Fish Biliary PAH Metabolites As an Indicator Of

Home , Acrylonitrile

USE OF FISH BIOMARKERS TO ASSESS THE CONTAMINANT EXPOSURE AND

EFFECTS IN LAKE ERIE TRIBUTARIES

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

Xuan Yang, M.S.

* * * * *

The Ohio State University 2004

Dissertation Committee: Approved by

Dr. Paul C. Baumann, Adviser ______

Dr. Susan W. Fisher, Adviser ______

Dr. Audeen W. Fentiman Advisers

Dr. William I. Notz Environmental Science Graduate Program

2004

ABSTRACT

As an attempt to assess the ecological status of the Lake Erie system and to evaluate the use of biomarkers in environmental monitoring and assessment, brown bullheads (Ameiurus nebulosus) were collected from ten Lake Erie tributaries, including the industrially contaminated sites: Detroit River (DET), Ottawa River (OTT), Black

River (BLA), Cuyahoga River (CUY), Ashtabula River (ASH), Presque Isle Bay (PIB),

Buffalo River (BUF) and Niagara River at Love Canal (NIA), and two non-industrialized reference sites: Huron River (HUR) and Old Woman Creek (OWC). Concentrations of polycyclic aromatic hydrocarbon (PAH) metabolites were measured in bile of fish from all the locations except HUR and PIB, and the condition factor (K), the hepatosomatic index (HSI), the gonosomatic index (GSI), the spleen-somatic index (SSI), incidences of external raised lesions (grossly visible tumors) and barbel deformities were surveyed in fish from all the ten locations mainly during 1998-2000. Additionally, two genetic assays, the comet assay and micronucleus assay, were used to examine the genotoxic exposure and effects in fish from ASH relative to those from the Conneaut River (CON) (a reference site) during 2002-2004.

Measurements of both benzo[a]pyrene (B[a]P) and naphthalene (NAPH) - type

PAH metabolites showed significantly higher PAH exposure in fish from the contaminated DET, OTT, BLA, CUY, ASH, and BUF than fish from NIA and the

ii reference site, OWC. Fish from OWC and ASH appeared to be healthier, with lower prevalence of tumors and/or barbel deformities than fish from DET, OTT, HUR, BLA,

CUY, PIB, BUF and NIA. No apparent trend existed between fish from the contaminated and reference sites in K, HSI, SSI and GSI. The comet assay with erythrocytes of brown bullheads revealed that fish from ASH suffered greater genotoxic exposure and damage than fish from the reference site, CON.

Concentrations of PAH metabolites in bile of fish were positively correlated with concentrations of selected sediment PAHs, and prevalence of raised lesions and barbel deformities in fish was positively correlated with concentrations of sediment PAHs and heavy metals. This study demonstrates that biliary PAH metabolites and incidences of external tumors and deformities in brown bullheads are effective indicators of contaminant exposure and impairments. The comet assay showed high sensitivity in assessing genotoxic exposure and damage. In contrast, the micronucleus test with erythrocytes of brown bullheads lacked sensitivity to moderate contamination.

On a basis of the biliary PAH metabolites and prevalence of raised lesions and barbel deformities in brown bullheads as well as concentrations of sediment contaminants, this study suggests that DET, CUY, OTT, and BUF were the most highly impacted sites. BLA and PIB appeared to be moderately impacted and OWC, ASH, HUR and NIA were the relatively least impacted sites. Although ASH was assigned in the same group with the two reference sites, OWC and HUR, it was still exposed to genotoxicants at a higher level according to the comet assay and so deserves future concern. The Niagara River showed improved conditions, which is very likely to have benefited from the extensive remediation undertaken at this site.

iii

Dedicated to my mom

ACKNOWLEDGMENTS

I would like to express my deepest gratitude and appreciation to my advisor, Dr.

Paul C. Baumann, for his devotion, guidance, encouragement, and financial support over

the years. I am very thankful to my co-advisor, Dr. Susan W. Fisher, for providing me with resources, advices, and support in my study and laboratory work. I wish to thank my dissertation committee members, Dr. Audeen W. Fentiman and Dr. William I. Notz for their time, effort, insightful suggestions and comments.

I appreciate the invaluable help from Edith Lin, Susan Cormier, Mary Lowry,

John Meier, and Lina Chang of the U.S. Environmental Protection Agency in performing the bile metabolite analysis and the comet and micronucleus assays. I am grateful to

Steve Smith, John Hickey, Dora Passino-Reader and Vicky Blazer of the U.S. Geological

Survey for their assistance in fish sampling. Many thanks to my colleagues, Michael

Rowan and Dan Peterson, for their pleasant cooperation and sharing the joy of working.

I especially appreciate the Environmental Science Graduate Program and The

Ohio State University for providing me with the precious studying opportunity and cherish the honor of the Fay Graduate Fellowship, established by Dr. Dale Fay and his family. I am grateful to my TA supervisors, Dr. James Christensen and Dr. Mohan Wali, and all my course instructors (too many to list here). My study is not possible without their help, support and instruction.

v Finally, I would like to thank my family members, my husband Shuo Liu, my father Puhua, my two sisters Lin and Qi for their love, patience, understanding, and encouragement. I also want to thank my friends, Yin Jiang, Qi Shen, Jie Xia, Hongxia

Duan and Yan Wang, my host family, Ellin Beard and her daughters Erin and Maria, for their sincere friendship and encouragement.

This research was supported by grants from NAWQA and BEST Program of the

U.S. Geological Survey, U.S. Environmental Protection Agency, and U.S. Fish and

Wildlife Service.

VITA

March 16, 1973 ………..... Born - Mudanjiang, Heilongjiang Province, China

1995 …………………….. B.S., Environmental Biology, Nanjing University, China

1998 …………………….. M.S., Environmental Biology, Nanjing University, China

2003 ……………………... Master of Applied Statistics, The Ohio State University

1995-1999 ………………. Graduate Research Associate, Nanjing University, China

1999-present …………….. Fay Fellow, Graduate Research/Teaching Associate, The

Ohio State University

PUBLICATIONS

1. Yang, X., Peterson, D.S., Baumann, P.C., and Lin, E.L.C. 2003. Fish biliary PAH metabolites estimated by fixed-wavelength fluorescence as an indicator of environmental exposure and effects. Journal of Great Lakes Research 29: 116-123.

2. Jin, H., Yang, X., Yu, H., and Yin, D. 1999. Identification of ammonia and volatile phenols as primary toxicants in a coal gasification effluent. Bulletin of Environmental Contamination and Toxicology 63: 399-406.

3. Yang, X., Jin, H., Yin, D., Yu, H., Cheng, H., Lou, X., and Xue, G. 1998. Cause identification of ecotoxicity of chemical industrial wastewater - a case study. Chinese Journal of Applied Ecology 9: 525-528.

4. Yang, X., Jin, H., Yin, D., and Cheng, H. 1998. Application of TIE techniques to the effluent of Nanjing Nitrogenous Fertilizer Plant. Journal of Hohai University 26: 121- 125.

vii 5. Cheng, H., Jin, H., Yin, D., and Yang, X. 1998. Toxicity of acrylonitrile, dichlorophenol and trichlorophenol to the early life stage of Carassius auratus and Bufo bufo gargarizans. Journal of Hohai University 26: 112-116.

6. Cheng, H., Jin, H., and Yang, X. 1998. Methodology of derivation of quality standards for protecting aquatic environment. Shanghai Environmental Sciences 4: 10-13.

7. Lou, X., Mei, Z., Xue, G., Xu, M., Xia, Z., Yang, L., and Yang, X. 1998. Application of TIE technique for identifying toxic reason of waste water of one chemical plant. The Administration and Technique of Environmental Monitoring 1: 17-20.

8. Yang, L., Jin, H., Yang, X., Lou, X., and Xue, G. 1997. Application of Toxicity Identification Evaluation in electroplating effluent. The Administration and Technique of Environmental Monitoring 5: 11-14.

FIELDS OF STUDY

Major Field: Environmental Science

viii

TABLE OF CONTENTS

Page

ABSTRACT...... ii

DEDICATION...... iv

ACKNOWLEDGMENTS ...... v

VITA...... vii

LIST OF TABLES...... xi

LIST OF FIGURES ...... xv

CHAPTERS:

1. INTRODUCTION ...... 1

Biomarkers...... 1 Lake Erie Tributaries ...... 3 Objectives ...... 6 References...... 9

2. BILIARY PAH METABOLITES IN BROWN BULLHEADS FROM LAKE ERIE TRIBUTARIES...... 14

Introduction...... 14 Methods...... 17 Results...... 20 Discussion...... 22 References...... 24

ix 3. THE CONDITION FACTOR AND ORGANO-SOMATIC INDICES OF BROWN BULLHEADS FROM LAKE ERIE TRIBUTARIES...... 33

Introduction...... 33 Methods...... 36 Results...... 39 Discussion...... 41 References...... 47

4. EXTERNAL RAISED LESIONS AND BARBEL DEFORMITIES IN BROWN BULLHEADS FROM LAKE ERIE TRIBUTARIES...... 65

Introduction...... 65 Methods...... 68 Results...... 71 Discussion...... 73 References...... 78

5. THE COMET ASSAY AND MICRONUCLEUS ASSAY WITH ERYTHROCYTES OF BROWN BULLHEADS FROM THE ASHTABULA AND CONNEAUT RIVERS ...... 90

Introduction...... 90 Methods...... 94 Results...... 97 Discussion...... 100 References...... 104

6. SUMMARY...... 115

Comparisons of Levels of Biomarkers between Lake Erie Tributaries...... 116 Associations between Biomarkers ...... 116 Effectiveness of Biomarkers...... 119 Environmental Status of Lake Erie Tributaries ...... 122 References...... 125

COMPLETE LITERATURE CITED...... 137

LIST OF TABLES

Page

Table 2.1. Concentrations of selected PAHs (µg/g dry weight) in sediments of the Detroit River (DET), Ottawa River (OTT), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River (CUY), Ashtabula River (ASH), Buffalo River (BUF), and Niagara River (NIA)...... 29

Table 3.1. Brown bullheads sampled from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 52

Table 3.2. Values of the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI) of brown bullheads sampled in May and late April from the Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Ashtabula River (ASH), and Presque Isle Bay (PIB)...... 53

Table 3.3. Values of the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI) of brown bullheads sampled in June from the Detroit River (DET), Old Woman Creek (OWC), Cuyahoga River - harbor (CRH) and - upstream (CRU), Buffalo River (BUF), and Niagara River (NIA)...... 54

Table 3.4. Pearson’s correlation coefficients between the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen- somatic index (SSI) of brown bullheads...... 55

Table 3.5. Concentrations of selected chemicals (µg/g dry weight) in sediments of the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 56

xi Table 3.6. Spearman’s correlation coefficients between concentrations of sediment contaminants and mean values of the female (F) and male (M) condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI) of brown bullheads across the sampling sites...... 57

Table 3.7. Comparisons of the hepatosomatic index (HSI) of brown bullheads from the Black River (BLA), Cuyahoga River (CUY) - harbor (CRH) and - upstream (CRU), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA) with their historical data...... 58

Table 3.8. Comparisons of the condition factor (K) of brown bullheads from the Black River (BLA), Cuyahoga River (CUY) - harbor (CRH) and - upstream (CRU), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA) with their historical data...... 59

Table 4.1. Brown bullheads sampled from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 81

Table 4.2. Relationships between the probability of fish having external raised lesions and barbel deformities and fish sex, age, length, and weight...... 82

Table 4.3. Comparisons between concentrations of selected chemicals (µg/g dry weight) in sediments and prevalence of external raised lesions and barbel deformities in brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River (CUY), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 83

Table 4.4. Spearman’s correlation coefficients between concentrations of sediment contaminants and prevalence of raised lesions and barbel deformities in brown bullheads across the ten Lake Erie tributaries...... 84

Table 4.5. Comparisons of the prevalence of external raised lesions (tumors) in brown bullheads from the Detroit River (DET), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River (CUY), Ashtabula River (ASH), Presque Isle Bay (PIB), and Buffalo River (BUF) with their historical data...... 85

xii Table 4.6. Comparisons of the prevalence of barbel deformities in brown bullheads from the Huron River (HUR), Black River (BLA), Cuyahoga River (CUY), Ashtabula River (ASH), and Presque Isle Bay (PIB) with their historical data...... 87

Table 5.1. Prevalence of external raised lesions and barbel deformities in fish from the Ashtabula River (ASH) and Conneaut River (CON)...... 107

Table 5.2. Frequencies of micronuclei in polychromatic erythrocytes (MNPCEs) and normochromatic erythrocytes (MNNCEs) and in both types of erythrocytes (MN) of brown bullheads from the Ashtabula River in the autumn of 2002...... 108

Table 6.1. Summary of the mean concentrations of B[a]P and NAPH - type metabolites (µg/mg protein), the mean values of the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI) and spleen- somatic index (SSI), prevalence of external raised lesions and barbel deformities (%) in brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River (CUY), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 128

Table 6.2. DNA damage measured by the comet assay in erythrocytes of brown bullheads from the Ashtabula River (ASH) and Conneaut River (CON)...... 129

Table 6.3. Correlations between concentrations of PAH metabolites and the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI) and spleen-somatic index (SSI) in brown bullheads...... 130

Table 6.4. Relationships between the probability of fish having external raised lesions and barbel deformities and concentrations of PAH metabolites, the condition factor (K) and the hepatosomatic index (HSI) in brown bullheads...... 131

Table 6.5. Comparisons between biomarkers including biliary PAH metabolites, the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI) and spleen-somatic index (SSI), external raised lesions and barbel deformities, the comet assay and micronucleus assay of the brown bullhead...... 132

xiii Table 6.6. Ranks for concentrations of contaminants (PAHs, PCBs, DDTs, heavy metals) in sediments, prevalence of external raised lesions and barbel deformities and mean concentrations of biliary B[a]P and NAPH - type metabolites in brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River (CUY), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 134

xiv

LIST OF FIGURES

Page

Figure 1.1. Map locations of Great Lakes Areas of Concern...... 13

Figure 2.1. Map locations of the Detroit River (DET), Ottawa River (OTT), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River-harbor (CRH), Cuyahoga River-upstream (CRU), Ashtabula River (ASH), Buffalo River (BUF), and Niagara River (NIA)...... 30

Figure 3.1. Map locations of the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River - harbor (CRH), Cuyahoga River - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 60

xv Figure 3.3. Values of the hepatosomatic index (HSI) of brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek in 1999 (OWC99) and 2000 (OWC00), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 62

Figure 4.1. Map locations of the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River - harbor (CRH), Cuyahoga River - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 88

Figure 4.2. Prevalence of external raised lesions and barbel deformities in brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek in 1999 (OWC99) and in 2000 (OWC00), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA)...... 89

Figure 5.1. Map locations of the Ashtabula River and Conneaut River...... 109

Figure 5.2. Measurements of DNA damage in erythrocytes of brown bullheads from the Ashtabula River in the autumn of 2002...... 110

Figure 5.3. Measurements of DNA damage in erythrocytes of brown bullheads from the Ashtabula River and Conneaut River in the summer of 2003...... 111

Figure 5.4. Measurements of DNA damage in erythrocytes of brown bullheads from the Ashtabula River and Conneaut River in the autumn of 2003...... 112

xvi Figure 5.5. Measurements of DNA damage in erythrocytes of brown bullheads from the Ashtabula River and Conneaut River in the spring of 2004...... 113

Figure 5.6. Measurements of DNA damage in erythrocytes of largemouth bass from the Ashtabula River and Conneaut River in the spring of 2004...... 114

xvii

CHAPTER 1

INTRODUCTION

Biomarkers

As a result of rapid industrialization and urbanization, increasing quantities of man-produced pollutants have been discharged into the environment. When these pollutants enter water bodies they can have direct or indirect impacts on the biota of aquatic systems. They often interfere with the normal functioning of an organism and its ability to live in harmony with the environment (Adams et al. 1990). The changes they cause in behavior, growth, and reproduction of an organism will eventually result in undesirable effects at higher biological organization levels. Therefore, there is a great need for sensitive and reliable methods to assess the impacts of pollution to the aquatic environment.

Programs to monitor environmental quality were developed in the early 1970s, based on chemical analyses of major contaminants including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), heavy metals, and organochlorine pesticides in water, sediments, and soils (Kaiser 2001). However, the chemical analyses of contaminants in an ecosystem cannot evaluate the impacts of chemicals on organisms. Most of the available data on harmful effects of contaminants on 1 organisms has been obtained from laboratory toxicity tests (Kaiser 2001). These tests are limited to evaluation of toxicity of certain chemicals to specific species under laboratory- controlled conditions. Since the actual exposure and effects in nature also depend on physicochemical characteristics of the environment and interaction between compounds, the chemical analyses and laboratory toxicity assays have limitations in assessing environmental damage.

The development of biomarkers in the late 1980s provides enormous possibilities for using biological responses to assess environmental exposure and effects. A biomarker can be defined as “a xenobiotically induced variation in cellular or biochemical components or processes, structures, or functions that is measurable in a biological system or sample” (Everaarts et al. 1993). Effects of pollutants are usually expressed first at the molecular/biochemical level. Changes at these levels can induce structural and functional changes at a higher level, such as hormonal regulation, immune system, and metabolism in an organism. These changes may finally impair the growth, reproduction and survival ability of the organism (Adams et al. 1990). A variety of changes observable or measurable at molecular, biochemical, cellular, or physiological levels in individuals have been studied as biomarkers for investigating the present or past exposure of the individuals to pollutants (Kaiser 2001).

The use of biomarkers in environmental monitoring has the following advantages

(McCarthy and Shugart 1990; Kaiser 2001). First, measurements of biomarkers provide scientific evidence for a link between toxicant exposure and relevant biological effects at an individual, a population or a community level. Because changes in biomarkers are often characteristic of exposure to a particular type of pollutant(s), biomarkers help to

2 establish a cause-and-effect relationship between environmental exposure and effects.

Second, biomarkers can indicate the exposure of organisms to toxic chemicals that do not bioaccumulate or are rapidly metabolized and eliminated, such as PAHs. The changes in biomarkers are the integrated consequences of exposure to the parent compounds as well as their metabolites. Third, biomarkers reflect the integrated effects of exposure to complex mixtures of contaminants and other environmental factors such as water temperature, water velocity, sediment, oxygen, and food availability. They present the cumulative effects of these factors on the target organism. Fourth, biomarkers at molecular and biochemical levels respond quickly to changes in the environment. The rapid response can offer early warning signals of environmental deterioration and potential effects of toxicants at sites. Because of these properties, the use of biomarkers strengthens assessments of the extent and nature of environmental degradation (Adams et al. 1990).

Lake Erie Tributaries

The Great Lakes system comprises Lakes Erie, Huron, Michigan, Ontario, and

Superior and their connecting waterways. The volume of water stored in Great Lakes

(about 6,020 trillion gallons, or 5,472 cubic miles) represents about 20% of the world and

90% of the Unite States fresh surface water supply. Lake Erie is the smallest of the Great

Lakes in volume (119 cubic miles). However, because of its fertile soils, the Lake Erie basin is intensively farmed and is the most densely populated of the five lake basins

(http://www.great-lakes.net/). Lake Erie and its tributaries are exposed to the greatest impacts from urbanization and agriculture.

3 Contamination by humans in the Great Lakes ecosystem limits its recreational uses and threatens its commercial and sport fisheries (http://www.great-lakes.net/). In

1983, the International Joint Commission (an organization established by the Boundary

Waters Treaty of 1909 between the United States and Canada) reported that 900 chemicals and heavy metals that are potentially dangerous to human health and biota had been identified in Great Lakes. The Commission designated 43 Areas of Concern (AOCs) along the shoreline of Great Lakes (IJC 1987a).

Among the 43 AOCs, eight are located on Lake Eire (Figure 1.1). They are Lake

Erie tributaries including the River Raisin in Michigan, Maumee River, Black River,

Cuyahoga River and Ashtabula River in Ohio, Presque Isle Bay in Pennsylvania, Buffalo

River in New York, and Wheatley Harbor in Ontario, Canada. Four AOCs, the St. Clair

River, Detroit River, Clinton River and Rouge River are located on the connecting channel between Lake Erie and Lake Huron in Michigan and one AOC, the Niagara

River, is located on the connecting channel between Lake Erie and Lake Ontario in New

York.

Surveys focused on the health of benthic fish, in particular brown bullheads

(Ameiurus nebulosus), and concentrations of contaminants in sediments of the Lake Erie

AOCs started in the early 1980s. The very first studies included those conducted at the

Black River (Baumann et al. 1982) and the Niagara River (Black 1983). Both studies found elevated prevalence of tumors in fish from the rivers contaminated with PAHs in comparison with the relatively clean sites. Additional studies followed on the Black,

Cuyahoga, Ashtabula, Detroit, Niagara and Buffalo Rivers, and Presque Isle Bay (Rice et al. 1986; Hickey et al. 1990; Baumann et al. 1991; Maccubbin and Ersing 1991; Mueller

4 and Mac 1993; Smith et al. 1994). Although prevalence of tumors varied among sites,

environmental toxicants, particularly PAHs, were suspected to be a causal factor of fish tumors in those industrially contaminated Lake Erie tributaries.

The Black River at Lorain, Ohio has received a long term study since 1980

(Baumann and Harshbarger 1998). High incidences of liver cancers (> 20%) were observed in the adult (3 years old or older) brown bullhead population in 1980, 1981 and

1982, a time period prior to the closure of an upstream coking facility in 1983. Cancer prevalence declined to 10% in 1987 but increased by about 5 times during 1992-1993, 2 to 3 years after the dredging of contaminated sediments in 1990. This increase is believed to be related to the exposure of fish to buried contaminants released by the dredging. In

1994, the frequency of cancers went down back to the level of 1987. Among age 3 fish

(which were not present during the 1990 dredging) none had liver neoplasms. The changing trend in liver neoplasm epizootics corresponded to the changes in concentrations of sediment PAHs. The long term study demonstrates how industrial operations and remediation could affect the environment and fish health and the necessity of conducting follow-up studies to assess the environmental status after remediation actions.

Various remedial activities have been carried out at the Lake Erie AOCs within the last decade. Highly contaminated sediments were removed from the Detroit, Black, and Niagara Rivers in the 1990s (http://www.epa.gov/glnpo/aoc/sedimentprojects.html).

Strategies to reduce point source contamination (effluents from industry and waste water treatment plants) and/or to control non-point source problems (such as agricultural runoff) have been undertaken at most of the sites. However, studies to evaluate the recent

5 environmental exposure and health of wild fish populations in the Lake Erie system were lacking. Many of the studies in the 1980s and the early 1990s were site specific and concentrated on fish pathology. There is a great need for broader studies to update information for the current ecological status of the Lake Erie.

Objectives

In this research, a suite of biomarkers in a catfish, brown bullhead (Ameiurus nebulosus) were used to assess the contaminant stress and effects at ten Lake Erie tributaries, including eight industrially contaminated AOCs, the Detroit River, Ottawa

River (belonging to the Maumee River system), Black River, Cuyahoga River, Ashtabula

River, Presque Isle Bay, Buffalo River and Niagara River, and two non-industrialized reference sites, Huron River and Old Woman Creek. The brown bullhead was selected as the target organism because: 1) it is fairly ubiquitous and abundant in the Lake Erie system, 2) it is a bottom dwelling species that is sensitive to environmental carcinogens and native to the water being examined, and 3) it is an indicator species selected by the

International Joint Commission (IJC 1987b).

The use of biomarkers to assess the contaminant exposure and effects in fish is addressed in the following five chapters. Chapter 2 is focused on measurements and comparisons of PAH metabolites in bile of brown bullheads sampled from all the sites except the Huron River and Presque Isle Bay during 1995-2000. Associations have been found between concentrations of sediment PAHs, concentrations of PAH metabolites in fish bile, and prevalence of liver lesions in fish, suggesting that PAH metabolites are an effective indicator of PAH exposure (Malins et al. 1984; Krahn et al. 1986; Lin et al.

6 2001). The aim of the Chapter 2 was to assess the exposure of fish to PAHs in the sampling Lake Erie tributaries.

Chapter 3 discusses four physiological variables measured in brown bullheads from all the ten sampling sites during 1998-2000, including the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI).

The condition factor and the three organo-somatic indices have been used as indicators of fish health and well-being (Le Cren 1951; Htun-Han 1978; Slooff et al. 1983; Adams and

McLean 1985; Goede and Barton 1990). The objectives of this chapter were to evaluate the health condition of fish from both the contaminated and reference sites and to determine whether or not contamination had impacts on the four variables.

In Chapter 4, prevalence of external raised lesions (grossly visible tumors) and barbel deformities was examined in brown bullheads from all the ten sites during 1998-

2000. This study was intended to reveal the carcinogenic disease and external abnormalities in fish associated with exposure to contaminants.

Chapter 5 demonstrates the use of two genetic assays, the comet assay and micronucleus assay, to investigate genetic damage in fish from the contaminated

Ashtabula River during 2002-2004. The Conneaut River had little industrial pollution and served as a reference site. The comet and micronucleus assays were developed in the

1970s and 1980s as simple and quick methods to evaluate DNA damage and chromosomal aberrations in organisms (Heddle 1973; Schmid 1975; Rydberg and

Johanson 1978; Ostling and Johanson 1984; Singh et al. 1988). They have shown potentials as indicators of genotoxic exposure and effects in fish (Pandrangi et al. 1995;

Nacci et al. 1996; Devaux et al. 1997; Rao et al. 1997).

7 The last chapter, Chapter 6 summarizes the results from Chapters 2 to 5.

Associations between the biomarkers utilized were examined. The environmental status

of the study sites and effectiveness of the biomarkers as indicators of contamination and

effects were evaluated.

On a basis of the biomarkers measured and examination of their relationships as

well as their relationships with sediment contaminants, this study pursued the following

goals:

1) Assess the contaminant exposure and adverse effects in wild fish populations of Lake Erie tributaries in the late 1990s and early 2000s.

2) Gain insights into the cause-and-effect relationship between contaminant exposure and effects.

3) Investigate efficient biomarkers in brown bullheads for environmental monitoring and assessment.

4) Provide first-hand information on the ecological status of the Great Lake tributaries and on the effectiveness of remedial measures undertaken at several AOCs.

8 References

Adams, S. M., and McLean R. B. 1985. Estimation of largemouth bass, Micropterus salmoides Lacepede, growth using the liver somatic index and physiological variables. Journal of Fish Biology 26: 111-126.

Adams, S. M., Shugart, L. R., Southworth G. R., and Hinton, D. E. 1990. Application of bioindicators in assessing the health of fish populations experiencing contaminant stress. In McCarthy, J. F., and Shugart, L. R. (eds): Biomarkers of Environmental Contamination, Lewis Publishers, Inc., Chelsea, MI, USA. pp 333-353.

Baumann, P. C., and Harshbarger, J. C. 1998. Long term trends in liver neoplasm epizootics of brown bullhead in the Black River, Ohio. Environmental Monitoring and Assessment 53: 213-223.

Baumann, P. C., Mac, M. J., Smith, S. B., and Harshbarger, J. C. 1991. Tumor frequencies in walleye (Stitzostedion vitrium) and brown bullhead (Ictalurus nebulosus) and sediment contaminants in tributaries of the Laurentian Great Lakes. Canadian Journal of Fisheries and Aquatic Sciences 48: 1804-1810.

Baumann, P. C., Smith, W. D., and Ribick, M. 1982. Hepatic tumor rates and polynuclear aromatic hydrocarbon levels of two populations of brown bullhead (Ictalulus nebulosus). In Cooke M., Dennis A. J., and Fisher, G. L. (eds): Polynuclear Aromatic Hydrocarbons: Physical and Biological Chemistry. Battelle Press, Columbus, OH, USA. pp 93-102.

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9 Hickey, J. T., Bennett, R. O., and Merckel, C. 1990. Biological indicators of environmental contaminants in the Niagara River: histological evaluation of tissues at the Love Canal-102 Street Dump Site compared to the Black Creek, reference site. U.S. Fish and Wildlife Service Report. Cortland, NY, USA. pp 124.

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Kaiser, J. 2001. Bioindicators and Biomarkers of Environmental Pollution and Risk Assessment. Science Publishers, Enfield, NH, USA. pp 10-20.

Krahn M. M., Rhodes L. D., Meyers M. S., Moore L. K., MacLeod W. D., and Malins D. C. 1986. Associations between metabolites of aromatic compounds in bile and the occurrence of hepatic lesions in English sole (Parophrys vetulus) from Puget Sound, Washington. Archives of Environmental Contamination and Toxicology 15: 61-67.

Le Cren, E. D. 1951. The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (Perca fluviatilis). The Journal of Animal Ecology 20: 201-219.

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Maccubbin, A. E., and Ersing, N. 1991. Tumors in fish from the Detroit River. Hydrobiologia 219: 301-306.

Malins, D. C., McCain, B. B., Brown, D. W., Chan, S-L., Myers, M. S., Landahl, J. T., Prohaska, P. G., Friedman, A. J., Rhodes, L. D., Burrows, D. G., Gronlund, W. D., and Hodgins, H. O. 1984. Chemical pollutants in sediments and diseases of bottom-dwelling fish in Puget Sound, Washington. Environmental Science & Technology 18: 705-713.

10 McCarthy, J. F., and Shugart L. R. 1990. Biological markers of environmental contamination. In McCarthy, J. F., and Shugart L. R. (eds): Biomarkers of Environmental Contamination. Lewis Publishers, Inc., Chelsea, MI, USA. pp 3- 14.

Mueller, M. E., and Mac, M. J. 1993. Tumor surveys and 10-day bioaccumulation bioassays in the assessment and remediation of contaminated sediments. International Association for Great Lakes Research 36th Conference, June 4-10, 1993. Program and Abstracts. pp 101.

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Ostling, O., and Johanson, K. J. 1984. Microelectrophoretic study of radiation-induced DNA damages in individual mammalian cells. Biochemical and Biophysical Research Communications 123: 291-298.

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11 Smith, S. B., Blouin, M. A., and Mac, M. J. 1994. Ecological comparisons of Lake Erie tributaries with elevated incidence of fish tumors. Journal of Great Lakes Research 20: 701-716.

Figure 1.1. Map locations of Great Lakes Areas of Concern. The “outlined portion” covers the Areas of Concern on Lake Erie.

CHAPTER 2

BILIARY PAH METABOLITES IN BROWN BULLHEADS

FROM LAKE ERIE TRIBUTARIES

Introduction

Polycyclic aromatic hydrocarbons (PAHs) generally refer to hydrocarbons containing two or more fused benzene rings (Neff 1979). They are a group of highly lipophilic chemicals that are ubiquitously present as pollutants in the environment, including air, water, soil, and sediments (IARC 1983a). PAHs with four or more condensed benzene rings are often mutagenic and/or carcinogenic (IARC 1983b). They are the suspected cause of various forms of tumors observed in fish populations

(Baumann 1989; Myers et al. 1994; Baumann et al. 1996). The hypothesis that tumors in wild fish are caused by PAHs has been supported by laboratory studies (Black 1983a,

1983b; Couch and Harshbarger 1985; Varanasi et al. 1987; Metcalfe 1989). Changes in behavior, growth, reproduction, and undesirable effects at higher levels of biological organizations have also been observed in fish when PAHs enter the aquatic environment

(Moore et al. 1989).

14 Although PAHs can be released by natural processes such as marine oil seeps

(Wilson et al. 1974), forest and grass fires (Youngblood and Blumer 1975), or be synthesized by some bacteria, plants, and fungi (Neff 1979), anthropogenic activity is thought the major source for PAHs in the environment (NRC 1983). PAHs are found in coal, fuel oil and other petroleum products and are released into the environment during combustion of fossil fuels or in certain industrial processes (ATSDR 1996).

Manufactured gas plants, coal coking operations, and creosote used in wood treatment have considerably increased the environmental exposure of PAHs. PAHs enter aquatic systems through direct municipal and industrial waste discharge, surface runoff, accidental oil spills, and aerial deposition (Abrajano and Bopp 2001).

Once PAHs get into the aquatic environment, they are rapidly associated with solid particles and are deposited in sediments because of their hydrophobic property

(McElroy et. al. 1989). Therefore, levels of PAHs in sediments have been traditionally used to assess the exposure of fish to PAHs in the aquatic environment. However, questions of heterogeneity or patchiness of PAHs in sediments and differential bioavailability of PAHs raise a valid concern as to whether the sediment PAH levels provide an adequate index of the aquatic environmental quality (Stein et al. 1992).

Measuring concentrations of PAHs accumulated in tissues of aquatic organisms does address the question of bioavailability. But because PAHs are rapidly metabolized to compounds not identifiable by routine analytical techniques, analyses of PAHs in fish tissues do not provide useful information on exposure of fish to PAHs (Krahn et al.

1986b).

15 PAHs are enzymatically transformed in fish to compounds that are more toxic or carcinogenic than the parent compounds including carcinogens (Buhler and Williams

1989). As a result, the toxicological significance of PAH contamination depends in large part on the metabolic fate of the compounds (Steward et al. 1990). The metabolism of

PAHs is believed to be an essential factor in the development of various forms of environmental cancers (Collier and Varanasi 1991). Laboratory studies demonstrate that

PAHs, when taken up by fish from the environment, are rapidly metabolized to a complex group of compounds in the liver that are secreted into the bile (Maccubbin et al.

1988; Varanasi et al. 1989). Therefore, whether the level of biliary PAH metabolites could be used to indicate the PAH exposure has been the subject of many studies in fish including the brown bullhead (Ameiurus nebulosus) (Arcand-Hoy and Metcalfe 1999;

Leadly et al. 1999), English sole (Parophrys vetulus), rock sole (Lepidopsetta bilineata) and starry flounder (Platichthys stellatus) (Krahn et al. 1986b; Stein et al. 1992), carp

(Cyprinus carpio) (Johnston and Baumann 1989; Britvic et al. 1993), hardhead catfish

(Arius felis) and Atlantic croaker (Micropogon undulatus) (Willett et al. 1997).

Several methods have been developed since the middle of 1980s for analyzing

PAH metabolites in the bile of fish. Krahn et al. (1984) created a comparatively rapid and inexpensive high-performance liquid chromatography procedure with fluorescence detection (HPLC/F) to estimate the relative amount of PAH metabolites in fish bile. Since then this method has been widely applied in the study of exposure of fish to PAHs. A decade later, Lin et al. (1994) reported a simpler and less costly method, synchronous fluoremetric spectroscopy (SFS), for bile analysis. Lin et al. (1996) reported another even

16 quicker method, fixed-wavelength fluorescence (FF) which makes possible the

measurement of a large number of samples within a short time.

Sediments in many industrialized tributaries of Lake Erie are contaminated with

organic chemicals such as PAHs, polychlorinated biphenyls (PCBs), and pesticides

(USEPA 2000). Brown bullheads (Ameiurus nebulosus) were sampled from eight Lake

Erie tributaries during 1995 - 2000, including the industrially contaminated Detroit River

(DET), Ottawa River (OTT), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Buffalo River (BUF) and Niagara River at

Love Canal (NIA), and the non-industrialized Old Woman Creek (OWC). Bile was collected from each fish and was analyzed using the FF method. Concentrations of biliary

PAH metabolites were compared between fish sampled from the same site in different years and between fish sampled from different sites. The purposes were to assess the exposure of fish to PAHs in the eight Lake Erie Rivers and to evaluate the effectiveness of biliary PAH metabolites as an indicator of PAH pollution.

Methods

Fish Collection

Brown bullheads were captured in fyke nets or by electro-shocking from eight

Lake Erie tributaries in springs and early summers of 1995 - 2000, including the Detroit

River (DET) in 2000, Ottawa River (OTT) in 1999, Old Woman Creek (OWC) in 1995,

1996, 1997, 1999 and 2000, Black River (BLA) in 1995, 1996, and 1997, Cuyahoga

River - harbor (CRH) and - upstream (CRU) in 1999, Ashtabula River (ASH) in 2000,

Buffalo River (BUF) in 1998, and Niagara River at Love Canal (NIA) in 1998 (Figure

17 2.1). Among these sites, OWC was the only location without industrial pollution and was

selected as a reference site. However, it received agricultural runoff, and PAH

contamination has been found near a highway bridge and a railway bridge with creosote

treated wood (Johnston and Baumann 1989). The rest of the sites were industrially

contaminated. Each was designated by the International Joint Commission as a Great

Lakes Area of Concern. But because the Black River has undergone significant

remediation, it has recently been reclassified as an Area of Recovery.

Fish collected from each site were measured for total length. Those that were

250mm or longer (about 3 years old or older, sexually mature) were euthanized and

necropsied. Bile was extracted by a syringe from the gall bladder of each fish and was

injected into a vial. Bile samples were immediately put in liquid nitrogen until they could

be transferred to the laboratory and stored at -80ûC. The sex of each fish was identified and at least one pectoral spine was removed for aging according to the methodology in

Baumann et al. (1990) and Kovoscky (2000).

Bile Analysis

Concentrations of PAH metabolites in bile were measured by the fixed- wavelength fluorescence (FF) method (Lin et al. 1996) at the U.S. Environmental

Protection Agency, National Exposure Research Laboratory, Cincinnati, Ohio. Bile samples were first diluted to 1:20 and assayed for protein content by a bicinchoninic acid

(BCA) method adapted for microtiter plates (Redinbaugh and Turley 1986). Each diluted solution was further diluted to 1:1000 for the FF measurement. Concentrations of benzo[a]pyrene (B[a]P) - and naphthalene (NAPH) - type metabolites were determined by fluorescence at 380/430 nm and 290/335 nm excitation/emission wavelengths,

18 respectively. The concentrations of PAH metabolites in bile were normalized by protein

content and reported as micrograms of B[a]P - and NAPH - type metabolites per

milligram of protein. The normalization on the basis of biliary protein can, to a large

extent, account for changes in the levels of PAH metabolites due to differences in the

feeding status of fish (Collier and Varanasi 1991; Stein et al. 1992).

Since compounds other than metabolites of B[a]P and NAPH can also fluoresce at

the fixed wavelengths, the measurements of PAH metabolites are semi-quantitative

(Krahn et al. 1986a, 1986b, 1992; Lin et al. 1996). The concentration of B[a]P - type

metabolites is used to estimate exposure of fish to four and five - ring PAHs (Krahn et al.

1992). Aromatic compounds which fluoresce at 380/430 nm include B[a]P, pyrene,

fluoranthene and their metabolites, etc. (Krahn et al. 1986b). The concentration of NAPH

- type metabolites measures exposure of fish to two-ring PAHs where the majority of chemicals are NAPH and its derivatives (Krahn et al. 1992; Lin et al. 1996). Henceforth, the “type metabolites” should be used to describe the compounds, mostly metabolites of

PAHs, which contribute to the measurements at the fixed wavelengths.

Sediment Collection and Analysis

Sediments were collected and analyzed as described by Smith et al. (2003) and

Passino-Reader et al. (2004). Briefly, five to eight surface sediment samples (top 2-3 cm layer) were collected using a stainless steel Eckman dredge from each site where fish

were sampled. The samples from the same site were mixed in a stainless steel bowl and

stored in chemically clean jars. The mixed samples were freeze-dried, homogenized and

extracted for 12 hours with methylene chloride in a Soxhlet extraction apparatus. The

extracts were treated with copper to remove sulfur and purified by silica/alumina column

19 chromatography to isolate the PAH fractions. PAHs were identified and quantified by capillary gas chromatography with a mass spectrometer detector in the SIM mode.

Statistical Analysis

Statistical analyses were performed using SAS version 8.1 (SAS Institute, Cary,

NC, USA). PAH metabolite data were log transformed to increase normality and homogeneity of variance. The general linear model (GLM) that contained the main effects of the sampling site, fish sex and age was used to detect variables that caused differences in concentrations of PAH metabolites. Tukey's multiple comparison test was conducted to compare the mean concentrations of PAH metabolites between fish from different sites and between fish from the same site sampled in different years. Student t- test was used to compare the mean concentrations of PAH metabolites in fish from BLA and OWC sampled in the same year. The correlation between B[a]P and NAPH - type metabolites in fish was examined by Pearson’s correlation procedure using individual fish data. Associations between fish PAH metabolites and sediment PAHs were examined by Spearman’s rank correlation procedure using the mean concentrations of PAH metabolites and the sediment concentration of PAHs at each site.

Results

The mean concentrations of biliary PAH metabolites in fish sampled from OWC and BLA in different years are shown in Figure 2.2. Concentrations of both B[a]P - and

NAPH - type metabolites in fish of BLA significantly decreased from 1995 to 1997

(Tukey’s test, p < 0.05). The levels of B[a]P - type and NAPH - type metabolites in 1997 were about 1/6 and 1/3 of the levels in 1995, respectively. Fish sampled from OWC in

20 1996 and 1997 had lower concentrations of B[a]P - type metabolites than fish sampled in

1995 and 2000, and fish from OWC in 1997, 1999 and 2000 had lower NAPH - type metabolites than fish from OWC in 1995 and 1996 (Tukey’s test, p < 0.05). In each year of 1995, 1996 and 1997, B[a]P and NAPH - type metabolites were significantly higher in fish from BLA than in fish from OWC (t-test, p < 0.0001), varying from 13 and 3 times higher in 1995, 21 and 2 times higher in 1996, to 7 and 4 times higher in 1997, respectively.

The mean concentrations of biliary PAH metabolites in fish from DET in 2000,

OTT in 1999, OWC in 1997, 1999 and 2000, BLA in 1997, CRH and CRU in 1999, ASH in 2000, BUF in 1998, and NIA in 1998 were compared in Figure 2.3. Fish from CRH and CRU had very similar levels of PAH metabolites (CRH: 0.48 and 58.8 µg/mg protein

B[a]P - and NAPH - type metabolites; CRU: 0.48 and 65.5 µg/mg protein B[a]P - and

NAPH - type metabolites). Therefore bile data from the two locations of the Cuyahoga

River (CUY) were combined. Data from OWC in 1997, 1999 and 2000 were also combined since there was not much variation among these three years and these years were close to the sampling years of the other study sites. BLA in 1997 was selected for the comparison because 1997 was the nearest available sampling year for BLA.

Significant differences were found among all the sites in mean concentrations of

B[a]P - and NAPH - type metabolites (GLM, p < 0.0001). There was no evidence that sex and age could influence PAH metabolite levels (GLM, p > 0.05). Fish from CUY and

BUF had the highest levels of B[a]P-type metabolites, followed by fish from OTT, and then by fish from DET, BLA, and ASH (Tukey’s test, p < 0.05). Fish from CUY and

BUF also had higher levels of NAPH-type metabolites than fish from DET, OTT, BLA,

21 and ASH. Fish from NIA and OWC had the lowest levels of either type metabolites

(Tukey’s test, p < 0.05).

Concentrations of B[a]P and NAPH - type metabolites were highly correlated in

fish (rPearson’s = 0.94, p < 0.0001). The mean concentrations of B[a]P - type metabolites in fish were positively associated with concentrations of selected PAHs in sediments (Table

2.1) across the sampling sites (rSpearman’s = 0.81, p < 0.05). The mean concentrations of

NAPH - type metabolites were positively associated with concentrations of selected sediment PAHs with marginal significance (rSpearman’s = 0.64, p < 0.1).

Discussion

Concentrations of PAH metabolites in fish bile were not found to be related to sex

and age, which suggests the robustness of biliary PAH metabolites to these non-

contamination factors. Brown bullheads from the non-industrialized reference site, Old

Woman Creek, had significantly lower concentrations of PAH metabolites in bile than

fish from all the industrialized sites except the Niagara River. PAH metabolites in the bile

of brown bullheads were significantly associated with PAHs in sediments, indicating that

biliary PAH metabolites are an effective indicator of fish exposure to PAHs.

Lin et al. (2001) measured concentrations of PAH metabolites in brown bullheads

from the Old Woman Creek, Cuyahoga and Black Rivers in 1990, 1991, 1993, and 1998

using the synchronous fluorometric spectroscopy technique. Similar to the present study,

fish from the Old Woman Creek showed some variation in concentrations of PAH

metabolites over time. However, this variation was small and PAH metabolites in fish

from the contaminated Cuyahoga and Black Rivers were consistently higher than those

22 from the Old Woman Creek in all the sampling years. Fish from the Cuyahoga and Black

Rivers had significantly decreased levels of PAH metabolites in 1993 and 1998 in comparison to fish in 1990 and 1991. The decrease could result from the shutdown of coking operations in the Cuyahoga River in 1992 and the dredging of contaminated sediments from the Black River in 1990 (Lin et al. 2001).

The bile data of the Black River during 1995-1997 in my study confirmed that fish from the Black River experienced lowered PAH exposure in the late 1990s. It

supports the recent Black River’s reclassification from an Area of Concern to an Area of

Recovery. However, higher concentrations of biliary PAH metabolites were still detected

in fish from the Black River, as well as in fish from the Detroit, Ottawa, Cuyahoga,

Ashtabula, and Buffalo Rivers compared to the reference site Old Woman Creek. This

demonstrates greater PAH exposure at these industrially contaminated sites. Fish from

the Buffalo and Cuyahoga Rivers appeared to be those most highly exposed to PAHs.

Fish from the Niagara River had levels of PAH metabolites as low as fish from

the Old Woman Creek. The Niagara River had been seriously contaminated by toxicants

leaking from Love Canal, a place used as a municipal and chemical dump site from the

1920s to 1950s. However, a variety of remedial measures have been applied since the

1980s. The migration of toxicants into the river from some of the major contaminated

sites was forecasted to be reduced by approximately 80% through the first quarter of

1999 (USEPA and NYSDEC 1998). The low concentrations of PAHs detected in both the

NIA fish and sediments provide evidence for an improved condition at the Niagara River.

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Stein, J. E., Collier, T. K., Reichert, W. L., Casillas, E., Hom, T., and Varanasi, U. 1992. Bioindicators of contaminant exposure and sublethal effects: studies with benthic fish in Puget Sound, Washington. Environmental Toxicology and Chemistry 11: 701-714.

Steward, A. R., Kandaswami, C., Chidambaram, S., Ziper, C., Rutkowski, J. P., and Sikka., H. C. 1990. Disposition and metabolic fate of benzo[a]pyrene in the brown bullhead. Environmental Toxicology and Chemistry 9: 1503-1512.

USEPA (United States Environmental Protection Agency). 2000. Great Lakes Areas of Concern. http://www.epa.gov/glnpo/aoc.

USEPA (U.S. Environmental Protection Agency), and NYSDEC (New York State Department of Environmental Conservation). 1998. Reduction of Toxics Loadings to the Niagara River from Hazardous Waste Sites in the United States. Niagara River Report. http://www.epa.gov/glnpo/lakeont/nrtmp/report.html.

Varanasi, U., Stein, J. E., and Nishimoto, M. 1989. Biotransformation and disposition of PAH in fish. In Varanasi U. (ed): Metabolism of Polycyclic Aromatic Hydrocarbons in the Aquatic Environment. CRC Press Inc., Boca Raton, FL, USA. pp 93-150.

Varanasi, U., Stein, J. E., Nishimoto, M., Reichert, W. L., and Collier, T. K. 1987. Chemical carcinogenesis in feral fish: uptake, activation, and detoxication of organic xenobiotics. Environmental Health Perspectives 71: 155-170.

27 Willett, K. L., McDonald, S. J., Steinberg, M. A., and Beatty, K. B. 1997. Biomarker sensitivity for polynuclear aromatic hydrocarbon contamination in two marine fish species collected in Galveston Bay, Texas. Environmental Toxicology and Chemistry 16: 1472-1479.

Wilson, R. D., Monaghan, P. H., Osanik, A., Priu, L. C., and Rogers, M. A. 1974. Natural marine oil seepage. Science 184: 857-865.

Youngblood, W. W., and Blumer, M. 1975. Polycyclic aromatic hydrocarbons in the environment: homologus series in soils and recent marine sediments. Geochimica Et Cosmochimica Acta 39: 1303-1314.

Site DET OTT OWC BLA CUY b ASH BUF NIA

Year 2000 1999 2000 1998 1999 2000 1998 1998

PAHs a 17.42 9.41 5.25 5.42 19.07 3.91 7.59 1.02 a Sediment data were taken from Smith et al. 2003 and Passino-Reader et al. 2004. The concentration of PAHs is the sum of concentrations of anthracene, benz[a]anthracene, benzo[a]pyrene, benzo[b]fluoranthene, chrysene, dibenz(a,h)anthracene, fluoranthene, fluorene, naphthalene, C1-, C2-, C3-, and C4- naphthalene, perylene, phenanthrene, and pyrene. These compounds and their metabolites have fluorescent responses at 380/430 nm or/and 290/335 nm wavelengths (Krahn et al. 1986b; Lin et al. 1996). b Concentration was measured in Cuyahoga River-harbor.

Figure 2.1. Map locations of the Detroit River (DET), Ottawa River (OTT), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River-harbor (CRH), Cuyahoga River- upstream (CRU), Ashtabula River (ASH), Buffalo River (BUF), and Niagara River (NIA).

30 160 25 140 B[a]P-type (µg/100mg protein) A' 120 NAPH-type (µg/mg protein) n o i 100 21 at

r B' 80 A' 22 ncent 60 24 o 23 C'

C A 40 A 20 B' 10 20 B 20 C C B' A BBAB A 0 OWC95 OWC96 OWC97 OWC99 OWC00 BLA95 BLA96 BLA97 Site

Figure 2.2. Concentrations of B[a]P - type and NAPH - type metabolites in bile of brown bullheads from the Old Woman Creek in 1995 (OWC95), 1996 (OWC96), 1997 (OWC97), 1999 (OWC99) and 2000 (OWC00), and in bile of brown bullheads from the Black River in 1995 (BLA95), 1996 (BLA96) and 1997 (BLA97). Heights of columns represent mean concentrations and error bars are standard errors. Numbers above bars indicate sample sizes. Mean concentrations with the same letter are not significantly different (Tukey’s test, p > 0.05).

120

B[a]P-type (µg/100mg protein) 20 100 A NAPH-type (µg/mg protein) n

o 36

i 80 AB at r 60 22 A A 20 BC

ncent 20 CD o 40 D 20 C B 50 D 20 20 C E C E C D D 0 DET OTT OWC BLA CUY ASH BUF NIA Site

Figure 2.3. Concentrations of B[a]P - type and NAPH - type metabolites in bile of brown bullheads from the Detroit River in 2000 (DET), Ottawa River in 1999 (OTT), Old Woman Creek in 1997, 1999 and 2000 (OWC), Black River in 1997 (BLA), Cuyahoga River - harbor and upstream in 1999 (CUY), Ashtabula River in 2000 (ASH), Buffalo River in 1998 (BUF), and Niagara River in 1998 (NIA). Heights of columns represent mean concentrations and error bars are standard errors. Numbers above bars indicate sample sizes. Mean concentrations with the same letter are not significantly different (Tukey’s test, p > 0.05).

CHAPTER 3

THE CONDITION FACTOR AND ORGANO-SOMATIC INDICES OF BROWN

BULLHEADS FROM LAKE ERIE TRIBUTARIES

Introduction

Length-weight relationships have been used to measure relative plumpness or

well-being of fish (Le Cren 1951; Wege and Anderson 1978). A formula, weight = a

constant × lengthn, was found to adequately describe the relationship between length and

weight in most fish (Le Cren 1951). The exponent n usually lies between 2.5 and 4.0, and

n=3 is suggested to be used under the assumption that fish maintain the same shape as

they grow up (Le Cren 1951; Htun-Han 1978; Wege and Anderson 1978). The ratio

between the weight and length of a fish is called a condition factor (K). Various types of

the condition factor have been calculated, and most of them were derived from the

constant in the length-weight formula with n=3, i.e. K = weight / length3 (Le Cren 1951).

Because of the existence of decimal numbers in the K calculated by the above formula, K

= weight / (0.000427 × length3) (Menzies 1920; Le Cren 1951), K= 100 × weight /

length3 (Htun-Han 1978), and K = 105 × weight / length3 (Adams and McLean 1985) have been introduced to obtain integer K values. A variety of factors have been found to

33 introduce changes in the condition factor, such as food availability, species competition, ambient temperature as well as exposure to xenobiotics (Adams and McLean 1985).

Livers play an important role in digesting and storing nutrients and metabolizing and excreting xenobiotics (Hinton and Lauren 1990). The liver is one of the major organs for storing nutrients obtained from gut absorption and providing energy for growth and reproduction of fish (Saborowski and Buchholz 1996). Livers contain a mixed function oxygenase system, cytochrome P-450, which metabolizes some xenobiotics to their toxic forms while inactivates some others. The metabolites of compounds are then carried by bile which is synthesized within hepatocytes and are released into the gut for excretion or enterohepatic recirculation. Because the important role livers play in fish growth and metabolism, livers have been given extensive attention in evaluating fish health as well as environmental quality.

Various biostructural alterations have been observed in livers of fish. Hepatocyte coagulative necrosis, hepatocyte regenerative foci, and neoplasia appear to be associated with exposure to toxic agents (Hinton and Lauren 1990). They involve an increase in cell numbers (hyperplasia) for the overgrowth of hypatocytes. An increase in cell size

(hypertrophy) could also happen in fish that are exposed to polycyclic aromatic hydrocarbons (PAHs) or polychlorinated biphenyls (PCBs) due to proliferation of organelles. Enlargement of livers by hyperplasia or hypertrophy could be reflected in an increased ratio of liver weight to body weight (Slooff et al. 1983; Hinton and Lauren

1990). Therefore, the relative liver weight, i.e. hepatosomatic index (HSI), has been used to indicate the contaminant exposure of fish. Non-contamination factors, such as sex,

34 seasonal change, nutritional status, and infection of parasites might also cause variation in

HSI values (Slooff et al. 1983; Everaarts et al. 1993; Hinton and Lauren 1990).

The gonosomatic index (GSI) (ratio of gonad weight to body weight) and spleen-

somatic index (SSI) (ratio of spleen weight to body weight) are another two organo-

somatic indices which have been used as indicators of fish health. Introduced by Meien in

1927, the GSI has been used to indicate the gonadal development and activity in fish

(Htun-Han, 1978). The GSI varies between females and males and is sensitive to the fish

spawning cycle, seasonal change, nutritional status, and water temperature. Previous

research showed that exposure to pollutants can result in gonadal alterations such as a

decreased GSI and morphological changes (Choudhury et al. 1993; Friedmann et al.

1996).

The spleen is a major hematopoietic organ in fish. It stores red cells and

disintegrates old blood cells. Alterations in the relative spleen size could signal a

dysfunction. A decrease in SSI may result from necrosis and perturbations in cell

processing which impairs an individual’s health. An increase, enlargement or swelling of

the spleen, on the other hand, indicates disease or immune system problems (Goede and

Barton 1990). Changes have been observed in the spleen-somatic index of fish exposed to cadmium and tetrachloroguaiacol (Johansen et al. 1994; Stepanova et al. 1998). The GSI and SSI provide additional information for the well-being and reproductivity of fish.

Brown bullheads (Ameiurus nebulosus) were collected from ten Lake Erie

tributaries during 1998-2000, including the Detroit River (DET), Ottawa River (OTT),

Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River -

harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB),

35 Buffalo River (BUF), and Niagara River (NIA). The condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI) were measured in fish from each site. Through comparing the K and the three organo-somatic indices of fish from the contaminated and relatively clean sites, the study was aimed to examine health conditions of fish under contaminant stress and to provide more information regarding the use of the physiological variables as indicators of contaminant exposure and effects.

Methods

Fish Collection

Brown bullheads were captured in fyke nets or by electro-shocking from ten Lake

Erie tributaries in springs and early summers of 1998 - 2000, including the Detroit River

(DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black

River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River

(ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River at Love Canal

(NIA) (Figure 3.1, Table 3.1). Among these sites, HUR and OWC were the only locations without industrial pollution and were selected as reference sites. However, they received agricultural runoff, and PAH contamination has been found at OWC near a highway bridge and a railway bridge with creosote treated wood (Johnston and Baumann

1989). The rest of the sites were industrially contaminated. Each was designated by the

International Joint Commission as a Great Lakes Area of Concern. But because the Black

River has undergone significant remediation, it has recently been reclassified as an Area of Recovery.

36 Fish collected from each site were measured for total length and body weight.

Those that were 250mm or longer (about 3 years old or older, sexually mature) were

euthanized and necropsied. The liver, spleen, and gonad were excised from each fish and

weighed. The sex of each fish was identified and at least one pectoral spine was removed

for aging according to the methodology in Baumann et al. (1990) and Kovoscky (2000).

Calculation of the condition factor and organo-somatic Indices

The condition factor (K) was calculated by K= 105 × weight / length3.

Hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI) were calculated by HSI = 100 × liver weight / body weight, GSI = 100 × gonad weight / body weight, and SSI = 100 × spleen weight / body weight.

Sediment Collection and Analysis

Sediments were collected and analyzed according to Smith et al. (2003) and

Passino-Reader et al. (2004). Briefly, the oxidized top 2-3 cm of the fine sediments in

depositional zones were collected using a stainless steel Eckman dredge from the study

sites during 1998-2000. At least five samples were randomly collected from each site and mixed together.

A portion of the sediment samples were digested with aqua regia (3:1 HCl:HNO3) in glass beakers on a hotplate and diluted for the analysis of trace metals. Cold vapor atomic absorption spectrometry (AAS) was used to determine the amount of mercury in the sediment samples, and graphite furnace AAS was used to determine the amount of arsenic, selenium, cadmium, and lead. High concentrations of cadmium and lead were determined by atomic emission using an argon plasma.

37 Another portion of the sediment samples were freeze-dried and extracted in a

Soxhlet extraction apparatus for the analysis of organic chemicals and pesticides in

sediments. Surrogate standards and methylene chloride were added and the samples were

extracted for 12 hours. The extracts were treated with copper to remove sulfur and were

purified by silica/alumina column chromatography to isolate the aliphatic and

aromatic/pesticide/polychlorinated biphenyl’s fractions. Capillary gas chromatography

(CGC) with an electron capture detector was used to measure concentrations of pesticides

and polychlorinated biphenyls (PCBs), and CGC with a mass spectrometer detector in the

SIM mode was used to measure concentrations of polycyclic aromatic hydrocarbons

(PAHs) in the sediment samples. When pesticide and PCB analyses coelute with each

other in the normal CGC with an electron capture detector, samples were analyzed by

CGC with a mass spectrometer detector in the SIM mode.

Statistical Analysis

Statistical analyses were performed using SAS version 8.1 (SAS Institute, Cary,

NC, USA). HSI and SSI data were log transformed to increase normality and

homogeneity of variance. The general linear model (GLM) that contained the main

effects of the sampling site, fish sex and age was used to detect variables that caused

differences in K, HSI, GSI and SSI. Two-sample t-test was used to compare the K, HSI,

GSI and SSI between females and males at each site (α level for each comparison was

0.008 and the overall α was 0.10 with the Bonferroni adjustment for 12 comparisons).

Tukey's multiple comparison test was conducted to compare the mean values of the K,

HSI, GSI and SSI between different sites. Associations between the K, HSI, GSI and SSI were examined by Pearson’s correlation procedure using individual fish data.

38 Associations between sediment contaminants and the K, HSI, GSI and SSI were examined by Spearman’s rank correlation procedure using concentrations of sediment contaminants and mean values of the four physiological variables at each site.

Results

A total of 455 brown bullheads were collected from the ten Lake Erie tributaries

(Table 3.1). Although fish varied from one site to another in age (GLM, p < 0.001), the mean ages of fish from all sites were between 4.6 and 5.8 (within about one year’s difference), except for the NIA from which fish were averaged at 3.4 (Table 3.1). Fish from the two locations of the Cuyahoga River, CRH and CRU, as well as BLA appeared to be larger in size than fish from the other sites (Table 3.1).

Fish from the 11 locations had different K, HSI, GSI, and SSI values (GLM, p <

0.001). Sex was a significant factor that influenced all the four physiological variables

(GLM, p < 0.01). Age showed some impact on the SSI (GLM, p < 0.05) but not on the K,

HSI, and GSI (GLM, p > 0.1). Since fish from most of the sites were close in age and no extreme influence of age has been demonstrated, age was not taken into consideration in the following analysis. The mean values of the K, HSI, GSI, and SSI of fish from each site were calculated separately in terms of sex and are shown in Figure 3.2-3.5.

There was no apparent variation between female and male fish in the condition factor (Figure 3.2). Only female fish from CRH displayed a significantly higher mean K value than male fish from the same site (t-test, p < 0.008). The mean HSI values of females were significantly higher than those of males in four samplings and significantly lower in one sampling (t-test, p < 0.008 for each comparison) (Figure 3.3). Because fish

39 were sampled in the spawning season, female fish consistently had much higher GSI values than male fish (Figure 3.4). Most sites showed higher male SSI values than female

SSI values but the difference was only significant at CRU (t-test, p < 0.008) (Figure 3.5).

Previous studies demonstrated that the K, HSI, GSI and SSI were sensitive to seasonal changes (Htun-Han 1978; Delahunty and Vlaming 1980; Saborowski and

Buchholz 1996). The four physiological variables, therefore, were compared between sites sampled in the same season as an attempt to reduce the seasonal effects. OTT, HUR,

BLA, ASH and PIB that were sampled in May and OWC that was sampled in the end of

April of 1999 were ascribed into a group for comparison (Table 3.2). The rest sites including DET, OWC in 2000, CRH, CRU, BUF and NIA sampled in June were compared as another group (Table 3.3).

For the sites sampled in May and late April (Table 3.2), both female and male fish from the BLA had higher K values than fish from the rest of the sites. Fish from BLA as well as fish from ASH also had high HSI values. Females from OTT, BLA and ASH had greater GSIs than females from OWC and males from OTT, BLA and ASH had greater

GSIs than males from PIB. No significant difference was found between sites in the male

SSI but OWC and ASH showed higher female SSIs than OTT and HUR.

Sampled in June, females from CRH had the highest K value but males from the same site had the lowest K (Table 3.3). Both female and male fish from NIA had high

HSIs. The mean male GSI and SSI values of OWC were significantly lower than those of

BUF. No significant difference was found between sites in the female GSI and BUF had a significantly higher female SSI than DET, OWC and CRU.

40 The K, HSI, and GSI were positively correlated with each other in fish (Pearson’s

correlation, p < 0.005) (Table 3.4). However, the SSI was not related to the K and HSI

(Pearson’s correlation, p > 0.10) and was negatively associated with the GSI (Pearson’s correlation, p < 0.005). Concentrations of contaminants in sediments varied among sites, with PAHs being the highest in the Detroit River and Cuyahoga Harbor, and PCBs being the highest in the Ottawa River (Table 3.5). PAH concentrations were positively associated with the mean values of male SSI and PCB concentrations were positively associated with the mean values of male GSI across the sampling sites (Spearman’s rank correlation, p ≤ 0.05) (Table 3.6).

Discussion

The physiological variables, K, HSI, GSI, and SSI of brown bullheads varied among the sampling sites. Age did not induce significant variation in the K, HSI and GSI, while sex was an influential factor for all the four variables. The lack of a relationship between age and K, HSI and GSI could result from the control of fish age in sampling

(only 3 years old or older, sexually mature fish were sampled).

Female fish had more than ten times higher GSIs than male fish during the sampling season (spawning period of brown bullheads) as would be expected with mature ovaries. At a number of sites, females had significantly higher HSIs than males. Only one site showed a difference between females and males in the K and SSI.

Annual changes in the K, HSI and other organo-somatic indices have been investigated by a few studies (Htun-Han 1978; Delahunty and Vlaming 1980; Dawson and Grimm 1980; Adams and McLean 1985; White and Fletcher 1985; Saborowski and

41 Buchholz 1996). Saborowski and Buchholz (1996) observed significantly higher HSI values in female than in male dabs (Limanda limanda) in winter, but the difference decreased in spring and disappeared in summer (June to August). Although they also found a similar pattern with the condtion factor, the difference in K between females and males was smaller than the difference in HSI and was pretty much gone by May

(Saborowski and Buchholz 1996).

The dependence of the HSI and K on the seasonal cycle may explain why female bullheads had significantly higher HSIs than male bullheads in OTT, HUR, OWC of

1999, and ASH (sampled in late April or May, Table 3.1) but not in the other sites (most of which were sampled in June, Table 3.1) and why most sites (sampled in late April,

May and June) had no difference between females and males in the condition factor. The annual variations and variations among populations of the same species in the spleen size

(Ruklov 1979; Lipskaya and Salekhova 1980) may also explain why some site had significant difference between females and males in SSI but others did not.

Positive associations were found between the K, HSI and GSI. The reason for the lack of association between the SSI and K or HSI and the negative association between

SSI and GSI is unclear. One possibility is that the size of the spleen is independent of the other internal organs in brown bullheads. Another possibility is that environmental contaminants were harmful to spleens and led to abnormality in the organ size. The latter hypothesis is supported by the positive association found between the male SSI values and sediment concentrations of PAHs.

The K, HSI, and SSI have received extensive attention as their role in indicating fish health and environmental conditions (Schmitt and Dethloff 2000). Elevated condition

42 factors have been observed in white sucker (Catostomus commersoni) and redbreast sunfish (Lepomis auritus) at sites contaminated with pulp mill effluent (McMaster et al.

1991; Adams et al. 1992). Decreased condition factors have also been found in white sucker and Atlantic cod (Gadus morhua) exposed to metal mixtures and petroleum, respectively (Kiceniuk and Khan 1987; Munkittrick and Dixon 1988; Miller et al. 1992).

Similarly, an increase has been seen in the SSI of redbreast sunfish exposed to bleached kraft pulp mill effluent (Adams et al. 1992) and reduced SSIs occurred in cunners

(Tautogolabrus adspersus) exposed to petroleum and in Atlantic cod exposed to crude oil as well as in gobies (Zosterisessor ophiocephalus) at a site polluted with PCBs, PAHs, and metals (Payne et al. 1978; Kiceniuk and Khan 1987; Pulsford et al. 1995).

More focus has been given to the HSI relative to the K and SSI. Positive associations between HSIs and concentrations of contaminants, in particular PAHs, have been reported in previous studies (Slooff et al. 1983; Fabacher and Baumann 1985;

Gallagher and Di Giulio 1989; Everaarts et al. 1993; Pinkney et al. 2001). To the contrary, decreases were detected in HSIs of fish exposed to specific compounds such as heavy metals and hydrogen sulfide (Larsson et al. 1984; Hoque et al. 1998).

In this study, although there was no clear trend between the contaminated, recovered, and reference sites in values of the K, HSI, GSI and SSI, positive associations were found between the male SSI and sediment PAHs, and between the male GSI and sediment PCBs. Alterations in spleen morphology have been found to be associated with alterations in the release of erythrocytes into circulation, numbers of intact white blood cells, and degradation of red blood cells in the spleen (Yamamoto and Itazawa 1983;

Peters and Schwarzer 1985; Maule and Schreck 1990). The study on relative abundances

43 of various developmental stages of erythrocytes in brown bullheads at seven of the study sites showed increased proportions of immature red blood cells in fish from polluted sites

(personal contact with Michael W. Rowan of our research group). The current study supports the finding that contamination in Lake Erie tributaries may have impacts on the erythropoiesis and immune system of fish.

Reductions in GSI have been reported in juvenile walleye (Stizostedion vitreum) treated with dietary methylmercury at environmentally exposed levels and in female prespawning white suckers (Catostomus commersoni) sampled at a bleached kraft pulp mill effluent contaminated site (Friedmann et al. 1996; Janz et al. 1997). The reductions in the relative gonad size observed in the above two studies were associated with impaired growth and elevated ovarian follicular apoptosis, respectively. However, a positive association was found in the present study between the male GSI values and concentrations of sediment PCBs. The reason for this increase in GSI is unknown.

Laboratory experiments would be needed to examine whether contamination of PCBs could lead to increased gonadal size or development in male fish.

No significant associations were observed between the K, HSI and concentrations of any selected groups of sediment contaminants (PAHs, PCBs, heavy metals, and pesticides). When the mean HSI values of brown bullheads from the Black, Cuyahoga, and Buffalo Rivers were compared with the three sites’ historical data, the HSI values decreased from the 1980s to the 1990s for each site (Table 3.7). This decrease coincides with the change in levels of PAH pollution (Table 3.7). The same trend occurred for PIB between 1995 and 1998. However, this was not the case for NIA, at which PAH levels were relatively low in both years (Table 3.7). One possible reason for the lack of

44 association in the current study is that variations in exposure levels were not great enough to be a determinative factor for HSI, allowing variations in other factors such as sampling season (or temperature), nutrition, and disease to obscure any relationship. This line of reasoning is consistent with the highest HSI recorded being associated with the highest

PAH concentration (by an order of magnitude, BLA 1982, Table 3.7) and the study by

Vignier et al. (1992) in which the HSI significantly increased in fish exposed to high concentration of oil but not in fish exposed to low concentration of oil compared to the control group.

In comparison with the HSI, the historical comparison of the condition factor demonstrates that brown bullheads sampled from the Black and the Cuyahoga Rivers in the late 1990s had higher K values than fish sampled previously (Table 3.8). This increase corresponds to the decrease in levels of PAH pollution. However, it was not true for PIB, BUF and NIA. Presumably at very high exposure levels, the influence of PAH contamination on the condition factor would increase.

Interactions of various contaminants present in the study sites might be another reason for the absence of associations between contamination and the K and HSI. Since increases and decreases in K and HSI have both been identified in fish following exposure to various toxicants, effects in different directions might offset each other once fish were exposed to a mixture of contaminants.

In summary, no clear change could be seen between the contaminated and reference sites in K, HSI, GSI and SSI values of brown bullheads. However, the male SSI and GSI values appeared to be positively associated with concentrations of PAHs and

PCBs in sediments, respectively. This suggests that exposure to elevated concentrations

45 of PAHs and PCBs in Lake Erie tributaries may cause increases in the relative spleen and testicular sizes. Relationships between K or HSI and pollution were lacking within the study river systems, as were relationships between female SSI or GSI and contaminant levels. While impacts of PAHs on HSI might be apparent when contamination levels are very high, the condition factor and organo-somatic indices in general seem to be affected by too many non-contaminant variables to be useful in delineating mildly from moderately contaminated locations.

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Site Sampling Sample Age Length (mm) Weight (g)

season size (mean ± S.D.) (mean ± S.D.) (mean ± S.D.)

DET June 2000 16 5.4 ± 1.5 300.7 ± 22.2 372.5 ± 97.6

OTT May 1999 51 4.6 ± 1.2 299.2 ± 23.0 357.4 ± 92.9

HUR May 1998 30 4.6 ± 2.0 310.1 ± 32.1 428.6 ± 140.9

OWC April 1999 a 40 5.3 ± 0.9 296.4 ± 30.6 356.9 ± 123.5

June 2000 47 5.4 ± 0.9 290.0 ± 18.2 327.4 ± 56.9

BLA May 1998 45 4.9 ± 1.4 356.0 ± 26.4 754.0 ± 182.4

CRH June 1999 40 5.6 ± 0.7 360.1 ± 50.2 634.8 ± 155.8

CRU June 1999 16 5.8 ± 1.2 357.7 ± 32.2 673.9 ± 229.3

ASH May 2000 45 4.9 ± 1.6 294.6 ± 26.6 358.6 ± 111.5

PIB May 1998 42 4.7 ± 1.6 310.3 ± 22.8 417.7 ± 99.6

BUF June 1998 43 Not available 305.8 ± 41.2 439.1 ± 139.9

NIA June 1998 40 3.4 ± 1.6 269.4 ± 35.1 284.6 ± 109.7 a Fish were sampled on April 29 and 30, 1999 from OWC.

Site Sampling Sex Mean ± Stand Error

season K HSI GSI SSI

OTT May 1999 Female 1.36±0.03 b 2.56±0.10 b 8.68±0.59 ab 0.09±0.01 b

HUR May 1998 Female 1.39±0.05 b 2.59±0.11 b 6.68±0.85 bcd 0.09±0.01 b

OWC April 1999 Female 1.32±0.03 b 2.77±0.10 ab 3.99±0.27 d 0.13±0.01 a

BLA May 1998 Female 1.67±0.03 a 2.87±0.14 ab 10.8±0.48 a 0.10±0.01 ab

ASH May 2000 Female 1.39±0.04 b 3.36±0.21 a 8.36±0.88 abc 0.14±0.02 a

PIB May 1998 Female 1.37±0.04 b 1.95±0.10 c 5.56±1.00 cd 0.10±0.01 ab

OTT May 1999 Male 1.27±0.02 c 1.67±0.08 b 0.29±0.02 a 0.12±0.01 a

HUR May 1998 Male 1.42±0.03 b 1.78±0.12 b 0.25±0.01 ab 0.09±0.01 a

OWC April 1999 Male 1.35±0.04 bc 1.73±0.09 b 0.23±0.02 ab 0.13±0.01 a

BLA May 1998 Male 1.61±0.07 a 2.98±0.09 a 0.29±0.02 a 0.12±0.01 a

ASH May 2000 Male 1.34±0.02 bc 2.65±0.11 a 0.29±0.02 a 0.12±0.01 a

PIB May 1998 Male 1.39±0.03 bc 1.69±0.08 b 0.21±0.02 b 0.11±0.01 a

Means with the same letter are not significantly different (Tukey’s test, p > 0.05).

Site Sampling Sex Mean ± Stand Error

season K HSI GSI SSI

DET June 2000 Female 1.34±0.08 ab 1.83±0.11 bc 9.68±3.25 a 0.07±0.01 c

OWC June 2000 Female 1.30±0.04 b 2.27±0.10 ab 4.28±1.33 a 0.09±0.01 bc

CRH June 1999 Female 1.54±0.03 a 1.95±0.13 bc 9.56±1.57 a 0.12±0.01 ab

CRU June 1999 Female 1.47±0.06 ab 2.01±0.14 abc 5.59±1.84 a 0.08±0.01 bc

BUF June 1998 Female 1.43±0.04 ab 1.65±0.04 c 5.78±1.49 a 0.14±0.01 a

NIA June 1998 Female 1.48±0.06 a 2.43± 0.13 a 8.50±1.76 a 0.11±0.01 ab

DET June 2000 Male 1.34±0.02 ab 1.83±0.12 b 0.24±0.02 ab 0.15±0.05 ab

OWC June 2000 Male 1.36±0.03 ab 2.14±0.09 ab 0.16±0.01 b 0.11±0.01 b

CRH June 1999 Male 1.26±0.07 b 1.95±0.10 ab 0.28±0.02 ab 0.14±0.01 ab

CRU June 1999 Male 1.43±0.04 ab 1.88±0.09 ab 0.27±0.03 ab 0.12±0.01 ab

BUF June 1998 Male 1.46±0.03 a 1.91±0.06 ab 0.29±0.07 a 0.17±0.01 a

NIA June 1998 Male 1.37±0.04 ab 2.34±0.08 a 0.22± 0.02 ab 0.14±0.01 ab

Means with the same letter are not significantly different (Tukey’s test, p > 0.05).

K HSI GSI SSI

K 1.000 0.193 a 0.332 0.039

<.0001 b <.0001 0.424

437 431 c 435 431

HSI 0.193 1.000 0.149 0.017

<.0001 0.002 0.726

431 431 429 427

GSI 0.332 0.149 1.000 -0.146

<.0001 0.002 0.002

435 429 435 429

SSI 0.039 0.017 -0.146 1.000

0.424 0.726 0.002

431 427 429 431 a Pearson’s correlation coefficient. b p value. c Sample size.

Table 3.4. Pearson’s correlation coefficients between the condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI) of brown bullheads.

Site a DET OTT HUR OWC BLA CRH CRU ASH PIB BUF NIA

Year 2000 1999 1998 2000 1998 1999 1999 2000 1998 1998 1998

PAHs b 17.42 9.41 1.01 5.25 5.42 19.07 3.33 3.91 2.28 7.59 1.02

PCBs 0.51 2.93 0.04 0.08 0.15 0.46 0.19 1.03 0.13 0.25 0.06

DDTs c 0.082 0.081 0.012 0.033 0.030 0.023 0.025 0.013 0.013 0.028 0.005

Heavy 1.09× 0.88× 0.66× 0.33× 0.69× 1.17× 0.46× 0.65× 1.12× 1.00× 0.35×

metals d 103 103 103 103 103 103 103 103 103 103 103 a Sediment data were taken from Smith et al. 2003; Passino-Reader et al. 2004. b Sum of concentrations of anthracene, benz[a]anthracene, benzo[a]pyrene, benzo[b]fluoranthene, chrysene, dibenz(a,h)anthracene, fluoranthene, fluorene, naphthalene, C1-, C2-, C3-, and C4- naphthalene, perylene, phenanthrene, and pyrene. c Sum of concentrations of o,p'- and p,p'- DDD (dichlorodiphenyldichloroethane), o,p'- and p,p'- DDE (dichlorodiphenyldichloroethylene), o,p'- and p,p'- DDT (dichlorodiphenyltrichloroethane). For the concentration below the detection limit, half of the detection limit was used. d Sum of concentrations of Ba, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Sr, V. For the concentration below the detection limit, half of the detection limit was used.

Sediment Sediment Sediment Sediment

PAHs PCBs DDTs heavy metals

F K -0.032 a 0.018 -0.522 0.280

0.922 b 0.957 0.082 0.377

M K -0.460 -0.484 -0.248 -0.168

0.133 0.111 0.437 0.601

F HSI -0.340 -0.154 -0.149 -0.544

0.279 0.632 0.643 0.068

M HSI 0.000 -0.042 -0.263 -0.231

1.000 0.897 0.409 0.470

F GSI 0.466 0.459 0.088 0.508

0.127 0.134 0.786 0.092

M GSI 0.417 0.656 0.102 0.328

0.177 0.021 0.753 0.298

F SSI 0.025 0.035 -0.351 -0.014

0.939 0.913 0.263 0.965

M SSI 0.574 0.370 0.238 0.249

0.051 0.236 0.456 0.436 a Spearman’s correlation coefficient. b p value.

Table 3.6. Spearman’s correlation coefficients between concentrations of sediment contaminants and mean values of the female (F) and male (M) condition factor (K), hepatosomatic index (HSI), gonosomatic index (GSI), and spleen-somatic index (SSI) of brown bullheads across the sampling sites.

Site Year Female HSI Male HSI Data source of HSI Sediment PAHs

mean ± S.E. mean ± S.E. (µg/g dry

(sample size) (sample size) weight) a

BLA 1982 5.70±0.26 (7) 4.66±0.39 (3) Fabacher and Baumann (1985) 225 b

BLA 1992 2.22±0.07 (49) 1.75±0.09 (32) Paul Baumann (unpublished) 11.5 b

BLA 1993 2.99±0.09 (50) 2.14±0.06 (55) Paul Baumann (unpublished) 11.5b (1992 data)

BLA 1994 2.24±0.15 (20) 2.36±0.08 (29) Paul Baumann (unpublished) 5.3 b

BLA 1998 2.87±0.14 (26) 2.98±0.09 (19) Present study 2.5 c

CUY 1984 2.93±0.12 (33) 2.37±0.09 (56) Paul Baumann (unpublished) 18.2 d

CRH 1999 1.95±0.13 (16) 1.95±0.10 (16) Present study 9.8 c

CRU 1999 2.01±0.14 (6) 1.88±0.09 (7) Present study 1.7 c

PIB 1995 2.47±0.11 (21) 2.49±0.09 (28) Eric Obert (unpublished) 5.6 e (1990 data)

PIB 1998 1.95±0.10 (19) 1.69±0.08 (20) Present study 1.0 c

BUF 1987 2.98±0.20 (12) 2.83±0.18 (13) John Hickey (unpublished) 6.1 f (1989 data)

BUF 1998 1.65±0.04 (18) 1.91±0.06 (23) Present study 3.4 c

NIA 1987 2.33±0.14 (21) 2.12±0.09 (26) John Hickey (unpublished) 1.3 g (1991 data)

NIA 1998 2.43±0.13 (13) 2.34±0.08 (26) Present study 0.5 c a Sum of concentrations of benz[a]anthracene, benzo[a]pyrene, chrysene, phenanthrene, and pyrene. b Baumann and Harshbarger (1998). Concentration of chrysene is not available and therefore not included for BLA in 1994. c Smith et al. (2003); Passino-Reader et al. (2004). d Fabacher et al. (1988). e Rice (1991). f USAED (1993). Concentration reported here is the average of nine locations. For the concentration below the detection limit, half of the detection limit was used. g Eufemia et al. (1997). Concentration is for wet weight instead of dry weight.

Site Year Female K Male K Data source of K Sediment PAHs

mean ± S.E. mean ± S.E. (µg/g dry

(sample size) (sample size) weight) a

BLA 1992 1.47±0.03 (49) 1.44±0.03 (32) Paul Baumann (unpublished) 11.5 b

BLA 1993 1.46±0.02 (50) 1.43±0.02 (55) Paul Baumann (unpublished) 11.5b (1992 data)

BLA 1994 1.58±0.04 (20) 1.48±0.02 (29) Paul Baumann (unpublished) 5.3 b

BLA 1998 1.67± 0.03(26) 1.61±0.07 (19) Present study 2.5 c

CUY 1984 1.20±0.03 (33) 1.22±0.01 (56) Paul Baumann (unpublished) 18.2 d

CRH 1999 1.54± 0.03(16) 1.26±0.07 (17) Present study 9.8 c

CRU 1999 1.47±0.06 (6) 1.43±0.04 (7) Present study 1.7 c

PIB 1995 1.41±0.04 (33) 1.35±0.02 (34) Eric Obert (unpublished) 5.6 e (1990 data)

PIB 1998 1.37±0.04 (20) 1.39±0.03 (20) Present study 1.0 c

BUF 1987 1.53±0.08 (12) 1.53±0.04 (13) John Hickey (unpublished) 6.1 f (1989 data)

BUF 1998 1.43±0.04 (19) 1.46±0.03 (24) Present study 3.4 c

NIA 1987 1.50±0.04 (21) 1.46±0.04 (26) John Hickey (unpublished) 1.3 g (1991 data)

NIA 1998 1.48±0.06 (14) 1.37±0.04 (26) Present study 0.5 c a Sum of concentrations of benz[a]anthracene, benzo[a]pyrene, chrysene, phenanthrene, and pyrene. b Baumann and Harshbarger (1998). Concentration of chrysene is not available and therefore not included for BLA in 1994. c Smith et al. (2003); Passino-Reader et al. (2004). d Fabacher et al. (1988). e Rice (1991). f USAED (1993). Concentration reported here is the average of nine locations. For the concentration below the detection limit, half of the detection limit was used. g Eufemia et al. (1997). Concentration is for wet weight instead of dry weight.

60 1.9

1.8 Female Male 26 19 1.7 * 16 1.6 6 14 7 19 24 1.5 15 14 7 18 20 20 26 K 23 15 31 9 27 1.4 20 16 17 28 1.3

1.2 1.1

T T R 9 0 A H U H B F A E U D OT H C9 C0 BL CR CR AS PI BU NI W W O O Site

Figure 3.2. Values of the condition factor (K) of brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek in 1999 (OWC99) and 2000 (OWC00), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA). Heights of columns represent means and error bars are standard errors. Numbers above bars indicate sample sizes. * depicts significantly higher K in females than in males (t- test, p < 0.008).

61 4.5 4 Female * 18 Male 3.5 * * 2619 * 20 3 15 27 23 13 16 26 I 2.5 31 1616 6 19 7 9 14 7 ** 23 HS 15 2 27 20 18 1.5 1 0.5 0

T T R 9 H U H B F A E 9 00 LA S I I D OT HU C B CR CR A P BU N WC OW O Site

Figure 3.3. Values of the hepatosomatic index (HSI) of brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek in 1999 (OWC99) and 2000 (OWC00), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA). Heights of columns represent means and error bars are standard errors. Numbers above bars indicate sample sizes. * depicts significantly higher HSI in females than in males and ** depicts significantly higher HSI in males than in females (t-test, p < 0.008 each).

62 16 Female 14 7 Male (× 0.1) * * 12 26 16 * * * 14 10 23 18 * * I 8 15 6 * 19 * GS 20 6 * 16 20 24 4 28 19 17 7 27 9 14 15 20 24 31 2

T T 9 A H B F A UR 00 RH RU S I DE OT H C9 C BL C C A P BU NI OW OW Site

Figure 3.4. Values of the gonosomatic index (GSI) of brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek in 1999 (OWC99) and 2000 (OWC00), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA). Heights of columns represent means and error bars are standard errors. Numbers above bars indicate sample sizes. GSI values of females were higher than males by an order of magnitude. In order to show data of both sexes in one bar chart, values of males were multiplied by 10 (real GSI values of males should be 0.1 × the values shown by bars). * depicts significantly higher GSI in females than in males (t-test, p < 0.008 each).

63 0.25 Female 9 0.2 Male 23 17 17 18 26 0.15 20 15 27 19 16 7 27 31 20 14 25 ** 20 SSI 22 15 14 16 0.1 6 7

0.05

T T R 9 H U H B F A E T 00 LA S U D O HU C9 C B CR CR A PI B NI OW OW Site

Figure 3.5. Values of the spleen-somatic index (SSI) of brown bullheads from the Detroit River (DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek in 1999 (OWC99) and 2000 (OWC00), Black River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB), Buffalo River (BUF), and Niagara River (NIA). Heights of columns represent means and error bars are standard errors. Numbers above bars indicate sample sizes. ** depicts significantly higher SSI in males than in females (t- test, p < 0.008).

CHAPTER 4

EXTERNAL RAISED LESIONS AND BARBEL DEFORMITIES IN BROWN

BULLHEADS FROM LAKE ERIE TRIBUTARIES

Introduction

Elevated prevalence of epidermal papillomas in wild fish was described by

Keysselitz in as early as 1908. Lucke and Schlumberger (1941) made the first description of the appearance, distribution, histopathological structure, and development of the lip and mouth tumors in catfish. Dawe et al. (1964) advanced the hypothesis that carcinogenic contaminants might be the cause of the neoplasms they observed in white sucker and brown bullhead from Deep Creek Lake in Maryland. As laboratory and field research on the carcinogenic effects of contaminants in fish proceeded, the use of the prevalence of tumors in wild fish has gained acceptance for evaluating environmental damage to aquatic habitats (IJC 1987).

Higher prevalence of oral, cutaneous and liver tumors has been reported in fish from industrially contaminated sites relative to reference sites (Black and Baumann 1991;

Maccubbin and Ersing 1991; Smith et al. 1994). Significant correlations were found between prevalence of liver neoplasms in fish and concentrations of aromatic hydrocarbons in sediments and concentrations of aromatic compound metabolites in fish

65 bile (Malins et al. 1984; Krahn et al. 1986). Baumann et al. (1996) summarized that skin papilloma prevalence exceeding 25% and liver neoplasm prevalence exceeding 5% in brown bullheads and white suckers could be used as an indicator of environmental degradation in Great Lakes areas. More recent research suggests that liver tumor prevalence of about 5% or less and external tumor prevalence of 12% or less in brown bullheads aged three years old or older is a good criterion for an “Area of Recovery”

(Baumann 2002).

Studies on the carcinogenesis of chemicals in fish demonstrate that the three basic stages of carcinogenesis in mammals (initiation, promotion and progression) are also involved in the formation of tumors in fish (Baumann and Okihiro 2000). The metabolism and carcinogenesis of polycyclic aromatic hydrocarbons (PAHs), particularly benzo[a]pyrene (B[a]P), in fish have been well documented. PAHs are predominantly metabolized in the liver. PAHs are oxidized by cytochrome P-450 monooxygenases in the liver to hydroxyl or epoxide derivatives. Some of these derivatives conjugate with polar endogenous molecules such as glutathione and become less toxic and easily excreted from the body. However, some epoxides are active and bind to macromolecules

(DNA, RNA and proteins) (Varanasi et al. 1985). The binding of PAH derivatives to

DNA (DNA adducts) causes genetic changes in cells and initiates the tumor formation.

Although the initiated cells can progress into malignant neoplasms directly, evidence shows that more often tumors pass through an intermediate stage called promotion (Moore and Kitagawa 1986). The changes in DNA induced by adducts are fixed in the genome by mitotic activities and the transformed cells undergo clonal expansion and form preneoplastic foci. These foci have more growth potential than the

66 surrounding normal tissue and finally develop into benign tumors under additional

mutations. In the last stage, progression, the slow growing benign tumors are transformed

into rapidly growing malignant neoplasms that further invade in distant sites through

subsequent metastasis (Baumann and Okihiro 2000).

External abnormalities besides tumors have also been studied as indicators of

environmental exposure. Members of the catfish family, including the brown bullhead,

have long and slender appendages around the mouth area which function as taste organs

called barbels (Smith et. al. 1994). Atema (1971) estimated that there were about 20000

taste buds present in barbels. Bullheads swim with the tips of the barbels touching the

surface of the sediment in order to locate food. Elevated prevalence of various forms of

barbel deformities has been observed in brown bullheads from contaminated sites.

Although the mechanism of the formation of barbel abnormalities is not as clear as that of tumors, Black (1983) reported epidermal hyperplasia were induced on heads and barbels of brown bullheads by repeatedly treating the skin with a PAH containing sediment extract. Smith et al. (1994) found a significant correlation between frequencies of stubbed barbels and concentrations of PAHs in sediments. These findings suggest that prevalence of barbel deformities might be an indicator of environmental exposure and effects as well.

Brown bullheads (Ameiurus nebulosus) were collected from ten Lake Erie

tributaries during 1998-2000, including the Detroit River (DET), Ottawa River (OTT),

Huron River (HUR), Old Woman Creek (OWC), Black River (BLA), Cuyahoga River -

harbor (CRH) and - upstream (CRU), Ashtabula River (ASH), Presque Isle Bay (PIB),

Buffalo River (BUF), and Niagara River at Love Canal (NIA). Fish were observed

67 externally for visible raised lesions, which appeared grossly to be papillomas, and

deformities in barbels. Prevalence of fish with raised lesions and barbel deformities was

compared among the sampling sites. Relationships were examined between the

prevalence of raised lesions and barbel deformities in fish, fish age, sex, length and

weight, and concentrations of contaminants in sediments. The objectives of this study

were to survey incidences of external tumors and barbel deformities in fish of Lake Erie

tributaries, to explore the biological factors that influence or co-vary with the risk of fish

having external lesions and deformities, and to provide additional information for the

future use of external anomaly as indicators of environmental quality.

Methods

Fish Collection and Anomaly Examination

Brown bullheads were captured in fyke nets or by electro-shocking from ten Lake

Erie tributaries in springs and early summers of 1998 - 2000, including the Detroit River

(DET), Ottawa River (OTT), Huron River (HUR), Old Woman Creek (OWC), Black

River (BLA), Cuyahoga River - harbor (CRH) and - upstream (CRU), Ashtabula River

(ASH), Presque Isle Bay (PIB), Buffalo River (BUF) and Niagara River at Love Canal

(NIA) (Figure 4.1, Table 4.1). Among these sites, HUR and OWC were the only locations without industrial pollution and were selected as reference sites. However, they received agricultural runoff, and PAH contamination has been found at OWC near a highway bridge and a railway bridge with creosote treated wood (Johnston and Baumann

1989). The rest of the sites were industrially contaminated. Each was designated by the

International Joint Commission as a Great Lakes Area of Concern. But because the Black

68 River has undergone significant remediation, it has recently been reclassified as an Area

of Recovery.

Fish collected from each site were measured for total length and body weight.

Those that were 250mm or longer (about 3 years old or older, sexually mature) were

examined externally. Visible raised lesions in mouths, on heads and remaining body

surfaces, and deformities including missing, shortening and knobs of scar tissue in nasal,

maxillary, and chin barbels were recorded. Raised lesions appeared grossly to be

papillomas but were not diagnosed by histopathology. Fish were then euthanized and

necropsied. The sex of each fish was identified and at least one pectoral spine was

removed for aging according to the methodology in Baumann et al. (1990) and Kovoscky

(2000). The number of fish with raised lesions and barbel deformities was counted at

each site. Prevalence of raised lesions was calculated by No. of fish with raised lesions ÷

total No. of fish × 100% and prevalence of barbel deformities was calculated by No. of

fish with barbel deformities ÷ total No. of fish × 100%.

Sediment Collection and Analysis

Sediments were collected and analyzed according to Smith et al. (2003) and

Passino-Reader et al. (2004). Briefly, the oxidized top 2-3 cm of the fine sediments in

depositional zones were collected using a stainless steel Eckman dredge from the study

sites during 1998-2000. At least five samples were randomly collected from each site and

mixed together.

69 the sediment samples, graphite furnace AAS was used to determine the amount of

arsenic, selenium, cadmium, and lead. High concentrations of cadmium and lead were

determined by atomic emission using an argon plasma.

Another portion of the sediment samples were freeze-dried and extracted in a