EPA/902-R-98-001
March 1998
Final Report
SEDIMENT QUALITY OF THE NY/NJ
HARBOR SYSTEM
An Investigation under the Regional Environmental Monitoring and Assessment Program
(R-EMAP)
Darvene A. Adams
U.S. Environmental Protection Agency - Region 2
Edison, NJ
Joel S. O'Connor
U.S. Environmental Protection Agency - Region 2
New York, NY
Stephen B. Wei sb erg
Southern California Coastal Water Research Project
Westminster, CA
March 1998
-------
Disclaimer
The use of a product or trade names does not indicate endorsement by the U.S.
Environmental Protection Agency. Reference to or use of various guidelines or
threshold values does not constitute a policy decision by the U.S. EPA to adopt
these values as criteria or standards.
11
-------
FOREWORD
The Environmental Monitoring and Assessment Program (EMAP) is a long-term, interagency
environmental monitoring and research program overseen by EPA's Office of Research and
Development (ORD). Its goal is to provide the public, scientists and Congress with information
that can be used to evaluate the overall condition of the Nation's ecological resources. The
program is designed to operate on a broad geographic scale.
EMAP has entered into partnerships with EPA Regional offices, other Federal agencies and
States to assess environmental quality at smaller, regional or local scales. These Regional EMAP
(REMAP) projects adapt the EMAP approach to assess specific areas more precisely than can be
accomplished by existing data or EMAP alone. These projects also provide the opportunity to
apply EMAP's statistical design and ecological indicators at localized scales. The REMAP
project for the New York-New Jersey Harbor complex is one often REMAP efforts in the
country (U.S.EPA, 1993a).
The study results presented in this report are based on a REMAP proposal which was jointly
developed by U.S.EPA-Region 2 and NY-NJ Harbor Estuary Program (HEP) participants. The
study was jointly funded by U.S. EPA/ORD/EMAP, the NY-NJ HEP and U.S.EPA-Region 2.
in
-------
EXECUTIVE SUMMARY
A number of studies have documented high concentrations of contaminants in sediments of the
New York-New Jersey Harbor and Bight Apex. Based on these findings, U.S.EPA-Region 2 and
the New York-New Jersey Harbor Estuary Program (NY-NJ HEP) identified the development of
a sediment management and monitoring strategy as an integral part of the Comprehensive
Conservation and Management Plan (CCMP) for the Harbor and Bight Apex. An unbiased
baseline of sediment quality was needed to measure progress of management actions. Existing
data were insufficient for developing this baseline.
To provide the baseline data needed to evaluate progress, 168 sites in the Harbor and Bight Apex
were sampled in the summers of 1993 and 1994, using a stratified random design. Fourteen
sampling sites were allocated in each year to each of six sub-basins (Newark Bay, Lower Harbor,
Upper Harbor, Jamaica Bay, western Long Island Sound, and the Bight Apex). Surficial
sediment contaminant concentrations, two sediment toxicity tests (Ampelisca abdita and
Microtox™), and benthic macrofaunal community structure were measured at each site.
Contamination was widespread, with most of the Harbor samples (102 of 112) having at least
one chemical exceeding an ERL (Effects Range-Low) concentration, a threshold at which
biological effects are possible, and 50% of the Harbor exceeding at least one ERM (Effects
Range-Median) concentration, a threshold above which biological effects are more likely. A
toxicological response was also observed for 45% of the Harbor. Newark Bay was the most
contaminated sub-basin, with 92% of its area exceeding an ERM concentration and 49% of its
area showing a toxicological response. In contrast, only 7% of the area in the Bight Apex
exceeded ERM concentrations and toxicity was only observed at one Bight Apex location, which
was located near an area of historical dredged material disposal.
Contamination was distributed across chemical classes. At least one pesticide, one metal and
total PCBs were present at concentrations above ERM for one-third of the Harbor area. The ERL
for Total PCB was exceeded at 87% of the Harbor. Mercury and chlordane were the only
individual chemicals for which more than 25% of the area in the Harbor exceeded an ERM
concentration. Twenty-six individual chemicals had mean concentrations for the entire Harbor
that exceeded their ERL concentrations. Mercury, DDT and total PCBs were the only chemicals
for which average concentrations exceeded ERM values.
The condition of benthic communities was strongly associated with chemical contamination. At
the 66% of the Harbor area where impacted benthic communities were observed, there also was a
toxicological response and/or at least one chemical exceeding its ERM concentration. In
contrast, only 14% of the Harbor area without a toxicological response and without a chemical
exceeding ERM concentration had impacted benthic macroinvertebrates.
IV
-------
The sampling design and methods used in this study were compatible with those of EPA's
Environmental Monitoring and Assessment Program-Estuaries (EMAP-E), allowing unbiased
comparison of conditions in the NY-NJ Harbor with those in the entire mid-Atlantic region.
Based on comparisons with EMAP-E data collected from 1990 through 1993 from the Virginian
Province (coastal area from Cape Cod to, and including Chesapeake Bay), the NY-NJ Harbor
was found to have higher average sediment concentrations for 58 of the 59 chemicals measured
in this study. NY-NJ Harbor sediments are responsible for more than 90% of the spatial extent
of exceedances of the total PCBs ERM and 69% of the mercury ERM exceedances in the
Virginian Province, even though the Harbor constitutes only 4% of the area in the Province.
An index of benthic quality specific to the Harbor was developed as a tool to evaluate the health
of benthic macroinvertebrates. This Benthic Index of Biotic Integrity (B-IBI) was similar to the
IBIs developed for freshwater biota. It was developed for four different salinity and grain-size
habitat combinations. Five measures were ultimately used in the index; number of taxa,
abundance, biomass, abundance of pollution-indicative taxa and abundance of pollution-sensitive
taxa. Overall, the B-IBI was able to distinguish correctly 93% of the stressed sites from reference
sites.
Sediment quality in the Harbor has undoubtedly improved due to actions taken as a result of
recent environmental legislation and improved stewardship. Further major improvements cannot
be expected immediately, and will probably be more subtle than improvements to date. The
Harbor bottom will continue to integrate loadings of contaminants, organic materials and
sediments from the watersheds and airsheds surrounding it. The most obvious "next steps" are to
estimate how rapidly sediment quality and associated biological health improve under current
watershed protection and pollution prevention activities. Some of these steps are included in the
NY-NJ HEP CCMP. Other efforts are being undertaken as a subsequent REMAP investigation.
-------
ACKNOWLEDGMENTS
An assessment of this scope relies heavily on the expertise that was associated with its
development, implementation and interpretation. This investigation has especially benefitted
from strong management support and partners involved in the planning and execution of the
study.
We thank Richard Caspe, Kevin Bricke, Roland Hemmett, Richard Spear, Mario Del Vicario,
Randy Braun and Seth Ausubel, U.S.EPA-Region 2 managers, for their effective support and
broad coordination of this investigation.
We would like to acknowledge valuable assistance in the design of the study from: Rick
Linthurst, John Paul, Tony Olsen and the EMAP Design and Statistics Group (all U.S.EPA-
ORD), and NY/NJ Harbor Estuary Program (HEP) participants. For sampling logistics and
coordination of field efforts, we are grateful for the exceptional and essential assistance of
Douglas Pabst, Eric Stern, and Helen Grebe, U.S.EPA-Region 2. Additional personnel provided
essential and long-term effort in the field and laboratory: Barbara Finazzo, John Birri, James
Ferretti, Diane Calesso, Warren McHose, Ariel Perez, Rob Roesener, Margo Hunt, Diane Salkie
(all U.S.EPA-Region 2), and Lora McGuinness of Rutgers University. We also thank the captain
and crew of the OSV ANDERSON. Data analyses were expertly performed by Versar, Inc., and
updated by Melissa Hughes of U.S.EPA-ORD. Jim Heltshe of the University of Rhode Island
provided additional statistical support. Cove Corp. provided taxonomic identification of
unusually difficult benthic samples. Interpretation and benthic index development were
substantially enhanced by the generous and intensive contribution of several regional experts on
benthic invertebrate ecology: Angela Cristini, Eugene Gallagher, Fred Grassle, Judith Grassle,
Robert Loveland, John Paul, Robert Reid, Ananda Ranasinghe, Joseph Vitaliano and Robert
Whitlatch. We are indebted to seven external peer reviewers for constructive criticism and
recommendations.
Contract assistance was provided by Vern Laurie and Tom Murray of ORD. An additional
funding mechanism was provided and overseen by the Hudson River Foundation (HRF).
VI
-------
DEDICATION
This report is dedicated to the memory of Dr. Barbara Metzger, Director of the Environmental
Services Division, until her untimely death on February 14, 1996. This report strives to represent
her dedication to protection of the environment and her conviction that the U.S.EPA use sound
scientific practices to achieve that goal.
vn
-------
TABLE OF CONTENTS
Page
FOREWORD iii
EXECUTIVE SUMMARY iv
ACKNOWLEDGMENTS vi
DEDICATION vii
1.0 INTRODUCTION 1-1
1.1 BACKGROUND 1-1
1.2 OBJECTIVES 1-3
1.3 RELATIONSHIP TO THE CCMP 1 -4
1.4 ORGANIZATION OF THE REPORT 1 -4
2.0 METHODS 2-1
2.1 DESIGNATION OF STUDY AREA 2-1
2.2 STUDY DESIGN 2-1
2.3 SAMPLING PROCEDURES 2-3
2.3.1 Water Column 2-3
2.3.2 Sediment 2-4
2.3.3 Benthos 2-4
2.4 PHYSICAL/CHEMICAL/BACTERIOLOGICAL LABORATORY 2-4
METHODS
2.4.1 Major and Trace Elements 2-7
2.4.2 Organic Compounds 2-7
2.4.3 Sediment Physical Parameters 2-8
Vlll
-------
2.4.4 Bacteriological Analysis 2-9
2.5 TOXICITY METHODS 2-9
2.5.1 Amphipod Sediment Toxicity Tests 2-9
2.5.2 Microtox™ Assays 2-10
2.6 BENTHIC MACROINVERTEBRATE ASSEMBLAGES 2-10
2.7 DATA ANALYSIS 2-11
2.7.1 Chemical Data 2-11
2.7.2 Toxicity Data 2-11
2.7.3 Benthic Macroinvertebrate Data 2-11
2.7.4 Condition Estimates 2-13
2.7.4.1 Mean Condition 2-13
2.7.4.2 Mass Estimates 2-15
2.7.4.3 Percent of Area Estimates 2-16
2.8 SELECTION OF THRESHOLD VALUES 2-17
2.8.1 Physical Data Thresholds 2-17
2.8.2 Chemical Data Thresholds 2-17
2.8.3 Sediment Toxicity Thresholds 2-18
2.8.4 Benthic Index Thresholds 2-18
3.0 PHYSICAL PARAMETERS 3-1
3.1 BACKGROUND 3-1
3.2 CHARACTERIZATION OF THE HARBOR 3-1
IX
-------
3.2.1 Depth 3-1
3.2.2 Percent Silt-Clay 3-2
3.2.3 Total Organic Carbon (TOC) 3-2
3.2.4 Water Column Profile 3-2
3.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND 3-5
AND THE BIGHT APEX
3.3.1 Depth 3-5
3.3.2 Percent Silt-Clay 3-5
3.3.3 Total Organic Carbon (TOC) 3-5
3.3.4 Water Column Profile 3-6
3.4 COMPARISON TO PREVIOUS STUDIES 3-6
4.0 SEDIMENT CHEMISTRY 4-1
4.1 BACKGROUND 4-1
4.2 CHARACTERIZATION OF THE HARBOR 4-3
4.2.1 Mean Condition 4-3
4.2.2 Areal Extent 4-3
4.2.3 Dioxins and Furans 4-6
4.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND 4-16
AND THE BIGHT APEX
4.3.1 Mean Condition 4-16
4.3.2 Areal Extent 4-16
4.4 ALTERNATIVE THRESHOLDS 4-16
4.4.1 Proposed Sediment Quality Criteria 4-17
-------
4.4.2 Acid Volatile Sulfides 4-17
4.4.3 Aluminum-Normalization 4-20
4.5 RELATIONSHIP BETWEEN CHEMISTRY AND GRAIN SIZE 4-22
4.6 COMPARISON TO PREVIOUS STUDIES 4-22
4.7 COMPARISON TO A LARGER GEOGRAPHIC AREA 4-24
5.0 SEDIMENT TOXICITY 5-1
5.1 BACKGROUND 5-1
5.2 CHARACTERIZATION OF THE HARBOR 5-1
5.2.1 Mean Condition 5-1
5.2.2 Areal Extent 5-2
5.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND 5-6
AND THE BIGHT APEX
5.3.1 Mean Condition 5-6
5.3.2 Areal Extent 5-6
5.4 RELATIONSHIP BETWEEN TOXICITY AND GRAIN SIZE 5-8
5.5 COMPARISON TO PREVIOUS STUDIES 5-8
6.0 BENTHIC MACROINVERTEBRATES 6-1
6.1 BACKGROUND 6-1
6.2 CHARACTERIZATION OF THE HARBOR 6-2
6.2.1 Diversity and Taxonomic Composition 6-2
6.2.2 Abundance and Biomass 6-5
6.2.3 Benthic Index 6-6
xi
-------
6.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND 6-9
AND THE BIGHT APEX
6.3.1 Diversity and Taxonomic Composition 6-9
6.3.2 Abundance and Biomass 6-9
6.3.3 Benthic Index 6-9
6.4 COMPARISON TO PREVIOUS STUDIES 6-9
7.0 ASSOCIATIONS 7-1
7.1 BACKGROUND 7-1
7.2 ASSOCIATION BETWEEN CHEMISTRY AND 7-1
BENTHIC CONDITION
7.3 ASSOCIATION BETWEEN CHEMISTRY AND TOXICITY 7-4
7.4 ASSOCIATION AMONG CHEMISTRY, TOXICITY AND 7-4
BENTHIC COMMUNITY STRUCTURE
8.0 DISCUSSION 8-1
9.0 LITERATURE CITED 9-1
APPENDICES
A Sampling station maps
B Analytical detection limits
C Development of the Benthic Index of Biotic Integrity (B-IBI)
D Aluminum-normalization procedure
E Tables: E-l) Area-weighted mean concentrations for all sediment contaminants
E-2) Percent of area exceeding ERM values for all sediment contaminants
F Dioxin bioaccumulation calculation
xn
-------
G Area-weighted mean abundances of benthic macroinvertebrate species
H Clostridium perfringem results
I Benthic Index values for individual stations
LIST OF FIGURES
2-1 Map of the six study sub-basins 2-2
3-1 Percent of area distribution of substrate type (as % silt-clay) 3-3
3-2 Percent of area distribution of total organic carbon levels (as %TOC) 3-4
4-1 Percent of area with any chemical or chemicals within groups exceeding an 4-4
ERL or ERM in the Harbor
4-2 Percent of area greater than ERL and ERM values for individual metals in 4-5
the Harbor
4-3 Percent of area greater than ERL and ERM values for individual or classes 4-7
of organics in the Harbor
4-4 Percent of area exceeding the mercury ERL and ERM 4-8
4-5 Percent of area exceeding the chlordane ERL and ERM 4-9
4-6 Distribution of mercury concentrations by station 4-10
4-7 Distribution of total PCB concentrations by station 4-11
4-8 Distribution of total PAH concentrations by station 4-12
4-9 Total area (km2) above the mercury and total PCB ERMs 4-13
4-10 Total mass (kg) of mercury and total PCB in surficial sediments of the Harbor 4-14
4-11 Comparison of percent of area in the Harbor exceeding a proposed Sediment 4-18
Quality Criterion or an ERM value
4-12 Comparison of percent of area with SEM in excess of AVS (SEM-AVS>0) 4-19
and percent of area with any metal greater than ERM
Xlll
-------
4-13 Relationship between substrate type (sand* 40% silt-clay, mud • 40% 4-23
silt-clay) and percent of area in the Harbor with at least one chemical
greater than ERM
4-14 Contribution of the NY/NJ Harbor to percent of area in the Virginian 4-29
Province with ERM exceedances
5-1 Percent of area exhibiting Ampelisca abdita toxicity 5-3
5-2 Distribution of stations with Ampelisca abdita toxicity 5-4
5-3 Percent of area toxic in the Microtox™ assay 5-5
5-4 Distribution of stations toxic to Microtox™ luminescence 5-6
5-5 Relationship between substrate type (sand • 40% silt-clay, mud • 40% 5-9
silt-clay) and percent of area in the Harbor with toxicity to A. abdita,
Microtox™ or A. abdita and/or Microtox™
6-1 Numbers of benthic macrofaunal species, by major taxon 6-4
6-2 Percent of area with impacted benthos (B-IBI<3) 6-7
6-3 Distribution of stations by Benthic Index of Biotic Integrity (B-IBI) values 6-8
7-1 Association between benthos and percent of area with one or more 7-2
contaminants greater than an ERM
7-2 Association between Ampelisca abdita toxicity and percent of area with one 7-5
or more contaminants greater than an ERM value
7-3 Association between sediment toxicity (A. abdita or Microtox™), benthic 7-7
community structure (using the B-IBI) and sediment chemistry (as one or
more contaminants greater than an ERM value)
7-4 Percent of area considered degraded because of impacted benthos, sediment 7-8
toxicity and sediment chemistry concentrations
LIST OF TABLES
2-1 Sub-basin areas and percent of study areas 2-1
xiv
-------
2-2 Summary of physical/chemical analytical methods 2-5
2-3 Analytical measurements for sediment samples 2-6
2-4 Individual benthic macroinvertebrate measures assessed 2-12
2-5 B-IBI habitat categories 2-12
3-1 Area-weighted means of depth and sediment physical parameters 3-1
3-2 Area-weighted means of water column physical parameters 3-5
4-1 ERL and ERM concentrations for sediment trace metals and organic 4-2
compounds
4-2 Mean concentrations of 2,3,7,8-TCDD in sediments of three sub-basins 4-15
4-3 Environmental concentrations associated with TCDD risk to aquatic life 4-15
and associated wildlife
4-4 Percent of area with anthropogenically enriched levels of metals 4-21
4-5 Comparison of mean sediment contaminant concentrations between the 4-29
Virginian Province and the NY-NJ Harbor
5-1 Mean % survival for Ampelisca abdita and mean % Microtox™ 5-2
bioluminescence inhibition
5-2 Percent of area toxic in the A. abdita and Microtox™ assays 5-6
6-1 Species richness (total number of species) 6-2
6-2 Means of benthic variables 6-3
6-3 Pollution-sensitive and pollution-indicative taxa 6-5
6-4 Percent of area within B-IBI categories 6-4
7-1 Individual chemicals associated with impacted benthos 7-3
7-2 Individual chemicals associated with sediment toxicity (Ampelisca abdita) 7-6
8-1 Relative ranking of sub-basins by % of area 8-1
xv
-------
8-2 Association between Harbor means of benthic metrics and number of 8-2
chemicals > ERM
8-3 Percent of Harbor area which exceeded selected thresholds 8-3
xvi
-------
1.0 INTRODUCTION
1.1 BACKGROUND
The New York-New Jersey Harbor system is an important economic, recreational, and aesthetic
resource supporting many kinds of habitat and species. Among the many important species of
fish and shellfish in this estuarine and coastal system are striped bass, white perch, tomcod, and
blue crabs in the estuarine portion; and sea bass, bluefish, menhaden, herring, sturgeon, shad,
hake, winter flounder, lobster, clams and oysters in the marine portion. Historically, the Harbor
supported several large commercial and recreational fisheries. Currently, there remain some
isolated, small-scale commercial fisheries (e.g., clams, crabs, menhaden) and a large recreational
fishery (MacKenzie, 1992). Since the Estuary is on the Atlantic flyway, it is also an important
resting and feeding area for migrating birds. Many birds, both migratory and regional, utilize the
Harbor environs for feeding and raising young. Birds commonly found in the region include
herons, egrets, ducks, plovers, sandpipers, gulls and geese. Bald eagles and peregrine falcons,
both federally-listed endangered species, are less common inhabitants.
The land uses above and surrounding the New York-New Jersey Harbor Estuary make the Harbor
particularly susceptible to toxic contamination. For more than a century, it has been the recipient
of pollutants generated by the human activities that exist around it. The Harbor is surrounded by
a population of more than 20 million people and concentrated refining and manufacturing
industries. It is also one of the most heavily utilized shipping ports on the east coast. Sources of
toxicants found in the Harbor include municipal and industrial discharges, atmospheric inputs,
non-point source runoff, hazardous waste sites, landfills, combined sewer overflows and
accidental spills. Additionally, Harbor sediments are contaminant reservoirs which can function
as secondary sources. Since the Bight Apex and Long Island Sound receive Harbor outflow, both
are affected by Harbor contaminants. One dedesignated (dredged material) and several inactive
(acid waste, cellar dirt and sewage sludge) dumpsites also are located in the Bight Apex.
Contaminated sediments pose a substantial threat to Harbor resources and are a management
challenge. Dredging and disposal of contaminated sediments are controversial issues. Adverse
changes in the biota of the system have been documented, and many of these changes have been
linked to toxic contamination (Mayer, 1982; U.S.EPA, 1990a). The consequences of
contamination in the NY-NJ Harbor are extensive. The states around the Harbor advise
restricted consumption of striped bass, bluefish and blue claw crabs from large portions of the
Estuary because the levels of PCB and/or dioxins exceed guidelines for human consumption.
Areas that were once productive shellfish beds no longer exist or have reduced populations that
are restricted for harvesting (MacKenzie, 1992). Bioaccumulation of contaminants in fish,
shellfish and Crustacea has been documented (Belton et al., 1985; NYSDEC, 1988; Hauge et al.,
1990; Zongwei et al., 1994; NOAA, 1996). Benthic macroinvertebrate communities appear to be
impacted by sediment contaminants (Franz and Harris, 1988; Steimle and Caracciolo-Ward,
1-1
-------
1989). Other investigations have described elevated levels of contaminants in sediments
(NOAA, 1991; Huntley et al., 1993; Bonnevie et al., 1995) and sediment toxicity (Scott et al.,
1990; Schimmel et al., 1994; Long et al., 1995b).
Most studies of toxic contamination in the NY-NJ Harbor system have focused on measuring the
concentrations of contaminants in the sediment. Characterizing sediment condition is a logical
way to describe toxic contamination in an estuarine system because the sediment is both a sink
for contaminants that adsorb to fine particles, and a source for toxic contaminants that are
rereleased to the water column when sediments are disturbed by natural events (e.g., seasonal
turnover, bioturbation, violent storms) or human activity (e.g., dredging, vessel traffic). In
addition, the food chains for many estuarine species begin in the sediment; therefore,
contaminants in the sediment can be propagated widely throughout an estuarine ecosystem.
Existing studies have been useful in establishing concern about contaminants in the NY/NJ
Harbor system. In a review of historical data on toxic contamination, Squibb et al. (1991)
identified 12 metals and 43 organic chemicals that are present in the Harbor water, biota or
sediments at concentrations that may affect the integrity of the system. NOAA's National Status
and Trends program, which has conducted sampling in the NY-NJ Harbor, identified it as having
some of the highest metals concentrations found nationwide. Based on these data and the
integral relationship between contaminated sediments and the health of the Estuary, the Harbor
Estuary Program, U.S.EPA-Region 2, states and local governments have made addressing the
biological effects of contaminated sediments a high priority (U.S.EPA-Region 2, 1996).
While these existing data are sufficient for the purpose they were designed for and to raise
concerns about sediment contamination in the Harbor, they are insufficient for developing an
effective contaminant strategy for the NY-NJ Harbor complex for several reasons:
• Much of the existing data is limited, outdated, or unreliable, causing Squibb et al. (1991)
to recommend characterizing the problem further before acting to correct it.
• Data from historic studies are insufficient for evaluating the areal extent of toxic
contamination throughout the NY/NJ Harbor and in each of its sub-basins because most
studies of sediment contamination in the system were initiated to resolve site-specific
problems rather than to support regional management decisions. Sampling has been
limited to specific "hot-spots" around known or assumed contaminant sources. These
data cannot be extrapolated to unsampled areas, which would be necessary to reliably
characterize the condition of the entire system and specific sub-basins. Although Squibb
et al. (1991) were able to identify a large number of contaminants in the Harbor, they
were unable to evaluate pervasiveness of the contamination.
• There is little opportunity to assess from existing data the biological effect of
contaminants that were measured in the sediment, or whether the effects differ in
1-2
-------
different portions of the estuary complex. This is because sediment contaminant data
were collected independently of biological data.
These shortcomings are major impediments to developing a management strategy. Information
about the distribution of the contamination problems among sub-basins is necessary for
determining whether the management emphasis should be focused on a regional, watershed, or a
local site-specific scale. Developing management strategies on the basis of "hot spot"
information alone may result in misdirection of management efforts, particularly if the problem is
more widespread than limited "hot spot" data would suggest. Also, information on biological
effects of contaminants is critical to identifying the scope of the problem and determining the
resources appropriate to remediate it. The ecological significance of contaminant levels
documented from purely chemical surveys is unknown in the absence of information on direct
toxicity of those contaminants and/or data documenting the relative status of biological
communities, such as the benthos, exposed to these materials. Areas where contaminant levels
are high but biological availability and toxicity are low may be addressed best with management
strategies different from those appropriate for areas where significant impacts to biota are
evident.
1.2 OBJECTIVES
This project was designed to support resource management decisions related to pollution control
and remediation throughout the NY-NJ Harbor and Bight Apex and to assist the Harbor Estuary
Program (HEP) in developing a contaminant monitoring strategy to be followed as part of the
Comprehensive Conservation and Management Plan (CCMP) for the NY-NJ Harbor system.
This investigation was designed around several objectives:
Objective 1. Estimate with known confidence the percent of area in each of six major
sub-basins of the NY-NJ Harbor system in which the benthic environment is "degraded",
"not degraded", or "not evidently degraded" with respect to benthic macroinvertebrate
assemblages, sediment toxicity, and concentrations of sediment contaminants, and,
Objective 2. Identify statistical associations among particular chemical contaminants and
degraded benthos or toxic sediments.
A third objective was identified because the HEP and the Region recognized that a tool to
represent benthic quality was needed:
Objective 3. Develop and validate a managerially useful index of benthic quality for the
NY/NJ Harbor system, based on the condition of benthic macroinvertebrate assemblages.
1-3
-------
1.3 RELATIONSHIP TO THE CCMP
The New York-New Jersey Harbor Estuary Program (HEP) has prepared a Comprehensive
Conservation and Management Plan (New York-New Jersey Harbor Estuary Program, 1996).
The CCMP included a section on management of toxic contamination. The goals of the HEP
plan for toxics are:
! To establish and maintain a healthy and productive Harbor/Bight ecosystem, with no
adverse ecological effects due to toxics.
! To ensure that fish, Crustacea and shellfish caught in the Harbor/Bight are safe for
unrestricted human consumption.
I
To ensure that dredged sediments in the Harbor are safe for unrestricted ocean disposal.
In order to take steps toward attainment of these goals, the HEP plan includes actions to reduce
continuing inputs of toxic chemicals to the Harbor and Bight from sources such as municipal
discharges, industrial discharges, combined sewer overflows, storm water discharges, and non-
point sources.
The data from this investigation will be used to support the HEP goals. For example, the benthic
index and toxicity tests will be interpreted in relation to a "no adverse effects" level. Also, where
available, numeric criteria and tests used in regulatory decision-making will be used to interpret
the data. This investigation's surficial sediment sampling represents recently deposited
sediments and contaminants. Therefore, interpretation of the A. abdita toxicity test results,
sediment chemistry and benthic macroinvertebrate structure information will help managers
assess the potential future distribution of dredged material unsuitable for ocean disposal. This
information, combined with an evaluation of the causes of toxicity, also will help focus strategies
to control continuing sources of contamination.
1.4 ORGANIZATION OF THE REPORT
The purpose of this report is to present summarized data and interpretation to address the three
objectives that were defined at the start of the project.
The report has nine chapters. Chapter 2 defines the indicators that were used and how they were
measured. Chapters 3, 4, 5 and 6 report results from each of the indicator classes, both in terms
of mean condition and percent of area above or below specified threshold values, and relates
these to previous studies in the Harbor. Chapter 7 analyzes the associations between the various
indicators and Chapter 8 provides discussion of the results in terms of management implications.
Chapter 9 contains all references cited in the report. Several appendices are included: A -
sampling station locations and maps, B - analytical detection limits, C - benthic index
1-4
-------
development, D - aluminum-normalization procedure, E - tables of means and % of area
exceedances of ERMs for all chemicals measured in the study, F - dioxin bioaccumulation
calculations, G - mean abundances of all benthic species, H - Clostridium perfringem results,
and I - benthic index values for individual stations. Appendix J contains explanatory information
for the data disk that is included inside the back cover. The disk, in Excel format, contains
unmanipulated data from this investigation.
1-5
-------
-------
2.0 METHODS
2.1 DESIGNATION OF STUDY AREA
Based on hydrogeography and similar source characteristics, the study area was divided into six
sub-basins (Figure 2-1): Upper Harbor, Newark Bay, Lower Harbor (includes Raritan and Sandy
Hook Bays), Jamaica Bay, western Long Island Sound and the New York Bight Apex. The New
York-New Jersey Harbor, for purposes of this investigation, includes the lower portions of the
Hudson, Passaic, Harlem, Hackensack and Raritan Rivers, upstream to a near-bottom salinity of
15 ppt, the East River to Long Island Sound, and Lower Harbor to the Atlantic Ocean. The New
York Bight Apex is defined as the area of ocean bounded on the northwest by the transect from
Sandy Hook, NJ to Rockaway Point, NY, the east by 73° 30' W longitude, and the south by 40
10' N latitude. The eastern boundary of the western Long Island Sound sub-basin is 73° 24' W
longitude (from Eaton's Neck Point, NY to Norwalk, CT). The area of each sub-basin was
determined using Geographic Information System (GIS) ARCInfo software (Table 2-1).
Table 2-1
Sub-basin Areas and Percent of Study Areas
Sub-basin
Lower Harbor
Upper Harbor
Jamaica Bay
Newark Bay
W. Long Island Sound
Bight Apex
Harbor Total*
Study Area Total
Area (km2)
318
104
47
32
476
1883
501
2861
% of Study Area
11.1
3.7
1.7
1.1
16.6
65.8
17.6
100.0
*The Harbor Total includes Lower and Upper Harbors, Jamaica Bay and Newark Bay.
2.2 STUDY DESIGN
There are two different strategies for sampling to estimate characteristics of the field. Often
sampling sites are selected by their anticipated ability to reflect regional characteristics. Samples
are presumed a priori to be "representative" of their surrounding areas. This is termed
judgmental or purposive sampling. The alternative strategy, termed probabilistic, ensures that
2-1
-------
Six Study Sub-basins
New York
New Jersey
Long island
s
Newark Bay
Sandy Hook
Rocteway Part Transect
Raritan Bay
Sandy Hook Bay
NY Bight Apex
Rqure2-1 Map of the six study sub-basins: Upper Harbor, Bay, Lower (irv
dudes Raritan and Sandy Hook Bays), Bay, Long Sound
aid the New York Apex.
-------
every element in the population sampled has some chance of actually being sampled. For
instance, in the present investigation, every potential benthic grab sample in the study area could
have been sampled (i.e., potential grabs not sampled were excluded only by chance, not by
judgement). While this probability sampling is not the most appropriate in all situations, it does
have important advantages over purposive sampling. Probability samples can provide unbiased
estimates of population characteristics with specified confidence limits. These confidence limits
become smaller as sample size is increased.
Sampling stations for the present investigation were selected probabilistically using a stratified
random approach. The strata corresponded to each of six sub-basins where independent
estimates of condition were needed. Fourteen stations were assigned to each sub-basin in each of
the two years of sampling, for a total of 28 stations in each sub-basin (Appendix A). Each year,
sites were selected by randomly placing a grid structure over the study area, selecting 14 grid
cells at random from each stratum, and selecting a random location from within the selected
cells. Cells were of equal area within strata, except for the Newark Bay stratum, where grid cell
size was altered to ensure sampling in the Arthur Kill, Passaic River, and the Hackensack River.
Sampling was conducted between late July and late September of 1993 and 1994. A summer
index period was chosen for several reasons. This time period has been identified as most
appropriate for this area (Holland, 1990). Pollution stress is expected to be at its highest because
dissolved oxygen values are low and contaminant exposure is at its maximum due to high
temperatures and low dilution flows. Benthic organisms are usually more abundant, which
increases the success of sampling. While some indicators vary between July and September,
most of the measures that this investigation focused on, such as benthos, toxicity and chemistry,
are stable during that time period. The U.S.EPA Environmental Monitoring and Assessment
Program (EMAP) evaluated benthic response and found it did not vary unacceptably between late
July and September (Weisberg et al., 1993). A summer index period also ensures compatibility
with EMAP, which is useful since it allows comparison to those results and referencing to
EMAP benthic macroinvertebrate data for the development of a benthic index.
2.3 SAMPLING PROCEDURES
The U.S.EPA vessels, R/V CLEAN WATERS and CSV PETER W. ANDERSON, were used for
sample collection. Sampling stations were located using LORAN-C and a Global Positioning
System (GPS) or Differential-GPS (D-GPS). Depth of the water column was determined using
sonar. Field procedures followed Reifsteck et al. (1993).
2.3.1 Water Column
A SeaBird model SEE 25 "Sealogger" CTD unit was used to obtain a vertical profile of depth,
dissolved oxygen, pH, temperature, and salinity at each station. Measurements were made from
within a meter of the water surface to approximately a meter above the sediment/water interface.
-------
Water clarity was measured using a 20-cm Secchi disk. Dissolved oxygen, temperature and
salinity at the surface were measured using a Winkler titration, NBS thermometer and a
refractometer, respectively, and compared with the CTD results.
2.3.2 Sediment
A 0.04-m2 or 0.1-m2, stainless steel, Young-modified van Veen grab was used to collect surficial
sediment for chemical analysis and toxicity testing. Multiple grabs were required to collect
enough volume for analysis. Overlying water was carefully drained by allowing suspended floe
to settle for approximately one minute and then carefully suctioning off the overlying water with
a clean section of Tygon® tubing. For Acid Volatile Sulfide (AVS)/Simultaneously Extracted
Metals (SEM) analysis, aliquots of the top 2 cm were taken from the undisturbed surface of
multiple individual grabs using a 60-cc syringe which had the narrow end removed to create a
mini-corer. AVS samples were not homogenized. When a sample container was filled to the
top, it was sealed with Teflon® tape and immediately frozen. The remaining top 2 cm of
sediment from each grab were removed using clean stainless steel spoons. A composite of all
grabs was homogenized in a clean glass mixing bowl for 10 minutes. Subsamples were removed
for metals, organics, grain size, TOC and toxicity tests, and transferred to clean sample
containers that were stored on ice. The van Veen grab was rinsed with ambient seawater
between grabs at a station and thoroughly cleaned with detergent and water between stations.
2.3.3 Benthos
Three benthic macroinvertebrate grabs per sampling station were collected using the 0.04-m2
Young-modified van Veen grab. Benthic grabs were alternated with sediment chemistry/toxicity
grabs. Benthic samples were gently washed through a 0.5 mm mesh sieve. The material that
remained was preserved in a 10% buffered formaldehyde-rose bengal solution.
2.4 PHYSICAL/CHEMICAL/BACTERIOLOGICAL LABORATORY METHODS
Methods used for chemical analyses are summarized in Table 2-2. Individual chemical
parameters are listed in Table 2-3 and detection limits are in Appendix B. PAHs, TOC, grain
size, and total recoverable metals were analyzed at the U.S.EPA-Region 2 Laboratory in Edison,
NJ. PCB, pesticides and butyltins were analyzed, under contract to the Hudson River Foundation
(HRF), by the Geochemical and Environmental Research Group (GERG) of Texas A&M
University, College Station, TX. Acid volatile sulfide (AVS), simultaneously extracted metals
(SEM) and total metals were analyzed by the Trace Element Research Laboratory (TERL) of
Texas A&M. Selected samples for dioxins and furans were analyzed by Battelle Labs,
Columbus, OH. The GERG and TERL laboratories both participated in the NOAA Status and
Trends Interlaboratory Comparison exercise.
2-4
-------
Table 2-2
Summary of Physical/Chemical Analytical Methods
Parameter
PAHs
PCB/Pesticides
Major and Trace
Elements
Major and Trace
Elements
Hexavalent
Chromium
Dioxins and
Furans
AVS/SEM
Butyltins
TOC
Grain size
Method
Methylene chloride extraction; determination by
GC/MS
Methylene chloride extraction; determination by
HRGC/ECD
Total metals: HNO3 and HF acid digestion: Hg-
CVAAS;Cu, Ni, Pb, Cr, Sb, Sn, As, Se, Ag, Cd-
GFAAS; Al, Fe, Mn, Si, Zn-FAAS
Total recoverable metals: HNO3/H2O2 or
microwave digestion: Hg-CVAF;Cu, Ni, Cr, Ag,
Al, Fe, Mn, Sb (1993); Zn-ICP; Pb, Cd, As (1993),
Se-GFAAS; As (1994), Sb (1994)-HYDAAS
Chelation with APDC, extraction with MIBK;
determination by FAAS
Extraction with toluene; determination by
FIRGC/FIRMS; second column confirmation for
2,3,7,8-TCDD
AVS-selective generation of H2S, gravimetric,
colorimetric or titrametric determination; SEM-
filtration of AVS digestate, determination by
FAAS, ICPAES or CVAAS
Tropolone extraction; determination by
HRGC/FPD or HRGC/MS
Acidification with H3PO4; determination using a
CO2 analyzer
Sieving and pipette analysis
Reference
TSB SOP C-48
(U.S.EPA-Region
2, 1994a)
GERG SOPs-
ST02, ST04
GERG SOPs-
ST08, ST09, ST10,
ST11
TSB SOPs C-5, C-
8, C-72, C-73, C-
74 (U.S.EPA-
Region 2, 1994b-f)
MCAWW218.4
(U.S.EPA, 1983)
Method 1613 -
Rev. A
(U.S.EPA, 1990b)
GERG SOPs-9 130,
ST11, ST09, ST10
GERGSOP-9013
MCAWW 415.1
(U.S.EPA, 1983)
U.S.EPA, 1993b
All analyses employed appropriate quality assurance samples. Quality assurance goals were
developed and followed for each analysis (Adams and Hunt, 1993). Except in isolated instances,
all quality assurance goals were met or exceeded. Data were entered into two separate databases
and then compared electronically to ensure accuracy in data entry.
2-5
-------
Table 2-3
Analytical Measurements for Sediment Samples
Polyaromatic Hydrocarbons (PAHs)
Acenaphthene
Acenaphthylene
Anthracene
Benz(a)anthracene
Benzo(b,k)fluoranthene
Benzo(g,h,i)perylene
Benzo(a)pyrene
Benzo(e)pyrene
DDT and its Metabolites
o,p'-DDD ~"
Biphenyl
Chrysene
Dibenz(a,b)anthracene
2,6-Dimethylnaphthalene
Fluoranthene
Fluorene
Ideno(l,2,3-c,d)pyrene
2-Methylnaphthalene
1 -Methylnaphthalene
1 -Methylphenanthrene
Naphthalene
Perylene
Phenanthrene
Pyrene
2,3,5-Trimethylnaphthalene
Chlorinated Pesticides other than DDT
p,p'-DDD
o,p'-DDE
p,p'-DDE
o,p'-DDT
p,p'-DDT
Aldrin
Alpha-Chlordane
Trans-Nonachlor
Dieldrin
Endrin
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Lindane (• -BHC)
Mirex
Major Elements
Trace Elements
Aluminum
Iron
Manganese
Silicon
Antimony
Arsenic
Cadmium
Chromium
Copper
Lead
Mercury
Nickel
Selenium
Silver
Tin
Zinc
PCS Congeners (20)
No. Congener Name
8 2,4'-dichlorobiphenyl
18 2,2',5-trichlorobiphenyl
28 2,4,4'-trichlorobiphenyl
44 2,2',3,5-tetrachlorobiphenyl
52 2,2',5,5'-tetrachlorobiphenyl
66 2,3',4,4'-tetrachlorobiphenyl
101 2,2',4,5,5'-pentachlorobiphenyl
105 2,3,3',4,4'-pentachlorobiphenyl
110/77 2,3,3',4',6-pentachlorobiphenyl/
3,3',4,4'-trichlorotetrabiphenyl
No. Congener Name
118 2,3',4,4',5-pentachlorobiphenyl
126 3,3',4,4',5-pentachlorobiphenyl
128 2,2',3,3',4,4'-hexachlorobiphenyl
138 2,2',3,4,4',5'-hexachlorobiphenyl
153 2,2',3,4,4',5'-hexachlorobiphenyl
170 2,2',4,4',5,5'-hexachlorobiphenyl
180 2,2',3,3',4,4',5-heptachlorobiphenyl
187 2,2',3,4,4',5,5'-heptachlorobiphenyl
195 2,2',3,3',4,4',5,6-octachlorobiphenyl
206 2,2',3,3',4,4',5,5',6-nonachlorobiphenyl
209 2,2',3,3',4,4',5,5',6,6'-decachlorobiphenyl
Dioxin and Furan Congeners*
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Other Measurements
AVS/SEM
Grain Size
Clostridium
TOC
Butyltins
*only analyzed on Upper Harbor, Jamaica Bay, & Lower Harbor samples
2-6
-------
2.4.1 Major and Trace Elements
Sediment samples were prepared for bulk metals analyses using two procedures: 1) digestion
with nitric and hydrofluoric acids (total metals) and 2) digestion with nitric acid (total
recoverable metals). Subsequent data analyses are based on total metals results. Mercury was
analyzed by cold vapor atomic absorption (CVAA). Copper, nickel, lead, chromium, hexavalent
chromium, antimony, tin, arsenic, selenium, silver and cadmium were analyzed by graphite
furnace atomic absorption spectroscopy (GFAAS). Other metals (aluminum, iron, manganese,
silicon and zinc) were determined by flame atomic absorption spectroscopy (FAAS). Metal
concentrations are reported on a dry weight basis. The sediment SRM used was National
Research Council of Canada (NRCC) MESS2.
2.4.2 Organic Compounds
For analysis of pesticides and PCBs, aliquots of sediment were dried using sodium sulfate and
soxhlet extracted using methylene chloride for six hours. The extract was concentrated using a
Kuderna-Danish technique and the methylene chloride replaced with hexane. Extracts were
cleaned up with a silica gel/alumina column eluting with a 50:50 mixture of pentane and
methylene chloride. This fraction, which was primarily the aromatic and chlorinated
hydrocarbons, was again concentrated using a Kuderna-Danish technique and the mixed solvent
was replaced with hexane. The chlorinated pesticides and PCBs were quantified using high
resolution capillary gas chromatography with electron capture detection (GC/ECD). The GC
column used was a 30 m, 0.25 mm ID. fused silica column with a DB-5 bonded phase. The data
are reported in ng/g dry weight. The sediment SRM used with these samples was National
Institute of Technology (NIST) 194la.
Twenty-two poly cyclic aromatic hydrocarbons (PAHs) were measured (U.S.EPA-Region 2,
1994a). A 10-g aliquot of sediment was dried with anhydrous sodium sulfate and soxhlet
extracted with methylene chloride for 16 hours. The extract was dried by using a sodium sulfate
drying column and concentrated using a Kuderna-Danish apparatus to 1 ml. A GC/MS with a 30
m, 0.25 mm ID. DB-5 fused silica capillary column was used for analysis. A mass range of 33
to 450 amu was used. Results are reported as ug/kg, dry weight. The SRMs used were NIST
194la and 2260.
Butyltin analysis included mono-, di-, tri- and tetrabutyltin. Samples were freeze-dried and
extracted using 0.2% tropolone in methylene chloride on a roller table for three hours. The
extract was concentrated using a Kuderna-Danish apparatus and treated with Grignard reagent to
hexylate the butyltins. Extracts were neutralized and cleaned up with a silica gel/alumina
column. The fraction was again concentrated using Kuderna-Danish techniques and the mixed
solvent was replaced with hexane. Final volume of the extract was 1.0 ml. Butyltin
quantification was done on a high resolution capillary gas chromatograph with either flame
photometric detection (HRGC/FPD), equipped with a tin selective 610 nm filter or a mass
2-7
-------
spectrometer (HRGC/MS). The GC column used was a 30 m, 0.32 mm ID. fused silica capillary
column with DB-5 or DB-5MS bonded phase. A mass of 121 m/z was monitored for
quantification with a secondary ion of 191 m/z monitored for confirmation. The standard
reference material, NRCC PACS-1, also was analyzed.
The analytical method for AVS analysis employed selective generation of hydrogen sulfide and
gravimetric, colorimetric or titrametric determination (depending on the expected concentration
of sulfide). Following AVS analysis and digestate filtration, SEM analysis was performed for
cadmium, copper, lead, mercury, nickel and zinc using FAAS, ICPAES or CVAAS. Results are
reported as umol/g (dry wt).
Analysis of selected sediments for seventeen dioxin and furan congeners was done according to
Method 1613-Revision A (U.S.EPA, 1990b). Frozen sediment samples were thawed and
centrifuged to remove excess water. Approximately 10 g of sediment was used for determination
of percent solids. Another 10 g was combined with quartz sand for extraction. All samples were
spiked with isotopically labeled analogs of 15 of the 17 2,3,7,8-substituted PCDDs/PCDFs prior
to extraction. The samples then were extracted for 20 hours using toluene in a Soxhlet/Dean
Stark apparatus. Extracts were spiked with 37CL4-2,3,7,8-TCDD cleanup standard, partitioned
against base and acid solutions, and processed through acid/base silica, basic alumina, and
carbon AX-21/Celite cleanup columns. The carbon AX-21 Celite columns were back eluted with
30 mL toluene rather than the method-specified 20 mL as the laboratory has found that the extra
toluene has increased the recovery of OCDD/F in the past. Extracts were spiked with 1,2,3,4-
TCDD-13C12/l,2,3,7,8,9-HxCDD-13C12 recovery standard and concentrated to a final volume of
20 uL. These extracts were analyzed by high resolution gas chromatography/high resolution
mass spectrometry (FtRGC/FIRMS) in the selected ion monitoring mode on a DB-5 capillary
column at an instrument resolution of approximately 10,000 (10% valley). Most samples were
diluted to reduce chromatographic interference problems. Because 2,3,7,8-TCDF is not
completely resolved from other tetrachlorinated isomers on the DB-5 column, second column
confirmation of 2,3,7,8-TCDF levels above 1 ng/kg dry wt. was performed on a DB-Dioxin
column. The standard reference material, EOF-2513 (Cambridge Isotope Laboratories), was
processed with each batch of samples.
2.4.3 Sediment Physical Parameters
Grain size analysis was performed according to U.S.EPA (1993b), except samples were not
digested with hydrogen peroxide. Samples were treated with sodium hexametaphosphate as a
dispersant. Sand was defined as the fraction that was retained on a 63-u sieve. Percent silt and
percent clay were determined using pipette analysis of the filtrate. Percent moisture was obtained
by accurately weighing 10 g of sediment, drying overnight at 105°C and reweighing. The total
organic carbon (TOC) method was based on the U.S.EPA method MCAWW 415.1 (U.S.EPA,
1983), modified for sediment using a boat sampling module.
-------
2.4.4 Bacteriological Analysis
Concentrations of Clostridium perfringem spores have been used as an indicator of sewage
contamination (Hill et al., 1993; O'Reilly et al., 1995). C. perfringens is a obligate anaerobe
bacterium found in fecal material. It can survive extreme environmental conditions. This study
evaluated the concentrations of the spores in Harbor sediments. The membrane filter method of
Emerson and Cabelli (1982) was used. Mean concentrations of C. perfringens spores are
expressed as confirmed counts per gram (wet weight) of sediment
2.5 TOXICITY METHODS
2.5.1 Amphipod Sediment Toxicity Tests
Batches of a tube-dwelling amphipod, Ampelisca abdita, were supplied by East Coast Amphipod
of Kingston, Rhode Island. The amphipods and control sediment were collected from the
Narrow River, Rhode Island and the U.S. Army Corps of Engineers Long Island Sound (LIS)
reference station. Control sediment was press-sieved through a 0.5 mm mesh stainless steel sieve
to remove resident amphipods and debris. Test sediment was press-sieved through a 2.0 mm
stainless steel sieve to remove large debris and predaceous organisms. If amphipods were
present, the test sediments were press-sieved through a 1.0 mm stainless steel sieve. Organisms
were acclimated at 20°C and 30 ppt salinity prior to testing. Temperature and salinity did not
change by more than 3°C and 3 ppt, respectively, during any 24 consecutive hours of
acclimation. Amphipods were fed the marine alga, Phaeodactylum tricornutum, during
acclimation. Ten-day acute, static, non-renewal sediment toxicity tests were conducted
according to ASTM (1991, 1992) and U.S.EPA (1993b) test protocols. For each toxicity test,
200 ml of composited, press-sieved sample were placed in 1 L glass test chambers and covered
with 600 ml of seawater. Five replicate test chambers were used for each sample. Each replicate
contained 20 organisms.
Post-test enumeration of amphipods was performed without knowledge of sample identity to
prevent bias. If less than 20 amphipods were found, the test sediment was stored in the dark for
up to 48 hours to encourage emergence of any remaining amphipods. Final organism counts
were confirmed by a second scientist. Minimum control survival for satisfying test performance
criteria was 90%. Sodium dodecyl sulfate (SDS) was used as a reference toxicant to evaluate the
sensitivity of each batch of amphipods. Reference toxicant results were all within the acceptable
range for this species. A. abdita assays were conducted by the U.S.EPA-Region 2 Bioassay
Laboratory in Edison, NJ and SAIC, Narragansett, RI. These two laboratories participated in an
interlaboratory comparison which showed that the laboratories produced comparable results.
2-9
-------
2.5.2 Microtox™ Assays
The Microtox™ assay for marine sediments is considered to be a rapid screening alternative to
standard acute toxicity testing with fish or invertebrates (Giesy and Hoke, 1990) and has been
recommended as a first stage assay in a tiered testing arrangement (Sloof, 1985). In this
investigation it was used to supplement the Ampelisca abdita acute amphipod assay. The
Microtox™ assay is based on the inhibition of light emission by the luminescent bacterium
Photobacterium phosphoreum in the presence of toxicants. Freeze-dried luminescent bacteria
are reconstituted in control and test solutions and incubated, then luminescence is measured on
serial dilutions after 5 to 15 minute exposures. The percent inhibition of light transmission,
converted to an EC50 value, is the measure of toxicity. Microtox™ assays were conducted by
ToxScan, Watsonville, CA.
The solvent extraction method adapted from Long and Market (1992) was employed. Before
extraction, excess water from the top of the samples was decanted and discarded. The sediment
was homogenized and a 3.3 g wet weight sample was weighed into a 50-ml Pyrex centrifuge tube
with a Teflon-lined screw cap. Samples were dried by and extracted with dichloromethane
(DCM). Solvent exchange and concentration were performed using a Kuderna-Danish flask
attached to a Snyder column. Extracts were tested in duplicate following Micobics Corp.
recommended procedures (1992). The sediment extracts were diluted 1:100 with Microtox™
diluent. Serial dilutions of 100, 50, 25, 12.5, 6.25, 3.13 and 0 percent of this stock solution were
made using Microtox™ diluent. The 0% dilution is a reagent blank used to measure spontaneous
decay in bacterial luminescence of any treatment. Percent decrease in luminescence relative to
the reagent blank was calculated and these data were used to obtain the 50% inhibition
concentration (i.e., EC50). Results were converted to mg dry wt./ml.
Control sediment from the U.S. Army Corps of Engineers Long Island Sound (LIS) reference
station was tested along with the Harbor samples. Ethanol reagent blanks with no sediment and
extraction blanks were prepared and tested. Reference toxicant testing using phenol was
conducted with each set of sediment assays and results were acceptable according to the test
protocols.
2.6 BENTHIC MACROINVERTEBRATE ASSEMBLAGES
Three replicate grabs for benthic macroinvertebrate community structure were obtained at each
station. The grabs were processed by being washed through a 0.5 mm screen on-board the
sampling vessel. Invertebrates from two of the replicates were sorted and identified, the third
replicate was archived. Procedures for sorting, identifying, and measuring the biomass of
benthic macroinvertebrates followed EMAP-E procedures (Klemm et al., 1993; Frithsen et al.,
1994). The macrobenthos were identified to the lowest practical taxonomic category. Rare or
previously undocumented specimens from the Harbor were put aside in a reference collection.
Ten percent of all samples were reprocessed and subjected to a second QA evaluation.
2-10
-------
Taxonomic identifications were verified using reference organisms obtained from EMAP's
reference collection. Sample processing was conducted by Versar, Inc. (Columbia, MD) and
Cove Corporation (Lusby, MD). Species identifications and enumerations were done by Cove
Corporation and biomass measurements were done by Versar, Inc.
Organisms were grouped by taxa for biomass determination. To standardize the biomass
measurements, all samples were preserved in a 10% solution of buffered formaldehyde for at
least two months before the biomass measurement. Hard-bodied organisms (bivalves <2cm and
gastropods) were acidified in 10% HCL until all visible traces of shell material were removed.
Bivalves larger than 2 cm were shucked before determination of biomass. Biomass was
determined as dry wt. after drying for at least 48 hours at 60°C.
2.7 DATA ANALYSIS
2.7.1 Chemical Data
For several classes of compounds, data analyses were performed on summed results. Total PCBs
were the sum of the concentrations of the 20 congeners in Table 2-3 multiplied by 2.0 (NOAA,
1989). Total PAHs were the sum of the concentrations of the 23 individual PAHs. Total
chlordane was the sum of the concentrations of heptachlor, heptachlor-epoxide, oxychlordane,
gamma-chlordane, alpha-chlordane, trans-nonachlor and cis-nonachlor. Non-detects were not
included in the calculation of total concentrations.
Data analyses for metals were based on total metals results.
2.7.2 Toxicity Data
Amphipod survival data were not transformed, since an examination of a large historical data set
from S AIC has shown that A. abdita percentage survival data meet the requirement of normality
(Thursby et al., 1997).
For Microtox™ analyses, the concentration and response data were log-transformed before using
analysis of covariance (ANCOVA) to conduct a pair-wise comparison to determine significant
differences between samples from each station and control sediment.
2.7.3 Benthic Macroinvertebrate Data
Nine individual measures (Table 2-4) and one composite index (benthic index of biotic integrity
or B-IBI) were used to evaluate the condition of benthic assemblages in the study area. Diversity
2-11
-------
was evaluated by using species richness (number of species) and the Shannon-Wiener diversity
index (Shannon and Weaver, 1949).
A multi-metric benthic index of biotic integrity (B-IBI) was developed for the NY/NJ Harbor
(Appendix C). The EMAP-E 1990-1993 Virginian Province data, excluding Chesapeake Bay
and site with salinities less than 15 ppt, were used to develop the index. The B-IBI incorporated
five of the benthic macroinvertebrate metrics in Table 2-4 into a single value that described the
condition of the benthos. These five metrics were those which most effectively distinguished
normal sites from all others. The metrics were evaluated for four different salinity and grain size
habitats (Table 2-5) and threshold values were defined for each.
Table 2-4
Individual Benthic Macroinvertebrate Measures Assessed
Species Diversity
Number of taxa (#)*
Shannon-Wiener Diversity (H')
Abundance and Biomass
Abundance (#/m2)*
Biomass (g/m2)*
Species Composition
Abundance of pollution-indicative taxa (%)*
Abundance of pollution-sensitive taxa (%)*
Trophic Composition
Abundance of deposit feeding taxa (%)
Abundance of suspension feeding taxa (%)
Abundance of carnivores/omnivores (%)
* Measures used in B-IBI.
Table 2-5
B-IBI Habitat Categories
Habitat
Salinity Class
Polyhaline
(15-28 ppt)
Euhaline
(28-35 ppt)
Sediment Type
Mud (>40% silt+clay)
Sand (<40% silt+clay)
Mud (>40% silt+clay)
Sand (<40% silt+clay)
The index was calculated by scoring each selected metric as 5, 3, or 1 depending on whether its
value at a site approximated, deviated slightly from, or deviated greatly from conditions at the
2-12
-------
best reference sites. The B-IBI value for each station is calculated as the mean score of the five
metrics. A mean score of 5 indicated that the site was approximately equivalent to the best
reference sites. A score of 3 or 1 indicated that the site slightly deviated or greatly deviated from
conditions at the best reference sites and would be considered to have impacted benthos. The
overall validation efficiency of the B-IBI was 93%. The average difference between replicates
was 0.32. Ninety-one percent of the replicates at the same site scored similarly. At most of the
sites where the replicates scored differently, the replicates had similar numerical values, but were
on either side of the index threshold of 3.
2.7.4 Condition Estimates
Two types of characterizations were done for this investigation. Individual sub-basins were
separately characterized for each parameter, resulting in six characterizations. The "Harbor"
characterization includes four of the six sub-basins that are commonly known as the Harbor
proper; Jamaica Bay, Newark Bay, Lower Harbor and Upper Harbor. The watersheds, sources,
physical and hydrological characteristics of western Long Island Sound and the Bight Apex were
significantly different from the Harbor proper.
The condition of each stratum and the Harbor as a whole was assessed in two ways: 1) mean
condition and, 2) percent of area exceeding threshold (or critical) values for selected parameters.
The spatial distribution of degraded and non-degraded stations was also evaluated using GIS
(Geographic Information System) display of individual station results.
This investigation used specific terminology to distinguish different bases for determining
"sediment quality." Sediments with unusually high chemical concentrations were considered
"contaminated." Significant results of sediment toxicity tests indicated "toxic" sediments.
Measurable departures from normal benthic macroinvertebrate assemblages indicated "impacted"
or "abnormal" benthic assemblages. Only when two or three of these sediment quality
indications were abnormal, were the sediments described as "degraded."
2.7.4.1 Mean Condition
Since the sampling stations within each stratum or sub-basin (except Newark Bay) were selected
with equal inclusion probabilities, the mean parameter values for a stratum, h, and its variance
were calculated as:
1=1
2-13
-------
:= E ———— (2)
1=1 n - 1
where
yih was the variable of interest (e.g., concentration of mercury), and
nh was the number of samples collected from stratum h.
The weighted mean value for L strata with combined area A is given by
where the weighting factors, Wh = Ah/A, ensure that each stratum h is weighted by its fraction of
the combined area for all L strata. An estimator for the variance of the stratified mean (3) is
E Wh2Var(yb) (4)
Strata were combined to develop estimates for the study area as a whole and for the New
York/New Jersey Harbor, which includes all strata except western Long Island Sound and the
Bight Apex, following Holt and Smith (1979). Confidence intervals were calculated as 1.64
times the standard error, where the standard error was the square root of the variance.
The samples from Newark Bay were treated as a cluster sample, in which the cells formed
clusters (areas) of unequal size. Mean parameter values were calculated as area-weighted means:
2-14
-------
where
q was the area of sampling cell /',
C was the combined area of all the cells sampled,
yih was the variable of interest (e.g., concentration of mercury), and
n was the number of cells sampled.
The standard error was calculated using the jackknife estimator (Cochran 1977; Efron and Gong
1983):
where
u, . = £ .c.y / (C- c.)
U) i^ i J
was the weighted mean value deleting they'th cell and
was the jackknife estimate of the mean y for the n cells.
2.7.4.2 Mass Estimates
Total mass of contaminants in surficial sediments were estimated from bulk density and volume
of sediment, and contaminant concentration. Wet sediment bulk density was calculated as:
D = (l-p)rs + prw
where: p = porosity (mean of 0.4 assumed)
2-15
-------
rs = density of sediment (quartz, etc.) particles (2.65 g/cm3)
rw = density of water (1 g/cm3).
So, D • (1-0.4)»2.65+0.4 = 2 g/cm3, or 4 g/cm2 (= 40»106kg/m2) in the top two centimeters of
sediment. The concentration of mercury, for example, averaged over surficial sediments in the
Harbor was 0.74 ppm, dry weight (Table E-l). So, mean mercury concentration • 0.74*(l-p) =
0.74O.6 = 0.44 ppm, wet weight. Therefore, the mass of mercury in surficial sediments of the
Harbor was approximately:
mercury concentration »unit mass of sediment • Harbor surface area
= 0.44 mg/kg »40»106 kg/km2 • 501 km2
= 8,800 kg Hg.
2.7.4.3 Percent of Area Estimates
Estimates of percent of area exceeding selected thresholds (e.g., mercury concentration greater
than ERM) were calculated asp = Bin (except in Newark Bay), where B was number of samples
exceeding the threshold and n was the total number of samples in the stratum. For strata with
equal inclusion probability, the exact confidence intervals for/? were calculated from the
binomial distribution using the formula of Hollander and Wolfe (1973). Below detection limit
values were included as zero for percent of area estimates.
The confidence interval for combined strata was calculated using the normal approximation to
the binomial, with the 90% confidence interval of stratified estimates of proportions, pst,
estimated as:
where
E wh ph
h=l
Var(p )= E Wh2Var(ph)
h=l
2-16
-------
The formulas for estimating means and variances for Newark Bay also were used to estimate the
percentage of area in Newark Bay with y values that fell into some defined class. An indicator
variable, I; was assigned the value 1 if the value of^ fell in a specified class, and 0 otherwise.
The sample mean and variance of I; was an estimate of the proportion of area in Newark Bay that
had y values within the specified class.
2.8 SELECTION OF THRESHOLD VALUES
To conduct the data analyses needed to produce percent of area estimates, threshold values or
"levels of concern" were required. The threshold values used were either proposed (proposed
SQC), established by regulation or Agency guidance (e.g., Ampelisca abdita toxicity), or were
screening guidelines (e.g., contaminant ERLs and ERMs).
2.8.1 Physical Data Thresholds
For grain size, a value of 40% silt-clay was used to distinguish between sand (<40% silt-clay)
and mud (>40% silt-clay) substrate. This cut-off was established using cluster analysis on
Environmental Monitoring and Assessment Program (EMAP) data from 525 randomly selected
sites, sampled between 1990 and 1993 in the Virginian Province.
2.8.2 Chemical Data Thresholds
For chemical contaminants, three conventions were evaluated: 1) the "Effects Range-Low
(ERL)" and "Effects Range-Median (ERM)" values of Long and Morgan (1991) and Long et al.
(1995a); 2) two conventions which incorporate equilibrium partitioning theory (U.S.EPA, 1994):
Proposed Sediment Quality Criteria (SQC) and Acid Volatile Sulfides (AVS); and, 3) aluminum
normalization for metals (Appendix D).
For determination of potential biological effects, this study's chemical data, except dioxins and
furans, were evaluated using the effects-based guidelines of Long and Morgan (1991) and Long
et al. (1995a). This approach utilizes data from laboratory spiked bioassays, equilibrium
partitioning models and synoptic chemical and biological data from field surveys. Ranges of
chemical concentrations are determined that are usually associated with biological effects
(Effects Range-Median or ERM), and at which biological effects begin to be seen (Effects
Range-Low or ERL). New York State has adopted some of these ERLs and ERMs for Sediment
Guidance Criteria (NYSDEC, 1994 and 1996). The Long and Morgan (1991) and Long et al.
(1995a) values were used because they include thresholds for most of the chemicals that were
measured, allowing this study to provide an integrated contaminant response. Alternative
thresholds and evaluation methods, such as proposed sediment quality criteria (U.S.EPA, 1994),
2-17
-------
SEM-AVS (DiToro et al., 1990; NOAA, 1995) and aluminum normalization (Appendix D) also
were applied.
Concentrations of seventeen dioxin and furan congeners also were measured in sediments of
three sub-basins: Jamaica Bay, Lower Harbor and Upper Harbor. Sediments that are
contaminated with dioxins and furans contain a complex mixture of congeners. Individual
congeners differ greatly in their toxicity and carcinogenicity and although specific individual
congeners may not be present in concentrations of concern, the combined effect of existing
concentrations may be toxicity. A "toxicity equivalency factor (TEF)" was applied to each
congener, then summed across all dioxin and furan congeners to give "toxicity equivalents
(TEQ)". This permitted estimation of total dioxin/furan toxicity (U.S.EPA, 1989; Cura et al.,
1995). The TEQs calculated were for human health application. TEFs for aquatic organisms are
still in the development stage and do not address all congeners. Therefore, comparison to interim
guidelines was made for 2,3,7,8-TCDD risk to aquatic life and associated wildlife. A level of
100 pg/g 2,3,7,8,-TCDD has been suggested as interim guidance for high risk to sensitive fish
species (U.S.EPA, 1993c).
2.8.3 Sediment Toxicity Thresholds
Significant toxicity for the amphipod, A. abdita, was defined as survival less than or equal to
80% of the mean control survival and statistically different (p<0.05) from controls
(U.S.EPA/U.S.ACE, 1991). For Microtox™, a significant effect was defined as an EC50
statistically less (p<.05) than the performance control and 70% or less of the control EC50. This
70% criterion is used by the Puget Sound Dredge Disposal Analysis (PSDDA, 1989).
2.8.4 Benthic Index Thresholds
Threshold values for each measure (metric) in the NY/NJ Harbor Benthic Index of Biotic
Integrity (B-IBI) were established based on the distribution of its values at reference sites.
Similar to the Index of Biotic Integrity (IBI) approach (Kerans and Karr, 1994), each measure
was scored as 5, 3, or 1 based on whether its value at a site approximated, deviated slightly from,
or deviated greatly from conditions at the best reference sites. Threshold values were established
at the 5th and 50th (median) values for reference sites in each habitat. Metric values below the
5th percentile compared to the reference sites were scored as a 1; values between the 5th and
50th percentile were scored as a 3; and values above the 50th percentile were scored as a 5. An
index value for a location was calculated by taking the mean of the scores for the individual
measures at a location. If the mean of all the benthic index metrics at a location was less than or
equal to 3, the location was considered to have impacted benthos.
2-1!
-------
3.0 PHYSICAL PARAMETERS
3.1 BACKGROUND
Many factors potentially influence chemical and biological measurements. The measurement of
physical parameters provides information necessary to interpret chemical and biological data
accurately. Sediment grain size and total organic carbon content can determine the magnitude
and distribution of contaminants (Burton, 1995). Fine-grained sediments generally retain more
contamination than sands because of the greater surface area to volume ratio of fine particles and
surface electric charges that can render them more chemically and biologically reactive (Plumb,
1981; Power and Chapman, 1995). Physical characteristics, such as salinity, sediment type and
depth, are important parameters because they can influence the distribution and abundance of
benthic assemblages (Snelgrove and Butman, 1994; Holland et al., 1989).
At each site where sediment was collected, water column depth, temperature, salinity, and
dissolved oxygen were measured. The water column measurements consisted of a single CTD
profile at each station. Physical characteristics of the sediments included grain size (as % silt-
clay) and total organic carbon (TOC) content.
3.2 CHARACTERIZATION OF THE HARBOR
3.2.1 Depth
All Harbor sub-basins, except the Upper Harbor, had similar mean depths (Table 3-1). The
Upper Harbor mean at 10 m, was 3-4 m deeper than other sub-basins in the Harbor. The mean
Table 3-1
Area-Weighted Means of Depth and Sediment Physical Parameters
(± 90% confidence interval)
Depth (m)
% Silt-Clay
% TOC
Harbor
6.9
±0.9
34.8
±6.1
1.9
±0.3
Jamaica
Bay
6.4
±1.1
30.3
±9.7
1.9
±0.7
Newark
Bay
6.7
±1.5
68.1
±8.6
2.3
±0.6
Lower
Harbor
5.9
±1.2
26.8
±8.8
1.7
±0.4
Upper
Harbor
10.1
±1.8
51.0
±10.1
2.5
±0.5
W.LI.
Sound
16.6
±2.4
63.2
±10.5
2.3
±0.7
Bight
Apex
22.2
±2.8
7.7
±3.4
1.2
±0.4
3-1
-------
depth for the entire Harbor was 7 m. Portions of the Harbor are dredged to maintain shipping
channels.
3.2.2 Percent Silt-Clay
The mean percent silt-clay varied greatly among sub-basins (Figure 3-1). Average percent silt-
clay in sediments of the entire Harbor was 35%. Newark Bay was the muddiest sub-basin with
68% silt-clay and Lower Harbor was the sandiest with only 26% silt-clay. These same patterns
were also apparent when results are expressed as areal extent. In terms of spatial extent, 39% of
the Harbor is predominantly mud (>40% silt-clay). Eighty-five percent of Newark Bay was
dominated by mud compared to 29% of Jamaica Bay and Lower Harbor.
3.2.3 Total Organic Carbon (TOC)
The average total organic carbon (TOC) in Harbor sub-basins ranged from 1.7 to 2.5%, with the
sub-basins not significantly different from one another (Table 3-1). When TOC was examined
on an areal basis, the sub-basins were not as similar, with Upper Harbor, Newark Bay and Lower
Harbor having a considerable percent of area with TOC exceeding 1.5% (Figure 3-2). Sixty-two
percent of the sediments in the Harbor contained between 0.5 and 3.4% TOC. There also were
no sites in Newark Bay where TOC was less than 0.5%, whereas TOC less than 0.5% occurred
over at least 10% of the area in every other sub-basin.
3.2.4 Water Column Profile
The water column results are all based on a single measurement at each station during the study
period.
All sub-basins were similar to one another with regard to mean bottom water temperature during
the sampling timeframe (Table 3-2). Means ranged from 20.8°C in Jamaica Bay to 23.4°C in
Newark Bay. Mean bottom water temperature for the entire Harbor was 22.0°C.
Mean bottom salinity for the entire Harbor was 26.2 ppt. Newark Bay had an average salinity of
22.4 ppt, which was significantly lower (p<0.01) than any of the other systems. The lowest
salinity value measured during the study was 1.3 ppt in the Passaic River; all other values
exceeded 12 ppt.
In general, dissolved oxygen concentrations are extremely variable temporally and spatially.
This study obtained a single measurement of dissolved oxygen at each station. New York City
has a more complete dissolved oxygen data set (Brosnan and O'Shea, 1994; 1995) which was
-------
T3
-a
c
ra
CO
m
i
m
-------
in
c\i
4j_
V
2
m
CO
n
v" in
2 d
in ji_
c\i v
an
in
_
v
co
Jl ^
A d
;uaoj9c]
o
•fi
cd
O
O
'£
ofl
C
O
C
en
• i— i
-a
ca
1
(Li
O
IH
d>
OH
CN
U
3
00
-------
used for developing the benthic index. Therefore, dissolved oxygen results from this
investigation were not reported here.
Table 3-2
Area-Weighted Means of Water Column Physical Parameters
(± 90% confidence interval)
Bottom
Temp. (°C)
Bottom
Salinity (ppt)
Harbor
22.0
±0.5
26.2
±0.4
Jamaica
Bay
20.8
±0.5
27.1
±0.5
Newark
Bay
23.4
±0.8
22.4
±0.8
Lower
Harbor
22.3
±0.7
26.9
±0.5
Upper
Harbor
21.3
±0.9
24.8
±1.5
W.LI.
Sound
17.5
±0.6
28.7
±0.6
Bight
Apex
11.1
±1.2
33.6
±0.9
3.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND AND THE BIGHT
APEX
3.3.1 Depth
Western Long Island Sound and the Bight Apex were both, on average, about 20 m deeper than
the Harbor (Table 3-1).
3.3.2 Percent Silt-Clay
The Bight Apex had a low mean % silt-clay compared to the Harbor (8% versus 35%) and was
the sandiest sub-basin in the study (Figure 3-1). The mean % silt-clay in western Long Island
Sound (63%) was comparable to Newark Bay (68%) and approximately double that of the
Harbor. Fifty percent of the area of western Long Island Sound was mud compared to 4% of the
Bight Apex (Figure 3-1).
3.3.3 Total Organic Carbon (TOC)
TOC levels in the Bight Apex were significantly less than in the Harbor (Table 3-1). The Bight
Apex had a mean of 1.2 % TOC. The mean in western Long Island Sound was comparable to
Newark Bay and Upper Harbor. Forty-six percent of the area in the Bight Apex had less than
0.5% TOC, compared to 29% in western Long Island Sound (Figure 3-2).
3-5
-------
3.3.4 Water Column Profile
The average bottom water temperature in the Bight Apex, at 11.1°C, was 11°C less than the
Harbor mean and the lowest of all the sub-basins (Table 3-2). The average for western Long
Island Sound was 17.5°C.
The Bight Apex had an average salinity of 34.6 ppt, and western Long Island Sound, 28.7 ppt
(Table 3-2). Both were higher than the Harbor mean or other sub-basins.
Because of the spatial and temporal variability of dissolved oxygen levels and the fact that this
investigation obtained a single dissolved oxygen measurement at each station, those results are
not included here.
3.4 COMPARISON TO PREVIOUS STUDIES
Several other investigations and monitoring programs have produced physical data for the Harbor
and/or Bight Apex. TOC and grain size in the Harbor have been measured as part of other
contaminant investigations (Long et al., 1995b; Strobel et al., 1995) and as study objectives
(Suszkowski, 1978; Jones et al., 1979; Coch, 1986). The most spatially and temporally extensive
database for dissolved oxygen, temperature and salinity is that of the New York City Department
of Environmental Protection (NYCDEP) which sampled 52 stations at least bi-monthly year-
round (weekly in the summer) in the Harbor in 1993 and 1994 (Brosnan and O'Shea, 1994;
1995). U.S.EPA conducted monitoring of dissolved oxygen and temperature in the Bight Apex
(U.S.EPA-Region 2, 1994g; 1995). The data from the present study were collected to aid in
interpretation of other study parameters and were not intended to represent comprehensive
temporal coverage.
Physical data collected during this investigation was similar to historical data. The Harbor mean
TOC for the present study was 1.9%. The Long et al. (1995b) investigation (using similar sub-
basin boundaries to the present investigation) produced a range of 0.07% to 5.0% and a mean of
2.6%. Long et al. found a range of 0.0% to 76.7% silt-clay in the Harbor, with a mean of 39.3%
silt-clay as compared to a mean of 34.8% in the present investigation. An earlier study by Coch
(1978), incorporating data from Suszkowski (1978), showed that Newark Bay was 66% silt-clay;
this study produced an estimate of 68%. The area that is approximately Upper Harbor in the
present study had 51% of its area predominantly mud, compared to 34% in the Coch
investigation. Other basin boundaries in the Coch investigation were significantly different to
preclude direct comparison with the present investigation. Additionally, grain size and TOC
distributions in the Harbor may have substantially changed.
Salinity and temperature measurements were similar between this and other investigations.
3-6
-------
4.0 SEDIMENT CHEMISTRY
4.1 BACKGROUND
Chemically contaminated sediments, directly and indirectly, pose a significant threat to Harbor
resources. Striped bass, bluefish and blue claw crabs from large portions of the estuary should
not be consumed because the levels of PCBs and/or dioxins exceed guidelines (NY-NJ HEP,
1996). Areas that were once productive shellfish beds no longer exist or have reduced
populations that are restricted for harvesting. Bioaccumulation of contaminants and effects on
benthic macroinvertebrate communities also have been observed. Dredging and disposal of
contaminated sediments is a major management issue because of the potential adverse biological
effects that could result in disposal areas.
For determination of potential biological effects, chemical data, except that for dioxins and
furans, were evaluated using the aquatic effects-based guidelines of Long and Morgan (1991) and
Long et al. (1995a). This approach utilizes data from laboratory spiked bioassays, equilibrium
partitioning models and synoptic chemical and biological data from field surveys. Two
concentrations are determined for each chemical that are associated with incidence of biological
effects in the dataset that was used for development (Table 4-1). The Effects Range-Low (ERL)
value is the concentration at which adverse biological effects begin to be seen, and the Effects
Range-Median (ERM) concentration is that usually associated with adverse biological effects.
New York State has adopted some of the ERLs and ERMs for Sediment Guidance Criteria
(NYSDEC, 1994 and 1996). The Long and Morgan (1991) and Long et al. (1995a) values were
used because they include thresholds for most of the chemicals that were measured, allowing this
study to provide an integrated contaminant response. Alternative thresholds and evaluation
methods, such as proposed sediment quality criteria (U.S.EPA, 1994), SEM-AVS (DiToro et al.,
1990; NOAA, 1995) and aluminum normalization also were applied.
Concentrations of seventeen dioxin and furan congeners were measured in sediments of three
sub-basins: Jamaica Bay, Lower Harbor and Upper Harbor. Sediments that are contaminated
with dioxins and furans contain a complex mixture of congeners. Individual congeners differ
greatly in their toxicity and although individual congeners may not be present in concentrations
of concern, their combined concentrations may be toxic. A "toxicity equivalency factor (TEF)"
was applied to each congener, then summed across all dioxin and furan congeners to give
"toxicity equivalents (TEQ)". This permits estimation of total dioxin/furan toxicity, expressed as
an equivalent amount of 2,3,7,8-TCDD (U.S.EPA, 1989; Cura et al., 1995). TEFs for aquatic
organisms are still in the development stage and do not address all congeners. A level of 100
pg/g 2,3,7,8-TCDD has been suggested as interim guidance for high risk to sensitive fish species
(U.S.EPA, 1993c).
4-1
-------
Table 4-1
ERL and ERM Concentrations for Sediment Trace Metals and Organic Compounds
(Long and Morgan, 1991; Long et al., 1995a)
Chemical Analyte
Trace Elements (ppm)
Antimony
Arsenic
Cadmium
Chromium
Copper
Lead
Mercury
Nickel
Silver
Zinc
Polychlorinated Biphenyls (ppb)
Total PCBs
DDT and Metabolites (ppb)
DDT
ODD
p,p'-DDE
DDE
Total DDT
Other Pesticides (ppb)
Chlordane
Dieldrin
Endrin
Polynuclear Aromatic Hydrocarbons (ppb)
Acenaphthene
Acenaphthylene
Anthracene
Benzo(a)anthracene
Benzo(a)pyrene
Chrysene
Dibenz(a,h)anthracene
Fluoranthene
Fluorene
2-Methyhiaphthalene
Naphthalene
Phenanthrene
Low molecular weight PAHs
High molecular weight PAHs
Pyrene
Total PAH
ERL Concentration
2
8.2
1.2
81
34
46.7
0.15
20.9
1
150
22.7
1
2
2.2
2
1.58
0.5
0.02
0.02
16
44
85.3
261
430
384
63.4
600
19
70
160
240
552
1700
665
4022
ERM Concentration
25
70
9.6
370
270
218
0.71
51.6
3.7
410
180
7
20
27
15
46.1
6
8
45
500
640
1100
1600
1600
2800
260
5100
540
670
2100
1500
3160
9600
2600
44792
4-2
-------
4.2 CHARACTERIZATION OF THE HARBOR
4.2.1 Mean Condition
Chemical contamination was found to be pervasive in the Harbor. The mean values for every
contaminant for which ERL and ERM thresholds exist, except cadmium, were above ERL levels
(Appendix E). The Harbor means for mercury, parent DDT and total PCBs exceeded ERM
values.
Of the Harbor sub-basins, Newark Bay had the highest average concentration of all the metals
measured, except for manganese and silicon (Appendix E).
The Upper Harbor had the highest mean concentrations of individual and total PAHs and endrin,
but for all other organic contaminants, Newark Bay had the highest mean concentration. For
chemicals in the DDT family (e.g., parent DDT, ODD, DDE and total DDT), Newark Bay had a
mean concentration that was 10 or more times higher than the next highest sub-basin.
The mean concentration of tributyltins in the Harbor was 30.1 ppb. Mean concentrations in
Jamaica Bay and Upper Harbor were similar (38.6 and 32.5 ppb). Newark Bay's average
concentration was about twice as high (69.3 ppb). Tributyltin threshold concentrations for
biological effects have not been defined.
4.2.2 Areal Extent
Chemical contamination was present throughout the Harbor. When expressed on an area basis,
87% of the Harbor exceeded an ERL concentration for at least one contaminant, and 50% of the
Harbor exceeded an ERM concentration for at least one contaminant (Figure 4-1).
Within the Harbor, Newark Bay and the Upper Harbor had the most pervasive contaminant
problem, with 92% and 79% of their areas, respectively, exceeding an ERM value for at least one
chemical. These two sub-basins, at 98% and 100%, also had the highest percent of area
exceeding at least five ERLs. The entire Harbor exceeded five or more ERLs at 57% of its area.
Estimates of the percent of area in the Harbor that exceeded an ERL and/or ERM for any metal,
pesticide, PAH and total PCBs showed that all contaminant groups appeared to contribute to
Harbor contamination (Figure 4-1). No single contaminant group predominated. Metals,
pesticides and total PCBs contaminated approximately the same percentages of the Harbor at
ERM levels.
Examination of the individual chemicals showed that mercury, chlordane and total PCBs were
the most pervasive at levels above ERMs. The most ubiquitous metal was mercury, with 75% of
the area of the Harbor exceeding the ERL and 34% exceeding the ERM (Figure 4-2). All other
4-3
-------
.H 00
s §
|3
II
CO Ofl
^ c
0
oj
OH
**
1-.
-------
03
a
o
ON
Ef
I
Ml
§
c/3
J
03
T3 O
-
«*
S.g
"S 00
--
o
S
aj "°
H.s
^1
-4_J
S .5
2 AH
g
D
eo
-------
metals also caused significant amounts of area, ranging from 23 to 54%, to exceed an ERL value.
Of the pesticides, chlordane resulted in the greatest percent area (32%) above an ERM (Figure 4-
3). Organic contaminants above ERL values affected from 56% to 83% of the Harbor area.
The distribution of individual chemicals was not uniform across sub-basins (Appendix E). Many
of the individual chemicals were sub-basin specific. Several, such as mercury and chlordane,
were ubiquitous. Mercury had the highest percent area of all the metals exceeding an ERM.
Focusing on mercury in each of the sub-basins showed that 91% of the area in Newark Bay and
46% of the area in the Upper Harbor exceeded the ERM concentration (Figure 4-4). Chlordane,
the most prevalent pesticide Harbor-wide at a level that has probable biological effects, exceeded
the ERM in 91% of Newark Bay. All sub-basins had some area above the ERM concentration
for chlordane (Figure 4-5).
It was possible to distinguish some general patterns of chemical distribution in sediments. The
pattern of mercury distribution in the Harbor indicated that a possible source or sources exist in
or above Newark Bay (Figure 4-6). Concentrations were elevated down the Arthur Kill across
Raritan Bay to Sandy Hook Bay, following the circulation pattern for this part of the Harbor.
Total PCBs (Figure 4-7) and total PAHs (Figure 4-8) exhibited similar patterns.
The actual area above and below specified threshold levels was calculated, in addition to percent
of area. Approximately 436 of the Harbor's 501 km2 were above the ERL for at least one
contaminant and approximately 250 km2 were above the ERM for a least one contaminant. The
total area for specific contaminants also was estimated. Mercury and total PCBs concentrations
above ERMs affected approximately the same total area of the Harbor (Figure 4-9). Although
Newark Bay had pervasive, elevated levels of these contaminants, because of its small relative
size, it did not contribute as much as other sub-basins to the total contaminated area.
Total mass of contaminants in the surficial sediments was also calculated. As an example, the
estimated mass of mercury in the Harbor was more than three times that of total PCBs (Figure 4-
10). Comparing sub-basins, a higher quantity of mercury and total PCBs was estimated for the
Bight Apex, despite the low concentration of these chemicals in that sub-basin.
4.2.3 Dioxins and Furans
Concentrations of seventeen congeners of dioxins and furans were measured at each station in
Jamaica Bay, Lower Harbor and Upper Harbor. The biotic effects of dioxins and furans are
roughly additive, although congeners differ greatly in their toxicity and carcinogenicity. These
features were of concern because most sediments, if contaminated with dioxins and furans, have
them present as complex mixtures and although individual congeners may not be present in
concentrations of concern, their combined concentrations may be toxic. A "toxicity equivalency
factor" has been quantified for each congener, allowing estimation of total dioxin/furan toxicity,
expressed as "toxicity equivalents" (U.S.EPA, 1989; Cura et al., 1995).
4-6
-------
-------
Ofl
c
o
03
E?
en
o
P-l
T3
w
I
I
(D
<1J
O
X
03
.,_,
(X
-------
ON
^
Pi
-------
New York
D BelowERL: <. 15ppm
Between ERL and ERM: .15 to .71 ppm
A Above ERM: > .71 ppm
New Jersey
Western Long Island Sound
Long Island
RaritanBay
Sandy Hook Bay
NY Bight Apex
Navesink raver
Shrewsbury River
0 5 10
Rgure 4-6. Distribution of sediment mercury concentrations by station. ERL and ERM values
(Long et al., 1995a) are equivalent to NY State Sediment Guidance Criteria lowest
and severe effects levels (NYSDEC, 1994 and 1996).
-------
Total PCB
New York
D Below ERL:< 22.7 ppb
* Between ERL and ERM: 22.7 to 180 ppb
A Above ERM: > 180 ppb
New Jersey
Western Long Island Sound
Long Island
RaritanBay
Sandy Hook Bay
* NY Bight Apex D
Naveank River
Shrewsbury River
0 5 10
Figure 4-7. Distribution of sediment Total PCB concentrations by station. TPCB is the
product of the sum of the 20 congeners in Table 2-3 and 2.0 (NOAA, 1989).
ERL and ERM values are according to Long et. al. (1995a).
-------
Total PAH
New York
D Below ERL:< 4022 ppb
Between ERL and ERM: 4022 to 44792 ppb
A Above ERM: > 44792 ppb
New Jersey
Western Long Island Sound
Long Island
RaritanBay
Sandy Hook Bay
. NYBightApex n
Navesink River
Shrewsbury River
0 5 10
Figure 4-8. Distribution of sediment Total PAH concentrations by station. TPAH is the sum
of the 23 individual PAHs in Table 2-3. ERL and ERM values are according to
Longet.al.(1995a).
-------
03
LD
cn
ro
CD
C
o
LJJ
CD
O
CL
"ro
-*— •
o
-*— *
"O
c
CT3
o
0)
_c
-»—*
0)
o
03
LO
E (5
^,1
cS 0)
O g,
05
cri
4
-------
"O
flj
en
O
§
-o
-------
Among the three sub-basins where dioxins were measured, mean concentrations of the most
toxic dioxin congener, 2,3,7,8-TCDD, were highest in Lower Harbor (Table 4-2). Similarly,
incorporating all congeners into the calculation of TEQs resulted in the Upper Harbor having a
significantly higher amount of 2,3,7,8-TCDD equivalents than the other two sub-basins.
Table 4-2
Mean Concentrations Of 2,3,7,8-TCDD In Sediments Of Three Sub-Basins
(± 90% confidence limits)
Jamaica Bay
Lower Harbor
Upper Harbor
2,3,7,8-TCDD (ng/kg, dry wt.)
4.0 ±2.6
7.5 ±3.4
5.5±1.8
Human Health Toxicity
Equivalents
16.4±9.1
17.0±7.1
22.2±5.7
Using the Biota to Sediment Accumulation Factor ( BSAF) approach, EPA has derived interim
guidance (U.S.EPA, 1993c) for assessing risk of 2,3,7,8-TCDD only, to aquatic life and
associated wildlife (Table 4-3). Comparison of the values presented in the interim guidance to
Table 4-3
Interim Environmental Concentrations Associated With TCDD Risk To Aquatic Life And
Associated Wildlife (table adapted from U.S.EPA, 1993c)
Organism
Sediment Concentration (pg/g dry wt.)
Low Risk
Fish
Mammalian Wildlife
Avian Wildlife
60
2.5
21
High Risk to Sensitive Species
Fish
Mammalian Wildlife
Avian Wildlife
100
25
210
Fish lipid of 8% and sediment organic carbon of 3% assumed where needed.
For risk to fish, BSAF of 0.3 used; for risk to wildlife, BSAF of 0. 1 used.
Low risk concentrations are derived from no-effects thresholds for reproductive effects (mortality in embryos and young) in sensitive species.
High risk concentrations are derived from TCDD doses expected to cause 50 to 1 00% mortality in embryos and young of sensitive species.
4-15
-------
the 2,3,7,8-TCDD sediment concentrations found in this study indicated less than low risk to fish
and avian wildlife, with low risk to mammalian wildlife.
Using a Theoretical Bioaccumulation Approach (TBP) (U.S.EPA, 1993c), this study estimated
that concentrations of 3 pptr and 21 pptr (as toxicity equivalents), would be representative of low
and high risk, respectively, to mammalian wildlife consuming food contaminated with dioxins
and furans (Appendix F). Applying these values to the data from the three sub-basins indicated
that none of the mean concentrations found would be considered "high risk" to mammalian or
avian wildlife.
4.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND AND THE BIGHT
APEX
4.3.1 Mean Condition
Overall, the Bight Apex was relatively uncontaminated when compared to the Harbor (Appendix
E). Western Long Island Sound had the lowest mean for total chlordane. For all other
contaminants measured, the Bight Apex had the lowest mean concentrations.
4.3.2 Areal Extent
Exceedances of at least one ERM were not as common in western Long Island Sound and the
Bight Apex (21% and 7%), but western Long Island Sound exceeded at least one ERL in 100%
of its area. The Bight Apex, which exceeded more than five ERLs in 18% of its area, did not
have as pervasive a pattern of ERL exceedances as the Harbor.
The percent of area above specific ERL and ERM values in the Bight Apex and western Long
Island Sound also was estimated. These two sub-basins had 4% and 0% of area exceeding the
mercury ERM value, but 18% and 64%, respectively, exceeded the ERL. The Bight Apex had
21% of its area above the ERL concentration for total chlordane and 7% above the ERM.
4.4 ALTERNATIVE THRESHOLDS
The results presented in the last two sections have been based largely on interpreting the
chemical concentrations relative to the thresholds suggested by Long et al. (1995a). The Long
and Morgan (1991) and Long et al. (1995a) values are emphasized because they include
thresholds for most of the chemicals that were measured, allowing this study to provide an
integrated contaminant response. Other thresholds and approaches for interpreting sediment
chemistry data for a more limited set of chemicals have been suggested. This section interprets
the data in the context of some of those alternatives.
4-16
-------
4.4.1 Proposed Sediment Quality Criteria
The U.S.EPA has proposed Sediment Quality Criteria (SQC) for five chemicals (U.S.EPA,
1994). This approach was based on equilibrium partitioning theory to establish individual
chemical concentrations in interstitial water that do not exceed water quality criteria (WQC)
(DiToro et al., 1991). SQC are normalized to the TOC content of the sediment. The approach
assumes that water quality criteria are protective of infaunal organisms, chemical concentrations
in the interstitial water are in equilibrium with that adsorbed to the sediment particles, and
porewater is the primary route of organism exposure. The calculation incorporates an organic
carbon normalization step.
Exceedances of SQC's were rare, with none of the chemicals exceeding the SQC threshold for
more than 3% of the area in the Harbor (Figure 4-11). For dieldrin and endrin, no samples
exceeded SQC's. In addition, none of the samples from the Bight Apex or Western Long Island
Sound exceeded SQC for any chemical.
The interpretation based on SQC's was very similar to that based on the ERM thresholds for
these chemicals. For three of the five chemicals, there was complete agreement between the two
approaches (Figure 4-11). For fluoranthene and phenanthrene, the estimates for percent of the
Harbor with exceedances between the two approaches differed by less than 3% and were not
significantly different. If comparing SQC to ERL thresholds, there was no agreement, as four of
the five chemicals exceed nearly 100% of the Harbor area at the ERL concentrations.
4.4.2 Acid Volatile Sulfides
Equilibrium partitioning theory also has been applied with regard to acid volatile sulfides in
sediments. AVS in combination with simultaneously extracted metals (SEM) is used to indicate
when several divalent metals (Cd, Cu, Ni, Pb, Zn) would not be bioavailable (DiToro, 1990). If
the difference between the molar concentrations of SEM and AVS (SEM - AVS) is <0, the
theory states that the sulfides should be binding all of the metal and none should be available to
cause toxicity (NOAA, 1995). When SEM is in excess (SEM - AVS > 0), the sediments are
described as potentially toxic. This theory does not take into account other contaminants that
could be causing an effect.
Thirty-six percent of the Harbor was found to have SEM in excess of AVS (Figure 4-12 ).
Within the Harbor, excess SEM was most prevalent in Lower Harbor, where it occurred over
54% of the area. Excess SEM occurred in only 7% of western Long Island Sound, but occurred
for more than half of the Bight Apex.
The SEM-AVS results were highly inconsistent with the ERM or ERL based metals results
(Figure 4-12). Based on ERL/ERM, it appeared that metal toxicity should have been highest (or
metal non-toxicity lowest) in Newark Bay, where ERM metals concentrations were exceeded for
4-17
-------
O
a
T3
OJ
2 a:
Q. LJJ
A A
OJ
C
T3
C
LU
0)
b
0)
d)
_C
O
<
3 00
.S c
-a o
O -H
OH -
2 3
OH tj)
00 >§
C &
'-3*9
Ofi
QJ
«J 3
E13
* *2
-------
c .M
03
-------
91% of the area. But SEM in excess of AVS occurred at only 13% of Newark Bay's area. In
other words, SEM-AVS theory would predict that 87% of Newark Bay should have been non-
toxic. However, metal ERLs and ERMs, indicated that 0% and 25%, respectively, was non-
toxic. Similarly, in the Upper Harbor, 50% of the area exceeded ERM, whereas only 4% had
excess SEM. In contrast, only a single station in the Bight Apex exceeded an ERM for metals,
whereas 54% of the area in the Bight Apex had excess SEM.
4.4.3 Aluminum-Normalization
Analyses so far have focused on identifying amount of area where contaminants are at
concentrations of biological concern, but another relevant question was what percent of the
Harbor has been subjected to anthropogenic enrichment for each chemical compound. Most
organic contaminants are of anthropogenic origin, so detection and enrichment are synonymous.
However, a portion of the metals in sediments results from natural weathering of crustal rocks,
with naturally higher concentrations of metals occurring in fine-grained, deposit!onal sediments.
One challenge in accurately assessing the spatial extent of contamination is separating the
anthropogenic contribution to observed concentrations of metals from concentrations attributable
to natural mineral weathering.
Several techniques have been developed to address this concern (Luoma, 1990, Schropp et al.
1990), the most popular of which is aluminum-normalization (Daskalakis and O'Connor, 1995;
Hanson et al., 1993; Loring, 1991; Schropp et al., 1990). Using this approach, aluminum is
treated as a conservative tracer of crustal decomposition, since anthropogenic contributions of
aluminum are small relative to natural pools in sediment. A set of non-contaminated sites are
identified and statistical relationships between each metal and aluminum are established for those
sites. Significant deviation from those relationships indicate anthropogenic enrichment. This
investigation used the relationships derived by Weisberg et al. (in prep.) for identifying sites with
anthropogenic metal enrichment (Appendix D).
Most of the Harbor was found to be enriched in at least one metal (Table 4-4). Nine of the 12
metals measured were enriched over more than 50% of the area of the Harbor. Zinc (80%),
mercury (75%), lead and silver (both 70%), were the metals enriching the most Harbor area.
Newark Bay had the highest number of metals enriching greater than 50% of its area (11 out of
the 12). The only metal for which Newark Bay did not have the highest percent of enriched area
was silver. Upper Harbor had the most enriched area (96%) for this metal. Upper Harbor also
was comparable to Newark Bay for enrichment by copper, tin and mercury.
Compared to the Harbor, the Bight Apex had an almost equivalent percent of area enriched with
antimony and arsenic. All other values were substantially below the Harbor values. Only one
metal, arsenic at 54%, was enriched in more than 50% of the Bight Apex's area. Western Long
Island Sound was more similar to the Harbor, with eight of the 12 metals enriching greater than
4-20
-------
50% of its area. Copper (93%) and zinc (86%) were the most pervasive in western Long Island
Sound, with the extent of copper enrichment being comparable to that found in Newark Bay.
Table 4-4
Percent of Area With Anthropogenically Enriched Levels of Metals
(parentheses represent 90% confidence intervals)
Antimony
Arsenic
Cadmium
Chromium
Copper
Lead
Mercury
Nickel
Silver
Selenium
Tin
Zinc
Harbor
54
(32-53)
52
(41-62)
46
(36-56)
66
(56-76)
66
(56-77)
70
(60-80)
75
(66-84)
7
(3-11)
70
(60-80)
63
(52-73)
38
(28-49)
80
(72-88)
Jamaica
Bay
32
(20-46)
25
(14-38)
36
(24-50)
43
(30-57)
50
(36-64)
50
(36-64)
43
(30-57)
4
(0-13)
54
(40-67)
54
(40-67)
29
(17-42)
50
(36-64)
Newark
Bay
85
(72-97)
88
(79-97)
94
(88-100)
89
(80-98)
97
(93-101)
95
(90-100)
98
(94-101)
48
(26-70)
94
(89-100)
80
(65-95)
51
(30-73)
96
(91-101)
Lower
Harbor
54
(40-67)
50
(36-64)
36
(24-50)
64
(50-76)
57
(43-70)
68
(54-80)
71
(58-83)
4
(0-13)
61
(47-73)
61
(47-73)
36
(24-50)
82
(69-91)
Upper
Harbor
54
(40-67)
57
(43-70)
68
(54-80)
75
(62-86)
93
(82-98)
79
(65-88)
93
(82-98)
7
(2-18)
96
(87-100)
68
(54-80)
46
(33-60)
82
(69-91)
W.LI
Sound
14
(6-27)
18
(9-31)
46
(33-60
57
(43-70)
93
(82-98)
68
(54-80)
54
(40-67)
0
(0-8)
71
(58-83)
54
(40-67)
32
(20-46)
86
(73-84)
Bight
Apex
46
(33-60)
54
(40-67)
7
(2-18)
18
(9-31)
14
(6-27)
25
(14-38)
18
(9-31)
4
(0-13)
18
(9-31)
21
(12-35)
14
(6-27)
25
(14-38)
4-21
-------
4.5 RELATIONSHIP BETWEEN CHEMISTRY AND GRAIN SIZE
Grain size is a controlling factor in the adsorption of contaminants onto sediments. Fine
sediments typically accumulate higher levels of contaminants than coarse sediments, due to a
higher surface area to volume ratio and surface charges that cause these particles to be more
chemically and biologically reactive than coarser particles (Power and Chapman, 1995).
Depositional areas, which accumulate fine particles, frequently have higher levels of
contaminants than coarse sediment zones.
The 39% of the Harbor that was predominantly mud (>40% silt/clay) had 95% exceedance of at
least one ERM (Figure 4-13). This can be compared to the sand portion of the Harbor where
only 16% of the area exceeded a contaminant ERM.
4.6 COMPARISON TO PREVIOUS STUDIES
No other investigations have sampled the Harbor for sediment contaminants using a probabilistic
sampling approach. Therefore, while the results of other investigations can be compared to the
present investigation to confirm general magnitude and variety of contaminants, other
investigations cannot be used to compare the areal extent of contaminants from the present
investigation. However, the ranges of concentrations and specific contaminants determined for
the present investigation generally agree with those obtained from other investigations in the
Harbor.
Another sediment quality investigation (Long et al, 1995), took place during approximately the
same time period as this investigation but sampled in the winter season. The primary purpose of
the Long et al. investigation was to evaluate sediment toxicity and the investigation was
conducted in two phases. The first phase sampled Harbor-wide and selected samples for
chemical analysis after toxicity test results were examined. The second phase focused on
Newark Bay and selected stations prior to sampling for chemical analysis to represent a gradient
of contamination.
Mercury in the Long et al. investigation of the entire Harbor, generally ranged from 1.0 to 5.0
ppm, with a few samples from the East River at around 5.0 ppm, and one sample from the Arthur
Kill at 15 ppm. The present investigation had mercury concentrations ranging from non-detected
to 6.7 ppm with a mean of 1.0 ppm. The highest mercury values (5.4 and 6.7 ppm) were found in
the Arthur Kill.
In the Long et al. investigation, total PCBs (sum of 20 congeners) generally ranged from 100 to
200 ppb. Several stations in the Arthur Kill and East River were above 450 ppb. The East River
had a high value of 1973 ppb. This investigation had a range of .03 to 2482 ppb PCBs (sum of
20 congeners) with a mean of 205 ppb. In the Upper Harbor sub-basin, three stations in the East
River had PCBs concentrations ranging from 373 to 430 ppb. A single station east of Governor's
4-22
-------
ooooooooooo
LU
A
6
ooooooooooo
OC&COh-CQLO-'frOOCMt-
<+_, 03
O -u
M- 00
}H
Al O
\
ra
I
Q
Z
<
O)
ta S
H
-------
Island had 425 ppb. One station in the Lower Hudson River was measured at 403 ppb and an
Upper Hudson River station had a concentration of 947 ppb. In the Newark Bay sub-basin, a
station in the Elizabeth Ship Channel had a value of 1435 ppb. Three stations in the Passaic
River were above 650 ppb, with one having the investigation's high concentration of 2481 ppb.
In the Long et al. investigation, total PAHs (sum of 24 congeners) generally ranged from 4,000 to
20,000 ppb. Five sites from the East River and one from the Kill van Kull exceeded 20,000 ppb.
The highest concentration of total PAHs was 1,123,355 ppb in the East River.
Concentrations of 2,3,7,8 TCDD (as TEQs) ranged from 13 pg/g at a single reference station in
Upper Harbor to 874 pg/g in the lower Passaic River.
Earlier investigations are summarized in a review by Squibb et al. (1991). They concluded that
many portions of the Harbor exceeded the ERM values that existed at that time.
4.7 COMPARISON TO A LARGER GEOGRAPHIC AREA
The probabilistic design, sampling methods and laboratory procedures used for the present
investigation were the same as those used by the U.S.EPA Environmental Monitoring and
Assessment Program (EMAP). This compatibility allowed direct comparison of the data
obtained under EMAP and this investigation. The EMAP Virginian Province effort
encompassed the coastal zone of the east coast from Cape Cod south to the mouth of Chesapeake
Bay. It included the NY-NJ Harbor Estuary.
Comparison to the Virginian Province indicated that the NY-NJ Harbor is heavily and
extensively contaminated. The NY-NJ Harbor had a statistically higher (p< 0.10) mean sediment
contaminant concentration than the Virginian Province for 50 of the 59 chemicals measured
(Table 4-5). In addition, for several chemicals, specifically mercury and total PCBs, the Harbor
had a large portion (69% and 100%, respectively) of the areal extent of ERM exceedances in the
Virginian Province, even though the Harbor constitutes only 4% of the area in the Province
(Figure 4-14).
East Coast tributyltin concentrations from purposive sediment sampling ranged from <10 to 770
ppb (Krone, Stein and Varanasi 1996). The levels measured by the present investigation were
comparable to the low end of the East Coast range. EMAP, using a probabilistic approach,
obtained a similar range for the Virginian Province (12 to 764 ng/g).
4-24
-------
Table 4-5
Comparison of Mean Sediment Contaminant Concentrations between the Virginian
Province (1990-1993) and the NY/NJ Harbor (1993-1994)
(± represents 90% confidence intervals for NY/NJ Harbor data; ± for Virginian Province data
represents the standard error)
METALS (ppm)
Aluminum
Antimony
Arsenic
Cadmium
Chromium
Copper
Iron
Lead
Manganese
Mercury
Nickel
Selenium
NY/NJ
Harbor
43456
±4229
1.49
±0.48
10.33
±2.05
0.79
±0.13
78.09
±10.11
72.53
±17.40
23483.6
±2897.0
78.84
±12.83
495.26
±44.14
0.74
±0.14
24.07
±2.90
3.82
±1.02
Virginian
Province
35697
±1238
0.54
±0.024
6.60
±0.30
0.21
±0.01
37.82
±1.73
19.57
±1.55
19664
±729
38.29
±1.31
556.45
±46.56
0.09
±0.01
14.39
±0.87
0.27
±0.02
Harbor
Larger
•
•
•
•
•
•
•
•
•
•
•
4-25
-------
Silver
Tin
Zinc
ORGANIC S (ppb)
Total PCBs
= (• congeners) x 2
Parent DDT
ODD
DDE
Total DDT
Aldrin
Alpha Chlordane
Chlordane
Dieldrin
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
1.59
±0.30
4.96
±1.54
170.06
±25.56
224.35
±42.25
9.57
±9.38
14.16
±5.98
8.53
±2.54
31.59
±16.64
0.50
±0.05
1.15
±0.22
5.11
±1.01
0.80
±0.12
0.45
±0.06
0.39
±0.05
0.46
±0.15
0.24
±0.03
2.34
±0.14
79.65
±4.61
17.57
±3.72
0.58
±0.07
0.99
±0.21
1.31
±0.24
2.62
±0.45
0.02
±0.01
0.29
±0.09
0.47
±0.15
0.31
±0.08
0.06
±0.02
0.08
±0.03
0.03
±0.01
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
4-26
-------
Lindane
Mirex
Trans-Nonachlor
Acenaphthene
Acenaphthylene
Anthracene
B enzo(a)anthracene
Benzo(a)pyrene
Benzo(e)pyrene
Benzo(k)fluoranthene
Benzo(g,h,i)perylene
Biphenyl
Chrysene
Dibenz(a,h)anthracene
2,6-Dimethylnaphthalene
Fluoranthene
0.43
±0.07
0.56
±0.17
0.71
±0.14
82.78
±65.43
122.93
±41.89
365.05
±220.76
486.83
±129.35
433.96
±116.40
302.69
±72.98
781.78
±177.51
303.05
±83.12
32.16
±11.74
544.76
±145.85
79.42
±31.10
198.15
±57.34
743.25
±278.61
0.06
±0.02
0.02
±0.01
0.12
±0.056
23.80
±13.83
6.96
±1.69
51.86
±29.99
99.59
±46.19
87.25
±28.38
66.62
±18.71
173.74
±52.91
65.65
±17.93
11.19
±2.28
110.17
±45.25
9.99
±1.49
22.50
±4.24
217.00
±105.53
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
4-27
-------
Fluorene
Ideno( 1 , 2, 3 -c, d)py rene
2-Methylnaphthalene
1 -Methylnaphthalene
1 -Methylphenanthrene
Naphthalene
Perylene
Phenanthrene
Pyrene
2,3,5-
Trimethylnaphthalene
Total PAHs
Monobutyltin
Dibutyltin
Tributyltin
Total Butyltin
176.41
±182.11
291.62
±90.08
89.91
±42.02
46.37
±24.30
156.10
±88.28
163.96
±100.34
333.54
±113.69
628.06
±520.48
767.60
±269.73
47.00
±29.87
7177.4
±2607.9
5.32
±1.37
16.33
±6.04
30.08
±8.52
55.90
±15.35
33.82
±14.93
71.22
±19.49
40.11
±8.28
18.75
±4.25
24.46
±9.84
47.25
±10.23
85.44
±11.57
194.25
±117.15
232.29
±113.38
10.39
±2.81
1704.30
±653.64
2.23
±0.33
5.11
±0.76
8.29
±0.95
15.62
±1.44
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
4-28
-------
IMM
O
_Q
0
58 g
"•si
-1-voQ.
2*2
r- i-
>" c c
Z. >^ *—
co~
|I2
I— O 03
CO
o
Q_
"co
-l-<
o
Q
Q
"ca
•5
0}
X
Q_
O
c
N
w
O
O
£
§
£?
O
-4—>
c
O
^1
(I)
o
i-
o
•e
CS
ffi
T3
CC
0)
(U
0
o
1-1
[ III III III ill
1 1 1 1 •
o o o o o
CO CO -t CM
c
_0
.0
-Nf'
4
(L>
exceedances.
(%)
UBLH
B9jyp
-------
-------
5.0 SEDIMENT TOXICITY
5.1 BACKGROUND
The analysis and interpretation of chemical data to determine potential biological response must
assume that there is a particular association between chemical contamination and biological
effects. Toxicity tests can provide this information directly and can control confounding factors,
such as temperature, salinity and dissolved oxygen. They also integrate the effects of complex
mixtures of chemicals in sediment, including chemicals that are not measured. However, toxicity
tests should not be used alone because individual species of test organisms vary in their
sensitivity to chemicals and the relevance of toxicity test results to field conditions is difficult to
establish (Chapman, 1995). The value of toxicity tests is best realized when they are interpreted
in conjunction with chemistry and in situ biological response (e.g., benthic macroinvertebrate
community structure).
This investigation used two measures of toxicity: (1) survival of the amphipod, Ampelisca
abdita, as a percentage of control survival, and (2) inhibition of light emission by the bacterium,
Photobacterium phosphor eum, when exposed to organic extracts of test sediment relative to
control sediment. Grain size analysis was used to examine the association between toxicity and
substrate type.
Sediments at a station were considered toxic using the Ampelisca abdita toxicity test if percent
survival was less than 80% compared to control. These criteria are similar to U.S.EPA/U.S.ACE
(1991). Sediments were considered "highly toxic" if A. abdita survival was less than 60%
compared to survival in control sediments.
Sediments were considered toxic using the Microtox™ assay if the EC50 was 70% or less and
significantly different (p<05) from the control EC50 (PSDDA, 1989). The degree of Microtox™
toxicity is measured as the dry weight of sediment that provides enough organic extract to inhibit
normal bacterial luminescence by 50%, i.e., EC50, (Long and Market, 1992). Measured
Microtox™ toxicity is expressed as a percentage of control EC50 (0.12 and 0.22 mg dry wt/ml in
this study). Therefore, EC50 values and percentages of control values are inversely proportional
to sediment toxicity.
5.2 CHARACTERIZATION OF THE HARBOR
5.2.1 Mean Condition
Mean percent survival of Ampelisca abdita (as percent of control survival) was comparable
within each sub-basin of the Harbor except Newark Bay (Table 5-1). Mean survival within
5-1
-------
Newark Bay was significantly less (p<. 10) than the Harbor as a whole. Lower Harbor exhibited
the highest mean survival.
Mean Microtox™ values varied substantially among Harbor sub-basins, but Newark Bay also
exhibited the greatest toxicity relative to the entire Harbor (p<.05). Jamaica Bay and Upper
Harbor were similar to one another. Lower Harbor had the least percent of area toxic in the
Microtox™ assay.
Table 5-1
Mean % Survival for Ampelisca abdita and
Mean % Microtox™ Bioluminescence Inhibition
(± 90% confidence intervals)
Ampelisca
abdita*
Microtox™
*
Harbor
87.9
±4.1
365
±86
Jamaica
Bay
84.9
±7.7
257
±100
Newark
Bay
66.5
±15.1
122
±56
Lower
Harbor
91.0
±5.9
452
±132
Upper
Harbor
86.6
±6.3
224
±96
W. Long
Is. Sound
97.0
±1.4
237
±113
Bight
Apex
94.9
±1.9
765
±119
1 Adjusted for control survival or control bioluminescence inhibition.
5.2.2 Areal Extent
Out of a total area of 501 km2, an estimated 75 km2 (15%) of the Harbor proper was toxic to A.
abdita and 40 km2 (8% of the total area) was highly toxic (Figure 5-1). Newark Bay and Jamaica
Bay have more widespread toxic sediments (46 and 25%, respectively) than the rest of the
Harbor. However, only Newark Bay has a larger percent area of highly toxic sediments than
other Harbor sub-basins (p<.10). Although relatively large percentages of Newark and Jamaica
Bay sediments were toxic, these were the smallest Harbor sub-basins. The total toxic area of
these sub-basins (26 km2) was approximately 1/3 of the acreage of toxic sediments in the entire
Harbor. Individual stations toxic to A. abdita were concentrated in the Kills (Newark Bay sub-
basin) and the mouth of Jamaica Bay (Figure 5-2). Highly toxic stations exhibited a similar
pattern, with the addition of several stations in the back bay portion of Jamaica Bay.
Based on the Microtox™ assay, 38% (190 km2) of the Harbor area was found to be toxic (Figure
5-3). Sub-basins in the Harbor were similar with regard to percent of area toxic in the
Microtox™ assay. The estimated percentages of sub-basins considered toxic using Microtox™
ranged from 39% in Upper Harbor to 50% in Jamaica Bay. Over the Harbor proper, the
Microtox™ assay characterized 2.5 times more area as toxic than the A abdita assay (Table 5-2).
5-2
-------
ro
*^>
1 3
I *
3 §
o o
CD -^
*"""""X ^*
.2 S
x CD
O 'F
o
o
o
CO
o
(O
o
'x
o
I
_g
-4—J
3
-4—1
tlJ
O .
(o/0) B9JV ]0
-------
Amphipod Toxicity
New York
NcoToxic: > 80% of control response (survival)
w Toxic 60 to 80% of control response (survival)
A Hghly toxic: < 60% of control response (survival)
New Jersey
Western Long Island Sound
Long Island
RaritanBay
Sandy Hook Bay
D . NYBightApex
Navesink River
Shrewsbury River
Rgure 5-2. Distribution of stations toxic in amphipod (Ampelisca abdita) survival assays.
5-4
-------
o
_g
_o
o
cj
O
c
o
(%)
M)
fc
-------
The majority of the toxic stations for Microtox™ were clustered on the south shore of Lower
Harbor, the perimeter of Jamaica Bay, the Kills and the Passaic River (Figure 5-4).
Using a positive result in either assay as an indication of toxic conditions, resulted in 45% of the
Harbor considered toxic (Table 5-2).
Table 5-2
Percent of Area Toxic in the A abdita and Microtox™ Assays
(± 90% confidence intervals)
Ampelisca
abdita*
Microtox™**
Ampelisca
abdita or
Microtox™
Harbor
15
(8-22)
38
(28-49)
45
(34-55)
Jamaica
Bay
28
(15-39)
50
(36-64)
50
(36-64)
Newark
Bay
46
(23-68)
44
(22-67)
49
(28-71)
Lower
Harbor
11
(0-18)
36
(24-50)
43
(30-57)
Upper
Harbor
15
(2-23)
39
(27-53)
46
(33-60)
W.LI
Sound
0
(0-8)
57
(43-70)
57
(43-70)
Bight
Apex
4
(0-8)
4
(0-13)
4
(0-13)
* Significant toxicity is percent survival • 80% mean control survival (U.S.EPA, 1991).
** Significant toxicity is an EC50 statistically less than the control and • 70% of the control EC50 (PSDDA, 1989).
5.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND AND THE BIGHT
APEX
5.3.1 Mean Condition
Mean A. abdita survival was higher in Bight Apex and western Long Island Sound sediments than
in Harbor sediments. Mean survival in these two areas also was higher than any of the individual
Harbor sub-basins (Table 5-1).
The least toxic sediments using mean Microtox™ results were in the Bight Apex. Mean western
Long Island Sound toxicity was above that for the Harbor as a whole, but was comparable to
Jamaica Bay, Upper Harbor and Newark Bay.
5.3.2 Areal Extent
Based upon A. abdita assays, the Harbor as a whole and each Harbor sub-basin had
proportionately more toxic area than either western Long Island Sound or the Bight Apex (Figure
5-6
-------
Mcrotox Toxicity
New York
a Non-toxic: >/=70% of control response (EC )
ou
Toxic: <70% of control response (EC )
New Jersey
Western Long Island Sound
Long Island
Upper Harixx
RaritanBay
Sandy Hook Bay
NY Bight Apex n
Navesink River
Shrewsbury River
0 5 10
Rgure 5-4. Distribution of stations inhibiting Mcrotox biduminescence.
-------
5-1). There was no significant difference between the percent of toxic sediments estimated in
western Long Island Sound and the Bight Apex. However, the areal estimate of toxic sediments
in the Bight Apex was 67 km2, nearly as much toxic area as estimated for the Harbor. The single
toxic site in the Bight Apex was located in an area of historical dredged material disposal.
Western Long Island Sound had no sites that exhibited^, abdita toxicity (Figure 5-2).
More area in western Long Island Sound was characterized as toxic in the Microtox™ assay than
in the Harbor as a whole or any other sub-basin. Only 4% of the Bight Apex was classified as
toxic. The Microtox™ assay indicates the same extent of toxicity in the Bight Apex as is
indicated by the A. abdita assay. Individual toxic sites were clustered in the portion of western
Long Island Sound closest to the Harbor and at a single location in the Bight Apex (Figure 5-4).
The Bight Apex site was also toxic to A. abdita. Microtox™ and A. abdita results for western
Long Island Sound substantially disagree, as 17 sites (57% of the area) were toxic in the
Microtox™ assay but none were toxic to A. abdita.
5.4 RELATIONSHIP BETWEEN TOXICITY AND GRAIN SIZE
Generally, sediment toxicity is expected to be greater and more prevalent in finer grained
substrates (Power and Chapman, 1995). The percent of area in the Harbor with A. abdita toxicity
was examined by substrate category (Figure 5-5). Of the 39% of the Harbor sediments that were
mud (>40% silt-clay), approximately 26% was toxic to A. abdita and 76% toxic to Microtox™.
Conversely, the sand portion (<40% silt-clay) of the Harbor (61%) exhibited toxicity to A abdita
and Microtox™ at 7% and 14% of its area, respectively. Overall, A. abdita toxicity was slightly
more predominant in mud than sand, but Microtox™ toxicity was significantly higher in mud than
sand. Regression analyses of toxicity vs. grain size at individual stations showed that A abdita
toxicity was not related to the fraction of silt-clay in sediments (P = .05), but Microtox™ toxicity
was significantly greater as the fraction of silt-clay increased.
5.5 COMPARISON TO PREVIOUS STUDIES
Previous studies of sediment toxicity in the Harbor generally have used non-random sampling
strategies. Although useful for other purposes, these non-probabilistic approaches prevent reliable
characterization of the Harbor, or even portions of the Harbor. This is true no matter how reliably
the sediment toxicity tests assayed the sampled sediment. However, the intensive sampling of
Newark Bay in the Long et al. (1995b) investigation did identify "hot spots" more fully than the
present investigation.
This study's sediment toxicity results were broadly similar to those of Long et al. (1995b). That
investigation and the present investigation both conclude that: (1) less than 50% of the entire
Harbor was toxic to Ampelisca survival or to Microtox™ luminescence, and (2) the Newark Bay
-------
E2:
OOOOOOOOOOO OOOOOOOOOOO
-------
sub-basin is the most toxic region of the Harbor. Both investigations had similar study areas; the
Long et al. (1995b) investigation did not include Jamaica Bay.
However, the Long et al. investigation and this study estimated somewhat different areas of
sediment toxicity in the individual sub-basins. Based upon Ampelisca survival tests, this study
estimated that 46% (23-68%) of the Newark Bay sub-basin was toxic (Figure 5-1), whereas Long
et al. (1995b) estimated that 85% of this sub-basin was toxic.
Approximately 40% of the Long et al. study area was toxic to Microtox™ luminescence. The
present investigation estimated that less than 20% of the Long et al.'s study area was toxic in the
Microtox™ assay, although 38% (28-49%) of the Harbor exhibited Microtox™ toxicity (Figure 5-
1).
5-10
-------
6.0 BENTHIC MACROINVERTEBRATES
6.1 BACKGROUND
Although sediment chemistry and toxicity assays provide useful insights into sediment quality,
they provide only limited understanding of ecological damage (Keeler and McLemore, 1996).
For most management purposes, a principal goal is protection and remediation of biological
resources. This goal requires a reliable understanding of biological effects of contaminants.
Because of several attributes, bottom-dwelling invertebrates (benthos) provide useful indications
of biological response to environmental conditions. Since the ultimate disposition of many
contaminants is into sediments where benthic macroinvertebrates live and feed, they are directly
exposed to contaminant effects. Because they are relatively sedentary and cannot avoid
exposure, benthos can provide an accurate indication of local environmental conditions. Bottom
dwelling organisms (benthos) are also relatively long-lived and, as an essential component of the
food web, are an important link between primary producers and higher trophic levels (Diaz,
1995). Additionally, benthos significantly affect oxygen, nutrient, and carbon cycles (Blackburn
and Henriksen, 1983). They exhibit a broad diversity of sizes, feeding modes and life history
characteristics, with a range of responses to environmental stress, making them especially
suitable as integrators of contaminant effects (Frithsen & Holland, 1992).
Many measures have been suggested for describing benthic communities. This study assessed
several individual structural measures to quantify the status of benthic macroinvertebrate
assemblages (Table 2-4). Species diversity, a measure of community structure, is indicative of
the species utilizing the available habitat. It is expressed here as number of species (species
richness) and as the Shannon-Wiener composite index (Shannon and Weaver, 1949). Evenness
(distribution among species) is a basic characteristic of benthic community structure. Biomass
also is an integral component of community structure, since it is the basis for energy flow and has
been shown to be responsive to pollution stress (Warwick, 1986; Dauer and Connor, 1980;
Luckenbach et al., 1990; Pearson and Rosenberg, 1978). Total abundance is also used as an
indicator for contaminant effects (Becker et al., 1990) and, along with biomass, is a measure of
total biological activity at a site. The use of benthic species that are pollution-tolerant or
pollution-sensitive has also been used to determine the ecological health of a location (Grassle
and Grassle, 1974 and 1976).
However, more than one measure or indicator, combined into an index of benthic invertebrate
structure, can distinguish more effectively than individual measures between normal and
abnormal benthic assemblages (Pearson and Rosenberg, 1978; Gray, 1995). A multi-metric
benthic index of biotic integrity (B-IBI), similar to the fresh water Index of Biotic Integrity (IBI)
(Karr, 1991; Kerans and Karr, 1994) was developed for the NY/NJ Harbor (Appendix C). Five
metrics which most effectively distinguished normal sites from all others were selected for the B-
IBI (Table 2-4). These metrics were evaluated for four different salinity and grain size habitats
6-1
-------
(Table 2-5). The index was calculated by scoring each selected metric as 5, 3, or 1 depending on
whether its value at a site approximated, deviated slightly from, or deviated greatly from
conditions at the best reference sites. The B-IBI value for each station is calculated as the mean
score of the five metrics. A mean score of 5 indicated that the site was approximately equivalent
to the best reference sites. A score of 3 or 1 indicated that the site slightly deviated or greatly
deviated from conditions at the best reference sites and would be considered to have impacted
benthos. These scoring criteria defined normal and abnormal benthic assemblages.
The overall validation efficiency of the B-IBI was 93%. The average difference between
replicates was 0.32. Ninety-one percent of the replicates at the same site scored similarly. At
most of the sites where the replicates scored differently, the replicates had similar numerical
values, but were on either side of the index threshold of 3.
6.2 CHARACTERIZATION OF THE HARBOR
6.2.1 Diversity and Taxonomic Composition
A total of 239 infaunal species were represented in the Harbor (Table 6-1). The mean number of
species per sample in the entire Harbor was 19.2 (Table 6-2). Mean species diversity (Shannon-
Wiener) in the Harbor was 2.3 (Table 6-2). Shannon-Wiener diversity was similar in all sub-
basins, but taxonomic composition varied greatly among sub-basins.
Table 6-1
Species Richness (Total Number of Species)
Number of Species
Harbor
239
Jamaica
Bay
137
Newark
Bay
91
Lower
Harbor
166
Upper
Harbor
152
W.LI.
Sound
180
Bight
Apex
231
One difference among sub-basins of the Harbor was the relatively few species present in Newark
Bay (Figure 6-1). The mean numbers of species per sample (species richness) was not
significantly less (p>0.1) in Newark Bay than in Jamaica Bay (the next least species-rich sub-
basin). However, Newark Bay species richness was significantly less (p<.05) than any of the
other three Harbor sub-basins. Nearly half the total number of species was polychaetes,
consistently in each sub-basin (Figure 6-1). Molluscs and arthropods were represented by
roughly equal numbers of species in each sub-basin. Depending upon the sub-basin, amphipod
species constituted from 10 to 18% of all species identified. Three taxa (Amphipoda, Mollusca,
and Polychaeta) include about 85% of all taxa identified (Figure 6-1). Area-weighted mean
abundances for all benthic macroinvertebrate species identified in the study also were calculated
(Appendix G).
6-2
-------
»5
W ^
3 5
« C
•c s
^ « 8
2l|
-9 *> 'S
H« o
«4H O
O _
5O O^
a o
C5 ON
M -H
2? ft
03 "^
H3 s3
i> O
&• CO
>-l *-<
U 0
P ffi
>-. s-<
U O
J ffi
> cd
6 PQ
^
03
O
'3 S*
$3 PS
i— i
o
1
ffi
ii
of?0
0 0
0 0
o^ ^
oC i--"
^ +1
0 0
0 0
of rn"
^ -H
0 0
0 0
o o
of of
in 04
41
0 0
0 0
O^ t-;
^ 41
0 0
0 0
o-T m~
m ^
0 0
0 0
o o
cT •*"
^ 4?
fN
a
on
'3
ft
CO
0)
_^
'on
S3
D
CO
i
_O
J
"o
OH
q 2
oo m
->
'on
i-i
>
5
on
'o
ft
CO
-------
, „ ,,
•
i
Q_
<
iMI
£2
.0)
•o
r-
BHH . ! . '"/ " " ::'"" " 13
Wil - | 1 ::::^:^;v:,,;.,./^...j;,•:%,:^j O
IHiiHiB1''':1
CO
(#) sapeds jo
-------
Lists of pollution-sensitive and pollution-indicative species (Table 6-3) were developed by
comparing relative abundance of taxa between reference sites and degraded sites in the EMAP-E
Virginian Province data. Pollution-indicative taxa were those for which average abundance,
average percent of abundance, and frequency of occurrence were all higher at degraded versus
reference sites. Pollution-sensitive taxa were those for which average abundance, average
percent of abundance, and frequency of occurrence were all higher at reference than degraded
sites, and for which percent of abundance at reference sites averaged at least 0.2%.
Table 6-3
Pollution-Sensitive and Pollution-Indicative Taxa
Pollution-Sensitive Taxa
Mollusca
Acteocina canaliculata
Tellina agilis
Spisula solidissima
Arthropoda
Ampelisca agassizi
Ampelisca verrilli
Byblis serrata
Rheopoxynius hudsoni
Polychaeta
Ampharete arctica
Aricidea catherinae
Caulleriella spp.
Clymenella torquata
Glycinde solitaria
Levinsenia gracilis
Macroclymene zonalis
Nephtyspicta
Ninoe nigripes
Polygordius spp.
Sabaco elongatus
Scalibregma inflatum
Spiophanes bombyx
Pollution-Indicative Taxa
Mollusca
Mulinia lateralis
Oligochaeta
Oligochaetes
Polychaeta
Capitella spp.
Polydora cornuta
Streblospio benedicti
6.2.2 Abundance and Biomass
The mean abundance and biomass for the Harbor was 40,000 organisms/m2 and 31 g/m2,
respectively (Table 6-2).
6-5
-------
The mean abundance of benthos was significantly lower (p<.01) in both Newark Bay and Upper
Harbor than in any other Harbor sub-basin (Table 6-2). Pollution-sensitive species were
significantly less abundant (p<.05) in Newark Bay than elsewhere in the Harbor, and
significantly more abundant in Lower Harbor (p<.05) than elsewhere in the Harbor. Pollution-
indicative species were generally distributed inversely to pollution-sensitive species: i.e.,
pollution-indicative species were least abundant in Lower Harbor and most abundant in Newark
Bay (p<.05). Biomass of the benthos was significantly lower (p<.05) in Jamaica and Newark
Bays than in Lower and Upper Harbors.
6.2.3 Benthic Index
A multi-metric benthic index of biotic integrity (B-IBI), similar to the fresh water Index of Biotic
Integrity (Karr, 1991; Kerans and Karr, 1994), was developed for the NY/NJ Harbor (Appendix
C). Values of the benthic index (B-IBI) at a sampling site can range from one (impacted
assemblage) to five (normal assemblage). Benthic structure in about half (53%) of the entire
Harbor area exhibited measurable departure from the structure at reference sites (Table 6-4).
Most of this area (47%) was in a category indicative of intermediate impact (B-IBI values of 2 to
3).
Measurable benthic impacts (B-IBI<3) were most widespread in Newark Bay, Upper Harbor and
Jamaica Bay (Figure 6-2). Estimates of impacted benthic area ranged from 39% for Lower
Harbor to 98% for Newark Bay (Table 6-4). The distribution of individual stations with
impacted benthos (Figure 6-3) shows the most highly impacted sites were located in the Newark
Bay sub-basin and in the back bay portion of Jamaica Bay. Newark Bay had only one station of
28 that was comparable to reference conditions.
Table 6-4
Percent of Area within B-IBI categories
(90% confidence intervals are in parentheses)
lto<2
impacted
2 to 3
moderately
impacted
•3-5
unimpacted
Harbor
6
(3-9)
47
(37-57)
47
(37-58)
Jamaica
Bay
18
(9-31)
46
(33-60)
36
(24-50)
Newark
Bay
18
(0-38)
80
(60-100)
2
(0-6)
Lower
Harbor
0
(0-8)
39
(27-53)
61
(47-73)
Upper
Harbor
14
(6-27)
61
(47-73)
25
(14-38)
W.LI
Sound
7
(2-18)
46
(33-60)
46
(33-60)
Bight
Apex
0
(0-8)
0
(0-8)
100
(92-100)
6-6
-------
ro
V
h-H
ffl
*— H
t
&
w
O
O
ea
ex
OJ
(o/0) B9JV P
cs
>o
-------
Benthic Index of Biotic Integrity
D Unimpacted benthos: B-IBI value = 3.00 to 5.00
® Moderately impacted benthos: B-IBI value = 2.00 to 2.90
A Highly impacted benthos: B-IBI value = 0.00 to 1 .90
Western Long Island Sound
RaritanBay
Sandy Hook Bay
D NYBightApex
Navesink River r .y^
Shrewsbury River
0 5 10
Rgure 6-3. Distribution of stations by Benthic Index of Biotic Integrity (B-IBI) values.
-------
6.3 CHARACTERIZATION OF WESTERN LONG ISLAND SOUND AND THE BIGHT
APEX
6.3.1 Diversity and Taxonomic Composition
More species were identified in western Long Island Sound and in the Bight Apex, than in any of
the Harbor sub-basins. The relative abundance of amphipods, molluscs and polychaetes was
similar in coastal waters and the Harbor (Figure 6-1). The mean number of benthic species per
station in the Bight Apex (28.9) was greater than in western Long Island Sound or within any sub-
basin of the Harbor (p<.05).
6.3.2 Abundance and Biomass
The mean abundance of benthos did not differ significantly (p=.05) between western Long Island
Sound, the Bight Apex and the Harbor. However, pollutant-indicative species in the Apex were
significantly less abundant than in any other sub-basin (p<.05). Conversely, pollution-sensitive
species were more abundant in the Apex than in any other sub-basin (p<.01). Benthic biomass in
both western Long Island Sound and the Apex was intermediate between sub-basins of the Harbor
(Table 6-2).
6.3.3 Benthic Index
The percent of area in each category of the benthic index was similar between the Harbor and
western Long Island Sound (Table 6-4). Nearly 50% of the area in each would be considered to
have impacted benthos, but most of the 50% was in the intermediate category of the B-IBI. The
Bight Apex had approximately 100% of its area in the highest category (similar to reference
conditions) of the B-IBI.
6.4 COMPARISON TO PREVIOUS STUDIES
Previous studies of benthic invertebrate structure in the Harbor primarily have used non-random
sampling strategies. Although useful for other purposes, these non-probabilistic data prevent
reliable generalizations beyond the specific locations sampled. However, some broad
comparisons have been made to the current investigation.
Comparisons between this investigation and other studies regarding benthic structure must
consider both natural and sampling variability, and differences in techniques. Uncertainty due to
natural and sampling variability tends to decrease as less specific structural features are
compared. For example, comparisons of abundance for a species, e.g., the amphipod Ampelisca
abdita, are less certain (and less ecologically significant) than comparative abundance of all
6-9
-------
amphipods, or of several amphipods which function similarly. Unfortunately, differences in
techniques cause the greatest problems in comparisons among studies. They preclude most
quantitative comparisons of even reliably estimable parameters. For instance, differences in
methods among studies within the Harbor would make most biomass comparisons less than
useful, although regional comparisons can be made.
This study's estimates of mean macrofaunal benthic abundance (# organisms/m2) were also not
quantitatively comparable with other studies in the Harbor. This investigation's estimate of
abundance for Lower Harbor was substantially higher (52,000/m2) than that (660/m2) of another
study in Lower Harbor (Steimle and Caracciolo-Ward 1989). Two probable reasons for this
disparity are: (1) entirely summer sampling by this investigation versus primarily winter
collections, when benthic densities are minimal, by Steimle and Caracciolo-Ward, and (2) use of
0.5 mm mesh benthic sieves in the present investigation versus 1.0 mm mesh sieves used by
Steimle and Caracciolo-Ward. Additional methodological contributions to this disparity are
possible. Similar sieve-size differences also probably contributed to this study's higher estimates
of mean abundance in Newark Bay (11,000/m2) versus 2,300/m2 in August samples from the
southern portion of Newark Bay (Cerrato 1986). The same sieve-size difference, plus differences
in grab size, precluded useful comparisons of species richness (species per benthic grab) between
Cerrato (1986) and the present investigation.
Within the Bight Apex, mean macrofaunal abundance as estimated by this investigation
(32,000/m2 ±8,200) was similar to an October 1994 estimate of 26,000/m2 within a 79 km2
rectangle surrounding the existing dredged material dumpsite (Hunt, 1996).
Several authors have postulated that benthic structural quality in the Harbor or parts of it has
degraded, or improved, since the late 1950s. After correcting and enlarging the available benthic
macrofaunal data set from 1957-60 and 1973-74 surveys, Steimle and Caracciolo-Ward (1989)
questioned the significance of all benthic structural differences presumably indicative of negative
trends in Lower Bay. An even more extensive benthic survey of Lower Harbor in 1986-87
(Cerrato, Bokuniewicz and Wiggins, 1988) did not indicate substantial changes in benthic
structure from the 1957-60 or 1973-74 surveys interpreted by Steimle and Caracciolo-Ward
(1989). Indeed, the mean, 1986-87 structural parameters estimated by Cerrato et al. reflected
normal conditions or deviated only slightly from them, as defined by this study's benthic index.
These observations were consistent with apparent improvements in summer minima of dissolved
oxygen concentrations. Although dissolved oxygen monitoring is limited to New York waters of
Lower Bay, improvements in summer bottom oxygen concentrations of Lower Harbor became
evident (depending upon the site) from 1945 to the late 1970s. Once improvements became
evident, they continued improving to 1995 (T.M. Brosnan, 1995, personal communication).
A number of benthic faunal surveys were conducted since 1972 in the Newark Bay area. These
data sets included less than 30 stations per survey and were confounded by differences in
sampling and analysis techniques, sampling locations and seasons, interannual variability, etc.
Hypothesized hypoxic impacts on the Newark Bay benthos, particularly in deeper areas, do not
6-10
-------
appear to have been evaluated during periods of extreme hypoxia (Cerrato, 1986). Despite the
limitations of these data, analyses indicated that total abundance and species diversity were
unusually low, at least until the May and August 1985 sampling of Cerrato (1986). Cerrato
(1986) concluded that data from two seasonal cruises may or may not indicate real, temporary or
long-term, differences from prior benthic structure which is itself inferred from limited,
purposeful sampling.
Cautious but interesting observations on the benthic infauna of Jamaica Bay were based upon
sampling during 1981 and 1982 (Franz and Harris 1988). The authors emphasized within-Bay
associations. The only strong, evident pollutant influence on benthic structure were total organic
carbon content of sediments. A. abdita was also sampled at three sites in Jamaica Bay during
spring and summer of 1988-89 (Franz and Tanacredi, 1992). This work documents the existence
of two productive cohorts in the Bay. The authors indicated that productivity of A. abdita, at least
at the sites sampled, was comparable to the total macrobenthic production for several North
Atlantic estuaries. These authors also suggested that the large amounts of particulate organic
carbon in Bay sediments stimulated this high Ampelisca productivity.
It seems probable that the principal departures from historically "normal" benthic structure in the
Harbor area had already taken place before the late 1950s, perhaps much earlier.
6-11
-------
-------
7.0 ASSOCIATIONS
7.1 BACKGROUND
Along with areal extent of contamination, an additional goal of this investigation was to
determine if the contaminants in the NY/NJ Harbor and Bight Apex system were associated with
biological effects. This was done most effectively by an integrated assessment. This type of
assessment provides more information concerning the ecological significance of contamination
than any of the measures can supply individually. An integrated assessment can include two or
more of the following components: sediment toxicity tests, sediment chemical analyses, tissue
chemical analyses, pathological studies, and community structure analyses (Chapman et al.,
1995). This investigation used three of the five components: sediment toxicity tests, sediment
chemical analyses and community structure analyses. This type of integrated assessment is
commonly known as the Sediment Quality Triad approach (Chapman, 1990).
The Sediment Quality Triad has had multiple estuarine and marine applications (Long and
Chapman, 1985; Chapman et al., 1987; Chapman et al., 1991). It offers several advantages that
are not realized when using a one or two component approach. In a complex sediment mixture,
such as is found in the NY/NJ Harbor, the triad approach incorporates interactions among
contaminants (such as additivity, antagonism and synergism) and the effects of any unidentified
chemicals. It is more comprehensive than individual measures, but does not assess non-
sedimentary ecosystem components such as fish and mammals or human health. Associations
are assessed here using a weight-of-evidence approach.
7.2 ASSOCIATION BETWEEN CHEMISTRY AND BENTHIC CONDITION
Contamination by chemical constituents appears to be a prominent factor affecting the health of
benthic macroinvertebrate assemblages in the NY-NJ Harbor. The percent of area with impacted
benthos was closely related to the level of contaminants in sediments of the Harbor. In the 53%
of the Harbor that had abnormal benthic assemblages, 79% of this area exceeded an ERM for at
least one contaminant (Figure 7-1). Only 16% of the area with normal benthic assemblages
exceeded an ERM for any toxicant.
Three individual chemicals or classes exceeded their ERM values in more than 50% of the
impacted benthic area (Table 7-1). Because these toxicants (mercury, chlordane and total PCBs)
exceeded their ERM values at relatively few sites without evidence of benthic degradation (16%
of the Harbor area), they were strongly associated with impacted benthos. No other chemicals
measured were half as widespread in association with impacted benthos at concentrations likely
to cause biotic effects.
7-1
-------
CD
O
a.
X
Q-
DD
o
'
I A
&
§ §
(%) E8JV JO JUaOJBd
a
o
o
o
-d
&C
o T
3'
Q
OH
co
O
-------
Table 7-1
Individual Chemicals Associated with Impacted Benthos
%of
Impacted Area
at which a
Contaminant
>ERM
% of Non-
Impacted Area
at which a
Contaminant
>ERM
%of
Impacted Area
at which a
Contaminant
>ERL
% of Non-
Impacted Area
at which a
Contaminant >
ERL
Metals
Mercury
Silver
Nickel
Lead
Zinc
Copper
Antimony
Arsenic
Cadmium
Chromium
51.1
23.6
8.3
6.3
4.9
4.5
1.5
1.5
0.2
0
14.6
1.6
0
1.6
0
1.6
0
0
0
0
92.8
78.8
82.0
82.0
67.5
78.5
33.4
69.0
41.5
66.2
55.1
14.5
16.2
22.4
17.9
17.6
6.7
12.2
1.9
11.5
Organics
Total PCBs
Total Chlordane
Total DDE
Total ODD
High Molec. Wt. PAHs
Total DDT
Low Molec. Wt. PAHs
Benzo(a)anthracene
Anthracene
Phenanthrene
Dibenz(a,h)anthracene
p,p'-DDE
Pyrene
Benzo(a)pyrene
Total parent DDT
Chrysene
Total PAHs
Fluoranthene
Acenaphthylene
Fluorene
Acenaphthene
2-Methylnaphthalene
Naphthalene
Endrin
DielHrin
69.6
55.6
24.0
19.9
17.4
13.3
11.0
7.4
6.3
6.3
6.0
5.8
5.8
5.6
5.2
4.9
3.5
3.5
2.9
2.1
2.1
2.1
1.4
0
0
6.7
6.7
0.3
3.5
6.3
1.9
6.3
6.3
4.7
4.7
3.2
0.3
6.3
6.3
0.3
3.2
1.6
1.6
3.2
1.6
0
0
0
0
0
95.2
95.5
83.4
90.5
76.9
95.5
81.6
63.9
71.0
57.0
55.6
77.1
34.4
41.6
51.1
59.4
69.0
40.0
79.9
86.9
81.2
48.9
31.0
100.0
994
26.4
25.4
13.8
16.9
20.1
31.1
15.1
6.6
12.1
8.2
11.4
13.1
6.6
6.6
38.3
6.6
6.6
6.6
14.4
18.3
13.7
6.6
6.6
99.3
85 6
7-3
-------
7.3 ASSOCIATION BETWEEN CHEMISTRY AND TOXICITY
Similar to the association between chemistry and benthic community structure, contaminants also
were associated with sediment toxicity. Within the 15% of the Harbor where A. abdita toxicity
was observed, 92% exceeded at least one ERM (Figure 7-2). Conversely, an ERM was exceeded
in 42% of the non-toxic area. Metals were most often associated with toxicity, and total PAHs
the least often associated.
The same three individual contaminants or classes (mercury, total chlordane, and total PCBs) that
were most strongly associated with benthic impacts also appeared to be associated with toxicity
(Table 7-2). However, all three also appeared to be, although less frequently, found in non-toxic
areas at concentrations above ERMs.
7.4 ASSOCIATION AMONG CHEMISTRY, TOXICITY AND BENTHIC
COMMUNITY STRUCTURE
Incorporating all three components of the Sediment Quality Triad strengthened the association
between contaminants and biological effects, and demonstrated that a high degree of consistency
existed among the components. This association was examined by partitioning the Harbor areas
with impacted benthic structure into percentages of these areas with and without one or more
toxicant concentrations exceeding ERM values, and percentages with or without evidence of
sediment toxicity based upon A abdita or Microtox™ assays (Figure 7-3). Most of the area with
impacted benthic structure also had evidence of both sediment toxicity and toxicant
concentrations likely to cause biological effects (66%). Approximately 86% of the area with
impacted benthic structure had evidence of sediment toxicity or toxicants likely to impact benthic
structure. Conversely, only 18% of those areas with normal benthos exhibited evidence of
sediment toxicity or any sediment toxicant exceeding its ERM value. Consequently, it is
apparent that most departures from normal benthic structure were associated with sediment
toxicity or unusually high toxicant concentrations in sediments.
At only 14 of the 168 Harbor stations were abnormal benthic assemblages observed without
evidence of: (1) potential biological effects (at least one chemical concentration greater than its
ERM value), or (2) actual sediment toxicity (Microtox™ or A. abdita). Environmental stresses
other than, or coincident with, toxicants could have been responsible for these abnormal benthic
index values. To assess the possibility of low dissolved oxygen induced benthic impacts,
existing dissolved oxygen data from the New York City Department of Environmental Protection
(Brosnan and O'Shea, 1994; 1995) were evaluated. Severe hypoxic stress (< 2 mg/1 D.O.) was
probable at only three of the 14 stations. Several of the remaining 11 stations were exposed to
exceptionally great scouring by strong currents, a stressor known to impact benthic structure.
The estimated prevalence of high toxicant concentrations (i.e., sediments with one or more
toxicant concentrations exceeding ERM values) was consistent with the estimates of areas with
7-4
-------
<
Q.
CO
o
a.
S
0)
1
CO
o
fc
o
M-
$n
1
O
O
o
a
O
-a
X ^_r
-*-> cd
W)
PI
a
s ^
0)
a °H
.2 §
-------
Table 7-2
Individual Chemicals Associated with Sediment Toxicity (Atnpelisca abdita)
% of Toxic
Area at which
a Contaminant
>ERM
% of Non-
Toxic Area at
which a
Contaminant >
ERM
% of Toxic
Area at which
a Contaminant
>ERL
% of Non-
Toxic Area at
which a
Contaminant >
ERL
Metals
Mercury
Silver
Lead
Zinc
Copper
Nickel
Antimony
Arsenic
Cadmium
Chromium
68.8
21.2
17.3
16.8
16.1
15.2
5.3
5.3
0.7
0
28.0
11.8
1.8
0.1
0.9
2.5
0
0
0
0
100.0
94.6
95.4
90.8
95.4
75.4
33.2
70.4
52.2
73.1
70.7
40.5
46.7
36.0
41.9
46.7
18.6
37.3
17.8
34.7
Organics
Total Chlordane
Total PCBs
High Molec. Wt. PAHs
Total ODD
Anthracene
Total DDE
Total DDT
Low Molec. Wt. PAHs
Benzo(a)pyrene
p,p'-DDE
Benzo(a)anthracene
Pyrene
Phenanthrene
Total parent DDT
Dibenz(a,h)anthracene
Chrysene
Total PAHs
Fluoranthene
Acenaphthylene
Fluorene
Acenaphthene
2-Methylnaphthalene
Naphthalene
Endrin
DielHrin
57.3
49.5
26.7
24.7
17.5
16.9
14.6
12.9
12.9
12.8
12.4
12.4
12.4
12.0
11.1
7.4
7.4
7.4
5.1
2.3
2.3
2.3
0
0
0
28.2
37.3
9.7
10.0
3.5
12.1
6.8
8.0
4.7
1.6
6.0
4.9
4.4
1.3
3.5
3.5
1.8
1.7
2.7
1.8
0.9
0.9
0.9
0
0
100.0
100.0
82.3
95.4
82.3
95.4
100.0
84.6
37.3
90.8
57.8
29.8
56.0
58.0
39.4
57.8
60.1
42.7
77.7
84.6
82.3
57.8
40.7
100.0
1000
56.0
65.5
44.5
49.0
36.4
42.8
59.1
44.3
23.0
39.3
33.2
19.8
30.2
42.8
34.0
30.5
36.0
21.1
44.1
49.3
43.7
24.0
15.8
99.6
91 6
7-6
-------
O
-------
-------
impacted benthos. Both indicators estimated that about 50% of the Harbor sediments were
affected on average, and provided similar results even within sub-basins (Figure 7-4). However,
sediments from a relatively small area of the Harbor (15%) reduced laboratory survival of A.
abdita. The A. abdita test detected sediment toxicity in only about 25% of the Harbor area with
ecologically significant chemical contamination and/or measured degradation of benthic
assemblages. This indicated that the A. abdita acute sediment toxicity test was a less sensitive
indicator of sediment quality than the B-IBI or ERM sediment chemistry concentrations. This
difference was significant for the Harbor as a whole (P<0.01) and was consistent within each
Harbor sub-basin and western Long Island Sound (P<0.05). The only exception was the lack of a
significant difference between the estimated areas with ecologically significant chemical
contamination and A abdita toxicity within Jamaica Bay (Figure 7-4). These findings indicated
that benthic structure was measurably impacted, and was predictable by chemical contamination,
before acute toxicity of A. abdita became evident.
7-9
-------
8.0 DISCUSSION
Previous studies have documented that sediment chemical contamination is intensive within
selected portions of the NY/NJ Harbor (NOAA, 1995). This study expands on these findings by
documenting the pervasiveness of the contamination. Nearly every sample collected in the
Harbor, as part of this investigation, had at least one chemical exceeding an ERL concentration
and one-half of the area in the Harbor had at least one chemical exceeding an ERM
concentration. Contamination was pervasive across chemical groups. More than one-third of the
Harbor had chemical concentrations exceeding ERM concentrations for each of the metals,
pesticides and PCBs chemical groups; there were 14 individual chemicals which exceeded their
ERL concentration over more than 25% of the Harbor area.
Examining a simple ranking of the sub-basins by areal extent of biologically significant levels of
contaminants, toxicity and abnormal benthic communities (Table 8-1) shows that Newark Bay is
consistently the most degraded sub-basin in the Harbor and Lower Harbor the least degraded.
The Bight Apex and western Long Island Sound appear relatively unaffected.
Table 8-1
Relative Ranking of Sub-basins by % of Area
(1 is most degraded, 5 is least degraded)
Chemistry1
Toxicity2
Benthos3
Mean Ranking
Jamaica
Bay
4
2
3
3
Newark
Bay
1
1
1
1
Lower
Harbor
3
4
5
4
Upper
Harbor
2
3
2
2
W.LI
Sound
5
6
4
5
Bight
Apex
6
5
6
6
' One or more contaminants > ERM.
2 Significant toxicity to A. abdita (% survival
-------
integrative indices (Elliott, 1994). The index approach was chosen for this investigation because
the integration provides a threshold for identifying degraded assemblages and allows analysis
based on areal extent of a problem. This investigation's conclusion regarding coupling of
chemistry and benthic response, though, appears to be independent of the index. There were
relationships between average values for several of the individual benthic metrics used in the
index and presence of high chemical concentrations (Table 8-2).
Table 8-2
Association between Harbor Means of Benthic Metrics and Number of Chemicals > ERM
(± represent 90% confidence interval)
Abundance
(# organism s/m2)
Species Richness
(as # species/sample)
Pollution-Indicative Species
(%}
1 or more
chemicals > ERM*
98,497
±64,798
24.33
±4.63
18.37
±807
2-5
chemicals >
ERM*
76,976
±27,915
21.24
±1.97
34.08
±658
6 or more chemicals
>ERM*
21,477
±3108
18.15
±2.43
58.31
±499
* May also have 1 or more contaminants >ERL.
This study's conclusions are also based on interpreting chemical concentrations relative to the
thresholds suggested by Long and Morgan (1991) and Long et al. (1995a). Some authors have
suggested that the likelihood of contaminant-related biological effects is more appropriately
assessed using equilibrium partitioning for organic chemicals (DiToro, 1991; U.S.EPA, 1994)
and acid-volatile sulfides for metals (DiToro et al., 1990, 1992), although other authors have
questioned these approaches (lannuzzi et al., 1995). The Long et al. (1995a) values were used
for this investigation because they included thresholds for most of the chemicals that were
measured, allowing an integrated contaminant response to be provided. The other approaches
have been developed for a relatively small number of chemicals, and rely almost entirely on
theoretical considerations without field assessment. When the two approaches were compared
for chemicals for which thresholds are available from both approaches, the Long et al. approach
consistently predicted a greater extent of contamination occurring at biologically relevant
concentrations (Table 8-3). The coincidence between chemistry, toxicity and biological response
was also greater for the Long et al. approach, suggesting that the partitioning approaches may
underestimate the availability of contaminants, but this may be partially a function of the lesser
number of chemicals for which thresholds have been developed. Re-examination of which
chemical groups are leading to biological and toxicological responses may be advisable as the
equilibrium partitioning approach is applied to a larger group of chemicals. The use of
cumulative distribution functions, inherent with a probability-based sampling design, provides
8-2
-------
flexibility in applying different thresholds to the same data set without recollecting samples or
recalculating data.
Table 8-3
Percent of Harbor Area Which Exceeded Selected Thresholds
ERM (Long & Morgan, 1991; Long et al., 1995a)
Proposed SQC (U.S.EPA, 1994 )*
AVS (DiToro, 1990, 1992)**
Percent of Area Exceeding Threshold (%)
50
3
12
* Proposed SQC exist for 3 aromatic hydrocarbons and 2 pesticides. Normalized for TOC.
"Applies to 5 divalent metals.
The most prevalent contaminants at levels of biological concern appeared to be mercury,
chlordane and total PCBs, which were consistent with findings of previous studies (Long et al.,
1995a) and data syntheses (Squibb et al., 1991) for NY/NJ Harbor. However, this study's
conclusions were based on associations which do not necessarily result from cause/effect
relationships. Correlations with other chemicals, or with mixes of other chemicals, can confound
the patterns that were observed. Associations identifying which chemicals are not causing effects
are more robust to confounding than are associations that imply cause. Still, mercury, chlordane
and total PCBs each were found at concentrations exceeding ERM levels at more than half of the
sites in the Harbor where impacted benthic assemblages were observed, but were not observed at
most sites which contained healthy assemblages, which is a compelling spatial coincidence.
The chemistry problems in the NY/NJ Harbor present a difficult management challenge as each
of the major chemicals of concern appears to originate from a combination of point- and non-
point sources. For instance, most of the mercury input to Newark Bay has been estimated to
come from point sources on the Hackensack and Passaic Rivers (Olsen et al., 1984), which is
consistent with the spatial patterns this investigation observed for mercury in the Harbor.
Mercury was mostly concentrated in Newark Bay and the mercury exceedances in places like
Raritan Bay followed a spatial pattern suggesting flow from Newark Bay as a source. In contrast,
HydroQual (1991) has estimated that 50% of the inputs for total PCBs enters from tributaries and
most of the chlordane (Bopp et al., 1982) is non-point in origin. The distribution of total PCBs
and chlordane was pervasive throughout the Upper Harbor, Newark Bay and Jamaica Bay, each
of which has a distinct watershed.
While this study assessed which chemicals were site- or basin-specific problems within the
Harbor complex, it did not address which chemicals had sources that were ubiquitous at scales
beyond the boundaries of the Harbor complex. The field methods, laboratory methods and QA
protocols used in the Harbor were modeled after those of EPA's Environmental Monitoring and
8-3
-------
Assessment Program (EMAP), facilitating comparisons between the NY/NJ Harbor and the
remainder of the mid-Atlantic coastal estuaries sampled by EMAP (Strobel et al., 1995), and
allowing assessment of which chemicals are NY/NJ specific problems and which are issues on a
wider geographic scale. Conducting the comparison with EMAP, this study found that the
Harbor had higher average concentrations for 58 of the 59 chemicals measured (Table 4-4). For
several chemicals, specifically mercury and total PCBs, the Harbor also had a large portion (69%
and 100%, respectively) of the areal extent of ERM exceedances in the Virginian Province, even
though the Harbor constitutes only 4% of the area in the Province (Figure 4-14). These findings
suggest that for these chemicals the spatial scale of management action should be focused on the
Harbor.
This study was focused on evaluating the relationship between contaminants and benthic
community condition, but findings from other studies suggested that there also may be
contaminant related food chain effects in the Harbor. Benthic macroinvertebrates are important
food for fish and birds. Some of the same contaminants this study found prevalent in the
sediments were also present in fish, shellfish and Crustacea (Belton et al., 1985; NYSDEC, 1988;
Hauge et al., 1990; Zongwei et al., 1994) and bird feathers (Burger and Gochfeld, 1993) within
and near the Harbor and Bight Apex.
While this study's data were sufficient to indicate a contaminant problem in the Harbor, this
study did not distinguish historical from current inputs. Crawford et al. (1995) has suggested that
inputs to systems like Newark Bay have decreased by 90% over the last decade. This
investigation sampled only the top two centimeters and it has been suggested that average
deposition rates in the Harbor are as high as 0.3 cm per year (Olsen et al., 1984). Based on this
estimate, most of the material this investigation sampled would have been deposited in the last
seven years, suggesting that inputs to the system are still a problem even after substantial
reductions. Better estimation of deposition rates and sediment transport within each sub-basin is
a necessary precursor for determining the most appropriate management strategy to address the
contaminant problem, whether it is pollution prevention, remediation, no action or a combination
of strategies.
This study assessed the quality of surficial sediments in 1993-94, but these qualities will persist
in potential dredged materials of the future. This investigation was principally concerned with
fine-grained particles (silts and clays, <63|i in diameter) because most toxicants are strongly
attached to them — so strongly that the fines carry the toxicants with them when transported
(Olsen et al. 1982). Although the dynamics of fine particles and their associated toxicants are
complex functions of several processes, the locations of maximal net sedimentation (the fastest
deposition) are predictable. Deposition is fastest wherever bottom currents are slow and little
wave energy reaches the bottom: in coves and channels, around piers, and near the ends of
salinity intrusions (Olsen et al. 1978, 1984; Abood et al. 1992).
Human intervention to improve sediment quality in the Harbor presumes at least broad
understanding of where sediments and contaminants come from, and their movements within the
8-4
-------
Harbor. Despite large uncertainties, usefully precise estimates of fine sediment fluxes exist. On
average, 1.5 ±0.5 x 106 metric tons of fine particles accumulate in the Harbor annually. About
3/4 of this material is riverborne, 1/4 comes from marine sources, and very small contributions
are from sewage solids, water column productivity and shore erosion (Olsen et al. 1984). These
estimates are comparable to those of Ellsworth (1986) and Bokuniewicz and Ellsworth (1986),
but a resulting fine-sediment budget did not balance. Bokuniewicz and Ellsworth suspect that
existing measurements underestimate oceanic fluxes into the Harbor, and up rivers and into
Jamaica Bay (by factors of about 2.5 to 4). Most of the newly introduced fines tend to mix with
fines already in the Harbor, and accumulate in dredged areas of Upper Bay, Newark Bay, and
Raritan Bay, at 10 to 100 times the accumulation rates elsewhere (Olsen et al., 1984; Abood et
al., 1992). Dredging is the principal mechanism for removing fine-grained sediment from the
Harbor. Lateral fine sediment mixing appears to be rapid throughout the Harbor (Bokuniewicz
and Ellsworth, 1986). These extensive fluxes rapidly scavenge toxicants from the water column
and tend to homogenize their concentrations in fine sediments. Sediments of shallow, wide areas
of the Harbor are generally in equilibrium with sea level rise, and have net sedimentation rates of
only l-3mm/yr. These areas have little or no net accumulation of fine particles. However, these
fines and their associated contaminants are continually resuspended, facilitating their lateral
transport throughout the Harbor (Olsen et al., 1984).
These dynamics explain why today's fine surficial sediments scavenge toxicants from Harbor
waters and store them in deep and protected areas such as channels and coves. As a consequence
most chemical properties of the surficial sediments measured by this investigation in 1993-94
will persist in channels until these sediments are dredged. Similarly, the qualities of future
dredged materials will reflect toxicant concentrations of fine sediments accumulated in previous
years. Consequently, significant improvements in dredged material quality will require
significant reductions in total toxicant loadings on the Harbor, wherever they come from.
8-5
-------
-------
9.0 LITERATURE CITED
Abood, K.A., E.A. Maikish, T.B. Vanderbeek and M.U. McGowan. 1992 Evaluation of
Induced Sedimentation in New York Harbor, pp. 105-133, In: C.L. Smith [Ed.] Estuarine
Research in the 1980s. State University of New York Press, Albany, New York.
Abood, K.A. and S.G. Metzger. 1996. Comparing impacts to shallow-water habitats through
time and space. Estuaries 19:220-228.
Adams, D. and M. Hunt. 1993. Quality Assurance Project Plan for Environmental Monitoring
Projects, "Sediment Quality of the NY/NJ Harbor." U.S.EPA-Region 2, Edison, NJ.
ASTM. 1991. Standard guide for conducting 10-day static sediment toxicity tests with marine
and estuarine amphipods, E1367-91. American Society for Testing Materials, Philadelphia, PA.
ASTM. 1992. Standard guide for conducting 10-day static sediment toxicity tests with marine
and estuarine amphipods, E1367-92. American Society for Testing Materials, Philadelphia, PA.
Ayres, R.U. 1987. An Historical Reconstruction of Anthropogenic Pollutant Emissions in the
Hudson-Raritan Basin, 1880-1980. Presented at 'Earth Transformed' Conference, Clark
University, October 24-31, 1987. 54pp.
Ayres, R.U. and S.R. Rod. 1986. Patterns of pollution in the Hudson-Raritan Basin.
Environment, 28(4):4-20, 39-43.
Becker, D.S., G.R. Bilyard, and T.C. Ginn. 1990. Comparisons between sediment bioassays
and alterations of benthic macroinvertebrate assemblages at a marine Superfund site:
Commencement Bay, Washington. Environ. Toxicol. & Chem. 9:669-685.
Belton, T.J., R. Hazen, B.E. Ruppel, K.R. Lockwood, J.A. Mueller, E. Stevenson, J.J. and
Post. 1985. A study of dioxin (2,3,7,8-Tetrachlorodibenzo-p-dioxin) contamination in select
finfish, crustaceans, and sediments of New Jersey waterways. New Jersey Department of
Environmental Protection. Office of Science and Research, Trenton, NJ.
Blackburn, T.H and K. Henriksen. 1983. Nitrogen cycling in different types of sediments
from Danish waters. Limnol. Oceanogr. 28:477-493.
Blumer, M., J. Sass, G. Souza, H. Sanders, F. Grassle, and G. Hampson. 1970 The West
Falmouth oil spill, persistence of the pollution eight months after the accident. Woods Hole
Oceanogr. Inst. Ref. No. 70-44.
9-1
-------
Boesch, D.F. and R. Rosenberg. 1981. Response to stress in marine benthic communities. In
G.W. Barret and R. Rosenberg, eds., Stress Effects on Natural Ecosystems. John Wiley and
Sons, New York, pp. 179-200.
Bokuniewicz, H.J. and J.M. Ellsworth. 1986. Sediment Budget for the Hudson System.
Northeastern Geol. 8(3 ): 15 6-164.
Bonnevie, N.L., R.J. Wenning, S.L. Huntley, and H. Bedbury. 1995. Distribution of
inorganic compounds in sediments from three waterways in northern New Jersey. Bull. Environ.
Contam. Toxicol. 51:672-680.
Bopp, R.F., H.J. Simpson, C.R. Olsen, R.M. Trier, and N. Kostyk. 1982 Chlorinated
hydrocarbons and radionuclide chronologies in sediments of the Hudson River and estuary, New
York. Environ. Sci. Technol. 16:582.
Bopp, R.F. and H.J. Simpson. 1989. Contamination of the Hudson River. The Sediment
Record pp. 401-416 In: Contaminated Marine Sediments - Assessment and Remediation
National Academy Press, Washington, D.C.
Brosnan, T.M., and M.L. O'Shea. 1994. New York Harbor Water Quality Survey: 1993.
Executive Summary, Report and Appendices. New York City Department of Environmental
Protection, Marine Sciences Section, Wards Island, New York.
Brosnan, T.M., and M.L. O'Shea. 1995. New York Harbor Water Quality Survey: 1994.
Executive Summary, Report and Appendices. New York City Department of Environmental
Protection, Marine Sciences Section, Wards Island, New York.
Brosnan, T.M., and M.L. O'Shea. 1996. Long-Term Improvements in Water Quality Due to
Sewage Abatement in the Lower Hudson River. Estuaries 19:4.
Burger, J., and M. Gochfeld. Lead and cadmium accumulation in eggs and fledgling seabirds
in the New York Bight. Environ. Toxicol. & Chem. 12:261-267.
Burton, G. A., Jr. 1995. Sediment collection and processing: factors affecting realism. In: G.
A. Burton, Jr., ed., Sediment Toxicity Assessment. Lewis Publishers, Ann Arbor, MI.
Cerrato, R.M. 1986. The benthic fauna of Newark Bay. Marine Sciences Research Center,
State University of New York, Stony Brook, NY. (Special Report 68, Reference 86-3) 105 pp.
Cerrato, R.M., H.J. Bokuniewicz and M.H. Wiggins. 1988. A spatial and seasonal study of
the benthic fauna of the Lower Bay of New York Harbor. Draft Report to New York District of
the U.S. Army Corps of Engineers. 112 pp.
9-2
-------
Chapman, P.M., R.N. Dexter, and E.R. Long. 1987. Synoptic measures of sediment
contamination, toxicity and infaunal community structure (the sediment quality triad) in San
Francisco Bay. Mar. Ecol. Prog. Ser. 37:75-96.
Chapman, P.M. 1990. The sediment quality triad approach to determining pollution-induced
degradation. Sci. Tox. Environ. 97-8:815-825.
Chapman, P.M., R.N. Dexter, H.B. Anderson and E.A. Power. 1991. Evaluation of effects
associated with an oil platform, using the sediment quality triad. Environ. Toxicol. Chem. 10:
407-424.
Chapman, P.M. 1995. Do sediment toxicity tests require field validation? Environ. Toxicol.
Chem., 14:1451-1453.
Chapman, P.M., E.A. Power, and G.A. Burton, Jr. 1995. Integrative Assessments in Aquatic
Ecosystems. In: G. A. Burton, Jr., ed., Sediment Toxicity Assessment. Lewis Publishers, Ann
Arbor, MI.
Chillrud, S.N. 1996. Transport and Fate of Particle Associated Contaminants in the Hudson
River Basin. Ph.D. Thesis, Columbia University, New York, NY. 277pp.
Coch, N.K. 1986. Sediment characteristics and facies distribution. Northeastern Geo., 8:3:109-
129.
Cochran, W.G. 1977. Sampling Techniques. John Wiley and Sons, New York, NY.
Crawford, D.W., N.L. Bonnevie, C.A. Gillis and R.J. Wenning. 1994. Historical changes in
the ecological health of the Newark Bay Estuary, New Jersey. Ecotox. Environ. Safety 29:276-
303.
Crawford, D.W., N.L. Bonnevie, and R.J. Wenning. 1995. Sources of pollution and sediment
contamination in Newark Bay, New Jersey. Ecotox. Environ. Safety 30:85-100.
Cura, J.J., W.J. Heiger-Bernays and K.W. Bucholz. 1995. Draft Program Guidance Manual.
Guidance on Managing Dredged Material Contaminated with Dioxins and Furans, (EPA
Contract No. 68-C2-0134. Work Assignment 2-127). Menzie-Cura and Associates, Inc.,
Chelmsford, MA and Battelle Ocean Sciences, Duxbury, MA.
Daskalakis, K.D. and T.P. O'Connor. 1995. Distribution of chemical concentrations in U.S.
coastal and estuarine sediment. Mar. Environ. Res. 40:381-398.
Dauer, D.M. and Conner, W.G. 1980. Effects of moderate sewage input on benthic
polychaete populations. Estuar. Coast. Mar. Sci. 10:335-346.
9-3
-------
Diaz, R.J. 1995. Ecosystem assessment using estuarine and marine benthic community
structure. In: G. A. Burton, Jr., ed., Sediment Toxicity Assessment. Lewis Publishers, Ann
Arbor, MI.
DiToro, D.M., J.D. Mahoney, D.J. Hansen, K.J. Scott, M.B. Hicks, S.M. Mayr, and M.S.
Redmond. 1990 . Toxicity of cadmium in sediments: the role of acid volatile sulfide. Environ.
Toxicol. Chem. 9:1487-1502.
DiToro, D.M., C.S. Zarba, D.J. Hansen, W.J. Berry, R.C. Swartz, C.E. Cowan, S.P. Pavlou,
H.E. Allen, N.A. Thomas and P.R. Paguin. 1991. Technical basis for establishing sediment
quality criteria for nonionic organic chemicals using equilibrium partitioning. Environ. Toxicol.
Chem. 109:1541-1583.
DiToro, D.M., J.D. Mahony, D.J. Hansen, K.J. Scott, A.R. Carlson, and G.T. Ankley. 1992
Acid volatile sulfide predicts the acute toxicity of Cd and Ni in sediments. Environ. Sci. &
Technol. 26:96-101.
Efron, B., and G. Gong. 1983. A leisurely look at the bootstrap, the jackknife and cross-
validation. Am. Sta. 37:36-48.
Elliott, M. 1994. The analysis of macrobenthic community data. Mar. Pollut. Bull. 28:62-64.
Ellsworth, J.M. 1986. Sources and sinks for fine-grained sediment in the Lower Hudson River.
Northeastern Geol. 8(3 ): 141 -15 5.
Emerson, D.J., and V.J. Cabelli. 1982. Extraction of Clostridium perfringens spores from
bottom sediment samples. Appl. Environ. Microbiol. 44:1144-1149.
Engle, V.D., J.K. Summers and G.R. Gaston. 1994. A benthic index of environmental
condition of Gulf of Mexico estuaries. Estuaries 17:372-384.
Franz, D.R. 1982. An historical perspective on molluscs in lower New York Harbor, with
emphasis on oysters. In G.F. Mayer, ed., Ecological Stress and the New York Bight: Science and
Management. Estuarine Research Foundation, Columbia, SC, pp. 181-197.
Franz, D.R. and W. H. Harris. 1984. Benthos study, Jamaica Bay Wildlife Refuge, Gateway
National Recreation Area, Brooklyn, NY (GATE-N-022-III). Final report to Gateway Institute
for Natural Resource Sciences, National Park Service.
Franz, D.R. and W.H. Harris. 1988. Seasonal and spatial variability in macrobenthos
communities in Jamaica Bay, New York - an urban estuary. Estuaries. 11:15-28.
9-4
-------
Franz, D.R. and J.T. Tanacredi. 1992. Secondary production of the amphipod Ampelisca
abdita Mills and its importance in the diet of juvenile winter flounder (Pseudopleuronectes
americanus) in Jamaica Bay, New York. Estuaries 15:193-203.
Frederick, K.D. and R.A. Sedjo (eds). 1991. America's Renewable Resources - Historical
Trends and Current Challenges. Resources for the Future, Washington, DC. 296 pp.
Frithsen J.B., and A.F. Holland. 1992. Benthic communities as indicators of ecosystem
condition (abstract only). In: D.H. McKenzie, D.E. Hyatt, and VJ. McDonald (eds.) Ecological
Indicators, Volume 1. Elsevier Applied Science, New York, New York.
Frithsen, J.B., L.C. Scott and M. Young. 1994. Methods for Processing Estuarine Benthic
Macroinvertebrate Samples from the EMAP Estuaries Virginian Province. Versar, Inc.,
Columbia, MD.
Frithsen, J.B., L.C. Scott and J.A. Ranasinghe. 1995. Laboratory Processing of R-EMAP
1994 New York/New Jersey Harbor System Benthic Samples. Versar, Inc., Columbia, MD.
Giesy, J.P., and R.A. Hoke. 1990. In: R. Baudo, J. Giesy and H. Muntau (eds.) Sediments:
Chemistry and Toxicity ofln-Place Pollutants. Lewis Publishers, Chelsea, Michigan.
Grassle, J.P., and J.F. Grassle. 1974. Opportunistic life histories and genetic systems in
marine benthic polychaetes. J. Mar. Res. 32:253-284.
Grassle, J.P., and J.F. Grassle. 1976. Sibling species in the marine pollution indicator
Capitella (Polychaeta). Science 192:567-569.
Gray, J.S. 1995. New Approaches to the Assessment of Marine Ecosystem Health, pp. 127-
135, In: D.J. Rapport, C.L. Gaudet and P. Calow (eds) Evaluating and Monitoring the Health of
Large-Scale Ecosystems. Springer-Verlag, Berlin.
Hanson, P.J., D.W. Evans, and D.R. Colby. 1993. Assessment of elemental contamination in
estuarine and coastal environments based on geochemical and statistical modeling of sediments.
Mar. Environ. Res. 36:237-266.
Hauge, P., J. Bukowski, P. Morton, M. Boriek, and G. Casey. 1990 Polychlorinated
biphenyls (PCBs), chlordane,, and DDTs in selected fish and shellfish from New Jersey waters,
1986-1987: results from New Jersey's Toxics in Biota Monitoring Program. New Jersey
Department of Environmental Protection. Trenton, NJ.
Holland, A.F., A.T. Shaughnessy, L.C. Scott, V.A. Dickens, J. Gerritsen, and J.A.
Ranasinghe. 1989. Long term benthic monitoring and assessment program for the Maryland
9-5
-------
portion of Chesapeake Bay: Interpretative report. CBRM-LTB/EST-2. Prepared for Maryland
Department of Natural Resources, Annapolis, Maryland.
Holland, A.F. (ed.) 1990. Near-coastal program plan for estuaries. EPA 600/14-90/033. U.S.
Environmental Protection Agency, Office of Research and Development, Environmental
Research Laboratory-Narragansett, Rhode Island.
Hollander, M., and D.A. Wolfe. 1973. Nonparametric Statistical Methods. John Wiley and
Sons, New York, New York.
Holt, D. and T.M.F. Smith. 1979. Post stratification. J.R. Statist. Soc. A. 142:33-46.
Hunsacker, C.T. and D.E. Carpenter. 1990. Environmental Monitoring and Assessment
Program Ecological Indicators. (EPA/600/3-90/060). U.S. Environmental Protection Agency,
Research Triangle Park, NC.
Hunt, C. 1996. Personal communication. Battelle Ocean Sciences, Duxbury, Massachusetts.
Huntley, S.L., N.L. Bonnevie, R.J. Wenning, and H. Bedbury. 1993. Distribution of
polycyclic aromatic hydrocarbons (PAHs) in three northern New Jersey waterways. Bull.
Environ. Contain. Toxicol. 51:865-872.
HydroQual, Inc. 1991. Assessment of pollutant loadings to New York - New Jersey Harbor.
Task 7.1. Mahwah, New Jersey.
lannuzzi, T.J., S.L. Huntley, N.L. Bonnevie, B.L. Finley and R.J. Wenning. 1995.
Distribution and possible sources of poly chlorinated biphenyls in dated sediments from the
Newark Bay estuary, New Jersey. Arch. Environ. Contain. andTox. 28:108-117.
Jacot, A. 1920. On the marine mollusca of Staten Island, New York. Nautilus 23:111-115.
Jones, C.R., C.T. Fray, and J.R. Schubel. 1979. Textural properties of surficial sediments of
Lower Bay of New York Harbor. Marine Sciences Research Center, State University of New
York, Stony Brook. Special Report 11.
Karr, J.R. 1991. Biological integrity: a long-neglected aspect of water resource management.
Ecol.Appl. 1:66-84.
Kerans, B.L. and J. R. Karr. 1994. A benthic index of biotic integrity (B-IBI) for rivers of the
Tennessee Valley. Ecol.Appl. 4:768-785.
Klemm, D.J., L.B. Lobring, J.W. Eichelberger, A. Alford-Stevens, B.B. Porter, R.F.
Thomas, J.M. Lazorchak, G.B. Collins and R.L. Graves. 1993. Environmental Monitoring
9-6
-------
and Assessment Program (EMAP) Laboratory Methods Manual: Estuaries. U.S. Environmental
Protection Agency, Environmental Research Laboratory, Cincinnati, OH.
Krone, C.A., J.E. Stein and U. Varanasi. 1996. Butyltin contamination of sediments and
benthic fish from the East, Gulf and Pacific coasts of the United States. Environ. Monit. Assess.
40:75-89.
Lee, W.Y., M.F. Welch, and J.A.C. Nicol. 1977. Survival of two species of amphipods in
aqueous extracts of petroleum oils. Mar. Poll. Bull. 8:92-94.
Long, E.R. and P.M. Chapman. 1985. A sediment quality triad: measures of sediment,
contamination, toxicity and infaunal community composition in Puget Sound. Mar. Pollut. Bull.
16:405-415.
Long, E.R. and L.G. Morgan. 1991. The potential for biological effects of sediment-sorbed
contaminants tested in the National Status and Trends Program. NOAA Technical
Memorandum, NOS OMA 52. U.S. National Oceanic and Atmospheric Administration, Seattle,
Washington.
Long, E.R. and R. Markel. 1992. An evaluation of the extent and magnitude of biological
effects associated with chemical contaminants in San Francisco Bay, California. NOAA
Technical Memorandum NOS ORCA 64. National Oceanic and Atmospheric Administration,
Seattle, WA.
Long, E.R., D.D. MacDonald, S.L. Smith, and F.D. Calder. 1995a Incidence of adverse
biological effects within ranges of chemical concentrations in marine and estuarine sediments.
Env. Manage. 19:81-97.
Long, E.R., D.A. Wolfe, J.K. Scott, G.B. Thursby, E.A. Stern, C. Peven, and T. Schwartz.
1995b. Magnitude and Extent of Sediment Toxicity in the Hudson-Raritan Estuary. NOAA
Technical Memorandum NOS ORCA 88. National Oceanic and Atmospheric Administration,
Silver Spring, MD.
Luckenbach, M. W., R.J. Diaz, E.C. Zobrist, and C.H. Button. 1990 Evaluation of the
benthic resource value of impounded and non-impounded tidal creeks in Virginia, USA. Ocean.
Shore Manage. 14:35-50.
MacKenzie, C.L., Jr. 1990. History of the fisheries of Raritan Bay, New York and New Jersey.
Mar. Fish. Rev., 52 (4): 1-45.
MacKenzie, C.L., Jr. 1992. The Fisheries of Raritan Bay. Rutgers University Press, New
Brunswick, NJ. 304 pp.
9-7
-------
Mayer, G.F. (ed.). 1982. Ecological Stress and the New York Bight: Science and Management.
Estuarine Research Federation, Columbia, SC.
McManus, J.W. and D. Pauly. 1990. Measuring ecological stress: variations on a theme by M.
Warwick. Mar. Biol. 106:305-308.
Microbics Corp. 1992. Microtox™ standard assay procedure. Carlesbad, California.
NOAA (National Oceanic and Atmospheric Administration). 1989 A summary of data on
tissue contamination from the first three years (1986-1988) of the Mussel Watch Project.
National Oceanic and Atmospheric Administration. Rockville, MD. 22pp.
NOAA. 1991. Second summary of data on chemical concentrations in sediments from the
National Status and Trends Program. Technical Memorandum NOS OMA 59. Ocean
Assessment Division, Rockville, MD.
NOAA. 1995. The Utility of'AVS/EqP in Hazardous Waste Site Evaluations. NOAA Technical
Memorandum NOS ORCA 87. Seattle, Washington.
NOAA. 1996. Contaminant Levels in Muscle and Hepatic Tissue of Lobster from the New
York Bight Apex. National Oceanic & Atmospheric Administration, National Marine Fisheries
Service, James J.Howard Marine Sciences Laboratory, Highlands, New Jersey. 72pp.
NY-NJ HEP (New York-New Jersey Harbor Estuary Program). 1996. Final Comprehensive
Conservation and Management Plan (CCMP). U.S. Environmental Protection Agency, New
York, NY.
NYSDEC (New York State Department of Environmental Protection). 1988 PCS in
Hudson River Striped Bass. Technical report 88-2 (BEP). New York State Department of
Environmental Conservation, Division of Fish and Wildlife, Albany, New York.
NYSDEC (New York State Department of Environmental Protection). 1994 Technical
guidance for screening contaminated sediments. NYSDEC, Division of Fish and Wildlife,
Division of Marine Resources, Albany, NY.
NYSDEC (New York State Department of Environmental Protection). 1996. Memorandum
dated April 25, 1996 from Arthur J. Newell amending the "Technical guidance for screening
contaminated sediments (NYSDEC, 1994)" to incorporate the Long et al. (1995) values for
marine and estuarine sediments. NYSDEC, Divison of Marine Resources, Setauket, NY.
O'Connor, T.P. 1996. Trends in chemical concentrations in mussels and oysters collected
along the U.S. coast from 1986 to 1993. Mar. Environ. Res. 41:183-200.
-------
Olsen, C.R., LL. Larsen, R.H. Brewster, N.H. Cutchall, R.F. Bopp and H.J. Simpson. 1984
A Geochemical Assessment of Sedimentation and Contaminant Distributions in the Hudson-
Raritan Estuary. NOAA Tech. Report NOS QMS 2. Rockville, MD. 101 pp.
Olsen, C.R., H.J. Simpson, R.F. Bopp, S.C. Williams, T.-H. Peng and B.L. Deck. 1978
Geochemical analysis of the sediments and sedimentation in the Hudson estuary. J. Sediment
Petrol. 48:401-
Olsen, C.R., N.H. Cutshall and LL. Larsen. 1982. Pollutant-particle associations and
dynamics in coastal marine environments: A review. Mar. Chem. 11:501-
Overton, W.S., D. White and D.L. Stevens. 1990. Design Report for EMAP: Environmental
Monitoring and Assessment Program. EPA/600/3-91/053. U.S. Environmental Protection
Agency, Office of Research and Development, Washington, DC.
Pearson, T.H. and R. Rosenberg. 1978. Macrobenthic succession in relation to organic
enrichment and pollution of the marine environment. Oceanogr. Mar. Biol. Ann. Rev. 16:229-
311.
Plumb, R.H., Jr. 1981. Procedures for handling and chemical analysis of sediment and water
samples. Technical Report EPA/CE-81-1. U.S. Army Engineer Waterways Experiment Station,
CE, Vicksburg, Mississippi.
Power, E.A., and P.M. Chapman. 1995. Assessing sediment quality. In: G. A. Burton, Jr.,
ed., Sediment Toxicity Assessment. Lewis Publishers, Ann Arbor, MI.
PSDDA (Puget Sound Dredged Disposal Analysis). 1989 Management plan report -
unconfmed open-water disposal of dredged material, Phase II. U.S. EPA-Region 10, Army
Corps of Engineers and Washington State Department of Environment, Seattle, Washington.
Ranasinghe, J.A., S.B. Weisberg, D.M. Dauer, et al. 1993. Chesapeake Bay Benthic
Community Restoration Goals. Versar, Inc., Columbia, MD. 49 pp.
Ranasinghe, J.A., S.B. Weisberg, J.S. O'Connor, and D.A. Adams. In Review. A Benthic Index
of Biotic Integrity (B-IBI) for the New York/New Jersey Harbor. Journal of Aquatic Ecosystem
Stress and Recovery.
Reifsteck, D.M., C.J. Strobel and D. Keith. 1993. Environmental Monitoring and Assessment
Program, Near Coastal Component, 1993 Virginian Province Effort, Field Operations and Safety
Manual. U.S. Environmental Protection Agency, ORD, Narragansett, RI.
Sanders, H., F. Grassle and G. Hampson. 1972. The West Falmouth oil spill. I. Biology. Woods
Hole Oceanogr. Inst. Tech. Rep. 72-80.
9-9
-------
Schimmel, S.C., B.D. Melzian, D.E. Campbell, C.J. Strobel, S.J. Benyi, J.S. Rosen and H.W.
Buffam. 1994. Statistical Summary: EMAP-Estuaries Virginian Province - 1991. (EPA/620/R-
94/005) U.S. Environmental Protection Agency, ORD, ERL-Narragansett, RI.
Schroop, S.J., F.G. Lewis, H.L. Windom, J.D. Ryan, F.D. Calder and L.C. Burney. 1990
Interpretation of metal concentrations in estuarine sediments of Florida using aluminum as a
reference element. Estuaries 13:227-235.
Scott, J., W. Berry, D. Cobb, D. Keith, G. Tracey, and N. Rubinstein. 1990 The application
of the amphipod ten-day toxicity test for dredged material evaluations. Contribution #1181.
U.S.EPA-ERL, Narragansett, RI.
Scott, L., J.B. Frithsen and J.A. Ranasinghe. 1994. Analysis of R-EMAP 1993 New York/New
Jersey Harbor System Benthic Samples. Versar, Inc., Columbia, MD.
Shannon, C.E. and W. Weaver. 1949. The Mathematical Theory of Communication. Urbana:
University of Illinois Press.
Skinner, L.C., S.J. Jackling, G. Kimber, J. Waldman, J. Shastay, Jr., and A.J. Newell. 1996
Chemical Residues in Fish, Bivalves, Crustaceans, and a Cephalopod from the New York-New
Jersey Harbor Estuary: PCB, Organochlorine Pesticides and Mercury. New York State Department
of Environmental Conservation, Division of Fish, Wildlife and Marine Resources, Albany, NY. 150
pp.
Sloff, W. 1985. The role of multispecies toxicity testing in aquatic toxicology. In J. Cairns, Jr.
(ed.), Multispecies Toxicity Testing. Pergamon Press, New York: 45-60.
Snelgrove, P. V.R. and C.A. Butman. 1994. Animal-sediment relationships revisited: cause versus
effects. Oceanogr. Mar. Biol. Ann. Rev. 32:111-117.
Squibb, K.S., J.M. O'Connor and T.J. Kneip. 1991. New York/New Jersey Harbor Estuary
Program Module 3.1: Toxics Characterization Report. Institute of Environmental Medicine, New
York University Medical Center, Tuxedo, NY.
Steimle, F.W. and J. Caracciolo-Ward. 1989. A reassessment of the status of the benthic
macrofauna of the Raritan Estuary. Estuaries 12:145-156.
Stevens, D.L., Jr. 1994. Implementation of a national monitoring program. J. Environ. Mgt.
42:1-29.
Strobel, C.J., H.W. Buffum, S.J. Benyi, E.A. Petrocelli, D.R. Reifsteck and D.J. Keith. 1995
Statistical Summary: EMAP-Estuaries Virginian Province-1990 to 1993. EPA/620/R-94/026. U.S.
9-10
-------
Environmental Protection Agency, National Health and Environmental Effects Research Laboratory-
Atlantic Ecology Division, Narragansett, RI.
Summers, J.K., J.F. Paul and A. Robertson. 1995. Monitoring the ecological condition of
estuaries in the United States. Toxicol. Environ. Chem. 49:93-108.
Suszkowski, D.J. 1978. Sedimentology of Newark Bay, New Jersey: an urban estuary bay. Ph.D.
dissertation, University of Delaware, Dover, Delaware.
Swartz, R.C., W.A. Deben, K.A. Sercu and J.O. Lamberson. 1982 Sediment toxicity and the
distribution of amphipods in Commencement Bay, Washington, U. S. A. Mar. Poll. Bull. 13:359-364.
Thursby, G.B., J. Heltshe and K.J. Scott. 1997. Revised approach to toxicity test acceptability
criteria using a statistical performance assessment. Environ. Toxicol. Chem. 16(6).
U.S.EPA (U.S. Environmental Protection Agency). 1983. Methods for chemical analysis of
water and wastes, EPA-600/4-79-020. U.S. Environmental Protection Agency, Environmental
Monitoring and Support Laboratory, Cincinnati, Ohio.
U.S.EPA (U.S. Environmental Protection Agency). 1989. Interim Procedures for Estimating
Risks Associated with Mixtures of Chlorinated Dibenzo-p-Dioxins and -Dibenzofurans (CDDs and
CDFs) and 1989 Update. U.S. Environmental Protection Agency, Risk Assessment Forum,
Washington, DC. EPA/625/3-89/016.
U.S.EPA (U.S. Environmental Protection Agency). 1990a. Managing Contaminated Sediments:
EPA Decision-Making Process. EPA 506/6-90/002. Washington, D.C.
U.S.EPA (U.S. Environmental Protection Agency). 1990b. Method 1613: Tetra through Octa-
Chlorinated Dioxins and Furans by Isotope Dilution HRGC/HRMS - Revision A. October 1990.
Office of Water, Regulations and Standards Industrial Technology Division, Washington, D.C.
U.S.EPA (U.S. Environmental Protection Agency). 1991 US EPA Contract Laboratory
Program, Statement of Work for organic analysis, multi-media, multi-concentration, document
number ILM03.0. U.S. Environmental Protection Agency, Washington, D.C.
U.S.EPA (U.S. Environmental Protection Agency). 1993a. R-EMAP: Regional Environmental
Monitoring and AssessmentProgram. EPA/625/R-93/012. U.S. Environmental Protection Agency,
Office of Research and Development, Washington, D.C.
U.S. EPA (U.S. Environmental Protection Agency). 1993b. EMAP Laboratory Methods Manual:
Estuaries. U.S. Environmental Protection Agency, Office of Research and Development,
Environmental Monitoring Systems Laboratory, Cincinnati, OH.
9-11
-------
U.S.EPA (U.S. Environmental Protection Agency). 1993c. Interim Report on Data and Methods
for Assessment of 2,3,7,8-Tetrachlorodibenzo-p-dioxin risks to aquatic life and associated wildlife,
EPA/600/R-93/055. U.S. Environmental Protection Agency, Office of Research and Development,
Washington, DC.
U.S.EPA (U.S. Environmental Protection Agency). 1994. Notice of sediment quality criteria.
Federal Register 59(11):2652-2656.
U.S.EPA (U.S. Environmental Protection Agency)-Region 2. 1994a-f. Technical SupportBranch
(TSB) Standard Operating Procedures (SOPs) C-48, C-5, C-8, C-72, C-73, C-74 (revisedFeb. 1994).
USEPA-Region 2 Technical Support Branch, Edison, NJ.
U.S.EPA (U.S. Environmental Protection Agency)-Region 2 1994g New York Bight Water
Quality, Summers of 1992 and 1993. U. S. Environmental Protection Agency-Region 2, Surveillance
and Monitoring Branch, Edison, New Jersey.
U.S.EPA (U.S. Environmental Protection Agency)-Region 2 1995 New York Bight Water
Quality, Summer of 1994. U.S. Environmental Protection Agency-Region 2, Surveillance and
Monitoring Branch, Edison, New Jersey.
U.S. EPA (U.S. Environmental Protection Agency), and U.S. Army Corps of Engineers (U.S.
ACE). 1991. Evaluation of dredged material proposed for ocean disposal - testing manual, EPA-
503/8-91/001. U.S. Environmental Protection Agency, Office of Water, Washington, DC and U.S.
Army Corps of Engineers, Washington, DC.
Warwick, R.M. 1986. A new method for detecting pollution effects on marine macrobenthos
communities. Mar. Biol. 92:557-562.
Weisberg, S.B., J.B. Frithsen, A.F. Holland, J.F. Paul, K.J. Scott, J.K. Summers, H.T.
Wilson, R.M. Valente, D.G. Heimbuch, J. Gerritsen, S.C. Schimmel and R.W. Lattimer. 1993
Virginian Province Demonstration Report, EMAP-Estuaries: 1990. (EPA/620/R-93/006) U.S.
Environmental Protection Agency, Office of Research and Development, Washington, DC.
Whitney, G.G. 1994. From Coastal Wilderness to Fruited Plain. A History of Environmental
Change in Temperate North America - 1500 to the Present. Cambridge University Press,
Cambridge, MA.
WRI (New York State Water Resources Institute). 1995. The State of the City's Waters 1994:
The New York Harbor Estuary. New York State Water Resources Institute, Center for the
Environment, Cornell University, Ithaca, NY. 62 pp.
9-12
-------
Zongwei, C., V.M.S. Ram aim jam, M.L. Gross, A. Cristini, and R.K. Tucker. 1994 Levels of
polychlorodibenzo-p-dioxins and dibenzofurans in crab tissues from the Newark Bay/Raritan Bay
system. Environ. Sci. Technol. 28:1528-1534.
9-13
-------
APPENDICES FOR:
SEDIMENT QUALITY OF THE NY/NJ
HARBOR SYSTEM
EPA/902-R-98-001
Darvene A. Adams
U.S. Environmental Protection Agency - Region 2
Edison, NJ
Joel S. O'Connor
U.S. Environmental Protection Agency - Region 2
New York, NY
Stephen B. Wei sb erg
Southern California Coastal Water Research Project
Westminster, CA
March 1998
-------
Appendix A
Sampling station maps
-------
R-EMAP SUMMER 1993 AND 1994 SAMPLING LOCATIONS, NY/NJ HARBOR
-------
R-EMAP SUMMER 1993 AND 1994 SAMPLING LOCATIONS, UPPER NEW YORK HARBOR
1993 data
1994 data
-------
R-EMAP SUMMER 1993 AND 1994 SAMPLING LOCATIONS, NEWARK BAY
1993 data
1994 data
-------
V
-------
R-EMAP SIMMER 1993 AND 1994 SAMPLMG LOCATIONS. WESTERN LONG GLAND SOUND
C&6
Lsooi
.LS026
v ..
& ,LS027 m
LS102
LS103
ALS112
ALS108
LS1HA
LS020
ALS106
. LS016 A LSI
ALSHO
,LS019_^ ^^
• LSOfl
!5 >LS018 A
^C^J
J
$z
LLSfl3
-LS03
-------
c/i
-------
R-EMAP SUMMER 1993 AND 1994 SAMPLING LOCATIONS. NEW YORK BIGHT APEX
.BA002
BBA005
A BA106
,BA007
ABAK)5
,BA012
BBAOM
.BA016
ABA107 BA108
.BA017
, BA025A BAffl
ABA109
A BATE
kBATKD
, BA026
BAH3
• BA030
BBA033ABA114
. BA035
BA|021
-------
Appendix B
Analytical detection values
-------
Analytical Detection Values for Sediment Samples
Parameter
PAHs (ug/kg, dry wt.)
Acenaphthene
Acenaphthylene
Anthracene
Benzo(a)anthracene
Benzo(b,k)fluoranthene
Benzo(g,h,i)perylene
Benzo(a)pyrene
Benzo(e)pyrene
Biphenyl
Chrysene
Dibenz(a,h)anthracene
2,6-Dimethylnaphthalene
Fluoranthene
Fluorene
Indeno(l ,2,3-C,D)pyrene
2-Methylnaphthalene
1 -Methy Inaphthalene
1 -Methy Iphenanthrene
Naphthalene
Perylene
Phenanthrene
Pyrene
2,3,5-Trimethy Inaphthalene
Pesticides (ng/g, dry wt.)
o,p'-DDD
p,p'-DDD
o,p'-DDE
p,p'-DDE
o,p'-DDT
p,p'-DDT
Aldrin
alpha-Chlordane
trans-Nonachlor
Dieldrin
Heptachlor
Heptachlor epoxide
Hexachlorbenzene
Lindane (gamma-BHC)
Mirex
AVS/SEM (ug/g, dry wt.)
AVS
SEM-Cd
SEM-Cu
SEM-Hg
SEM-Ni
SEM-Pb
SEM-Zn
1 DL
12,24
12
12
12
12,31
12,25
12
12
12,24
12
12,32
12
12
12,24
12,30
12
12,24
12,30
12
12
12
12
12,33
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
0.2
0.5
0.07
0.5
1
5
Parameter |
Major and Trace Elements (ug/g, dry wt.)
Aluminum (Al)
Antimony (Sb)
Arsenic (As)
Cadmium (Cd)
Chromium (Cr)
Copper (Cu)
Iron (Fe)
Lead (Pb)
Manganese (Mn)
Mercury (Hg)
Nickel (Ni)
Selenium (Se)
Silver (Ag)
Tin (Sn)
Zinc (Zn)
PCBs (ng/g, dry wt.)
2,4'-dichlorobiphenyl (8)
2,2',5-trichlorobiphenyl (18)
2,4,4' -trichlorobiphenyl (28)
2,2',3,5'-tetrachlorobiphenyl (44)
2,2',5,5'-tetrachlorobiphenyl (52)
2,3',4,4'-tetrachlorobiphenyl (66)
2,2',4,5,5'-pentachlorobiphenyl (101)
2,3,3',4,4'-pentachlorobiphenyl(105)
2,3',4,4',5-pentachlorobiphenyl (118)
2,2',3,3',4,4'-hexachlorobiphenyl(128)
2,2',3,4,4',5'-hexachlorobiphenyl(153)
2,2',4,4',5,5'-heptachlorobiphenyl(170)
2,2',3,3',4,4',5-heptachlorobiphenyl(180)
2,2',3,3',4,4',5,5'-heptachlorobiphenyl(187)
2,2',3,3',4,4',5,6-octachlorobiphenyl(195)
2,2',3,3',4,4',5,5',6-nonachlorobiphenyl(206)
2,2',3,3',4,4',5,5',6,6'-decachlorobiphenyl(209)
Butyltins (ng/g, dry wt.)
Monobutyltin
Dibutyltin
Tributyltin
Tetrabutyltin
Dioxin & Furan Congeners (ng/kg, dry
wt.)
Detection limits for dioxins and furans varied by
sample. See accompanying data disk for
detection
limits.
DL
200
0.1
0.24
0.018
0.1
0.44
40
0.15
3.5
0.01
0.4
0.1
0.013
0.1
1.5
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
5
5
5
5
Two values for a detection limit represent values achieved for 1993 and 1994 samples, respectively.
-------
Appendix C
Development of the Benthic Index of
Biotic Integrity (B-IBI)
-------
Running Head: NY/NJ Harbor B-IBI
A BENTHIC INDEX OF BIOTIC INTEGRITY (B-IBI) FOR THE
NEW YORK/NEW JERSEY HARBOR
Stephen B. Weisberg1*
J. Ananda Ranasinghe1
Joel S. O'Connor2
Darvene A. Adams3
*Versar, Inc.
9200 Rumsey Road
Columbia, Maryland 21045
2U. S. Environmental Protection Agency
Water Management Division
290 Broadway
New York, NY 10007
3U. S. Environmental Protection Agency
Environmental Services Division
2890 Woodbridge Avenue
Edison, NJ 08837
^Present Address;
Southern California Coastal Water Research Project
7171 Fenwick Lane
Westminster, CA 92683
(714) 894-2222
-------
ABSTRACT
A multi-metric benthic index of biotic integrity (B-IBI) for the New
York/New Jersey Harbor was developed by comparing the response of nine
candidate measures of benthic condition (metrics) between a set of
minimally-affected reference sites and a set of sites with known anthropogenic
stress. The index was developed independently for each of four habitats defined by
salinity and substrate. All nine candidate metrics differed significantly between
reference sites and stressed sites in the calibration data set for at least one habitat.
Six metrics differed significantly between reference and stressed sites in all habitats-
species richness, species diversity, biomass, percent of abundance as
pollution-sensitive taxa, percent of abundance as pollution-tolerant taxa, and percent
of abundance as camivores/omnivores. The index was calculated by scoring each of
five selected metrics as 5, 3, or 1 depending on whether its value approximated,
deviated slightly from, or deviated greatly from conditions at the best reference
sites. Validation using independent data from 72 sites in the NY/NJ Harbor
complex showed that the index was 93% effective at distinguishing
anthropogenically stressed sites from reference sites.
-------
INTRODUCTION
Numerous studies have documented a high degree of toxic
contamination of sediments in the New York/New Jersey Harbor
complex (lanuzzi et al. 1995, Huntley et al 1993, Bonnevie et al.
1993, Williams et al. 1978). Billions of dollars have been spent
to reduce source loads, which have declined by more than 90% in
the last decade (Crawford et al. 1995). The proximal goal of
these expenditures has been reduction of toxic contaminant inputs
and ambient sediment concentrations, but the ultimate goal is
protection of biological and human health resources. Few
studies, though, have examined the extent of contaminant effects
on the quality of biological assemblages (Steimle and Caraceiolo-
Ward 1989, Franz et al. 1988); most biological assessments
conducted in the New York Harbor have focused on food chain
effects of contaminants in tissues (Stainken and Rollwagen 1979,
Burger and Gochfeld 1993, Zongwei et al. 1994, Peven et al.
1996).
The condition of benthic macrofaunal invertebrate
assemblages is a good candidate for assessing the present
condition and future changes in biotic condition of the NY/NJ
Harbor. Benthos have limited mobility and cannot avoid adverse
conditions (Gray 1979} so their condition accurately reflects
local environmental conditions. Benthos live in sediments, where
exposure to contaminant and hypoxia stress is generally most
-------
severe. Benthos are an important component of the food web,
serving as an important link between primary producers and higher
trophic levels (Holland et al. 1980, Baird and Ulanowicz 1989).
Benthos also significantly affect oxygen, nutrient, and carbon
cycles and may control the coupling of benthic and pelagic
processes (Rhoads and Young 1970, Kemp and Boynton 1981,
Blackburn and Henriksen 1983).
One factor limiting use of benthos for assessing condition
of New York Harbor sediment is a lack of clear expectations for
benthic assemblage characteristics in non-stressed habitats.
These expectations are a necessary first step in using benthic
community measures in assessments because expectations establish
criteria for distinguishing between non-stressed sites and those
with varying degrees of anthropogenic alteration. Such criteria
could also be used to identify areas most in need of restoration
and provide a quantitative endpoint for restoration.
One approach that has been used extensively in fresh water
to define expectations at non-stressed sites is the Index of
Biotic Integrity (IBI) (Kerans and Karr 1994, Simon and Lyons
1995). This approach defines community characteristics expected
at sites free from anthropogenic stress, and scores metrics that
quantify those expectations based upon observations at non-
stressed reference sites. Characteristics of biota at 'other'
sites are then compared with these expectations to provide an
-------
assessment of site conditions. In this paper, we use that
approach to develop a benthic index of biotic integrity (B-IBI)
for application to summer estuarine benthic communities of the
New York/New Jersey Harbor complex.
-------
METHODS
• Data Sources
The B-IBI was developed using data from EPA's Environmental
Monitoring Assessment Program (EMAP), which collected benthos,
sediment chemistry and sediment toxicity samples at 525 randomly
selected sites in the Virginian biogeographic province in August
and September between 1990 and 1993 (Paul et al 1992}. At each
site, triplicate samples of benthic macrofaunal communities were
collected using a 440-cm2, stainless steel, Young-modified
VanVeen grab, and sieved in the field using a 0.5-mm screen and
preserved in a 10% solution of buffered formaldehyde stained with
rose bengal. .A 50-ml core from each grab was frozen in a plastic
bag for analysis of silt-clay content. Sediment samples for
analysis of sediment chemistry and toxicity and were also
collected using the VanVeen grab. A teflon spoon was used to
remove the top 2 cm of sediment to a clean glass jar with a
teflon lid, which was stored frozen. Dissolved oxygen and
salinity were measured near the bottom of each site using a
SeaBird CTD.
In the laboratory, macroinvertebrates were identified to the
lowest practical taxonomic level and counted. Biomass was
r
measured for 30 dominant species; other taxa were combined by
feeding type and major taxonomic group (i.e., subsurface,
-------
deposit-feeding polychaetes). Biomass was determined as shell-
free dry weight after drying at 60 °C for 48 hours. Bivalves
longer than 2 cm were shucked and smaller shells removed by
acidification in 10% HC1 before determining biomass. Percent
sand in the-sediment was estimated as the fraction retained on a
63 u sieve. Percent silt and percent clay were determined using
pipette analysis of the filtrate.
Sediment samples were analyzed for the NOAA Status and
Trends Program list of chemicals {O'Connor and Ihler 1991) using
standard methodologies (Table 1). Sediment toxicity was measured
using the ten-day acute, static, non-renewal Ampelisca abdita
test following ASTM (1990) protocols. For each toxicity test,
200 ml of composited, press-sieved sample was placed in 1 L glass
test chambers and covered with 600 ml of seawater. Five
replicate test chambers were used for each sample, with 20
organisms placed into each replicate.
The index was validated using independent data from 168
randomly selected sites in the New York/New Jersey Harbor complex
between August and September in 1993 and 1994. The validation
data set included the same variables collected using the same
field and laboratory methods as described above, except that only
two benthic macrofaunal samples were processed for each site.
-------
Index Development
The B-IBI was developed by testing and quantifying
previously established principles that benthic assemblages
respond to improvements in habitat quality in at least four ways:
(1) species diversity increases as new taxa that are unable to
tolerate poor habitat quality flourish (Pearson and Rosenberg
1978); (2) the abundance and biomass of organisms increases
(Pearson and Rosenberg 1978, Warwick and Clarice 1991); (3) the
dominant species at the site change from pollution-tolerant to
pollution-sensitive (Boesch 1973, Warwick 1986, Oauer 1993); and
4) the diversity of feeding guilds increases (Brown et al. in
press). These hypotheses were tested by comparing benthic
assemblages at reference sites with those at anthropogenically
stressed sites, selecting attributes that differed significantly
between the two groups for inclusion in the index, and
establishing thresholds for the selected attributes based on the
range of attribute values at the reference sites.
Reference sites were selected by eliminating locations near
known point-source discharges, and selecting from the remaining
sites those where bioassay survival exceeded 80% of controls, and
no contaminant exceeded Long et al.'s (1995) Effects Range-Median
(ER-M) concentration, and no more than two contaminants exceeded
Long et al's Effects Range-Low (ER-L) concentration, and total
organic content of the sediment was less than 2.5%, and dissolved
-------
oxygen concentration at the time of sampling exceeded 5 ppm.
Sites were also screened to exclude those that occurred in areas
of known frequent hypoxia, such as western Long Island Sound.
The anthropogenically stressed sites used for comparison of
#
response were identified as sites where any sediment contaminant
exceeded Long et al.'s (1995) ER-M concentration and survival in
sediment toxicity tests was less than 80% of control, or
dissolved oxygen content was below 2 ppm.
Two criteria were used to compare attribute values between
reference and stressed sites. First, a Mann-Whitney U-test was
used to test for difference in median. Second, the Kolmogorov-
Smirnov two-sample test was used to test for other distributional
differences. The latter is particularly important for attributes
such as abundance and biomass, for which the anticipated response
at stressed sites could be higher or lower than at reference
sites, depending on the severity of the stress.
Nine candidate metrics from the four categories of benthic
response were tested (Table 2). The feeding guild and pollution-
sensitivity metrics required classification of collected species'
into groups. Feeding modes were assigned using literature
descriptions of feeding behavior (Jorgensen 1966; Bousfield 1973;
Fauchald and Jumars 1979; Dauer et al. 1981). Lists of
pollution-indicative (Table 3) and pollution-sensitive (Table 4)
taxa were developed by comparing relative abundance of taxa
-------
between the reference sites and stressed sites in the calibration
data set. Pollution-indicative taxa were selected as those for
which average abundance, average percent of abundance, and
frequency of occurrence were all higher at stressed than
reference sites (Table 3). Pollution-sensitive taxa were
selected as those for which average abundance, average percent of
abundance, and frequency of occurrence were all higher at
reference than stressed sites, and for which percent of abundance
at reference'sites averaged at least 0.2% (Table 4).
Attributes were tested separately for each of four habitats
defined by salinity and substrate type (Table 5). The four
habitats were established using cluster analysis (Bray-Curtis
similarity coefficient, flexible sorting, fl«-0.25) on species
abundances in the calibration data set to identify major site
groupings, followed by ANOVA to determine whether salinity, grain
size or depth differed significantly among the site groupings
(Ranasinghe et al. in prep). Results from the cluster analysis
were also used to identify geographical limitations for selection
of reference sites; reference sites were selected from estuarine
and coastal areas between Chincoteague Bay and Cape Cod, because
euhaline and polyhaline benthic assemblages within the Virginian
Province, except for Chesapeake Bay, exhibited a high degree of
similarity (Ranasinghe et al. in prep).
10
-------
Thresholds for each selected metric were established based
on the distribution of its values at the reference sites. The
IBI approach involves scoring each metric as 5, 3, or 1,
depending on whether its value at a site approximates, deviates
slightly froia, or deviates greatly from conditions at the best
reference sites (Karr et al. 1986). Threshold values were
established as approximately the 5th and 50th (median) percentile
values for reference sites in each habitat. For each metric,
values below the 5th percentile were scored as 1; values between
the 5th and 50th percentiles were scored as 3, and values above
the 50th percentile were scored as 5. The scored values of the
metrics were combined into an index by computing the mean
attribute score across all selected metrics. Assemblages with an
index score less than 3 are considered stressed, as they have
metric values that on average are less than that at the poorest
reference sites.
Two of the attributes, abundance and biomass, respond to
stress bimodally, where the response can be greater than
reference at sites with moderate degrees of stress and less than
reference at sites with high degrees of stress (Pearson and
Rosenberg 1978, Dauer and Conner 1980, Stull et al. 1986, Ferraro
et al. 1991). These two attributes were scored as 5 for those
values falling between the 25th and 75th percentile response at
reference sites, and as a 3 for those values between the 5-25th
and 75-95th percentiles at reference sites. Abundance values
11
-------
lower than the 5th percentile or higher than the 95th percentile
were scored as a 1; bioraass values higher than the 95th
percentile were scored as a 3 since high biomass can occur
naturally at non-stressed sites where biomass is dominated by
large bivalves,
Index validation was conducted in three ways. First, we
examined index values at reference sites and anthropogenically-
stressed sites in the validation data set, which was independent
of the data set used to develop the index. Our criteria for
defining reference sites and known stressed sites from the
validation data set were the same as those for the calibration
data set; our hypothesis was that reference sites should have
index values of three or greater, while stressed sites should
have values less than three. Second, we examined the
relationship between the index and TOC concentration,
hypothesizing that stressed assemblages would occur at higher
concentrations of TOC. We examined the B-IBI relationship with
TOC in a correlative, rather than in a categorical, fashion
because threshold levels for anticipated biological response to
TOC levels are not well established. Third, we calculated the
correlation between replicates in the validation data set to
examine stability of the index over small spatial scales.
12
-------
RESULTS
One hundred and twenty-five sites from the calibration data
set met our criteria for reference sites. There were at least 25
reference sites for each habitat class, except for the polyhaline
mud habitat, for which only eleven reference sites were available
(Table 5). Twenty-five sites met our criteria as
anthropogenically-stressed (Figure 1), though only two were
identified for the euhaline sand habitat (Table 5).
All nine candidate metrics differed significantly between
reference sites and stressed sites for at least one habitat in
the calibration data set (Table 2). Species richness (number of
species per sample), species diversity, biomass, percentage of
abundance as pollution-sensitive taxa, percent of abundance as
pollution-indicative taxa, and percent of abundance as
carnivore/omnivores significantly differentiated reference and
stressed sites in all four habitats.
Our initial list of metrics selected for the B-IBI, and
their thresholds, are presented in Table 6. In developing the
index, we chose to include the abundance metric in the index for
all habitats even though it statistically distinguished reference
and stressed sites in only three of the four habitats; we did so
because the pattern of response was similar in all habitats, and
the response in the fourth habitat was significant at p = 0.2.
-------
We excluded species diversity from the index because it was
highly correlated with species richness, and species richness was
slightly more effective at differentiating reference and stressed
sites.
Validation
The initial index developed from the calibration data set
classified 89% of the validation sites correctly, with
classification efficiency equalling or exceeding 80% in each
habitat except polyhaline sand (Table 7}. When we examined the
validation efficiency of each metric individually, we found that
the proportion of abundance as carnivore/omnivores was the least
effective metric for differentiating reference from stressed
sites and was the metric that differed most in classification
efficiency between the calibration and validation data sets
(Table 8). We also found that when the carnivore/omnivore metric
was removed from the index, classification efficiency of the
index at validation sites improved slightly (Tables 7).
Therefore, we removed this metric from the index, improving
overall validation efficiency to 93%.
The final index was significantly correlated with total
organic carbon in both the calibration (r «• -0.50) and validation
(r = -0.54) data sets. Ninety-two percent of the samples for
which TOC exceeded 3%, and all of the samples for which TOC
14
-------
exceeded 4%, had an index value less than 3, indicating a
stressed benthic assemblage.
Index scores were significantly correlated (r » 0.84}
between replicates; average difference in index scores between
replicates was 0.32. Ninety-one percent of replicates at the
same site classified the same; at most sites where replicates
classified differently, the replicates had similar index values,
but were close to, and on either side of, the index threshold
value of 3.
15
-------
DISCUSSION
The premise of the IBI approach is that there are selected
quantifiable characteristics of biotic assemblages which are held
in common at reference sites and which differ from those at
anthropogenically stressed sites. Our study found that this was
the case for at least five different metrics, each of which was
effective at discriminating stressed sites in all of the habitats
we studied. Cumulatively, these metrics were 93% effective at
differentiating reference and stressed sites.
Another premise of the IBI approach is that biotic
communities respond to stress in numerous ways, often in a staged
fashion, and that multiple metrics are required to appropriately
integrate these responses (Barbour et al. 1995). Pearson and
Rosenberg (1978) erected a paradigm along these lines for marine
benthos, with different metrics providing better discrimination
of effect at varying distances from sources of stress. Our
results are consistent with the multi-metric premise, as we found
that the combination of metrics provided greater discrimination
than any of the metrics alone (Tables 7 and 8).
We found the most efficient metrics were those based on
pollution-tolerance of species occurring at the sites (Table 8).
Our empirical approach to defining pollution-indicative taxa
differs from most previous efforts at categorizing marine species
16
-------
groups, in which pollution tolerance has been largely inferred
from life history characteristics (Dauer 1993), The two
approaches are not inconsistent, as indicated by the similarity
of our list of pollution-indicative taxa and the lists of
opportunistic taxa from other studies of east coast benthic
macrofauna (Grassle and Grassle 1974, McCall 1977, Dauer 1993) .
One possible reason for the similarity in lists is that our
approach for identifying pollution-indicative taxa does not
discriminate between pollution-tolerant taxa and those that
recolonize quickly following stress events. The similarity of
the lists suggests that the latter is the predominant mechanism.
Our list of pollution-sensitive taxa is less consistent with
previously developed lists of equilibrium taxa. Perhaps the
difference results from incomplete knowledge of life histories
for many benthic organisms, as Seitz and Schaffner (1995) have
suggested. Despite this difference, the pollution-sensitive taxa
metric had a higher classification efficiency than the pollution-
indicative taxa metric for the validation sites. Perhaps this is
because the pollution-indicative taxa are ubiquitous colonizing
taxa, and their presence alone is not necessarily an indicator of
poor habitat conditions at the site; only when the pollution-
indicative taxa constitute a sizable portion of the assemblage do
they become reliable indicators. In contrast, the pollution-
sensitive taxa show a high fidelity to reference sites and may be
the first to die or leave the site as stress occurs.
17
-------
In developing the index, we chose a species richness metric
in preference to species diversity. We did so because the
richness metric was more effective at distinguishing reference
from stressed sites in the calibration (and subsequently in the
validation) data set. Species richness has the disadvantage,
though, of being gear-specific, whereas diversity is less so
(Swing et al. 1988). Me felt comfortable including richness
because we used the same sampling device at all. of our sites. If
the index is applied to historical data, or to data collected
using a different gear type, we recommend substituting a
diversity metric in place of species richness. The thresholds
for the diversity metric based on our calibration data set were
1.9 and 3.2 for all habitats. In our validation data set,
substituting the diversity metric for species richness reduced
validation efficiency to 89%.
Biomass is a metric in our index that is not measured by
some benthic programs because of cost. We found it to be the
least effective of our metrics at distinguishing stressed sites.
It was also the metric that varied most among replicates (Table
9), probably because it can be so easily skewed by a single large
individual, calculating the final index without the biomass
metric, as if these data were not available, reduced validation
efficiency to 89%.
18
-------
Although index development was conducted on a habitat-
specific basis, the metric response values at reference sites
were largely habitat-independent. Applying metric threshold
values averaged across habitat reduced classification efficiency
of the index by only 2% in the calibration data set and not at
all in the validation data set. Weisberg et al. (In press),
conducting a similar effort to establish thresholds for benthic
assemblage response variables at reference•sites in Chesapeake
Bay, also found consistency in response among higher salinity
habitats. Lopez (1988) suggested that many of the factors that
structure benthic communities are similar over gradients as sharp
as those from freshwater to saltwater. Such cross-habitat
comparative studies are rare in benthic ecology; the consistency
of our assemblage metric thresholds across habitats, despite
substantial differences in species composition in the different
habitats, suggests that further comparative work is warranted.
There has been recent debate as to whether the condition of
benthic communities is more appropriately assessed using
multivariate examination of individual species responses, or by
using assemblage level metrics, as was used here (Morris 1995,
Gerritsen 1995). We suggest that these approaches are not
mutually exclusive and may be best employed together;
multivariate approaches are sensitive enough to illuminate even
minor changes in species composition, whereas the assemblage
level approach provides perspective on the importance of those
19
-------
changes, The multivariate approach, though, may be harder to
employ, loth approaches require description of reference
condition. Assemblage level metrics appear to be relatively
robust to physical habitat variation; species composition is not.
The high degree of habitat specificity of individual species may
lead to difficultly in defining reference condition for the
multivariate approach, with false positives resulting if there
are minor differences in natural physical habitat between the
reference and potentially affected sites.
While our B-IBI was validated using data only from the NY/NJ
Harbor, it was developed based on data from a large portion of
the mid-Atlantic coast. One issue that remains unresolved is
whether it is applicable over the larger geographic scale of the
calibration data set, which will be difficult to address because
their are few independent data sets from the east coast with
concurrently collected benthic and stressor data that could be
used for validation. One such data set, to which we applied the
B-IBI, was collected from the Delmarva peninsula (Chaillou and
Weisberg 1995) located at the southern boundary of our
calibration data set. We found that the index validated at all
sixteen sites in that data set that met our criteria as reference
or stressed sites, suggesting that the index is applicable to at
least the southern portion of the province. In contrast to our
NY/NJ Harbor validation, however, the percent of abundance as
pollution-sensitive taxa was only 69% efficient at discriminating
20
-------
sites, indicating that either the taxa list, or the thresholds
used for this metric, may not be uniformly applicable at extreme
ends of the province. Attempts at validation with data sets from
other areas will be required to assess the degree of index
modification necessary to assure that the index is applicable to
the remainder of the Virginian Province.
21
-------
ACKNOWLEDGEMENTS
We would like to thank F. Grassle, J, Grassle, E. Gallagher
A. Cristini, R. Loveland, R. whitlatch and J. Vitaliano who
served as an advisory group and provided many valuable
suggestions during this work. We would also like to thank K.
Summers and J. Paul for access to the EMAP data which was used
to develop the index, and J. Frithsen, N. Roth and C. DeLisle for
their helpful comments on the manuscript. This work was
supported by Contract No. 68-DQ-30013 from the U.S. Environmental
Protection Agency.
22
-------
LITERATURE CITED
ASTM (American Society of Testing and Materials) 1990. Standard
guide for conducting 10-day static sediment toxicity tests with
-*
marine and estuarine amphipods. ASTM Standards Volume 11.04:
1052-1075.
Baird, D. and R. E. Ulanowicz. 1989. The seasonal dynamics of
the Chesapeake Bay Ecosystem. Ecological Monographs 59:329-364.
Barbour, M.T., J.&. stribling and J.R. Karr. 1995. Multi-metric
approach for establishing biocriteria and measuring biological
condition, pp. 63-80 in W.P. Davis and T.P. Simon (eds),
BJtOlogj.cal Assessment_.a.nd_Cr iter ia. Lewis Publishers, Boca Raton,
PI.
Blackburn, T. H. and K. Henriksen. 1983. Nitrogen cycling in
different types of sediments from Danish waters. Limnology and
Oceanography 28:477-493.
Boesch, D. F. 1973. Classification and community structure of
macrobenthos in the Hampton Roads area, Virginia. Marine,,gj.g|ogy
21:226-244.
23
-------
Bonnevie, N.L., R.J. Wenning, s.L. Huntley, and H. Bedbury. 1993.
Distribution of inorganic compounds in sediments from three
waterways in northern New Jersey. Bulletin of Environmental
Contamination and Toxicology 51:672—680.
Bousfield, E. L. 1973. Shallow-water Gammaridean Amphipoda of New
England. Cornell University Press, Ithaca, New York. 312 p.
Brown, S.S., G. R. Gaston, C. F. Rakocinski, R. W. Heard and J.
K. Summers. In press. Macrobenthic trophic structure responses
to environmental factors and sediment contaminants in Northern
Gulf of Mexico estuaries. Estuaries.
Burger, J. and M. Gochfeld. 1993. Lead and cadmium accumulation
in eggs and fledgling seabirds in the New York Bight.
Environmental Contamination and Toxicology 12:261-267.
Chaillou, J.A. and S.B. Weisberg. 1995. Assessment of the
ecological condition of the Delaware and Maryland coastal bays.
Prepared for IPA Region III, Annapolis, MD.
Crawford, D.W., N.L. Bonnevie, and R.J. Wenning. 1995. Sources
of pollution and sediment contamination in Newark Bay, New
Jersey. Scotoxicoloav and Environmental Safety 30:85-100.
24
-------
Dauer, D. M. 1993. Biological criteria, environmental health ana
estuarine macrobenthie community structure. Marine Pollution
Bulletin 26:249-257.
Dauer, D. M.-, and W. G. Conner. 1980. Effects of moderate sewage
on benthic polychaete populations. Estuarj.ne and Coastal Maring
Science 10:335-346.
Dauer, D. M., C. A. Maybury, and R. M. Swing. 1981. Feeding
behavior and general ecology of several spionid polychaetes from
Chesapeake Bay. Journal of Experimental Marine Biology and
Ecology 54:21-38.
Swing, R. M., J. A. Ranasinghe and D. M. Dauer. 1988. Comparison
of five benthic sampling devices. Prepared for the Virginia
Water Control Board, Richmond, Virginia.
Fauchald, K. and P. A. Jumars. 1979. The diet of worms. A study
of polychaete feeding guilds. Oceanography a^nd Marine Biology
Annual Review 17:193-284.
25
-------
Ferraro, S. P., R. c, Swartz, F. A. Cole, and D. W. Schults.
1991. Temporal changes in the benthos along a pollution gradient:
Discriminating the effects of natural phenomena from sewage-
industrial wastewater effects. Estuarine Coastal and Shelf
Science 33:383-407.
Franz, D.R. and W.H. Harris, 1988. Seasonal and spatial
variability in macrobenthos communities in Jamaica Bay, New York
-an urban estuary. Estuaries 11:15-28.
Gerritsen, J. 1995. Additive biological indices for resource
.management. Journal of the North American Bentholooieal sogj^y
14:451-457.
Grassle, J.F. and J.P. Grassle. 1974. Opportunistic life
histories and genetic systems in marine benthic polychaetes.
Journal of Marine Research 32:253-284.'
Gray, J. S. 1979. Pollution-induced changes in populations.
Transactions of the Roval Philosophical Society of London
286:545-561.
Holland, A. F. , N. K. Mountford, M. H. Heigel, K. R. Kaumeyer and
J. A. Mihursky. 1980. The influence of predation on infaunal
r*
abundance in upper Chesapeake Bay. Marine Biology 57:221-235.
26
-------
Huntley, S.L., N.L. Bonnevie, R.J. Wanning, and H. Bedbury. 1993.
Distribution of polycyclic aromatic hydrocarbons (PAHs) in three
northern New Jersey waterways. Bulletinof Environmental
Contamination and Toxicology 51:865-872.
lanuzzi, T.J., S.L. Huntley, N.L. Bonnevie, B.L. Pinley and R.J.
Wenning. 1995. Distribution and possible sources of
polychlorinated biphenyls in dated sediments froa the Newark Bay
estuary, New Jersey. Archives of Environmental Contamination and
Toxicology 28:108-117.
Jorgensen, C. B. 1966. Biology of Suspension Feeding. Pergamon
Press, Oxford.
Kemp, W. M. and w. R. Boynton. 1981. External and internal
factors regulating metabolic rates in an estuarine benthic
community. Oecologia 51:19-27.
Kerans, B. L., and J. R. Karr. 1994. A benthic index of biotic
integrity (.B-IBI) for rivers of the Tennessee Valley. Ecological
Applications 4:768-785.
Long, I. R., D. D. McDonald, s. L. Smith, and F. D. Calder. 1995.
Incidence of adverse biological effects within ranges of chemical
concentrations in marine and estuarine sediments. Enytronnie_nt.ail
Management 19:81-95.
27
-------
Lopez, G. R. 1988. Comparative ecology of the macrofauna of
freshwater and marine muds. Limnology and Oceanography 33:946-
962.
McCall, P.L. 1974. Community patterns and adaptive strategies of
the infaunal benthos of Long Island Sound. goufgaJ. of Marine
Research 35:221-266.
Norris, R.H. 1995. Biological monitoring: the dilemna of data
analysis. Journalr_o_£rfche North American Benthologj.cal Society
14:440-450.
Paul, J.F., K.J. Scott, A.F. Holland, S.B. Weisberg, J.K.
Summers, and A. Robertson. 1992. The estuarine component of the
U.S. EPA's Environmental Monitoring and Assessment Program.
Chemistry and Ecology 7:93-116.
Pearson, T. H. and R. Rosenberg. 1978. Macrobenthic succession in
relation to organic enrichment and pollution of the marine
environment. Oceanography and Marine Biology Annual Review
16:229-311.
Peven, C.S., A.D. Uhler, R.E. Hillman and W.G. Steinhauer. 1996.
Concentrations of organic contaminants in Mytllus edulis from the
Hudson-Raritan estuary and long Island Sound. Science Of the
Total Environment 179:135-147*
28
-------
Rhoads, D. C. and D. K. Young. 1970. The influence of deposit
feeding organisms on sediment stability and community trophic
structure. Journal of Marine Research 28:150-177.
Seitz, R. D/ and L.C. Schaffner. 1995. Population ecology and
secondary production of the polychaete Loimia medusa
(Terebellidae). Marine Etiology 121:701-711.
Simon, T.F. and J. Lyons. 1995. Application of the index of
biotic integrity to evaluate water resources integrity in
freshwater ecosystems, pp. 245-262 in W.p. Davis and T.P. Simon
(eds), Biological Assessment and Criteria. Lewis Publishers, Boca
Raton, Fl.
Stainken, D. and J. Rollwagen. 1979. PCS residues in bivalves
and sediments of Raritan Bay. Bulletin of Environmental
Contamination and Toxicology 23:690-697.
Steimle, F.W. and J. Caracciolo-Ward. 1989. A reassessment of
the status of the benthic macrofauna of the Raritan estuary.
Estuaries 12:145-156.
Stull, J.K., C. I. Haydock, R.W. Smith and D.E. Montagne. 1986.
Long-term changes in the benthic community on the coastal shelf
of Palos Verdes, Southern California. Marine Biology 91.:539-551.
29
-------
Warwick, R. M. 1986, A new method for detecting pollution effects
on marine macrobenthie communities. Marine Biology 92: 557-562,
Warwick, R. M. and K. R. Clarke, 1991. A comparison of some
methods for analysing changes in benthic community structure.
Journal of the Marine Biological Association of the United
Kingdom 71:225-244.
Weisberg, S.B., J.A. Ranasinghe, D.M. Dauer, L.C. Schaffner R.J.
Diaz and J.B. Frithsen. In Press. An estuarine benthic index of
biotic integrity (B-IBI) for Chesapeake Bay. Estuarjgg.
Williams, S.C., H.J. Simpson, C.R. Olsen and R.F. Bopp. 1978.
Sources of heavy metals in sediments of the Hudson River estuary.
Marine Chemistry 6:195-213.
Wilson, J. G. and D, W. Jeffrey. 1994. Benthic biological
pollution indices in estuaries, p. 311-327. In «T- M. Kramer
(ed-}* Biomonitorinqof Coastal Waters and Estuaries. CRC Press,
Boca Raton, Florida.
Word, J. Q. 1978. The infaunal trophic index, p. 19-39. In W.
Bascom (ed.), Coastal Water Research Project, Annual Report for
the Year 1978, Southern California Coastal Water Research
Project, El Segundo, California.
30
-------
Zongwei, C., V.M.S. Ramanujam, M.L. Gross, A. Cristini, and R.K.
Tucker, 1994. Levels of polychlorodibenzo-p-dioxins and
dibenzofurans in crab tissues from the Newark Bay/Raritan Bay
system. Environmental Science and Technology 28:1528-1534.
31
-------
Table l. Physical/Chemical Analytical Methods
Parameter Method
PAHs Methylene chloride extraction; determination
by GC/MS
PCBs/Pesticides Methylene chloride extraction; determination
by HRGC/ICD
Major and Trace HN03 and HF acid digestion: Hg - CVAAS;
Elements eu, Ni, Pb, Cr, Sb, Sn, As, Se, Ag, Cd -
GFAAS; Al, Fe, Mn, Si, Zn - PAAS
Dioxins & Furans Extraction with toluene; determination by
HRGC/HRMS; second column confirmation for
2,3,7f8-TCDD
TOC Acidification with H3P04; determination using
a CO2 analyzer
Grain size Sieving and pipette analysis
32
-------
Table 2. Mean benthic assemblage values at reference sites (top
number) and stressed sites (bottom number). Top
number shaded indicates pair is different by Mann-
Whitney test; bottom number shaded indicates different
by Kolmogorov-Smirnov test.
Polyhaline Polyhaline Euhaline Euhaline
Sand Mud sand Mud
Species Diversity
Number of Taxa
Shannon-Weiner
Abundance and Biomass
Abundance (#/m2)
Biomass (g dry
Specie* Composition
Percent of abundance
as pollution-
indicative taxa
Percent of abundance
as pollution-
intolerant taxa
Trophic Composition
Percent of abundance
as carnivore/
omnivores '••
Percent jaf abundance
as deposit feeders
Percent of abundance
as suspension
feeders
9111
8656
4.9
15.2
11.4
68.1
18.5
1.9
,17.0
"•5.5
42.1
34.6
40.4
59.8
21.2
6.0
2.81
1.01
7319
6638
22. .i; ;':.:•
15.1
79.3
18.3
0.1
14.8
5.2
40.6
24.7
44.6
66.5
28.3
14.7
3.04
0.85
11686
•^y^£w$
1.7 ";|?
12.4
52.2
30.2
0.2
13.2
0.5
27.1
27.8
59.5
38.4
24.0
7.5
2.73
1.11
^Sm^
- . /., :SlA K
:'::,:jiii-:*™-:;»SS':
14.6
90.5
8.8
0.1
13 „ 1
2.0
53.5
56.3
33.3
41.7
33
-------
M-4
o
ence
IM
M
O
u
o
^* f1^
vo r>
« *
00 P*
t , t
dance pen
-indicatj
c c
3 O .M
& -H
n
(U
If)
If)
vo
CO
00
fvj
CD
o*
u
V) 4J
0 O
Si 1
••H M
O 0
U)
0)
4J
W
0)
-P
0)
Rf
1
cr>
-------
i
o
•p
3 4J
<-i QJ
*H W
O
a is
4-1
M-l fl
0 *Q
*
<-i ir>
O O O ff>
in (<•> 00
« • « «
(*» M> in i-»
OOOOolOOOOOOOO
ddddddddddddd
o <* o
* t •
o o o
o o o o
« * * *
o a o o
« «)
u w
O(NOOOOO<-(OOOO'-l
r-
« -H
>M at
«M
«o co
o vo n
* « *
o m«-«
n * e»
* • *
00 1*1 GO
N «-• CO
O O O «
* » * *
o o o o
m w ««-»
* * * *
«O
U X
C (0
(0 4-1
•O
C 0)
3 >
43 *H
A 4->
•H
c en
A c
& V
s n
0)
o
* Q
«
•0 *H —<
01
WH
§
*H M 0, b> « U O
o m M -w m N
O'N Vi
|5
«
in ty 03
4) Q, Qt
Q, O»'H -1 Ol
«
+»
«
J
U
>1
^
0
CM
i 3
q u
*H W W
tlM C
~1 -H O
O Cn
w m
-------
m
0
n
e
o
e
o
U
09
0
a
n
a
4J
0)
w
IB
+J
(0
•o
c
o
•H
4J
C
0
0
W
9)
0
o
e
0
fc
0
(0
0
c
•H
W
01
-p
•N
W
o
^
10
41
•H
A
1
w
I?
«
0
|
0
10
o
4J
Tl
n
«*«>
o
Al
•d
3
£
u
r-t
•H
to
O
V
•0
C
1C
en
to
fH
U
•H
10
O
Al
1
-------
n
i-i
i
n
in in in in
V V V V
0 0
O 0
O M O
- 0 -
rt in
o o
o o
in u o
H°£
O O
o o
in u o
- 0 »
i-4 O
m
0 O
O 0
OHO
'» 0 »
o» in
CO CO
* *
O r4 O «1
V V V V
o o o o
A A A A
as
i
x
0)
j"j
+J
T1 n
U
U
21
0
•^
i
in
in
O O in in
r4 w x
c c
-4-4 V O
*•!•* C C
« M»4»4
n JS JR <-t i-t
«><>••«
1 i-4 ^1 45 JS
S
££,§
n
I
•^
4J —
3i«
3 0*0
O d *fl 3 "0
5 " w * ctJ
gggSS
M B-H -H o «
m > -« -< C C
0 ^4 * *-^-4
S°3* J3Mf4
« >•>•«*
I^llii
fl e
-------
m
a) 9
0) M X
0 0)
e c
Q-O
S e-0
42 * 9 *O
lwsg'§
^g i OJ WX
0 >5 5 oj t)
*M ^J J M •«
QJ Q 1-1 t~t K K
o n 2 2 "2*2
G O-S.-S.'i'i
3 ^
-------
I
CQ
0)
A
•O
O
-H
•H
CQ
U
(T3
U
>.
^J
p^j
O
0)
O
O
0)
U
(0
4-1
R3
-0
C
O
•H
(0
-0
•H
rH
U
•H
i-l
1
f-l
• i
Correct
«
0
, u
N
0
ft
4*
i
*
4J
•H
*
•H
1
*ff
s
U
>1
r-t
JJ
lorrec
V
4J
Percei
^•^
2
Ajutuo/a
0
•H
C
M
U
•o
•Pi
•H
H
i
H
U
n
«
+J
O
•H
N
0
X
o
O
o
o
H
O
ov
n
o
o
so
o
CO
00
00
en
0)
CJ
w
o
4-1
«H
>
•o
10
C
0)
U
M
0)
CM
(U
r-l
A
A3
4-»
•H
?
H
(Q
4J
«
4J
•O
(0
0)
c
(0
ft
pH
o
CM
•O
3
•o
c
(8
(0
1
10
I
r-l
H
«B
-------
to
O
c
I
I
-------
0)
c
•H
10
0)
o
fl
u
o
a
0)
M
I
C
0
•rt
4-*
(0
0
O
o
•H
N
i
X
0)
to
0)
•H
O
CO
o
M
s
(Q
H.
•H
O
0)
c
•H
0)
0
c
c
ia
A
en
•o
c
U3
(0
10
6
O
«H
a
O
•H
•d
H
i
C
o
•H
4J
(0
o
(tJ
•o
I
C
o
o
M
0)
CM
-P
•H
W
C
0)
CO
I
c
o
O
04
tn
03
u
(C
•o
I
C
4>
O
k
0)
CU
-------
Appendix D
Aluminum-normalization procedure
-------
comparison of sediment metal;aluminum relationships between the
eastern and gulf coasts of the United States
S. B. Weisberg1*
H. T. Wilson2
D. G. Heimbuch2
H. L. Windom3
J, K. Summers4
9200 Rumsey Road
Columbia, MD 21045
2Coastal Environmental Services
1099 Winterson Rd.
Linthicum, MD 21090
3Skidaway Institute of Oceanography
10 Ocean Science Circle
Savannah, GA 31411
4US SPA
Sabine Island
Gulf Breeze, FL 32561
*Present Address:
Southern California Coastal Water Research Project
7171 Penwick Lane
Westminster, CA 92683
Stevew@sccwrp.org
-------
INTRODUCTION
Metal contamination of sediments is a concern to the normal
function of estuarine and nearshore systems [1,2J. A portion of
the metals in sediments comes from natural weathering of crustal
rocks, with naturally higher concentrations of metals occurring in
finer-grained fractions of sediments. One challenge in assessing
the spatial extent of metal, contamination is separating
anthropogenic contributions of metals from natural contributions.
Several techniques have been developed for making this
distinction [3], the most popular of which is aluminum-
normalization [4,5,6,7,8,9]. Using this approach, aluminum is
treated as a conservative tracer of the natural metal-bearing
phases (i.e., aluminosilicates) in the fine sediment fraction.
Anthropogenic contributions to aluminum concentrations are trivial
compared to the natural contribution, and the natural metal-to-
aluminum ratio should be relatively constant within a region and
similar to the crustal ratio [10] or to the ratio observed in
source rocks in the regional watershed. Using the normalization
approach, a set of uncontaminated sites are identified, and
statistical relationships between each metal and aluminum are
identified for those sites. Significant deviation from those
relationships indicates anthropogenic enrichment.
Metal-to-aluminum ratios have been determined on several
spatial scales ranging from individual estuarine systems [11,12,13]
-------
to entire countries [7,8,14]. Differences in metal-to-aluminum
ratios have been found among studies conducted on these different
spatial scales, which could be explained by regional differences in
the ratios within source rocks or by differences in the
»
fractionation of metals between soluble and particulate phases
during weathering. These geologic explanations, though, are
confounded by 'differences in data analysis approaches used by
various investigators in defining the relationships. The most
important methodological differences among such studies are in the
assumed functional form of the relationship and in the means of
ensuring that only non-contaminated sites are included in the data
sets used to derive the relationships.
The uncertainty concerning differences in normalization
techniques among studies hampers inter-regional comparisons of
anthropogenic influence. If the metal-to-aluminum ratios in
crustal or source rock, or weathering characteristics differ
between regions, then locally derived aluminum relationships would
be the most appropriate basis for such comparisons. Alternatively,
if differences in metal-to-aluminum ratios are artifacts of data
analysis techniques used to define the relationship, then using
locally derived relationships would bias inter-regional
comparisons. This paper addresses these concerns by applying a
common analytical methodology to data collected on the Atlantic and
Gulf coasts to identify the most appropriate spatial scales for
developing aluminum-normalization curves.
-------
METHODS
Data Sources
Sampling was conducted in two biogeographical provinces: the
Virginian Province, extending from Cape Cod to Cape Henry on the
Atlantic coast (Figure I), and the Louisianian Province, extending
from Tampa Bay to the Mexican border along the Gulf Coast (Figure
2) . Sampling in the Virginian Province was conducted from late
July to early September annually between 1990 and 1993. Sampling
in the Louisianian Province was conducted from July 1 through
August 30 annually between 1991 and 1994. Between 100 and 160
sites were sampled in each province each year. Sampling sites were
selected using a stratified random design in which the estuaries
were classified as large estuaries (surface area >250 Jem2); large
tidal rivers (surface area >250 km2 with an aspect ratio of 18Jl or
greater); and small estuarine systems, which included all other
systems with a surface area of at least 2.5 km2. Sampling sites
within each stratum were selected randomly.
Sediment samples were collected at each site using a 440-cm2
Young-modified VanVeen grab. A teflon spoon was used to remove the
top 2 cm of sediment to a clean glass jar with a teflon lid, which
was stored frozen. Metals were analyzed in the laboratory by
HF/HNO3 digestion, followed by inductively coupled plasma mass
emission spectrometry (Ag, Al, Cr, Cu, Fe, Ni, Pb, Zn), microwave
-------
digestion using HN03/HC1 followed by graphite furnace atomic
absorption spectrophotometry (Cd, Sb, Se, Sn) , or cold vapor atomic
absorption spectrophotometry (Hg) . silver, antimony, selenium, and
tin were measured in the Virginian Province only during the last
three years... Reagent and procedural blanks were analyzed to check
for laboratory contamination during processing. Approximately
every tenth sample was split and processed as a laboratory
duplicate. In addition, National Research Council of Canada
Certified Reference Material BCSS-1 was analyzed with approximately
every 10 samples to assess accuracy and precision.
Analsis
The relationship between the concentration of aluminum and a
response metal was estimated based on a linear model:
Y = (J *A1 + m * e
where ,
y * concentration of the response metal
p = slope relating the response metal to aluminum
Al = aluminum concentration
m - intercept
e - random measurement error
-------
Anthropogenically contaminated sites were removed from the
data set by comparing the residual of the regression with an
estimate of laboratory measurement error. This approach was based
on the premise that if the data set did not include
anthropogenfcally enriched sites, the mean square error (MSE) fron
the regression would equal measurement error. If the MSE exceeded
measurement error, the site with the highest residual in the model
was removed, and the model was reparameterized. This procedure was
repeated until the MSE was no greater than measurement error.
Laboratory measurement error . was estimated based on repeated
measurements from blind laboratory duplicates and standard
reference materials.
To compare metal-to-aluminum relationships between the two
provinces, we applied our estimation method separately to data from
each year, providing four independent slope and intercept estimates
for each province. These annual estimates for each province served
as replicates to test whether the intercept differed significantly
from zero, and whether slope or intercept differed between
provinces, based on a t-test (a-0.05). Initial applications of
the model included an intercept term. If the average intercept for
a given metal did not differ significantly from zero in our initial
runs, the regression was recalculated with a no-antercept model.
If the intercepts were significantly different from zero, but not
different between provinces, the regression was recalculated after
setting the intercept egual to the average intercept between
-------
provinces. If the intercepts for both provinces were equivalent,
the four independent yearly estimates were used to assess whether
slopes differed significantly between provinces.
-------
RESULTS
Mean and maximum aluminum concentrations differed between the
two provinces by about 15%. Mean concentration of other metals
differed by* a substantially greater margin, with differences of
100% or more for 5 of the 12 metals examined (Table 1). For every
metal except aluminum, the mean and maximum observed concentrations
were higher in the Virginian Province,
Six of the 10 metals we examined had intercepts that differed
significantly from zero in at least one of the provinces (Table 2).
In all cases where the intercept was significant, the intercepts
were positive values. For only three metals did the intercept
differ significantly between provinces. Most intercept values were
small compared with the mean value for the province; however, for
silver in the Louisianian Province and selenium in the Virginian
Province the intercept values were almost half of the mean values
for the province.
Of the six metals that had an equivalent intercept between
provinces, only three (Hg, Pb, Ni) had significantly different
slopes (Table 3). For each of these metals the slope was higher in
the Virginian Province than in the Louisianian Province. For
nickel, the slope difference was only 30%; for lead and mercury,
the difference was almost 100% (Table 3) .
-------
The number of samples removed from the regression based on
deviation greater than measurement error differed substantially
among chemicals and between the provinces (Table 4}. Thirty-six
percent of the sites in the Virginian Province and 22% in the
Louisianian ..Province were eliminated from the regression for at
least one chemical. Most of the sites that were eliminated for one
chemical were eliminated for several chemicals (Table I). The
spatial pattern of eliminated sites was highly clustered, with most
sites occurring around the major cities of New York, Philadelphia,
Baltimore, Galveston, Mobile, and New Orleans (Figures 1 and 2).
10
-------
DISCUSSION
Most of the differences in metal-to-aluminum ratios between
the mid-Atlantic and Gulf coasts of the United States were small.
Slopes differed by more than 30% only for mercury, lead, silver,
and selenium, and comparisons for the latter two were confounded by
differences' in intercept. Differences in slope or intercept were
mostly limited to metals with small natural concentrations; there
were no significant differences in slope or intercept for the
naturally most abundant metals (e.g., copper, zinc, chromium).
We used a new approach for ensuring that only unenriched sites
were used to estimate the natural metal-to-aluminum relationships
within a province. Previous authors have used a variety of
techniques for accomplishing this objective. Some authors have
removed sites with large concentrations or sites where biological
effects are suspected [9], This may lead to a shallower slope if
naturally occurring high concentrations are removed from the
regression. Other authors have screened their sites based on the
uses of the surrounding land by equating low population density or
absence of known point sources with a lack of anthropogenic input
[5,7,8,14]. This approach is probably suitable for sparsely
populated areas, but becomes highly subjective in densely populated
areas such as the Virginian Province.
11
-------
Our approach is most similar to that of Schropp et al. [5], in
which sites were sequentially eliminated from the regression until
the residuals were distributed normally. Our approach, however
uses additional information to identify a quantitative stopping
rule for data removal; Schropp et al.'s approach of examining
kurtosis is more subjective. Our approach, though, requires an
unbiased estimate of measurement error, which can be hard to
develop because many laboratories fail to quantify error or do so
as part of a performance evaluation in which the analyst knows
which samples are being used for the test.
Our approach also assumes that the' study area encompasses
enough unenriched sites to define a baseline relationship. This
may not be the case for lead in the Virginian. Province because
atmospheric deposition is a primary source of lead. If atmospheric
deposition enhanced concentrations equally everywhere, then our
approach would quantify th* deposited lead as an addition to the
intercept term. If atmospheric inputs varied within a region, or
if these additions bound disproportionately to the fine-grained
sediment, our approach would quantify the additional lead as an
increase in slope. The higher slope we observed in the Virginian
Province probably reflects widespread enrichment, and the lead-to-
aluminum relationship we defined for the Virginian Province may
underestimate enrichment.
12
-------
An alternate approach g , .
- «-, —..-.. ,.m
Narragansett Bay
— - :':;;:; r zinc'
-PI., were Ms.cte, by and withln ^ ' '" °* th6
r^r...ion line (Flgure 3) ^ " *" DB"Ure"ent S" ««
' rel«ionship for Had was steeper
ha suested by the , =onflrming our con;;;nr
slope than suggested
'"
ratios t «« «-^ to
rat.os IB pre-industria! cores fron the Mississinci »•
Texas rial n * "isslsslppi R1Ver [17] and
Texas ,„,. Data for aZ! metals, except copper, „„.
the measurement error «^
°" Beta--
ror «^
(Piaure4, , °" Betal-t0-alu-in™ relationships
13
-------
but had a shallower slope than the data from the Mississippi River.
Our shallower slope for copper than in sediments from the
Mississippi River is not an artifact of our data analysis because
we eliminated very few data points in identifying the relationship
between copp*er and aluminum,
We also compared our metal-to-aluminum relationships with
those identified by other authors working in our geographic study
areas and found considerable similarity for all metals except lead
in the Virginian Province. For lead, most previous studies
suggested a relationship more similar to the slope we found for the
Louisianian Province. Interestingly, all of the previous studies
found slopes for chromium in the Virginian Province that were
equivalent to or less than those we found in the pre-industrial
cores (Figure 5). It is unclear why samples of pre-industrial
sediment contained larger chromium-to-aluminum ratios than those
estimated in all other studies, but suggests the earlier data may
contain a systematic analytical error. Standard reference
materials were not readily available during the earlier studies;
therefore, researchers had no way to assess the guality of their
data, and because of its refractory nature, chromium is a difficult
metal to analyze accurately. Perhaps our disagreement with copper
data for the Mississippi River can be explained similarly.
One substantial difference between the relationships we
defined and those defined in other studies is the magnitude and
14
-------
sign of the intercept term
,. ,„:».;„
-
•— -
- -
of
concentrations
.
for . negative int
relatlonshlp is ;as;; -•
conservative
which n&tu
contain large
„.
introducefl
analytical
~
.;;
.
15
-------
One shortcoming of our analytical approach is that we were
unable to incorporate a measurement error tern for aluminum. To
determine if our results were sensitive to this shortcoming, we
used the same analytical approach employing iron, which is also
abundant in crustal rock, as the conservative tracer and tested to
see if the same samples fell outside the background relationship.
Eighty-three percent of the samples that we identified as enriched
by aluminum-normalization were also identified as enriched by
normalizing to iron. Another 9% were identified as enriched by the
iron analyses only. Re-running our models, eliminating only
samples that were identified as enriched in both the iron and
aluminum analyses, had a negligible effect on the slopes of our
metal-to-aluminum relationships.
One issue that we chose not to address in our analysis was
roean-to-variance relationships. The data suggested a small mean-
to-variance relationship in laboratory measurement error for most
metals. Adjusting our model to exclude points based on a mean-to-
variance relationship resulted in eliminating most samples closest
to the origin. This difficulty arose because measurement error was
not a linear function of concentration; rather there was a "nugget
effect" in which measurement error relative to the mean increased
at lower concentrations. We had too few replicate data at low
concentrations to quantify the nugget effect. Modelling
measurement error would be a fruitful area for refining our
approach.
16
-------
One advantage of our approach for examining netal-to-aluminum
relationships is that our results can be applied easily to other
data sets that either are too small or are collected from
geographic areas that are too enriched to identify metal-to-
aluminum relationships. Within our study areas, the base metal-to-
aluminum relationship is constant for most metals. The only thing
that changes among studies is the allowable deviation from these
relationships. We suggest that there are three components of
allowable deviation: (1) variance of the slope estimate, which can
be estimated from the variability among our four independent slope
estimates (Table 6); (2) variance of the intercept (where
appropriate), which also can be estimated from our four yearly
estimates (Table 6); and (3) measurement variance of the specific
study. The probability that a sample has an enriched concentration
of a metal can be estimated by dividing the difference between the
observed and predicted concentrations of the metal by the square
root of the sum of the three sources of error and comparing the
quotient to standard normal critical values. Samples with
quotients exceeding 1.96 have a 95% probability of enrichment.
17
-------
ACKNOWLEDGEMENTS
The authors thank Gail Sloane for helpful comments on the
manuscript. This work was supported by the U.S. Environmental
Protection Agency under contract #68-00-30013.
IS
-------
LITERATURE CITED
[1] Furness, R.w. and P.S. Rainbow. 1990. Heavy
in
marine environment. CRC Press. Boca Raton, FL.
[2] O'Connor, T. P. and c. N. Ehler. 1991. Results from the NOAA
National Status and Trends Program on distribution and effects
of chemical contamination in the coastal and estuarine United
States. Environmental Monitoring and Assessment 17; 33-49.
[3] Luoma, S.N. 1990. Processes affecting metal concentrations in
estuarine and coastal marine sediments. pp 51-66 in R. W.
Furness and P. s. Rainbow (eds) Heavy metals in the marine
environment. CRC Press. Boca Raton, Fl.
[4] Windom, H.L., S.J. Schropp, F.D. Calder, J.D. Ryan, R.G.
Smith, L.C. Burney, F.G. Lewis and C.H. Rawlinson. 1989.
Katural trace metal concentrations in estuarine and coastal
marine sediments of the southeastern united states.
Environmental Science and Technology 23:314-320,
[5] Schropp, S. J., F.G. Lewis, H. L. Windom, J. D. Ryan, F. D.
Calder, and L. C. Burney. 1990. Interpretation of metal
concentrations in estuarine sediments of Florida using
.aluminum as a reference element. Estuaries 13:227-235.
19
-------
0. H.
estuarine and coastal sedlfflents.
Science 48: 101-115.
of elemental contamination in estuarine and ^
environments based on geochemical ,nd statistical
Marine Environmental Research 36:237-266
K.O. an. T.P. 0,Connor.
eaementa! sedinent contamination in the coa.ta! Unit., states
Environmental science and Technology 29:470-477.
summers, „.. T.L. Wade, y>D_ ^
P-s. »,ormali2ation of Mt.l concentration. in Mtuarin.
sediments from the Gulf of Mexico. Estuaries.
Tavio, .... and ....
•volution of the earth's crust: Rare earth ele».nt evidence
from sedimentary rocK8. Philosophical Transaction, of the
Royal Society, London A 301:381-399.
.....
»»• Ponution history of the savannah Rive, estuary
Environmental science and Technology 13:588-594. '
20
-------
.[»] KlinKha^er, «. p. and M. L.
distributions in the Hudson River estuary. Estuarine, coastal
and Shelf Science 12:629-643.
[", Trefry, j.H. and ..,. Preslev. 1986
fro. San Antonio Bay and the northwest Gulf of Mexico
Environmental Geology 1:283-294.
U4] Din, 2.B. !,M. Use of
fro. estuarine and coasta! s.diaents of straits of «.la)M.
Marine Pollution Bulletin 24:484-491.
t»] Goldberg, E.D., «. Gamble, ,.,. Griffin ^ M_ ^^ ^^
PoUution history of Narragansett Bay as recorded in its
sediments. Estuarine and coastal Marine science 5!5<9-56i.
II.] Goldberg, ,.„.. v. Hodge/ „.
o.P. Bricker, G. Matisoff, G.R. Hoiden and R. Braun. 1978
A pollution history of Chesapeake Bay. ceochi»ica et
Cositochiinica Acta 42:1413-1425.
t»J Trefry, ,.,. 1977. The
Mississippi River and their fate in the Gulf of Mexico. Ph.D
Dissertation. Texas AH, University, College Station, TX.
[18] Presley, B.J. pers. conm.
21
-------
Table 1.
Mean and maximum concentrations (ppm, except for
aluminum, which is percent) of metals measured
in each province. Table is based on all data
collected.
Mean Concentration Maxiaun Concentration
Virginian fcouiaianian Virginian I,owi«iani«n
Province Province Province Province
Aluminum
Silver
Cadmium
Chromium
Copper
Mercury
Nickel
Lead
Antimony
Selenium
Tin
Zinc
4.1
0.4
0.5
48.2
30.9
0.1
18.3
62.6
1.0
0.4
3.3
115.6
4.6 9 . 8 13.8
0.1 9.7 o.9
0-2 8.0 1.5
43*5 365.0 149.0
11.3 680.0 104.0
0.1 3.3 0.4
16.? 136.0 51.2
16.4 13,600.0 610.0
0.6 152.0 3.8
0*3 9.1 1 8
" * * J. . O
1-4 48.7 13.5
64.3 1,090.0 625.1
22
-------
Table 2. Annual intercept estimates for each province (ND =
no data). Asterisk indicates the mean intercept
value was significantly different from zero in the
Virginia Province; & indicates the same for the
Louisianian Province.
Virginian Province Louisianian Prorinc*
Metml 90 91 92 93 91 92 93 94
Ag* ND -0.005 0.029 -0.008 0.083 0.019 0.055 0.049
Cd*A 0.098 0.114 0.107 0.262 0.058 -0.004 0.085 0.086
Cr -0.694 -1.762 0.976 -4.85 5.340 3.009 -0.488 1.389
Cu -1.48 -1.07 -1.21 0.137 0.203 -0.359 -0.957 0.448
Hg** 0.013 0.017 0.007 0.000 0.018 0.012 0.015 0.000
Ni -5.88 -2.04 3.31 -4.29 -0.381 0.334 -1.18 2.36
6.97 2.85 1.58 -0.41 3.11 1.19 1.40 2.57
ND 0.066 0,158 0.265 0.216 0..083 0.162 0.160
ND 0.159 0.179 0.352 0.057 0,062 0.056 0.106
Sn* ND 0.087 0.210 0.225 0.081 0.010 0.189 -0.040
Zn 6.47 -3.57 3.05 -2.05 1.28 0.89 • 4.01 0.63
23
-------
•o
Q>
w
ft)
9
*4
«
0,
0
O
o
*
o
O
o
n
o
00
o
o
o
fH
o
d
m ao
o* irt
O i-l
C
V
•O
01
O
c
•H
O
u
a
«
o
in
i-t
O
. O
i-s
CM
n
o
o
I-l
CM
tn
r*
o
CM
Cn
O
o
o
in
CM
CM
n
i-i f*.
m u>
CM O
10
CO
r-4
o
o
CM
CM
It
O
in
f»
0
s
Gh
a
a
O
o
CO
iH
O
*
o
o
d
O
r»
(N
O
o
o
d
in
(M in
O -4
0
IM
W
0)
4-1
e
W
0)
0)
a
o
iH
W
i— I
IS
c
c
•
n
m
m
*
10
&
o
n
0)
4-)
I.
4J
10
B
•H
4J
S
*
O
5 ••«
^ &
> **
0
a.
a
•rt
N
•H
O
^
»H •
«
V
o
CM
O
*
o
o
z
01
to
o
o
*
o
en
o
m
«
O
n
CM
•
O
o
2
c
CM
«
fM
fWlf
O
*
in
«
CM
O
*
V
Ml
^^
r»
CM
4
n
<-i
c
-------
Table 4. Percent of sites removed from the regression for each
metal in each province.
Virginian Province touiaianimn Province
Silver
Cadmium
Chromium
Copper
Mercury
Nickel
Lead
Antimony
Selenium
Tin
Zinc
20,0
12.5
17.4
41.8
13.7
0.5
32.9
10.5
35.7
12.5
51.6
0.0
0.0
6.1
0.8
0.0
0.0
0.3
2.8
17.1
0.0
11.2
25
-------
Table .5. Frequency distribution of number of metals removed
from the regression
Number of
Metals Removed
Percent of sites
Virginian Province Louiaianian Province
41.4
66.1
1
2
3
4
5
6
7
8
9
10
11
14.9
11.5
7.5
5.3
3.6
4.3
3.8
2.8
3.2
1.3
0.4
IS. 6
9.2
2.0
1.4
0.9
0.8
0.2
0.2
0.0
0.0
0.0
26
-------
Table 6. Variance associated with parameter estimates for the
metaljaluminum models in each province
Metal
Silver
Silver
Cadmium
Chromium
Copper
Mercury
Mercury
Nickel
Nickel
Lead
Lead
Antimony
Selenium
Selenium
Tin
Tin
Zinc
Province
iouisianian
Virginian
Both
Both
Both
Louisianian
Virginian
Louiiianian
Virginian
Louisianian
Virginian
Both
Louiaianian
Virginian
Louisianian
Virginian
Both
Slope
0.0146
0.0330
0.0323
9.2442
2.6451
0.0082
0.0164
3.5914
4.6826
2.8223
4.6600
0.0776
0.0385
0.0308
0,295?
0.5009
13.0336
Slop*
Intercept Variance
0.0114 0.0
0.0
0.1008 0.0
0.1233
0.0338
0.0103 0.0
0.0103 0.0
0.022?
0.0390
2.0954 0.0129
2.0554 0.2461
0.1586 0.0
0.0703 0.0001
0.2296 0.0
0.0008
0.173? 0.0003
0.248S
Intercept'
Variance
0.0002
0
0,000?
-
-
0.0
0.0
• -
-
0.5509
O.SS09
0.0007
0.0001
0.0038
-
0,0019
«•>
27
-------
Figure 1. Number of metals found to be anthropogenically enriched
at study sites in the Virginian Province.
Figure 2. Ntitaber of metals found to be anthropogenically enriched
at study sites in the Louisianian Province.
Figure 3. Metal-to-aluminum relationships in pre-industrial
sediment cores from the Virginian Province and samples used in the
present study. Dashed lines represent 95% confidence intervals
based on laboratory measurement error. Circles are from Goldberg
et al.'s Core #1314 [16]. Squares are from Goldberg et ai.'s Core
#1411 [16]. Asterisks are from Goldberg et al.'s Core #7408 [15J.
Figure 4. Metal-to-aluminum relationships in pre-industrial
sediment cores from the Louisianian Province and those from the
present study. Dashed lines represent 95% confidence intervals
based on laboratory measurement error. Squares are data from the
Mississippi River [17]. Asterisks are data from Texas [18].
Figure 5. Chromium-to-aluminum relationships among several studies
and in deep sediment cores. Symbols are the same as in Figure 3.
28
-------
Appendix E
Tables:
E-l) Area-weighted mean concentrations
E-2) Percent of area exceeding ERM values
-------
2
•4^
0>
o
a
O
« S
11
g-S
u«
-g o
s
ffi
^
^ ^
H
hJ ffi
|l
o
ctf cd
a «
*->
i—
o
1
K
ft
t/3
i-l
H
W
in vo
vo m
CN -H
Tf CN
l-» CO
CN ^t-
vo -H
in i-«
o m
t-- m
vo ^3"
in -H
vo vo
o vo
r- •<*
oo vo
co -H
CO O
VO CN
vo -H
CN '-i
oo r~-
co -H
in CN
•3- CN
CO •*
^r -H
^
_d
j=
_ ON
oo ""1
*-^ Cm~^>
(^^ II
o ^
• Q
""
t oo
-?
• C5
-H
§2
»." f^>
£^p II
" f~^
^ -H
^
Antimoi
00 £J
od q^
^> S
• Q
""
Tt" ^^
P """•
r^ ON
O ^f
O CN
! 1 1
in
in ^
0 ^
co m
CO O
O CN
— ' -H
Arsenic
^ 2
^_~ f~ ^
^j ij
o 2]
•—i* ^^>
""
ro ^^
ON "1
• (*" *j
^j _U
CN ^
^^." C^
fl II
r^ ^
a
Cadmiu
Tf VO
in ON
co -H
vo °
00 ^j1
^T fN|
CN ^
ON ^j
• in
r^- ' — '
^-H OO
CO' O
CO ^H
— ' -H
ON ^H
O ON
22;
^ -H
a
3
Chromi
ON'^
5 ^2
o ' — '
^ -H
CN in
o r^
^-i m
-1 -H
gl
00
CN ^|
^P
41
0
^ 4H
cu
O
U
O CN
'vO C"^*
**«O ^J"
o ^r
— ' 4H
ON in
^_, vo
00 CN
CN -H
^ ON
_• oo
g o
CN ~H
ON ON
o in
r^ o
CN •*
CN -H
CN 00
0 0
OO CO
ON oo
co 41
CO O
OO CO
OO CN
^ -H
vo o
OO ON
•?t oo
CO CN
CN -H
o
HH
ON >— i
^-< in
CN -H
oo ON
CO CO
t^ ON
m -H
^ ^ ^"
ON ^j1
VO ^j1
CN O
ON C~~-
CO O
ON VO
— ' -H
ON <2
CN ^
^
CO
00 ^
id
hJ
oo ^t-
T— i in
CO OO
oo ON
^- -H
22 ^.
°° CN
ON ^
^ ON
t^ in
in oo
o c~~
CN >— i
co r~-
<— < CO
oo vo
-* -H
^r CN
ON *— i
ss
vo Tt-
OO CN
o vo
ON VO
co -H
vo ^J-
in ^t
^^
(D
en
60
1
-------
*_H X
m <
§1
^ w
^H
OH 03
£ ffi
11
hJ ffi
^ o3*
E m
03
03 c3
i— »
O
'I
ffi
O
5
a
o
U
t— <
1
w
H
CN °
M
t^*- ^""^
^ -H
00^
CN ^
m ^
^ -U
cn^
OS ^
^ ii
OO "^
CN ^
?3
c
H
C^ (— ^
«> 2
m CN
r^ CN
— ' -H
oo oo
^O CN
~ -H
^o oo
in en
CN t^
vo m
~ -H
o oo
oo in
O in
m -H
os m
oo in
^^ ^J"
£"fj f*^.
— -H
o m
o in
r^ CN
-< HH
0
a
N
OH
•S?
VI
U
HH
O
o
^o ^»
in jjj1
in "~!
s ^
?s
^ ^H
^o oo
rt OO
o m
CN ^r
— ' -H
^Nl °°
S ^
s§
•* t--
m in
CN l>
^ -H
in m
m CN
•*' CN
CN ^t"
CN -H
CN
X
, — v
¥j
ffl *
r\ M)
U g
, — i O
H^ W
O ^
H II
r*~i ^^
^ -H
^°
0 ?
^^
"2
^ -H
s;^
"" ^H
OS ^
o°
[^ OO
in ^n
^ -H
H
Q
Q
OH
|y-, C-J
-^
gs
m ^
•^t" O
OS SO
*— i m
— ' -H
^ 0
^H OS
CN Os
CN OO
— i -H
CN ^j1
SO OO
^H OS
Q
Q
Q
-4— *
O
H
-------
£ X
§ &
in n co
-H
so
CM
CO
vd
"
CN
^r in
i— i m
in m
o ^r
— ' -H
CO
ON OO
O CO
^H CN
ON in
^ -H
fe o
PH S
P ffi
oo
r-
o
ON in
^ -H
VO
-H
Si
^ -H
»n CO
CN ON
in m
— ' -H
ON
ON
!>• ON
r~ ON
O
^H (
CN -H
OO (N
(N u~>
O •*'
— ' -H
.-H ON
oo ^q
(> in
-- -H
00
o
•^f co
r- m
,-H O
m ^1-
(N ON
CO O
*-i ^r
^ -H
OO ON
CN r--
^f ON
ON ON
CN -H
^ (N
CN -H
ro
00
-H
-------
5 x
ffl ~>
B m
03
O
S m
03
iH
O
ffi
1
f— <
c
0
u
1— 1
1
[T')
H
^ So
-5
in O
t-- ^o
r-" -H
m
ON^
oo qj
in MS
t--- ON
r^ -H
0
o °.
-5
^ r^
"" 4?
ON OO
'O ON
CN CN
O I>
m -H
C
1
R§
VO ^
*s
m
^2
^ 41
s|
f^l O
o t-~
o •*
in oo
-^ 41
ON 'I
^ 4H
o oo
r-H CN
\o oo
in oo
— ' 41
C
1
03
§
1-Methylphei
-------
•g x
-§) &
ffl <
4
4
>>
41
0 l>
41
i— i >
OH
13
t
H
cs
ffi
<
PH
onobuty
X)
Q
Tributy
Tetrabuty
-------
If)
1— 1
«-,
"«
•+*
o
BD
C3
O
-J
^H
ON
ON
l-H 1/3
a ~<3
§ fc
DC ^
^ K
C^ g
.si
"O
^ M
^ S
11
M
o ^
hJ ffi
^ ^
J2J
03
O
c CQ
c^
t~— j
i_<
o
^
ffi
i/5
nJ
H
w
Og
^^ l
^^
^~*^
^^ 1
0?
^ ^
>
a
Antimo
^^ l
C""*l
oo
o
^^ i
f^i
C^) 1
^^ '
(N r?
1—1 oo
N — '
^^
° S
~^~^^
r^
^_, i
^
Arsenic
00
e
0?
o
oo
^^
oo
^^ 1
f""^
"^
^^
(N ^
1
^^
^^ 1
f~^
^— N
^^
^^
S
_g
T3
C3
O
0?
0?
o
Og
o 2
^^
~^
t^l
^^ 1
^^
^^
f"^l 1
f^)
/ — \
o ?
2>
1
'S
1
o
"I
^^ 1
^^
^" T1
oo"
°0
^^
(N in
" '
^.
o
/' — s
IT) l
r— H
(3
a,
0
O
of
0?
o
0?
r- "V
Ci
0?
^^ 1
^
O^
" — ^
£N
^^" 1
o
X N
^J. |
cs
"* T1
o
0?
o
o1
£ S
s — '
rn~
0,
^~N
"* ',
CN
o
.^H
0?
S
CO
o
oo ^
^H 1
ON
ts
^ ^
^
(N m
^oi
oo
^ jr
^— ^
-^
o
r^ (N
^ S.
u
-------
Ig.
CQ <
m
oo
(N
CM
0 O
11
(N
(N
oo
r--
(N
ON
m
(N
(N
0
00
oo
"71
m
oo
>o
ON
ON
ON
m
ON
(0
1)
CQ
"3-
(N
ON
(N
10
OO
(N
m
m
V
o
(N
ON
CN
l-H
O
1
ffi
en
m
ON
"
(N
0
in
00
m
(N
-O
_g
O
O
(N
w
o
• i-H
N
ICS
ORG
(N
X
1?
(U
M C
S S)
^ g
,—i O
$ 1X1
^r
H
Q
Q
-4-j
s
13
OH
Q
Q
Q
—
o
H
w
Q
Q
cx
Q
Q
o
H
H
Q
Q
o
H
e
Chlord
Dieldrin
W
-------
- g,
DO
oo
m
00
s s
11
S o
> '-o
^1
(T)
T1
o
(N
(N
0
ON
oo
0
CN
(N
ffl
0
s
•§
»
CM
VO
oo
CN
(N
r-
(N
oo
"3
o
O
(N
w
H
Acenaphthene
Anthracene
Acenaphthylene
0)
fl
O
o
N
0)
03
(U
(U
-------
PQ <
0
CO
'T
o
oo
t--
(N
So
11
oo
f?
4
as
"-1 CM
II
°a
1-1
00
r-
(N
0
03
H
s
I
c
-------
Appendix F
Dioxin bioaccumulation calculation
-------
Theoretical Bioaccumulation Potential (TBP) Calculation
Because the relative toxicity of congeners to humans may differ from toxicity to aquatic
organisms, different toxicity equivalents have been defined for humans and for [aquatic]
"ecological systems." Human toxicity equivalents are probably closer to those of other mammals
and birds than the equivalents for aquatic organisms. Preliminary efforts to define toxicity
equivalency factors for aquatic "ecological systems" are based solely upon laboratory mortalities
of early life-stage fishes (U.S. EPA 1993; Cura, Heiger-Bernays and Bucholz 1995, p.2-11).
These preliminary toxicity equivalency factors for aquatic ecosystems are so uncertain (indeed,
completely unknown for several congeners) that they are not used here. Further, it is clear that
fish-eating birds and mammals, including humans, are at much greater risk from dioxins/furans
than the benthos (U.S. EPA 1993; Cura, Heiger-Bernays and Bucholz 1995). Consequently this
investigation attempts to estimate dioxin/furan concentrations in sediments which would not be
a risk to humans or fish-eating wildlife.
Measured concentrations of 2,3,7,8-TCDD and the "human health toxicity equivalents" of all
dioxin/furan congeners - expressed as weighted additive equivalents - are summarized in Table
4.3.1. The biotic effects of 2,3,7,8-TCDD alone are not interpretable. Concentrations of this
single isomer are shown only because they are comparable to commonly reported 2,3,7,8-TCDD
values.
We attempt to estimate a "safe" concentration of dioxins/furans (expressed as human health
toxicity equivalents) in sediments, based upon a presumably protective range of concentrations in
fishes, and an estimated relationship between sediment and fish concentrations. First, we specify
dioxins/furans concentrations in fishes that seem to pose low and high risks to humans and
wildlife. The range of 0.7 to 7 pptr dioxins/furans in fishes is presumed to embrace low to high
risks for piscivorous mammals - probably the organisms at greatest potential risk (U.S. EPA
1993, Table E-l). Following the NYS Department of Health, we presume that 10 pptr in fishes
protects against effects of dioxins/furans in adult humans unless exceptionally large quantities of
fishes are eaten. Thus we consider a protective range for mammalian wildlife of one pptr (low
risk) to 7 pptr mean fish concentration (high risk), and presume that 7 pptr is also a low risk to
most adult humans.
We then work down the food web from fishes to estimate a "safe" range of dioxins/furans in
sediments. Wide ranges have been measured for dioxin/furan biomagnification from benthic
invertebrates to fishes. We use an intermediate value of two (U.S. EPA 1993; Cura, Heiger-
Bernays and Buckolz 1995). Thus, our "high risk" concentration of 7 pptr in fishes would result
from 7/2 = 3.5 pptr in the benthos. Dioxin/furan concentrations in the benthos are estimated
from: (1) the "accumulation factor" of dioxin/furan transfer from sediment to benthos, (2)
dioxin/furan concentration in sediment, (3) lipid content of the benthos, and (4) fraction of
organic carbon in the sediments. This relationship has been expressed as an estimator of
"theoretical bioaccumulation potential" (TBP) of benthic infaunal organisms (U.S. EPA 1993):
TBP = AF(C//oL)/%TOC
-------
where: TBP = 2,3,7,8-TCDD human health equivalents in benthic tissue (pptr, wet wt), AF =
accumulation factor, or dioxin/furan concentration in benthos as fraction of concentration
in sediment,
Cs = dioxin/furan concentration in sediments,
%L = percent lipid in fishes, and
%TOC = percent total organic carbon in sediments.
In our "high risk" case, TBP=3.5 pptr. Measured accumulation of 2,3,7,8-TCDD and 2,3,7,8-
TCDF from sediments to marine invertebrates has ranged from 0.24 to 1.0 times the sediment
concentration (Pruell et al. 1993). We assume an intermediate sediment to polychaete
"accumulation factor" (AF) of 0.5. Percentages of lipid in the benthos are typically near 1%, and
3% total organic carbon is common in Harbor sediments.
Using these assumed and typical values, we can solve for a range of presumably safe, but
mammalian wildlife "high risk" dioxin/furan concentrations in sediments:
3.5pptr = 0.5(Cs'01)/.03
Cs = 21pptr.
Similarly, our estimate of "low risk" concentration in the benthos (TBP) is half the low risk
protective concentration in fishes (1/2=0.5 pptr). So, low risk sediment concentrations are:
0.5 pptr = 0.5(CS'01)/.03
Cs = 3.
As mentioned above, we presume that even the high risk sediment concentrations (21 pptr) pose
low risks for most adult humans. Hence these sediment concentrations apply to piscivorous birds
and mammals. Several major uncertainties are inescapable in estimating these presumably
"high" and "low" risk sediment concentrations for wildlife. Some uncertainties are so great that
we can not even rank their severity. One obvious uncertainty is variability in wildlife exposures
to dioxins/furans, and assimilation efficiencies, from foods, sediments and water. This
obviously varies with the top carnivore involved, food available to the carnivore, differential
bioaccumulation in the food web, etc. (U.S. EPA 1993). Also, dose-response relationships such
as the above equation for theoretical bioaccumulation potential have little empirical support from
few environments. Even if the TBP equation is robust, all the predictor variables are spatially
heterogeneous, probably ensuring imprecise estimates of mean dioxin/furan concentrations in the
benthos even if field measurements were extensive. Further, there are few direct measurements
of variability in the most important variables, e.g., concentrations of dioxins/furans in
commercial and recreational fishes. Hence, there is little basis for estimating the distributions of
these variables.
-------
Appendix G
Area-weighted mean abundances of all
benthic macroinvertebrate species (mean #/.04 m )
-------
xj
>>
2
I!
I
§
«n
\q
o"
q
d
en
o
O
d
fB
s
es
S
d
O
C5
"5
gi
1
**!
d
S
s
d
s
O
d
q
d
ON
tn
oo
^4
C4
q
d
&
•a
2
£
I
BO
*
I
2
I
I
J8
,£
e
-------
I
o
d
»n
»n
8
a
q
d
q
d
i]
q
d
q
d
q
d
I
It
q
d
UU
3
00
q
d
fS
t*
q
d
I
d
>n
q
d
(N
tn
C4
d
q
d
11
11
tn
o
Tf
i
d
Ov
q
d
q
d
q
d
I
3
Ov-
en
00
in
d
i
oo
I
H
I
5
a
I
S
<
*
1
3
I
ff
I
O
S
£
•3
I
•3
£
i
x
1
I
-3
2
i
i
>»
£
I
1
f
1
I
£
i
u
-------
>-,
s
*
•mm
d
s
d
en
8
d
s
1
8
8
d
tn
d
§
d
I
d
t^
^r
>-,
£
q
d
8
d
q
d
q
o*
o>
en
d
•*
^^
s
q
d
8
q
d
i
1
d
q
d
I
8
J
i
1C
1
OQ
£
I
<
fr
I
I
41
i
I
so
at
U]
£
O
O
O
s
o
§
i
i
f
i
X
1
£
u
I
I
>s
1
«2
£
£
^
T»
•i
•3
•3
5
•3
-------
I
o
o*
p-
^
in
s
I.
a
'•O
(•>
s
a
**
p-
d
q
d
p-
*m
q
d
tn
oo
<*i
ON
d
8
oo
r-i
1
"5
ao
«*
q
d
9J
I
q
d
q
d
in
d
o
d
q
d
2 g
.y &
so
8
d
I
I
d
I
i
i
M
a
2
I
.2
I
x>
1
3
3
I
2
1
2
I
g,
1
£
1
I
I
I
5
I
I
-------
a.
<*-!
q
6
o
o
tn
O
s
q
o
Sc
q
d
s
q
6
1
q
o
€4
r*
o
o
q
o
I
J§
1
1
1
1
I
I
St,
1
I
£
I
o
t
I
•i
•a
1
I
I
1
I
!
•§
I
I
-------
f-
W>
go
s
r-
rm
q
d
q
d
*
O
f-
d
1l
q
o
I
c
q
e>
q
d
q
d
S
OS
r-
I
d
q
d
06
q
d
c
•3
Z
13
I
I
I
I
1
ee
!
!
35
•a
•a
1
f
i
a
I
(.
a
a
i
§•
s
u
I
£.
.-3
•5
«£
£
•i
I
X
1
•3
£.
-3
•i
I
f
I
•3
£
•3
-------
I
t
1]
s
q
d
£
q
d
q
d
1
d
s
d
q
d
ft
rv«
q
d
"
m
•«t
8
d
o
d
s
t-
q
d
q
d
8
d
B
d
I
d
en
8
d
3
q
d
i
Si
§
d
9
d
T
d
Z
§
2
CO
J
i
I
U
c
>>
1
t
I
I
.-I
i
I
-------
9
i
ox
t«-
q
d
t-
d
ox
es
en
d
q
d
i
"
Ox
r^
q
d
Ox
p-
q
d
Ox
r*
q
d
vO
d
8
«S
O*
I
I
1
9
d
I
q
d
i
d
a
2
d
1
d
xo
d
£
d
z
I
1
I
8,
I
1
£
!
I
=
S.
.2
'£
S.
1
£
I
>>
1
i
I
I
<£
3
s,
1
i
I
S.
•i
<£
5
•9
I
—
"3
o
*•••*
1
j
^
t
I
I
-------
ll
i
I
i
8
oo
§
o
SS
8
6
5
d
s
q
o
q
d
3
q
o
P-
tn
q
o*
55
en
O
q
o
r-
i
d
a
p-
*
no
if
i
9
d
q
d
O;
d
I
m
I
I
,2
,
1
I
I
u
I
1
I
I
I
I
•I
I
-------
I
«j
I
—
d
q
d
a
s
q
d
oe
d
oe
p*
8
90
o
d
o
•M
d
S
8
00
q
d
Z
§
en
*
V)
V9
f,
V)
3
K
2
<
8-
•a
%
i
I
t
•3
5
•3
O
-------
I
S
S
r*
vq
cs
1
s
q
d
s
d
vq
es
q
d
g
q
d
J *
•gs
00
5
«*»
q
d
tn
d
q
d
*
S
8
5;
q
d
8.
d
s
d
00
d
I
q
d
i
o
en
O
q
d
q
d
ft
q
d
S
q
d
8
q
d
g
I
&
I
•8.
u
a.
1
U
I
i
9
o
1
o
i
o
1
o
S
o
O
1
o
o
3
O
i
o
o
o
-
s
o
o
O
0
o
i
o
1
1
o
!
o
o
-------
I
•o
tn
8
a
g
i;
r^i
i
s
8
i*.
i
o
z
§
a.
I
so
I
O
o
I
O
S
1
1
O
,a
sa
jp^
S
i
1
.
£
o
-------
-------
-------
-
s
8
a
S3
«*>
9
d
s
d
8
s
d
q
d
8
o
d
Z
I
1
A
I
Q
'H
o
'5
u
V)
-------
II
1
s
d
q
d
Z
S
I
3
1
1
I
-------
&
8
d
O\
d
t";
d
q
d
o
d
5
I
o
d
o
d
Os
q
d
S
d
1
d
S
d
q
d
.2
I
1
t
I
I
r
u
i
i
-------
i
a
8
c4
Ov
-------
i
i
q
d
c-
«i
S
q
d
55
OS
r-
O
e
3
a
r-
o
d
¥">
d
d
1
o
d
§
I
8
es
c»
q
d
CM
q
d
OH
r-
q
d
i
S
q
d
§
d
r*
q
d
o\
S
§
d
o
o
I
2
r-
I
I
§•
I
o
1
I
1
I
-------
fc
3
O*
q
O
en
O*
§
S
j;
I!
i
I
d
-------
i;
i
o
ci
o
d
f
-------
Appendix H
Clostridium perfringens results
-------
Clostridium perfringens results
Concentrations of Clostridium perfringens spores have been used as an indicator of sewage
contamination (Hill et al., 1993; O'Reilly et al., 1995). C. perfringens is a obligate anaerobe
bacterium found in fecal material. It can survive extreme environmental conditions. This study
evaluated the concentrations of the spores in Harbor sediments. The laboratory procedure was
the membrane filter method of Emerson and Cabelli (1982). Mean concentrations of C.
perfringens spores are expressed as confirmed counts per gram (wet weight) of sediment.
The Lower Harbor had the lowest mean spore count of the sub-basins in the Harbor (Table J-l).
The other three sub-basins of the Harbor all had similar mean spore concentrations, although
variability was high. The mean spore concentration in western Long Island Sound was an order
of magnitude lower than the Harbor mean.
Table J-l
Area-weighted Mean Concentrations of C. perfringens
(± represent 90% confidence intervals)
Mean number of C.
perfringens spores
(# spores/g-wet
weight)
Harbor
2440
716
Jamaica
Bay
4171
5187
Newark
Bay
5977
3335
Lower
Harbor
935
355
Upper
Harbor
5156
2015
W.LI.
Sound
237
67
Bight
Apex
556
536
Background concentrations of C. perfringens in surficial sediments from the outer New York
Bight continental shelf of Georges Bank are 10-20 spores/g (dry weight) (Cabelli and Pedersen,
1982). Mean concentrations in the Harbor, western Long Island Sound and the Bight Apex were
significantly above background (even after converting from wet weight to dry weight).
Literature Cited
Cabelli, V.J., and D. Pedersen. 1982. The movement of sewage sludge from the New York
Bight dumpsite as seen from Clostridium perfringens spore densities. In Oceans '82 conference
record, p. 995-999. Inst. Electr. Electron. Eng., Piscataway, New Jersey.
Emerson, D.J., and V.J. Cabelli. 1982. Extraction of Clostridium perfringens spores from
bottom sediment samples. Appl. Environ. Microbiol. 44:1144-1149.
Hill, R.T., I.T. Knight, M.S. Anikis, and R.R. Colwell. 1993 Benthic distribution of sewage
sludge indicated by Clostridium perfringens at a deep-ocean dump site. Appl. Environ.
-------
Microbiol. 59(1):47-51.
O'Reilly, J.E., I. Katz, and A.F.J. Draxler. 1995. Changes in the abundance and distribution
of Clostridium perfringens, a microbial indicator, related to cessation of sewage sludge dumping
in the New York Bight, p. 113-132. In U.S. Dept. Of Commerce NOAA Technical Report
NMFS 124.
-------
Appendix I
Benthic Index (B-IBI) values for individual stations
-------
UPDATED 93/94 NY/NJ DATA 1
Benthic Index 11:12 Tuesday, February 13, 1996
OBS STATION RINDEX52
3.4
4.0
3.2
3.2
3.4
3.8
4.4
3.8
4.6
4.4
4.0
3.4
4.2
3.8
3.4
3.4
4.4
4.4
3.4
4.6
3.8
4.4
4.6
4.2
4.0
3.0
4.6
4.0
2.4
2.4
1.4
1.4
1.2
2.4
2.0
3.0
2.2
3.0
3.0
3.0
3.8
2.6
1.8
1.0
2.6
2.6
2.2
2.8
2.6
3.0
2.8
3.0
3.4
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
40
41
42
43
44
45
46
47
48
49
50
51
52
53
BA002
BAD 05
BA007
BAD 10
BAD 12
BAD 14
BAD 16
BA017
BAD 21
BAD 2 5
BAD 2 6
BA030
BA033
BAD 3 5
BA102
BA103
BA104
BA105
BA106
BA107
BA108
BA109
BAUD
BA111
BA112
BA113
BA114
BA115
JB002
JB006
JB008
JB012
JB015
JB018
JB022
JB026
JB031
JB033
JB039
JB041
JB042
JB043
JB101
JB103
JB104
JB106
JB108
JB110
JB111
JB112
JB113
JB114
JB115
-------
UPDATED 93/94 NY/NJ DATA 2
Benthic Index 11:12 Tuesday, February 13, 1996
OBS STATION RINDEX52
2.8
3.2
3.2
5.0
3.2
3.8
3.4
2.6
3.0
3.4
2.8
2.4
4.2
2.4
3.0
2.8
1.6
4.2
3.2
4.2
4.2
2.4
2.6
2.0
3.2
2.8
2.2
2.4
1.8
2.8
2.4
2.2
2.0
2.4
2.2
3.0
2.6
2.4
2.6
1.2
2.6
2.0
2.4
1.6
1.2
1.8
2.6
2.4
2.0
2.6
2.6
1.8
2.2
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
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
106
JB117
JB119
JB120
LS001
LS004
LS006
LS010
LS011
LS016
LS018
LS019
LS020
LS024
LS026
LS027
LS030
LS035
LS101
LS102
LS103
LS104
LS106
LS107
LS108
LS109
LS110
LS111
LS112
LS113
LS114
LS115
NB018
NB021
NB025
NB027
NB036
NB039
NB044
NB045
KB047
NB052
NB053
NB065
NB066
NB075
NB102
NB103
NB104
NB105
NB106
KB107
NB108
NB109
-------
UPDATED 93/94 NY/NJ DATA 3
Benthic Index 11:12 Tuesday, February 13, 1996
OBS STATION RINDIX52
2.8
2.4
2.2
2.4
1.6
2.6
2.8
3.6
3.2
4.0
4.6
4.4
3.0
2.6
2.8
2.8
3.0
4.4
3.8
2.6
2.8
3.0
3.0
2.6
3.4
4.8
3.4
3.0
2.2
2.2
2.8
3.0
2.6
3.0
2.0
2.8
4.0
3.2
3.2
1.8
2.6
2.4
1.4
2.2
2.6
2.6
3.2
4.0
2.2
2.2
2.2
2.8
2.6
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
NB110
NB111
NB112
NB113
NB114
NB115
RB001
RB002
RB007
RB010
RB011
RB012
RB016
RB019
RB024
RB027
RB029
RB030
RB032
RB033
RBI 01
RB102
RB103
RBI 04
RB105
RBI 06
RB107
RB108
RB110
RB111
RB112
RB114
RB116
RB117
UH003
UH004
UH008
UH010
UH011
UH014
UH018
UH019
UH020
UH022
UH023
UH026
UH029
UH030
UH101
UH102
UH103
UH104
UH105
-------
UPDATED 93/94 NY/NJ DATA 4
Benthic Index 11:12 Tuesday, February 13, 1996
OBS STATION RINDEX52
160 UH106 3.4
161 UH107 1.4
162 UH108 2.2
163 UH109 2.2
164 UH110 2.0
165 UH111 2.6
166 UH112 1.8
167 UH113 2,4
168 UH114 3.4
-------
Appendix J
Data disk explanatory information
-------
Apnl 27, 1995 1
Combined 1993,'94 NY/NJ REMAP Dataset
CONTENTS PROCEDURE
Alphabetic List of Variables and Attributes—
Variable Type Len Pos Format Label
10:43 Thursday,
74 ACENTHE Mum
73 ACENTHY Num
48 AG Num 8
93 AG RECOV Num
60 AL Num 8
119 ALDRIN Num
120 ALPHACHL Mum
94 AL RECOV Num
172 AMB DO Num
170 AM1~SAL Num
171 AMB~TEMP Num
77 ANTHRA Num
49- AS Num 8
95 AS RECOV Num
33 AVS Num 8
40 AVS MM Num
65 BASAREA Num
1 BASINCOO Char
80 BENANTH Num
82 8ENAPY Num
86 BENEPY Num
110 BENZOFL Num
85 BENZQP Num
108 BIQ1QDS1 Num
87 BIPHENYL Num
165 BTJTOT Num
175 8 BAG Num
176 B~CQND Num
21 B "DEPTH Num
22 B DO Num
26 i~QRP Num
24 B~PH Num
23 B SAL Nym
8 596
8 588
390 Silv
8 74«
486 Alui
8 956 t
8 964
8 756
8 1380
8 1364
8 1372
8 620
398 Ara
8 764
270 A\
8 326
8 526
2 0 J
8 644
8 660
8 692
8 884
8 684
8 868
8 700
-8 1324
8 1404
8 1412
8 174
8 182
8 214
8 198
8 190
Aetnaphthene (ppb)
Aeenaphthyfene (ppb)
»r (ppm)
Silver partial (ppm)
Aluminum (ppm)
Aidfin (ppb)
Aipha-chlordan» (ppb)
Aluminum partial (ppm)
Amb DO (mfl/l)
Amb Salinity (ppt)
Amb T«mp. (C)
Anthracene (ppb)
nic (ppm)
Arsenic partial (ppm)
AVS (ppm)
AVS (mmol)
Tota! Basin Ar«a (sq km)
Basin Code
8enzo[a]anthracene (ppb)
B«nzo[a]pyrene (ppb)
Benzo[e]py»n» (ppb)
B«nzo[b,k]flyoranthent (ppb)
Biphenyt (ppfo)
Total Buty! tins
Bottom BAC
Bottom Conductivity (mS/om)
Bottom depth (m)
Bottom DO (mQ/L)
Bonom ORP (mv)
Bottom pH
Bottom Salinity (ppt)
-------
April 27, 1995 2
a 1993/94 NY/NJ REMAP Dataset
CONTENTS PROCEDURE
# Variable Type t«n Pos Format Label
10:43 Thursday,
25 B_TEMP Num 3 206
50 CD Nym 8 406
96 CD RECOV Num a 772
69 CHAN TYP Char 8 558
121 CHLJTOTC Num 8 972
81 CHRYSENE Nym 8 652
109 CLOSTR Num s 876
51 CR Num « 414
97 CR_RECOV Num 8 780
52 CU~ Num 8 422
Bottom Tamp (C)
fr \*"v
Cadmium (ppm)
Cadmium partial (ppm)
Channel Type
Total Chlordartt (ppb)
Chrysen* (ppb)
Qottridium (f /gm)
Chromium (ppm)
Chromium partial (ppm)
Copper (ppm)
^f £UTi?EC0^ Num 8 788 Copper partial (ppm)
3 DATE Num 8 12 DATE7. Date
159 DBT Num a 1276
168 DDD_TQT Num 8 1348
167 ODE'TOT Num a 1340
169 DDT_STOT Num 8 1356
122 DDTJTOT Num e 980
7 DEPTH Num a 61
84 DI8ENZ Nym 8 676
123 OIELDRIN Num 8 988
88 DIMETH Num 8 708
124 ENDRIN Num 8 996
61 FE Num 8 494
99 FE_RECOV Num 8 796
78 FLUORANT Num 8 628
?i aUQRENE Nym 8 604
125 HEPTACHL Nym 8 1004
126 HEPTAEPO Num 8 1012
127 HEXACHL Num 8 1020
63 HG Num 8 510
101 HG RECOV Num S 812
83 INDENO Num 8 668
4 LAT Char 20 20
128 UNDANE Num 8 1028
i LONG Char 20 40
160 MBT Nym 8 1284
89 MENAP1 Num 8 716
72 MENAP2 Num 8 580
90 MEPHEN1 Num 8 724
129 MIRBC Num 8 1036
S9 MN Num 8 478
100 MN RECOV Num 8 804
71 NAPH Num 8 572
53 Nl Num a 430
102 Nl RECOV Nym 8 820
130 OPDDD Num 8 1044
131 OPDDE Num 8 1052
132 OPDDT Num 8 1060
163 OPDDTTOT Num 8 1308
117 PAH HMWC Num 8 940
116 PAH LMWC Num 8 932
118 PAHJTOTC Num a 948
54 PB Num 8 438
Dibutyltin (ppb)
Total ODD (ppb)
Total DOE (ppb)
Total DOT parent (ppb)
Total DDT (ppb)
Depth (m)
Dibenz[a,h]anthrac«ne (ppb)
Dieldrin (ppb)
2,S-Dlmethy»naphthalene (ppb)
Endrin (ppb)
Iron (ppm)
Iron partial (ppm)
Fluorantfiene (ppb)
Ruorene (ppb)
Heptachlor (ppb)
Heptaehlor Epoxide (ppb)
Hexachlorobeniene (ppb)
Mercury (ppm)
Mercury partial (ppm)
lnd«no[1,2,3-C,D]pyrene (ppb)
Undane - Gamma-BHC (ppb)
Monobutyttin (ppb)
1-MethylnaprrtfMtorm (ppb)
2-Methylnaphthalene (ppb)
1-MtthyJphenanthrent (ppb)
Mirex (ppb)
Manganese (ppm)
Manganese partial (ppm)
Naphthalene (ppb)
Nickel (ppm)
Nickel partial (ppm)
o,p, ODD (ppb)
o,p, DDE (ppb)
o,p, DDT (ppb)
Total OPDDT (ppb)
High Molecular Wt PAHs (ppb)
Low Molecular Wt PAHs (ppb)
Total PAHs (ppb)
Lead (ppm)
t
-------
27, 1985 3
Variable .
Combing 1993/94 NY/NJ REMAP Datase.
CONTENTS PROCEDURE
i »n D*. r
ten Pos Format Ubef
10:43 Thursday,
103 PB RECOV Num 8 82a
152 PCB8 Num 8 8122f8
142 PCB18 Num 8 iuo
148 PTRoa LI
'•« ruHZe Nym « 1nw
H9 PCB44 Num s 96
150 PC852 Num 8 1204
151 PCB66 Num 8 1212
133 PCB101 Num 8 1068
134 PCB105 Num g 076
'36 PCB118 Num 8 ml
137 PCB126 Num 8 iioo
138 PCB128 Num 8 1 M
139 PCB138 Num e i «
140 PCB153 Num 8 1124
'41 PCB170 Num g 1132
143 PCB180 Num 8 1143
144 PC8187 Num 8 1156
145 PCB19S Num 8 1154
146 PCB206 Num 8 i 72
147 PCB209 Num e MM
t3S PCB11077 Num 8 ,£1
166 PCB_TOTC Num 8 m2
29 PCTCONA Num ' 8 Itf
32 PCTCON'M Num 8 262
30 PCTSURA Num 8 246
i1 PERYLENE Num g 732
76 PHENANTH Num 8 612
6 POSEQUIP Char i m
JJ 5™ «™ 8 1228
154 PPODE Num 8 i23g
155 PPDOT Num a 247
164 PPOOTTOT Num 8 i7l6
79 PYRENE Num 8 «
179 RINDEX45 Num a U37
70 SAMPLI Char 6 56?
177 SAMPTYPE Char g ^20
55 SB Num e 446
104 SB_RECOV Num 8 836
57 SE Num a 462
11 SIAS Char to 92
9 SECCHI Num 8 £,
66 SiGAflEA Nym 8 534
27 SEGMENT Num 8 222
34 SEM_CD Num 8 27?
41 SBM_CO_M Num a 334
35 SEM_CU Num 8 286
42 SEMJ3U M Num 8 342
39 SEMHCf Num a 318
46 SEM_HQ M Num 8 374
37 SEM_NI Num 8 302
44 SEM_NI M Num 8 358
36 SEM PB Num 8 294
43 SEM_PB_M Num 8 350
Uwd partial
PCS Congener 8 (ppb)
PC8 Conftntr 18 (ppb)
PC8 Cong«n«r 28 (ppb)
PCS Congener 44 (ppb)
PCS Conganw 52 (ppb)
PCS Cortg«n»r 66 (ppb)
PCS Cong«n*r 1{J1 (ppb)
PCS Congtrmr 105 (pp&)
PCB Congener 118 {ppb)
PCS Conawtr 126 (ppb)
PCB Con0«n*r 12S (ppb)
PCS Congtrwr 133 {ppb)
PCS Conflarttr 153 (ppb)
PCB Congtntr 179 (ppb)
PCB Congener 130 (ppb)
PCS Congener 187 (ppb)
PCB Congener 195 (ppb)
PCi Congener 206 (ppb)
PCB Congener 209 (ppb)
PCB Congener 110/77 (ppb)
Total PCts (ppb}
AmpelJeoa Surv as % Of Control
Microtox Surv as % o* Control
Ampelfsc* % Survival
P«ryfene (ppb)
Phenamhrene (nob)
Pos Unit
P.p. 000 (ppb)
P,P, ODE (ppb)
P,P, DDT (ppb)
Total PPODT (ppb)
Pyrene (ppb)
RQI Analogue # 2 Value
Sample ID
Sample Type
Antimony (ppm)
Antimony partial (ppm)
Saiinium (ppm)
Sea condition
Seeehf depth (m)
Segment Area (sq km) (NB only)
Segment (NB only)
SEM Cd (ppm)
SEM Cd (mmol)
SEM Cu ft>pm)
SEM Cu {mmol)
SEM Hg (ppm)
SEM Hg (mmol)
SEM Ni (ppm)
SEM Nl (mmol)
SEM Pb fcpm)
SEM Pb (mmol)
-------
1 27, 1995 4
Combined 1993/94 NY/NJ REMAP Oataset
10:43 Thursday,
CONTENTS PROCEDURE
# Variable Typ, ten Pos Format Ubs(
47 SEM TOT Num
3« SEM~ZN Num
45 SEM 2N M Num
105 SB RECOV Num
62 Si Num 8
28 SIG AMP Num
31 SIG~MIC Num
64 SILfCUY Num
56 SN Num
2 STATION Char
67 STA UT Num
68 STA LNG Num
18 S AMBDO Nym
14 S_AMBSAL Num
'6 S AMBTMP Num
173 S BAG Num
174 S COND Num
12 SJ3EPTH Num
17 S_DO Num
20 S^QRP Num
19 S PH Num
13 S SAL
18 S TEMP
111 T2PAHC
'12 T3PAHC
113 T4PAHC
114 T5PAHC
115 T6PAHC
158 TBT
161 TCDD
162 TCOF
157 TETBT
Num
Num
Num
Num
Num
Num
Num
Num
Num
Num
Num
8 382
8 310
8 366
8 044
502
8 230
8 254
8 518
8 454
10 2 $F8
8 542
8 550
8 150
8 118
8 134
8 1388
8 1396
8 102
142
166
158
110
126
892
8
900
908
916
924
8 1268
8 1292
8 1300
._._-. j m,y(, ,| 8 1260
156 TNONCHL Num 8 t252
107 TOC Num 8 aeo
8 TRASH Char t eT
92 TRI235 Num 9 740
10 WEATHER Char 14 78
178 YEAR Num 8 1429
58 ZN Num 8 470
106 ZN.RECOV Num 8 852
Total SEM (mmol)
SEM Zn (ppm)
SEM Zn {mmol)
Salinium partial (ppm)
Silicon (ppm)
Amptlisca Significant* {1 -
Mierotox Significant* (1 =8la)
P»re«nt Siitotay Content
Tin (ppm)
Station M*ntifi«r
Station Latitude
Station Longitude
Surface BAG
Surfat* Conductivity {mS/emj
Surface depth (m)
Surfaet DO (mg/L)
Surfac* ORP (mV)
Surface pH
Surface Salinity (ppt)
Surfac* T«mp (C)
2-Rlno PAHs (ppb)
3-Ring PAHs (ppb)
4-Ring PAHt ftjpb)
5-Rlng PAHs (ppb)
8-Wng PAWs ftjpb)
Tribgtyltin (ppb)
Dioxin (2,3,7,8-TCDD) (ng/kg)
Furan (2,3,7,8-TCDF) (ng/kg)
Tetrabutyltln (ppb)
Trans-Nonachlor ftjpb)
Total Organic Carbon topm)
Trash?
2,3,5 Trfmathyfnaphthalan* topbi
W«atn«fcond,
2no (ppm)
Zinc partial (ppm)
------- |