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).
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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.
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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)
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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
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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).
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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)
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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
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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).
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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
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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.
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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£
-------
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
-------
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
-------
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
-------
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
-------
(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
-------
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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o
o
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z:
o
o
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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
-------
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
-------
Meteorological Data For The Current Year
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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
-------
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
-------
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
-------
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
LJ_
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
-------
t/5
MZ
(uu)z
CO .
-o .
CM .
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00 •
o
CO
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10
o-
o
D
(tu)z
o
o.
CO
£
O
-o-
m-
T-
co-
CN-
O
•t
o
CO
c
o
If
co -»
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CN
1-oooocNTrOooo
•---*
(UJ)Z
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00
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o
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•^•'•CNCNCNCNCNn
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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
Q -0;
0,4
0,4
- ADO = 0.52AS°/o0 +0.22
r = 0,93
O
1 2
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
-.2
- ADO = 0.55AS°/00 + 0.22
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
-------
CO
C\
CM
O
co
O
CM
(uu)Z
OO
CM
CM -O
co co
CO
CM
CM
O
CO m
o-
o
CM
Q
CO
CM
CM
CO
O
CO
o
LO
o
CM
O ^J CO CM
CM CM CM CO
O
CO
o
O
CM
CM
COCNOO -NT CO CN -O
«- -c- CM CM CM CO CO
Q
CO
(UJ)Z
CO
CO co
I I
Q
O
1 CO CM O
CM CM CO CO
Figure 21. Concentration of DO across the halocline.
B-44
-------
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
-------
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
-------
ANNUAL TREND
CBP Segment Designation "CB-2"
0)
25-]
a
o
o
O
M.5
-10
CD
cT
2
—*
O
(Q
-0.6
0.2 J
D)
1
o
.c
a
o
a.
O
O
0.1-1
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
Figure 34. Location of 210Pb and metai profile cores (Helz 1980)
B-62
-------
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
_l
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
-------
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
-------
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
-------
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
.
9 4
I I
* *
, ,
* •
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
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Brush, G.S., and F.W. Davis. 1981. Stratigraphic Evidence of Human
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Cargo, D.G., and R.B. Biggs. 1969. Hydrographic Phenomena in the
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Dimensions of the Chesapeake Bay: Areas and Volumes of Segment
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Draxler, Roland R., and Jerome L. Heffler. 1981. Workbook for Estimating
the Climatology of Regional-Continental Scale Atmospheric Dispersion
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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,
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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,
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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
-------
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
-------
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
-------
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
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C-31
-------
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
-------
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
-------
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
-------
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
-------
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
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Vascular Plants, Chesapeake Bay, Maryland. Grant No. R805970. Final
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Joseph, E.B. 1972. The Status of the Sciaenid Stocks of the Middle
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Lippson, A.J. , M.S. Haire, A.F. Holland, F. Jacobs, J. Jensen, R.L.
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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.
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III. pp. 925-948.
Muncy, R.J. 1962. Life History .of the Yellow Perch, Perca flavescens, in
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C-73
-------
Munro, R. 1981. Data from the Maryland Dept. of Nat. Res. USFWS Annual
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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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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.
-------
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
-------
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
-------
i
0
0-5
5-15
15-40
>40
Figure 1. The toxicity index (Tx) averaged over Chesapeake Bay segments.
D-9
-------
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
-------
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
-------
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
-------
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
-------
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
LJ
>
ex.
LJ
CO
DQ
O
O
Ld to
2
LJ
a:
a.
CO
ce
Lu
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
C£
LU
>
o:
= ^
SI
UI
r rn
— o
C£ ^
Q- <
i, i co^
— Q
Ul
o:
Q_
CO
ce
o
oo
I
o
CO
Q_LUQ:OX
o
CM
> si en LLJ cc en \ :
[
o
Figure 14. Juvenile indices for white perch in the Nanticoke River,
D-46
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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
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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
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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
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SECTION 5
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D-50
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in:
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•A.
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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.
D-53
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