding positively to strong fall,  winter (which are  combined as
 "late"), late, and annual flows.  This  argues for the estuarine transport
 of  the  larval and juvenile forms of these species by  the upstream migration
 of  the  bottom waters (Tyler and Seliger 1978).

 Multiple Regression Analysis

 Analytical methodology—
     A multivariate regression analysis  was used to  identify the freshwater
 variables that best explain the observed  trends in  the juvenile index.
 Flow relationships were characterized in  terms of the maximum and minimum
 values  of the freshwater flow to the head of the estuary determined as
 moving  averages per month (7, 14, 21,  28  days).   Temperature was calculated
 as  the  average monthly value using reference air temperatures from National
 Airport for the Potomac and Nanticoke Rivers and Baltimore City values for
 the  upper Bay and the Choptank River,  respectively.
                                      D-35

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TABLE 14a.  RESULT OF LINEAR REGRESSION ANALYSIS OF JUVENILE INDEX AGAINST
            AIR TEMPERATURE

Species
Alewife
Spot
Spot
Atl.
Menhaden
Bluefish
Catfish
Spot
Atl.
Menhaden
Bluefish
Catfish
Spot
Atl.
Menhaden
Bluefish
Spot
Str. Bass
Age 0
Atl.
Menhaden
Yel. Perch
Age 0
Weakfish
Mummichog
Yel. Perch
Age 0
Spot
Str. Bass
Age 0
Spot
Basin
Choptank
Choptank
Choptank

Potomac
Potomac
Potomac
Potomac

Potomac
Potomac
Potomac
Potomac

Potomac
Potomac
Potomac

Potomac

Upper Bay

Upper Bay
Upper Bay
Choptank

Nanticoke
Nanticoke

Nanticoke
Choptank
Time Corr. Coeff.
Feb. & March
Feb. & March
Feb. , March, April

Feb. & March
Feb. & March
Feb. & March
Feb. & March

Feb. March, April
Feb. March, April
Feb. March, April
Feb. March, April

March
March
March

April

March

March
April
February

February
March

April
Spring
-0.46
0.43
0.44

0.49
0.66
0.45
0.48

0.58
0.73
0.52
0.49

0.54
0.56
0.48

-0.49

0.51

0.46
-0.42
-0.48

-0.52
0.42

-0.44
0.52
P & 0.05
0.0281
0.0381
0.0351

0.0165
0.0007
0.0312
0.0209

0.0037
0.0001
0.0109
0.0170

0.0078
0.0051
0.0210

0.0178

0.0136

0.0286
0.0447
0.0216

0.0101
0.0475

0.0360
0.0103

                                    D-36

-------
TABLE 14b.  RELATIONSHIP AS REPRESENTED BY R V ALUES AND DETERMINED BY
            CORRELATION ANALYSIS (P * 0.05) FOR FINFISH JUVENILE INDEX
            VERSUS FLOW (N = 24)
    Species
Annual
 Flow
Winter
 Flow
Spring
 Flow
Summer
 Flow
Fall
Flow
Early
 Flow
Late
Flow
Choptank River
    Alewife
    W. Perch
    Menhaden
    Mummichog

Nanticoke River
    Anchovy

Potomac River
 0.48
-0.49
    Striped Bass
    Bluefish
    Silversides  -0.46
-0.42
 0.50
 0.51
-0.44
          0.43
         -0.53
-0.43
                   0.38
                            0.40
                            0.56
                            0.46
                  -0.49
                                    -0.46
Upper Bay
    Spot
    Striped Bass
    Bluefish
    Silversides  -0.54
          0.51
         -0.49
          0.47

         -0.41
                                     0.59
                           -0.53
                                              0.60
                           -0.42
Combined Basins
Striped Bass
Bluefish 0.42
Menhaden
Spot 0.45
Silversides -0.60

0.45
0.52
0.60
0.42
-0.49


0.43
0.46
0.67
-0.43 -0.54


0.52
0.41
0.65
-0.51

                                    D-37

-------
    Juvenile index data used in this analysis  covered  the  period  of  1958  to
1981 for Atlantic menhaden,  spot,  bluefish,  Bay anchovy, striped  bass,
;hite perch, yellow perch,  catfish,  mummichog,  alewife, and Atlantic
silversides.  Emphasis in the analysis is  placed on  freshwater  spawners and
selected forage fish because these species spawn within the Bay system,
i.ncluding the fluvial streams;  they are hypothesized to have  sensitive
-~oung life stages when exposed to  higher concentrations of natural and
:mthropogenic factors than marine  spawners.
    The climatic data were obtained from Washington  National  Airport  on the
"otomac and from Baltimore-Washington International  Airport for the  upper
•paches of the Bay.  Flow was from the Environmental Protection Agency's
"10RET data bases at the NCC for each of the four basins at  the fall line.
"!<>w data were corrected to include the basin  of half  the  GBP RET segments
. •• uf'I ]  as the TF segments.
    Water quality data for the analysis were computed  from the  CBP nutrient
data sets and included TF-2, RET-2,  CB-1,  CB-2,  EE-2,  ET-5, ET-6,  and
KT-7,  For each year, monthly geometric means  were computed for use  in the
'egression models.  It must  be noted that  for  the water quality data there
is "c>L a continuous record of data available.
     In Lien of a non-continuous record of  the  water  quality data,  the
initial analyses included only the juvenile  indices, air temperature
(surrogate of water temperature),  and stream flow.  For all months,  the
juvenile indices were regressed in a step-wise fashion using  a  maximum R2
improvement against streamflow, and air temperature.  This technique was
developed by J.H. Goodnight  of the SAS Institute and is considered to be
superior to the step-wise procedures and almost as good as all  possible
regressions.  This max R2 method proceeds by finding the one  variable
model with the highest R2, then the two variable model is  found by adding
the variable that would maximize the R2 for  the regression.   Once the
model is obtained, Max R2 compares all possible switches of variables to
see  if another would further increase the R2 until no  further improvement
can be made.
    The selection of models  is documented in maximum R2 flow  sheets  for
each basin showing the order of variables coming into  the  model,  variable
substitutions, and the associated  R2 for the one through ri*-'1  model.   It
may be noted that the maximum number of variables for  each basin  and
species was not constant.  For this work,  the  number of variables was
limited by seven.  Fewer number of variables in the  model  indicated  the
failure of the model and/or  its components to  meet an  alpha probability
level of less than 0.10.
    Predictive regression models for each juvenile index species  in  each
basin were obtained from the results of maximum r-squared  regressions.
Models were selected based on explainability of the  variables to  the
juvenile indices and the change of the r-square values.  Through  the use  of
these models, regressions were performed,  and  equations were  derived from
which predictions can be made using the air  temperature and stream flow.
The derivation of these models was iterative until the optimally
explainable model was found.  Once the predictive models were derived,
residuals and predictions were obtained.  The  predictive data were plotted
against the raw juvenile index data using SAS  Graph  for comparisons. For
each model, the R square, F  value, and probability,  as well as  individual
variable probabilities were tabulated.
                                      D-38

-------
    Through the use of the residuals from each statistically  significant
equation, the water quality variables were tested.   Because of  the
infrequent data in the Choptank and Nanticoke  Rivers,  the water quality
tests in these rivers was excluded.  Monthly Max R2  step-wise regression
of water quality variables including salinity, total nitrogen,  total
phosphorus, dissolved oxygen,  and chlorophyll  was performed against the
residuals from the physical models to see if improvement can be made on the
models.  Because of the infrequent number of years available, we feel that
these results may be considered suggestive only.

Striped Bass  (Morone saxatilis)

    Mihursky et al. (1981)  showed that the highest five-day flow in April
and the minimum December temperature explained about 80 percent of the
variance associated with the success of the striped  bass juvenile index
(Figure 8) in the Potomac River.  The present  analysis confirms that
freshwater flow and temperature are important  variables that explain the
variability associated with the success of the striped bass juvenile index
in the Potomac,  however, the analysis required five (5) variables
(combinations of flow and temperature) to achieve an R^ of 0.81 (Table
15)(Figure 9).  Additional years are included  in the GBP analysis, probably
accounting for the small difference between the results of Mihursky et al.
(1981) and this study.  The importance of the  minimum  21-day flow in May
(My-MN Q21) may be simply a partial reciprocal of the maximum 28-day flow
of May, or the minimum 21-day flow may be important  in its own right.
    A possible explanation for these relationships has been given by
Mihursky et al. (1981) including the role of increased freshwater flow in
April expanding the spawning range for egg and young larvae development and
the role of low December temperatures in tying up organic detritus, which
can later serve as a food substrate for microheterotroph growth.  The
latter is presumably food for copepods, which  serve  as an important food
for larval striped bass (Heinle et al. 1976).   The minimum 21-day flow in
May may be a correlate of the high flow for this  month.
    The same variables were used in the analysis  of  flow and temperature
relationships for the upper Bay, and Choptank  and Nanticoke Rivers.  The
R-squared values were significant (Table 15) for  the upper Bay, Choptank
and Nanticoke Rivers, but were only 0.50, 0.56,  and  0.34, respectively.
The result of the predictive equations are shown  in  Figures 10, 11, and 12.
In the upper Bay, the April minimum 7-day flow and May minimum 7- and
14-day flows appeared in the regression equation  without a maximum flow
being represented.  This difference is speculated to result from the high
tidal currents naturally associated with the Elk  River and Chesapeake and
Delaware Canal, the primary site of spawning in the  upper Bay.  High
currents presumably maintain the neutrally buoyant eggs suspended in the
water column (Mansueti 1958).   The lack of a positive relationship between
the juvenile index abundance and maximum April flows in the upper Bay is
possibly the result of the transport of eggs and  larvae toward the Delaware
Bay during periods of high flow from the Susquehanna.  The lack of
temperature relationships in the upper Bay regressions is not clear, and
only temperature relationships were expressed  in  the predictive equations
fo,r the Potomac and Nanticoke  Rivers.   Minimum flow  relationships explain
56 percent of the variance in the Choptank,  which is similar to the case
for the upper Bay except the coefficients are  different by several orders
of magnitude.
                                     D-39

-------
0) 0 .
0.24

w
w
"O
0) i
9-
k_

w,

6


Z-  8-f
         1961
                                   1970

                                   1962
4-
 /long term mean juvenile index
.*	
 Figure 8.   Three-dimensional plot of December temperature deviation from

             long-term average temperatures (+_ °C), Potomac River flow in

             April (cfs), and the juvenile striped bass abundance index.

             (From Mihursky et al. 1981).
                                              D-40

-------
TABLE 15.  POTENTIAL PREDICTION EQUATIONS FOR STRIPED BASS JUVENILE INDICES AS
           DESCRIBED BY MULTIPLE REGRESSION
                                  Individual
                                      P  /T/
POTOMAC RIVER
    Striped Bass
                   56.65249
    + (0.00062 x MY - MXQ28)
    + (-0.00057 x MY - MNQ21)
    + (-1.14294 x OC - ATMP)
    + (1.13943 x AP - ATMP)
    + (1-.01890 x NV - ATMP)

UPPER CHESAPEAKE BAY
    Striped Bass = -4.32031

    + (0.00027 x AP - MNQ7)
    + (0.00133 x MY - MNQ7)
    + (-0.00096  x MY - MNQ14)
                               4.79
                              -2.26
                              -4.56
                              -3.18
                               2.82
                               3.44
                               3.31

                              -2.73
CHOPTANK RIVER
    Striped Bass = -4.19136

    + (0.10966 x AP - MNQ7)
    + (-0.071338 X AP - MNQ14)
NANTICOKE RIVER
    Striped Bass = 103.6655

    + (-1.14745 x AP - ATMP)  -2.35
0.0002
0.0379
0.0003
0.0058
0.0124
0.00026
0.00035

0.0130
                               4.52    0.002

                               •3.47    0.023
                                                                 Multiple
                                                              P   F    R-square   DF
13.73    0.0001    0.3110    21
 6.80    0.0024    0.5050    22
              13.19    0.0002    0.5568    22
                                       0.0291
               5.15    0.0157    0.3399    22
Months:
  NV = November
  AP = April
  JL = July
                       DC = December
                       MY = May
                       AG = August
    MR = March
    JN = June
    SP = September
                           ATMP = air temperature
                           CHL  = chlorophyll a_
                           TP   = total phosphorus
                           TN   = total nitrogen
MX = maximium
MN = minimum
Q  = flow
DO = dissolved oxygen
SALIN = salinity
7, 14, 21, 28 = moving average of days for freshwater flow
                                    D-41

-------
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                                 D-42
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-------
    A comparison of flow and temperature relationships among the  four
basins suggest that climatic variables explain a substantial amount  of  the
variability associated with the striped bass juvenile  index.  However,
there is little correspondence in specific variables appearing in the
predictive equations for all four basins.   This may reflect  a true
difference in the response of the juvenile striped bass to  real differences
in the physical features of these systems.  Other possibilities exist such
as masking of the response to physical variables through human intervention
or quite simply an inability to sort out the "signal from the noise."
Further work is required to increase our understanding of these
relationships.

White Perch (Morone americana)

    Flow and temperature relationships showed R-square values of  0.57 and
0.64 for the Choptank and Nanticoke Rivers, respectively.  Values for the
Potomac and Upper Bay were less than 0.50  (Table 16).   In  the Choptank,  a
positive maximum May 28-day flow and a negative December and April air
temperature relationship were observed and, interestingly enough,  similar
variables occurred in the Potomac for striped bass,  a  closely related
species.   No clear explanation is available for the minimum  April 21-day
flow in the Choptank.   These results are shown graphically in Figures L3
and 14.
    The flow and temperature relationships for the Nanticoke are
inconsistant in that several maximum flow  variables exhibit  negative
coefficients (Table 16).  No temperature relationships appeared with the
flow variables.
    Though significant (p _ 0.05), the R-squares for the model describing
flow and temperature relationships for the Potomac and upper Bay  were 0.48
and 0.46,  respectively.   This suggests that climatic factors may  be  less
important for white perch juveniles in these two systems than in  the
Chqptank and Nanticoke.

Ambient Water Quality  Variables and Juvenile Index

    We hypothesized that water quality variables may explain an important
component of the variability associated with the juvenile indices.  This is
based on the knowledge that the tolerance  of a given species may  be
exceeded,  e.g., dissolved oxygen,  salinity, and temperature,  or there may
be an indirect relationship expressed through the food web,  e.g.,  nutrients
and chlorophyll a_.   We did not test for toxic chemicals  because the
temporal spatial coverage of these materials is too  low to define
meaningful relationships.  These materials are discussed elsewhere in this
report (Chapters 2 and 3, Appendix B).
    The approach used  was to regress ambient water quality variables
against the residuals  associated with the  multiple regression equations
that predicted the success of the juvenile index based on freshwater flow
and temperature.   The  SAS procedure was followed.   The approach chosen was
based on the relatively low number of annual observations, often  less than
10, which could be related to the climatic variables (N approximated 21 to
24 Annual observations).
                                     D-43

-------
TABLE 16.  POTENTIAL PREDICTION EQUATIONS FOR WHITE PERCH JUVENILE INDICES AS
           DESCRIBED BY MUI IPLE REGRESSION
                                  Ina^ idual
                                      P  /T/
POTOMAC RIVER
    White Perch = 54.12456
    + (0.00059 x MY - MXQ7)    3.67
    + (-0.00130 x JN - MNQ28) -1.62
    + (-1.40576 x JA - ATMP)  -1.68

UPPER CHESAPEAKE BAY
    White Perch = -193.11905

    + (0.000010 x AP - MXQ28)  1.16
    + (3.03348 x MY - ATMP)    2.62
    + (-0.00026 x AP - MNQ21) -1.60
    + (-0.00069  x MY - MNQ21)
                              -2.08
    + (0.00151 x MY - MNQ7)    3.08

CHOP TANK RIVER
    White Perch =  197.73527

    + (0.01513 x MY - MXQ28)   2.09
    + (-1.10864 X DC - ATMP)  -2.24
    + (-2.48733 X AP - ATMP)  -3.25
    + (-0.04094 X AP - MNQ21) -3.90

NANTICOKE RIVER
    White Perch = -3.54591

    + (0.08212 x JN - MNQ21)   2.93
    + (-0.02935 x JN - MXQ7)  -2.31
    + (-0.04837 x MY - MXQ28) -4.17
    + (-0.13289 x MY - MXQ14)  5.33
    + (-0.07899 x MY - MXQ7)  -4.64
0.0016
0.1210
0.1084
0.2640
0.0179
0.1274

0.0527
0.0068
0.0521
0.0386
0.0047
0.0011
0.0090
0.0330
0.0006
0.0001
0.0002
                          Multiple
                       P   F    R-square
5.77    0.0056    0.4767
                            DF
22
2.93    0.0436    0.4629
22
5.69    0.0043    0.5723
12
6.43    0.0014    0.6411
22

Months :
NV
AP
JL
= November
= April
= July
DC
MY
AG
= December
= May
= August
MR =
JN =
SP =
March
June
September
MX = maximium              ATMP = air temperature
MN = minimum               CHL  = chlorophyll a_
Q  = flow                  TP   = total phosphorus
DO = dissolved oxygen      TN   = total nitrogen
SALIN = salinity
7, 14, 21, 28 = moving average of days for freshwater flow
                                     D-44

-------
   Ul
   o
^3
   —>
<
   Ld
    o
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    >
    ex.
    LJ
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    2
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    a:
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          o
          00
o
to
O
CM
                                                                    I
                                                                   o
                   rsir — i—Ld  Q.LJQ;OX      zr5H03Luo:co\:
 Figure 13.   Juvenile  indices for  white perch in the  Choptank River.
                                        D-45

-------
   CO
   LJ
   o
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 Figure 14.   Juvenile  indices for white perch in the Nanticoke River,
                                    D-46

-------
Striped Bass

    The statistically significant relationships (p   0.05)  are  shown  in
Table 17.  Only the Nanticoke River lacked any significant relationships.
Dissolved oxygen explained 81 percent of the variability associated  with
the climatic residuals in the upper Bay and the Potomac for September and
June, respectively.  Total nitrogen appeared in the residual relationship
for the Potomac and Choptank Rivers, respectively.   Chlorophyll a_ and
salinity co-occurred in the upper Bay.
    It is difficult to ascribe cause and effect relationships  to  the
present analyses.   We view the approach more as a screening  tool  to  provide
guidance for further study.  The linkage between dissolved  oxygen and
nutrients was made in Chapter 1.   The limited field observations  for
dissolved oxygen in the reach of the estuaries where the larval and
juvenile striped bass occur limit our ability to define a  limiting
condition for survival.

White Perch

    Seven predictive models were developed to show  regressing  water quality
variables against  climatic residuals for the Potomac (Table  18).  Salinity
appeared in three  models that may be an auto-correlate with  freshwater
flow.  Phosphorus  occurred in four,  and nitrogen occurred  in two models.
The monthly significance of these relationships is  not clear.  Many of the
R-square values are 0.50 or greater making them interesting candidates for
further study.
    In the upper Bay, the March total nitrogen explained 83 percent of the
variability.   The  tidal freshwater and  brackish reaches of  the upper Bay
are generally believed to be phosphorus limited, more so than  nitrogen in
terms of  phytoplankton biomass yield.   Thus,  the high R-square for nitrogen
is difficult  to explain and may be a surrogate for  some other  factor or
simply a  chance occurrence.
                                     D-47

-------
TABLE 17.  AMBIENT WATER QUALITY VARIABLES* THAT SIGNIFICANTLY  IMPROVE THE
           LINEARITY OF THE RESIDUALS FROM THE POTOMAC RIVER PREDICTION
           EQUATIONS FOR STRIPED BASS JUVENILE INDICES
                     Variables
POTOMAC RIVER

    Model one
    Model two

UPPER CHESAPEAKE BAY
JN - DO
JL - TN
                                   9.05
                                   5.14
                      R - Square
0.5307
0.3635
                                                           DF
 9
10
0.0169
0.0496
    Model two    JL-CHL,  JL-SALIN  7.10
    Model three       SP - DO     21.56
CHOPTANK RIVER

    Model one
AC - TN
                                   7.81
                       0.6698
                       0.8118
0.6612
                                                                      0.0207
                                                                      0.0056
           0.0491
*Note these variables are not continuous over the period of record  for
 juvenile indices and, for this reason,  these water quality variables in
 the models must be considered suggestive only.
                       DC = December
                       MY = May
                       AG = August
                     MR = March
                     JN = June
                     SP = September
Months:
  NV = November
  AP = April
  JL = July

MX = maximium
MN = minimum
Q  = flow
DO = dissolved oxygen
SALIN = salinity
7, 14, 21, 28 = moving average of days for freshwater flow
                           ATMP
                           CHL
                           TP
                           TN
            air temperature
            chlorophyll a_
            total phosphorus
            total nitrogen
                                    D-.48

-------
TABLE 18.  AMBIENT WATER QUALITY VARIABLES* THAT SIGNIFICANTLY IMPROVE THE
           LINEARITY OF THE RESIDUALS FROM THE POTOMAC RIVER PREDICTION
           EQUATIONS FOR WHITE PERCH JUVENILE INDICES
                     Variables
POTOMAC RIVER

    Model one
    Model two
    Model three

    Model four
    Model five

    Model six

    Model seven

UPPER CHESAPEAKE BAY

    Model one
    Model two
MR - TN
AP-SALIN
MY - TP
MY-SALIN
JN - TP
JN - TP
JN-SALIN
JL - DO
JL - TP
DC - TN
MR - TN
SP-SALIN
 6.67
 8.83

 5.10
 8.37

15.63

 4.69
15.31
30.47
10.18
                      R - Square
0.5263
0.5577

0.5930
0.5114

0.8171

0.5395
0.7185
0.8839
0.6706
                         DF
 9
 9
10
 7
 5
 6
CHOPTANK RIVER

    No significant Model found (limited # available WQ years)
0.0417
0.0208

0.0430
0.0201

0.0026

0.0450
0.0079
0.0053
0.0245
*Note these variables are not continuous over the period of record for
 juvenile indices and, for this reason,  these water quality variables in
 the models must be considered suggestive only.
Months:
  NV = November
  AP = April
  JL = July

MX = maximium
MN = minimum
Q  = flow
DO = dissolved oxygen
SALIN = salinity
 DC = December
 MY = May
 AG = August
         MR = March
         JN = June
         SP = September
     ATMP = air temperature
     CHL  = chlorophyll a_
     TP   = total phosphorus
     TN   = total nitrogen
7, 14, 21, 28 = moving average of days for freshwater flow
                                    D-49

-------
                               SECTION 5

                         LITERATURE CITED


Ay ling,  G.M.   1974.   Uptake  of Cadmium, Zinc, Copper, Lead, and Chromium in
    the  Pacific Oyster,  Crassostrea gigas, Grown in the Tamas River,
    Tasramania.   Water Res. 8:729-738.

Bieri, R.H.,  C.H.  Hein,  R.J.  Huggett, P. Shou, H. Slone, C. Smith, and C-W
    Su.   1982a.  Toxic Organic Compounds in Surface Sediments from the
    Elizabeth and Patapsco Rivers and Estuaries.  Grant // R806012.  Final
    Project Report to U.S. Environmental Protection Agency's Chesapeake Bay
    Program.   Virginia Institute of Marine Science, Gloucester Point,
    Virginia.  136 pp. + Appendices.

Bieri, R.H.,  0. Bricker,  R.  Byrne, R. Diaz, G. Helz, J. Hill, R. Huggett,
    R. Kerhin,  M.  Nichols, E. Reinharz, L. schaffner, D. Wilding, and C.
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                        DISCLAIMER
This document has been reviewed with the U.S.  Environmental  Protection
Agency policy and approved for publication.  Mention of trade names or
commercial products  does not constitute endorsement or recommendation
for use.
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