WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT FINAL REPORT: VOLUME I
The Fate, Transport, and Ecological Impacts
of Airborne Contaminants
in Western National Parks (USA)
Burial Lake, Noatak National Preserve
Photo: Adam Schwindt
Dixon H. Landers
Staci Simonich
Daniel Jaffe
Linda Geiser
Donald H. Campbell
Adam Schwindt
Carl Schreck
Michael Kent
Will Hafner
Howard E. Taylor
Kimberly Hageman
Sascha Usenko
Luke Ackerman
Jill Schrlau
Neil Rose
Oregon State
UNIVERSITY
osu
Tamara Blett
Marilyn Morrison Erway
Technical Editor:
Susan Christie
UNIVERSITY OF
WASHINGTON
EPA/600/R-07/138
January 2008
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The Fate, Transport, and Ecological Impacts of Airborne
Contaminants in Western National Parks (USA)
Dixon H. Landers
USEPA, NHEERL
Western Ecology Division
Corvallis, Oregon
Staci Simonich
Dept. of Env. & Molecular Toxicology &
Dept. of Chemistry, Oregon State Univ.
Corvallis, Oregon
Daniel Jaffe
University of WA-Bothell
Bothell, Washington
Linda Geiser
US Forest Service Pacific NW Region
Air Program
Corvallis, Oregon
Donald H. Campbell
U.S. Geological Survey
Denver, Colorado
Adam Schwindt
Dept. of Microbiology
Oregon State University
Corvallis, Oregon
Carl Schreck
Oregon Cooperative Fish and Wildlife
Research Unit, USGS-BRD
Oregon State Univ. Corvallis, Oregon
Michael Kent
Dept. of Microbiology
Oregon State University
Corvallis, Oregon
Will Hafner*
University of Washington-Bothell
Bothell, Washington
Howard E. Taylor
U. S. Geological Survey
Boulder, Colorado
Kimberly Hageman*
Dept. of Env. & Molecular Toxicology
Oregon State University
Corvallis, Oregon
Sascha Usenko*
Dept. of Chemistry
Oregon State University
Corvallis, Oregon
Luke Ackerman*
Dept. of Chemistry
Oregon State University
Corvallis, Oregon
Jill Schrlau
Dept. of Chemistry
Oregon State University
Corvallis, Oregon
Neil Rose
University College London
London, United Kingdom
Tamara Blett
NPS-Air Resources Division
Lakewood, Colorado
Marilyn Morrison Erway
Dynamac Corporation
c/o USEPA, NHEERL
Western Ecology Division
Corvallis, Oregon
Technical Editor: Susan Christie
* Current Affiliations:
Hafner: Science Applications International Corp., Bothell, Washington
Hageman: Dept. of Chemistry, University of Otago, Dunedin, New Zealand
Usenko: Environmental Science Dept., Baylor University, Waco, Texas
Ackerman: FDA-Center for Food Safety and Applied Nutrition, College Park, Maryland
This report is the final report for the Western Airborne Contaminants Assessment Project (WACAP), and is
available online at http://www.nature.nps.gov/air/Studies/air_toxics/wacap.cfm and
http://www.epa.gov/nheerl/wacap/
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Proper citation of this document is:
Landers, D.H., S.L. Simonich, D.A. Jaffe, L.H. Geiser, D.H. Campbell, A.R. Schwindt,
C.B. Schreck, M.L. Kent, W.D. Hafner, H.E. Taylor, K.J. Hageman, S. Usenko, L.K.
Ackerman, J.E. Schrlau, N.L. Rose, T.F. Blett, and M.M. Erway. 2008. The Fate,
Transport, and Ecological Impacts of Airborne Contaminants in Western National Parks
(USA). EPA/600/R-07/138. U.S. Environmental Protection Agency, Office of Research
and Development, NHEERL, Western Ecology Division, Corvallis, Oregon.
DISCLAIMER: Funding for this work was provided by the National Park Service of the
Department of the Interior, the U.S. Environmental Protection Agency, and the U.S.
Geological Survey. It has been subjected to review by these government entities and
approved for publication. Approval does not signify that the contents reflect the views of
the U.S. Government, nor does mention of trade names or commercial products constitute
endorsement or recommendation.
Credit for the various photos that appear throughout the document goes to:
Adam Schwindt
Dixon Landers
Don Campbell
Jen Ramsey
Ruth Jenkins
John Warrick
Marilyn Erway
Neil Rose
Tamara Blett
Linda Geiser
Bill Baccus
WESTERN AIRBORNE CONTAMINANT ASSESSMENT PROJECT
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Acknowledgements
The completion of WACAP represents a tremendous coordinated effort by many individuals.
Each WACAP site presented unique challenges, but because each park that participated in
WACAP sampling provided support and assistance in planning, collecting, and transporting
samples, the sample collection effort was successfully completed. We have tried to list everyone
that contributed to the success of WACAP, and apologize to anyone we have inadvertently
missed. Megan Carney (USEPA), Andrew McCartney (NFS), and Nathan Truelove (USEPA)
each spent one summer as a camp manager. Besides providing the organization and food, they
willingly provided help to any other area when needed. Jennifer Ramsey (OSU) was a regular
field participant at almost all the core park sites, and provided consistent fish dissections and
blood collections, regardless of the field conditions. Doug Glavich and Adrienne Marler (USFS)
sampled vegetation and deployed air samplers at 12 of the 20 WACAP parks, providing
consistency in field methods and sample quality. Glen Wilson (OSU) and Dave Schmedding
(OSU) were important contributors to the development of the organic contaminant analyses, in
addition to providing much appreciated help in sampling at several locations. Bud Rice (NPS)
and Andrea Blakesley (NPS) provided invaluable assistance in organizing and sampling in the
Alaska parks. Ralph Vaga (USEPA) collected bathymetry data for many of the lake sites.
Suzanne Pierson (Indus) and Barbara Rosenbaum (Indus) provided GIS and graphical
summaries. Greg Brenner (Pacific Analytics) was the statistical consultant for data analyses.
Scientific peer review of the research plan and final report was a critical part of this project; peer
reviewers are listed below. We also thank USEPA and USGS internal reviewers who helped
ensure the quality of the final report.
WACAP Research Plan Peer Reviewers
Colin Gray, Research Coordination Head, Aquatic and Atmospheric Sciences Division,
Environment Canada, Vancouver, BC, Canada
Joan Grimalt, Professor, Spanish Council for Scientific Research, Institute of Chemical and
Environmental Research, Barcelona, Spain
Steve Kahl (Chair), Director, Center for the Environment, Plymouth State University, Plymouth,
NH
Kathy Tonnessen, Research Coordinator, NPS Rocky Mtn. Cooperative Ecosystem Studies Unit,
University of Montana, Missoula, MT
James Wiener, Wisconsin Distinguished Professor, University of Wisconsin-La Crosse,
LaCrosse, WI
WACAP Final Report Peer Reviewers
Robert Gerlach, ADEC Env. Health Laboratory, Anchorage, AK
Joan Grimalt, Institute of Chemical and Environmental Research, Barcelona, Spain
Jules Blais, Environmental Toxicology Program, University of Ottawa, Ottawa, Canada
Kathy Tonnessen, Research Coordinator, NPS Rocky Mtn. Cooperative Ecosystem Studies Unit,
University of Montana, Missoula, MT
Christopher Schmitt, Columbia Environmental Research Center, USGS, Columbia, MO
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT iii
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National Park Service
Chris Shaver, Chief, Air Resources
Division
John Reber
Judy Rocchio
John Vimont
Elizabeth Waddell
Air Service for Alaska Sites
George Cook, helicopter pilot, DENA
Jay Martin, Arctic Air Alaska
Buck Maxson, Arctic Air Guides
Jim Rood, Northwest Aviation
John St. Germaine, NW Aviation
Brad Shults, NFS (pilot NO AT, GAAR)
Bill Thompson, Settles Flying Service
Hollis Twitchell, NFS (pilot DENA)
Western Arctic National Parklands
Lois Dalle-Molle
Tom Heinlein
Linda Jeschke
Kean Mihata
Peter Neitlich
Sarah Nunn
Jerry Post
Roger Rosentreter (BLM)
Chris Young
Gates of the Arctic National Park and
Preserve
Jobe Chakuchin
David Krupa
Steve Ulvi
Gary Youngblood
Denali National Park and Preserve
Edric Lysne
Mik Shain
Brian Vontersch
Glacier National Park
Bill Michels
Shanti Berryman (USFS)
Jenny Blake
Dan Fagre
D. Gigneaux
Jill Grennon
Karen Holzer
Dan Jacobs
A. Mabelli
Clay Miller
Jack Potter
Heath Powers (USFS)
Corey Shea
Tim Sullivan
Olympic National Park
Bill Baccus
John Ciarno
Jerry Freilich
Jess Haggerman
Cat Hawkins Hoffman
Gay Hunter
Ruth Jenkins
Joshua Smith
Carl Strunk
John Warrick
Marissa Whisman
Mount Rainier National Park
Barbara Samora
Paul Baugher and the Crystal Mountain Ski
Patrol
Carla Brooks
Mike Carney
Cori Conner
Mike Gauthier
Jill Grenon (USFS)
David Gunderson
Jim Hull
Paul Kennard
Glenn Kessler
Larissa Lasselle (USFS)
Rich Lechleitner
Rebecca Doyle Lofgren
Stefan Lofgren
Uwe Nehring
John Piastuck
Paige Ritterbush
Ben Wright
IV
WESTERN AIRBORNE CONTAMINANT ASSESSMENT PROJECT
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Rocky Mountain National Park
Karl Cordova
Ken Czarnowski
Kee Elsisie
Dave Larsen
Kevin Pellini
Bob Love
Jim Sanborn
Terry Terrell
Judy Visty
Sequoia and Kings Canyon National Park
Annie Esperanza
Hassan Basagic
Danny Boiano
Bryan Czibesz
Heather Dumais
Sean Eagan
David Graber
Andi Heard
Tim Loverin
Evan Schmidt
Rich Thiel
North Cascades National Park
Mike Larrabee
Niki Bowerman
Steve Dorsch
Anthony Reece
Jon Riedel (USGS)
Jeannie Wenger
Field Assistance
Bruce Coblenz (OSU)
Debbie Miller (NFS)
Monte Miller
Roberta Porter (OSU)
Alena Pribyl (OSU)
Marge Storm (Dynamac, Inc.)
Snow Sampling, USGS
George Ingersoll
M. Alisa Mast
Chad Baillee
Robert Black
David Clow
Chester Crabb
John DeWild
Christine Dolliver
Ben Glass
Doug Hultstrand
David Manthorne
Patrick Moran
Leora Nanus
Rick Neam
Mark Olson
Shane Olund
Blase Reardon
Jeff Schmidt
Lisa Smith
Willamette Research Station Analytical
Laboratory (Dynamac, Inc.)
Karen Baxter
Brian Bowers
Scott Echols
Rachael Gruen
Toni Hoyman
Rick King
Kathy Motter
Kent Rodecap
Jason Schacher
USGS Trace Element Environmental
Analytical Chemistry, Boulder, CO
Ronald Antweiler
Dale Peart
Terry Plowman
David Roth
Simonich Environmental Chemistry
Laboratory
Lisa Marie Deskin
Eric Lynch
Eli Moore
University of Minnesota Research Analytical
Laboratory
Roger Eliason
Vegetation Sampling and PASD Deployments
in Secondary Parks
Lisa Acree (NFS), Yosemite NP
Kay Beeley (NPS), Bandelier NM
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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Susan Boudreau (NFS), Glacier Bay NP
Phyllis Bovin (NFS), Great Sand Dunes NPP
Fred Bunch (NFS), Great Sand Dunes NPP
Vida Davila (NFS), Big Bend NP
Chiska Derr (NFS), Glacier Bay NP
Karen Dillman (USFS), Tongass NF,
Katmai NPP, Wrangell-St. Elias NPP
Tom Ferguson (NFS), Katmai NPP
Steve Hunt (NFS), Wrangell-St Elias NPP
Helen Lons (NFS), Katmai NPP
Michael Murray (NFS), Crater Lake NP
Nancy Nordenstein (NFS), Lassen NP
Susan O'Ney (NFS), Grand Tetons NP
Dave Rak (USFS), Tongass NF
Devi Sharp (NFS), Wrangell-St Elias NPP
Kathy Spengler (NFS), Katmai NPP
Lee Tarnay (NFS), Yosemite NP
Katy Werner (NFS), Yosemite NP
VI
WESTERN AIRBORNE CONTAMINANT ASSESSMENT PROJECT
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Table of Contents
Volume I
Chapter Page
Acknowledgments iii
Abbreviations and Acronyms xvi
Executive Summary E-l
1 Introduction 1-1
1.1 Background 1-1
1.2 Approach 1-3
1.3 Park Selection 1-6
1.4 Site Selection within Parks 1-9
1.5 Measurements and Contaminants 1-14
1.6 Timeline, Implementation, and Reporting 1-14
1.7 Data management and Quality Assurance/Quality Control 1-15
1.8 WACAP Direction and Funding 1-16
1.9 Organization of This Report 1-16
2 Park Summaries 2-1
Introduction 2-1
Core Parks 2-1
Core Park Summary Key 2-2
Summary for NO AT and GAAR 2-4
Summary for DENA 2-8
Summary for GLAC 2-12
Summary for OLYM 2-16
Summary for MORA 2-20
Summary for ROMO 2-24
Summary for SEKI 2-28
Secondary Parks and Summary Key 2-32
Summary for WRST 2-34
Summary for GLBA 2-35
Summary for KATM 2-36
Summary for STLE 2-37
Summary for NOCA 2-38
Summary for GRTE 2-39
Summary for CRLA 2-40
Summary for LAVO 2-41
Summary for YOSE 2-42
Summary for GRSA 2-43
Summary for BAND 2-44
Summary for BIBE 2-45
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT vii
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3 Contaminants Studied and Methods Used 3-1
3.1 Introduction 3-1
3.2 Contaminants Studied 3-1
3.2.1 Semi-Volatile Organic Compounds (SOCs) 3-1
3.2.2 Mercury 3-7
3.2.3 Metals 3-9
3.3 Data Quality Summary 3-10
3.3.1 SOC Data Quality 3-11
3.3.2 Mercury Data Quality 3-11
3.3.3 Metals Data Quality 3-14
3.4 Methods Used 3-15
3.4.1 Air Modeling 3-15
3.4.2 Snow 3-16
3.4.3 Air 3-19
3.4.4 Vegetation 3-21
3.4.5 Lake Water 3-25
3.4.6 Sediment 3-27
3.4.7 Fish 3-30
3.4.8 Moose 3-33
3.4.9 Other Data Sources 3-34
3.5 Data Handling and Statistical Methods Used 3-37
3.5.1 Data Handling of Contaminant Concentrations Below the Detection Limit.. 3-37
3.5.2 Evidence and Magnitude of the Cold Fractionation Effect 3-37
3.5.3 Comparison of Park and Site Means for SOC and Element Concentrations
in Vegetation and Air 3-39
3.5.4 Correlations 3-40
3.5.5 Paired T-tests 3-40
4 Contaminant Distribution 4-1
4.1 Introduction 4-1
4.2 Semi-Volatile Organic Compounds (SOCs) 4-1
4.2.1 SOCs in Snow 4-2
4.2.2 SOCs in Air 4-8
4.2.3 SOCs in Vegetation 4-8
4.2.4 SOCs in Fish 4-32
4.2.5 SOCs in Sediments 4-33
4.2.6 Source Attribution for SOCs 4-43
4.3 Trace Metals, Including Mercury 4-49
4.3.1 Mercury and Trace Metals in Snow 4-49
4.3.2 Mercury and Particulate Carbon in Snow 4-51
4.3.3 Trace Metals in Vegetation 4-51
4.3.4 Mercury and Trace Metals in Fish 4-55
4.3.5 Mercury, Trace Metals, and Spheroidal Carbonaceous Particles
in Sediments 4-57
4.3.6 Source Attribution for Mercury, Trace Metals, and SCPs 4-75
4.4 Nutrient Nitrogen and Sulfur 4-77
4.4.1 Spatial Distributions of Nitrogen and Sulfur in Lichens 4-77
viii WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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4.4.2 Source Attribution for Nitrogen 4-77
4.5 Atmospheric Transport 4-78
4.6 Summary 4-86
5 Biological and Ecological Effects 5-1
5.1 Introduction 5-1
5.2 Bioaccumulation and Biomagnification 5-1
5.2.1 Processes of Bioaccumulation and Biomagnification 5-1
5.2.2 Effects of Bioaccumulation and Biomagnification 5-2
5.2.3 Evidence of Bioaccumulation in Vegetation 5-14
5.2.4 Evidence of Biomagnification 5-20
5.3 Biological Effects 5-22
5.3.1 Effects of Contaminants and the Utility of Biomarkers 5-22
5.3.2 Overview of General Fish Health 5-25
5.3.3 Biomarkers 5-25
5.4 Ecological Effects 5-52
5.4.1 Mercury 5-52
5.4.2 Selected SOCs with Contaminant Health Thresholds for Piscivorous
Biota 5-55
5.4.3 Human Health Risks from Consumption of SOCs in Fish 5-61
5.4.4 Potential Ecological Effects of SOC and Metal Contaminant Loads
on Aquatic Systems in the Parks 5-68
5.5 Nitrogen Deposition Effects and Relationships 5-74
5.5.1 Ecological Effects of Enhanced Nitrogen Deposition in the Western
United States 5-74
5.5.2 Evidence of Enhanced Nitrogen Deposition in Some Parks from
Lichen N 5-75
5.5.3 Correlations between Agricultural Chemicals and Measures of
Agricultural Intensity, Atmospheric Pollutants that Contain Nitrogen,
and Human Population 5-75
5.6 The Influence of Environmental Factors on Fish Hgtot 5-78
5.7 Summary 5-80
5.7.1 Bioaccumulation 5-80
5.7.2 Adverse Biological Effects Observed in Fish 5-81
5.7.3 Potential Adverse Ecological Effects 5-81
5.7.4 Health Risks to Humans 5-81
6 Recommendations and Conclusions 6-1
6.1 WACAP Recommendations to NFS 6-1
6.1.1 Introduction 6-1
6.1.2 Presence of Key Contaminants 6-1
6.1.3 Locations of Contaminant Accumulation 6-2
6.1.4 Ecological Threat from Contaminants 6-4
6.1.5 Sources of Contaminants 6-6
6.1.6 Understanding Contaminant Processes in Ecosystems 6-6
6.2 Conclusions 6-7
References R-l
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT ix
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1-1 WACAP Conceptual Diagram of Airborne Contaminant Assessment Approach 1-4
1-2 WACAP Sites Mapped on North American Shaded Relief Map and EPA
Level 1 Ecoregions (Biomes) 1-10
1-3 Relationships among Latitude, Longitude, and Mean Annual Temperature
in the 8 National Parks and 14 Sites Sampled in WACAP 1-11
1-4 Linkages among Major WACAP and Ecosystem Components, Contaminant
Pools, and Pathways 1-13
3-1 Current Status of SOC Contamination in WACAP Parks 3-8
3-2 All 2,922 One-Day Back-Trajectories for MORA 3-15
3-3 Passive Sampler 3-21
3-4 Scanning Electron Micrograph of a Spheroidal Carbonaceous Particle (SCP) 3-29
4-1 Current-Use Pesticides (CUPs): Average Concentrations and Fluxes of Sum
Endosulfans, Chlorpyrifos, and Dacthal across Parks and Media 4-3
4-2 HCHs: Average Concentrations and Fluxes of a-HCH and g-HCH across
Parks and Media 4-4
4-3 Historic-Use Pesticides (HUPs): Average Concentrations and Fluxes of HCB,
Dieldrin, and Sum Chlordanes across Parks and Media 4-5
4-4 PCBs: Average Concentrations and Fluxes of Sum PCBs across Parks
and Media 4-6
4-5 PAHs: Average Concentrations and Fluxes of Sum PAHs across Parks
and Media 4-6
4-6 PBDEs: Average Concentrations and Fluxes of Sum PBDEs in Fish and
Sediments across Parks 4-7
4-7 Annual Percent of Total Concentration in Snow for Current- and Historic-Use
Pesticides at SEKI and MORA 4-7
4-8 Regional Patterns of SOCs in Ambient Air as Indicated by Concentrations
Accumulated in XAD Resin in Suspended Passive Air Sampling Devices 4-9
4-9 Simple Linear Regression of Individual SOCs Determined in the XAD Resin
by Latitude 4-11
4-10 Comparison of Total Pesticide Concentrations in Lichen and Conifer Needle
Vegetation from WACAP Parks 4-12
4-11 Comparison of Total Pesticide Accumulation in Lichen Species by Park from
North to South along the Pacific Coast and from North to South in the
Rocky Mountains 4-14
4-12 Pesticide Concentrations in Lichens from Core and Secondary WACAP Parks
Overlaid on a Map of Agricultural Intensity 4-16
4-13 Pesticide Concentrations in Conifer Needles from Core and Secondary
WACAP Parks Overlaid on a Map of Agricultural Intensity 4-17
4-14 Uses and Estimated Application Intensity in 2002 of the Current-Use Insecticide
Chlorpyrifos in the Conterminous 48 States vs. Mean Concentration in
Vegetation from WACAP Parks 4-19
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4-15 Uses and Estimated Application Intensity in 2002 of the Current-Use Herbicide
Dacthal in the Conterminous 48 States vs. Mean Concentration in Vegetation
from WACAP Parks 4-20
4-16 Uses and Estimated Application Intensity in 2002 of the Current-Use Insecticide
Endosulfan in the Conterminous 48 States vs. Mean Concentration in
Vegetation from WACAP Parks 4-21
4-17 Uses and Estimated Application Intensity in 2002 of the Current-Use Herbicide
Triallate in the Conterminous 48 States vs. Mean Concentration in Vegetation
from WACAP Parks 4-22
4-18 Uses and Estimated Application Intensity in 2002 of the Current-Use Herbicide
Trifiuralin in the Conterminous 48 States vs. Mean Concentration in Vegetation
from WACAP Parks 4-23
4-19 Concentrations of PAHs in Lichens from Core and Secondary WACAP Parks
Overlaid on a Map of Population Density 4-24
4-20 Comparison of Total Pesticide Accumulation in Conifer Needles by Species
and Park 4-27
4-21 Needles of (A) Subalpine Fir, (B) Sitka Spruce, (C) Douglas-fir, (D) Western
Hemlock, and (E) Lodgepole Pine 4-28
4-22 Elevational Gradients for Sum Dacthal, Sum Endosulfan, and Sum Chlordane
Concentrations in Lichens 4-31
4-23 Focusing Factor-Corrected Flux Profiles of Current- and Historic-Use SOCs
In Matcharak Lake and Burial Lake Sediment Cores at GAAR and NO AT 4-34
4-24 Focusing Factor-Corrected Flux Profiles of Current- and Historic-Use SOCs
in McLeod Lake and Wonder Lake Sediment Cores at DENA 4-35
4-25 Focusing Factor-Corrected Flux Profiles of Current- and Historic-Use SOCs
in Snyder Lake and Oldman Lake Sediment Cores at GLAC 4-36
4-26 Focusing Factor-Corrected Flux Profiles of Current- and Historic-Use SOCs
in PJ Lake and Hoh Lake Sediment Cores at OLYM 4-37
4-27 Focusing Factor-Corrected Flux Profiles of Current- and Historic-Use SOCs
in LP19 and Golden Lake Sediment Cores at MORA 4-38
4-28 Focusing Factor-Corrected Flux Profiles of Current- and Historic-Use SOCs
in Lone Pine Lake and Mills Lake Sediment Cores at ROMO 4-39
4-29 Focusing Factor-Corrected Flux Profiles of Current- and Historic-Use SOCs
in Emerald Lake and Pear Lake Sediment Cores at SEKI 4-40
4-30 Percentage of Total Pesticide Concentration Related to Regional Sources 4-44
4-31 Mean Concentrations of Historic-Use and Current-Use Pesticides in
Two-Year-Old Conifer Needles from WACAP Parks 4-45
4-32 Fraction Ratios of IcdP/(IcdP+BeP) (average ± standard deviation) Calculated
From Snow, Lichen, and Pre- and Post-1955 Sediment in Snyder and Oldman
Lake Catchments in GLAC Compared to Measured Ratios 4-47
4-33 Average Concentrations and Fluxes of Mercury across Parks and Media 4-50
4-34 Unfiltered Total Mercury vs. Particulate Carbon Concentrations for All
WACAP Snowpack Samples, 2003-2005 4-52
4-35 Comparison of Selected Elements in Lichens in WACAP Parks with Elements
in Other National Parks and Forests in Western North America 4-54
4-36 Trace Metals in Fish Liver 4-56
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT xi
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4-37 Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg in Sediment
Cores from Burial Lake (NOAT) and Lake Matcharak (GAAR) 4-58
4-38 Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg in Sediment
Cores from Wonder and McLeod Lakes (DENA) 4-59
4-39 Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg and SCP in
Sediment Cores from Snyder and Oldman Lakes (GLAC) 4-60
4-40 Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg and SCP in
Sediment Cores from PJ and Hoh Lakes (OLYM) 4-61
4-41 Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg and SCP in
Sediment Cores from Golden Lake and LP19 (MORA) 4-62
4-42 Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg and SCP in
Sediment Cores from Mills and Lone Pine Lakes (ROMO) 4-63
4-43 Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg and SCP in
Sediment Cores from Pear and Emerald Lakes (SEKI) 4-64
4-44 Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from
Burial Lake (NOAT) and Lake Matcharak (GAAR) 4-67
4-45 Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from
Wonder and McLeod Lakes (DENA) 4-68
4-46 Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from
Snyder and Oldman Lakes (GLAC) 4-69
4-47 Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from
PJ and Hoh Lakes (OLYM) 4-70
4-48 Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from
Golden Lake and LP19 (MORA) 4-71
4-49 Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from
Mills and Lone Pine Lakes (ROMO) 4-72
4-50 Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from
Pear and Emerald Lakes (SEKI) 4-73
4-51 1-, 5-, and 10-Day Cluster Plots for NO AT and GAAR 4-79
4-52 1-, 5-, and 10-Day Cluster Plots for DENA 4-80
4-53 1-, 5-, and 10-Day Cluster Plots for GLAC 4-81
4-54 1-, 5-, and 10-Day Cluster Plots for OLYM 4-82
4-55 1-, 5-, and 10-Day Cluster Plots for MORA 4-83
4-56 1-, 5-, and 10-Day Cluster Plots for ROMO 4-84
4-57 1-, 5-, and 10-Day Cluster Plots for SEKI 4-85
5-1 Diagram of Increasing Effects of Contaminants, from Individual to Ecosystem
Level 5-3
5-2 Relationship between Fish Age and Total Whole Body Hg in Trout from All
Lakes 5-4
5-3 Mean SOC Concentrations in Lake Water, Snow, Sediments, Lichens,
Conifer Needles, and Fish from Emerald Lake (SEKI) 5-21
5-4 Pesticide Concentrations in XAD Resin, Conifer Needles, and Lichens from
Oldman Lake (GLAC) 5-23
5-5 Incidental Pathology Affecting Multiple Organs from Multiple Lake Trout at
Matcharak Lake (GAAR) 5-26
5-6 External Copepod Parasites and Internal Parasites in Lake Trout from Burial
xii WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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Lake (NOAT) and Matcharak Lake (GAAR), Respectively 5-27
5-7 Representative Hematoxylin-Eosin Stained Brook Trout Organs Showing the
Relative Difference between Fish with Very Few or No Macrophage Aggregates
and Extensive Accumulations of MAs (a-f) and Outlined High Magnification
Hepatic MAs (g-i) 5-29
5-8 Mean Percent, %MAs + 95% Confidence Intervals, for Fish with
Corresponding Hg and SOC Data: (a) Spleen, (b) Kidney 5-30
5-9 Average Stage of Gonad Maturation ± 95% Confidence Intervals in Trout
for which Corresponding SOC and Hg Data are also Available 5-32
5-10 Co-Linearity between (a) Splenic MAs, Hg, and Age in Brook Trout, and
(b) Log-Linear Relationship between Hg and Brook Trout (Salvelinus fontinalis)
Splenic MAs 5-33
5-11 Mean Vitellogenin + 95% Confidence Intervals and Concentrations from
Individual Male Trout from Lakes or Streams in National Parks in the Western
United States and Other Sites 5-38
5-12 Scatterplots Comparing Suspected Endocrine Disrupters and Plasma
Vitellogenin (Vtg) a Commonly Used Indicator of Estrogenic Contaminants
in Male Trout 5-39
5-13 Counties or Boroughs (Alaska) Where Museum and/or WACAP Fish Samples
Were Collected and Gonads Analyzed for Sex and Intersex 5-40
5-14 Categories of Relative Gonad Abnormality 5-43
5-15 Intersex Male Greenback Cutthroat Trout from Twin Lakes, Colorado,
Captured in the Late 1800s 5-45
5-16 Intersex Male Trout from Lone Pine Lake, ROMO (a-c) and Oldman Lake,
GLAC(d) 5-46
5-17 Box and Whisker Plots of Select Groups of Organochlorines Analyzed by
Brook Trout (a,b) or Oncorhynchus spp. (c,d) 5-48
5-18 Fish Whole-Body Lake Mean and Individual Fish Total Mercury and
Contaminant Health Thresholds for Various Biota 5-54
5-19 Fish Whole-Body Lake Mean and Individual Fish Sum PCB Concentrations,
with Contaminant Health Thresholds for Various Wildlife 5-57
5-20 Fish Whole-Body Lake Mean and Individual Fish Sum DDT Concentrations,
with Contaminant Health Thresholds for Various Wildlife 5-58
5-21 Fish Whole-Body Lake Mean and Individual Fish Sum Chlordane
Concentrations with Contaminant Health Thresholds for Various Wildlife 5-59
5-22 Fish Whole-Body Lake Mean and Individual Fish Sum Dieldrin Concentrations,
with Contaminant Health Thresholds for Various Wildlife 5-60
5-23 Concentrations of Historic-Use Pesticides for Dieldrin and a-HCH in Individual
Fish and Lake Average Fish Compared to Contaminant Health Thresholds
for Cancer for Fish Consumption for Recreational and Subsistence Fishers 5-62
5-24 Concentrations of Historic-Use Pesticides for Hexachlorobenzene (HCB) and
Heptachlor Epoxide (HCLR E) in Individual Fish and Lake Average Fish
Compared to Contaminant Health Thresholds for Cancer for Fish Consumption
for Recreational and Subsistence Fishers 5-63
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT xiii
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5-25 Concentrations of Historic-Use Pesticides (p,p'-DDE, chlordanes, mirex) in
Individual Fish and Lake Average Fish Compared to Contaminant Health
Thresholds for Cancer for Fish Consumption for Recreational and Subsistence
Fishers 5-64
5-26 Concentrations of Current-Use (dacthal, endosulfans) and Historic-Use
(methoxychlor) Pesticides in Individual Fish and Lake Average Fish
Compared to Contaminant Health Thresholds for Chronic Disease for Fish
Consumption for Recreational and Subsistence Fishers 5-65
5-27 Concentrations of Current-Use Contaminants PBDEs, g-HCH, and Chlorpyrifos
(CLPYR) in Individual Fish and Lake Average Fish Compared to Contaminant
Health Thresholds for Chronic Disease (and Cancer Thresholds for g-HCH)
for Fish Consumption for Recreational and Subsistence Fishers 5-66
5-28 Trophic Model for Lakes with Two-Fish Guilds Representing Alaska Systems 5-69
5-29 Mean Annual Concentrations of Ammonium Nitrate and Ammonium Sulfate
in Ambient Fine Particulates Measured by IMPROVE at WACAP Parks,
1998-2004 5-76
5-30 Average Total Mercury Values for Whole Fish Plotted Against Total
Phosphorus (TP) in the Lake Water for All Lakes in the Core Parks 5-79
1-1 WACAP Report Authors 1-5
1-2 Ecosystem Components Sampled for WACAP 1-6
1-3 WACAP Sites in Core Parks 1-8
1-4 Vegetation WACAP Sites in Core and Secondary Parks 1-9
1-5 WACAP Lake Sites: Selected Physical and Surface (1 m) Chemical
Characteristics Collected during WACAP Site Visits According to Methods
Listed in Chapter 3 1-12
1-6 WACAP Timeline and Site Sampling Strategy 1-14
3-1 Analytical Laboratories by Media and Analyte 3-1
3-2 WACAP Analytical Laboratories 3-2
3-3 Semi-Volatile Organic Compounds (SOCs) Measured in WACAP 3-3
3-4 Environmentally Significant Metals 3-9
3-5 Summary of Data Quality Indicators for SOCs by Media 3-12
3-6 Summary of Data Quality Indicators for Mercury by Media 3-13
3-7 Summary of Data Quality Indicators for Metals by Media 3-14
3-8 Starting Locations for Back-Trajectories and Precipitation Data Used
For Cluster Analysis 3-16
3-9 Summary of Passive Air Sampling Device Distribution among WACAP Parks 3-20
3-10 Vegetation Sample Summary 3-23
3-11 Species of Fish Captured 3-31
4-1 Compound Groupings Used in Chapter 4 4-1
4-2 Mean SOC Concentrations in Lichens and Conifer Needles from Each
WACAP Park 4-13
4-3 Linear Regression Model Results 4-29
4-4 Simple Linear Regression Results of Lichen SOCs on Park and Elevation 4-30
4-5 Comparison of SOC Data from Mills and Lone Pine Lakes 4-49
xiv WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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4-6 Total Integrated SCPs in WACAP Lake Sediment Cores 4-65
5-1 Comparison of Concentrations of Selected Organochlorines in Fish from
the Literature to Fish from WACAP Parks 5-6
5-2 Comparison of Concentrations of Total Mercury in Fish from the Literature to
Fish from WACAP Parks 5-11
5-3 SOC Concentrations in One- and Two-Year-Old Needles of White Fir (Abies
Concolor) and Lodgepole Pine (Pinus contortd) from the Emerald Lake Basin
of Sequoia National Park 5-14
5-4 Paired T-Test Results Comparing SOC Concentrations in One- and Two-
Year-Old Needles of White Fir (Abies concolor) and Lodgepole Pine (Pinus
Contortd) 5-15
5-5 Estimates of Total SOC Concentrations in Second-Year Needles from Western
North American Coniferous Forests 5-17
5-6 Endosulfans: Per Hectare Comparison of Estimated Annual Endosulfan
Accumulation in Second-Year Conifer Needles and Typical 2002 Endosulfan
Application Rates of This Pesticide in the Western United States 5-19
5-7 Correlations between Total Pesticide Concentrations in XAD Resin, Conifer
Needles, and Lichens from Wonder, Snyder, Oldman, and Lone Pine Lake
Watersheds 5-22
5-8 Characteristics of Intersex Trout Analyzed from Current and Historic Sampling.... 5-41
5-9 Categorization of Trout Testes by Abnormality, Geographic Region, and
Current or Historic Sampling 5-44
5-10 Comparison of Sites with Intersex Fish from the Rocky Mountains 5-44
5-11 SOC Concentrations in Moose Meat and Liver 5-51
5-12 Metal Concentrations in Moose Meat and Liver 5-53
5-13 Species Represented in Each Guild of the Loop Analysis 5-56
5-14 Number of Fish Exceeding Human Cancer Thresholds 5-67
5-15a Effects of a Negative Press Perturbation on Fish in a One-Fish Guild
Ecosystem 5-71
5-15b Effects of a Negative Press Perturbation on Fish in a Two-Fish Guild
Ecosystem 5-71
5-16 Rank Correlations among SOC Concentrations in Vegetation, Agricultural
Intensity, Mean 1998-2004 Ammonium Nitrate Concentrations in Fine
Particulates Measured by IMPROVE, and Population Density for the 20
WACAP Parks 5-77
II •
Appendix 1A Summary of Site Characteristics in Core and Secondary Parks 1 A-1
Appendix 3 A Summary of Sampling and Analysis Plan by Environmental Medium 3 A-1
Appendix 3B Sampling Information, Methods, and Data Quality 3B-1
Appendix 4A Detailed Information on Contaminants in Vegetation, Including
Elevation Trends 4A-1
Appendix 5A Fish Biological Data 5A-1
Appendix 5B Correlations between Hg and Age 5B-1
Appendix 5C Correlations between Macrophage Aggregates and Hg 5C-1
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT xv
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Abbreviations, Acronyms, and Symbols
°c
ueq/L
uL
um
137
210
Cs
Pb
226Ra
241Am
a-HCH
Al
ANOVA
ARD
As
B
Ba
BAND
BDL
Be
BeP
Bi
BIBE
C
Ca
CAAS
CBL
Cd
Ce
CI
CIC
degrees Celsius
microequivalencies per liter
microliter
micrometer
radionuclide of cesium
radionuclide of lead
radionuclide of radium
americium, radioactive decay product of 241Pu (plutonium)
hexachlorocyclohexane-alpha (also a-HCH)
aluminum
analysis of variance test
Air Resources Division (of the National Park Service)
arsenic
boron
barium
Bandelier National Monument
below detection limit
beryllium
benzo[e]pyrene
bismuth
Big Bend National Park
carbon
calcium
combustion atomic absorption spectrophotometry
Chesapeake Bay Laboratory
cadmium
cerium
confidence interval
constant initial concentration
XVI
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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Cl chloride
CLDN chlordane
CLPYR chlorpyrifos
cm centimeter
cm3 cubic centimeter
Co cobalt
Cr chromium
CRLA Crater Lake National Park
CRS constant rate of supply
Cs cesium
Cu copper
CUP current-use pesticide
CWSC Colorado Water Science Center Laboratory (USGS)
DCM dichloromethane
DCPA dacthal
DENA Denali National Park and Preserve
DL detection limit
DOC dissolved organic carbon
dw dry weight
Dy dysprosium
EA ethyl acetate
ECNI electron capture negative ionization
ECRC Environmental Change Research Centre
ECSMTP Standards, Measurements, and Testing Program of the European Commission
EDL estimated detection limit
El electron impact
EMAP-SW Environmental Monitoring and Assessment Program - Surface Water
ENDO endosulfan
EPA Environmental Protection Agency
EPA-ORD EPA Office of Research and Development
Er erbium
ERRC Environmental Radioactivity Research Centre
Eu europium
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
XVII
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FF
g
GAAR
GC/MS
Gd
GFF
g-HCH
GIS
GLAC
GLBA
GPC
GRSA
GRTE
ha
HCB
HC1
He
HF
Hg
HgS
Hgtot
Ho
HUP
IAS
IBC
IcdP
ICP-AES
ICP-MS
IMPROVE
JMP
K
KATM
focusing factor
gram
Gates of the Arctic National Park and Preserve
gas chromatography mass spectrometry
gadolinium
glass fiber filter
hexachlorocyclohexane-gamma, or lindane (also y-HCH)
geographic information system
Glacier National Park
Glacier Bay National Park and Preserve
gel permeation chromatography
Great Sand Dunes National Park and Preserve
Grand Teton National Park
hectare
hexachlorobenzene
hydrochloric acid
helium
hydrofluoric acid
mercury
mercury sulfide
total mercury
nitric acid
holmium
historic-use pesticide
inorganic ash spheres
industrial/urban use compounds
indeno[l,2,3-cd]pyrene
inductively coupled plasma-atomic emission spectrophotometry
inductively coupled plasma-mass spectrometry
Interagency Monitoring of Protected Visual Environments Program
statistical software package (SAS Institute, Gary, North Carolina)
potassium
Katmai National Park and Preserve
XVIII
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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kg kilogram
kg/ha/yr kilogram per hectare per year
Kow octanol-water coefficient
kV kilovolt
L liter
La lanthanum
LAVO Lassen Volcanic National Park
Li lithium
m meter
MA macrophage aggregate
MDL method detection limit
MeHg methyl mercury
Mg magnesium
mg/kg milligram per kilogram
mg/L milligrams per liter
mL milliliter
mm millimeter
Mn manganese
Mo molybdenum
MORA Mount Rainier National Park
N nitrogen
N2 dinitrogen (or nitrogen gas)
Na not applicable (also NA)
Na sodium
NCEP National Centers for Environmental Prediction
NCLR nonachlor
Nd neodymium
ng/g nanogram per gram
ng/L nanograms per liter
ng/(j,L nanogram per microliter
NH4+ ammonium
NHEERL National Health and Environmental Effects Research Laboratory
Ni nickel
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
XIX
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NIST National Institute of Standards and Technology
nm nanometer
NOs~ nitrate
NOAA National Oceanic and Atmospheric Administration
NO AT Noatak National Preserve
NOCA North Cascades National Park
NOX nitrogen dioxide
NPS National Park Service
NRC National Research Council
NRCC National Research Council of Canada
O2 oxygen
OC organochlorines
OLYM Olympic National Park
ORNL Oak Ridge National Laboratory
OSU Oregon State University
PAH polycyclic aromatic hydrocarbon
PASD passive air sampling device
Pb lead
PBDE polybrominated diphenyl ether
PBT persistent, bioaccumulative, and toxic
PC particulate carbon
PCB polychlorinated biphenyl
PE percent enrichment
Pg picogram
pg/g ww picogram per gram wet weight
POP persistent organic pollutant
Pr praseodymium
PRISM Parameter-elevation regressions on independent slopes model
PTFE polytetrafluoroethylene
QA/QC quality assurance/quality control
QAPP quality assurance project plan
Rb rubidium
Re rhenium
xx
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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RIA radioimmunoassay
RO reverse osmosis
ROMO Rocky Mountain National Park
RSD relative standard deviation
S sulfur
Sb antimony
SCP spheroidal carbonaceous particle
SD standard deviation
Se selenium
SE standard error
SEC Simonich Environmental Chemistry Laboratory
SEKI Sequoia and Kings Canyon National Parks
SiC>2 silica (silicon dioxide)
Sm samarium
SO42" sulfate
SOC semi-volatile organic compound
Sr strontium
SRM standard reference material
STLE Stikine-LeConte Wilderness, Tongass National Forest
SWE snow water equivalent
Tb terbium
TC total carbon
Te tellurium
TIC total inorganic carbon
Tl thallium
Tm thulium
TOC total organic carbon
U uranium
UMNRAL University of Minnesota Research Analytical Laboratory
USEPA U.S. Environmental Protection Agency
USFS U.S. Forest Service
USGS U.S. Geological Survey
USGS-BRD USGS Biological Resource Division
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
XXI
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USGS-CWSC
USGS-NRP
USGS-WWSC
V
Vtg
w
WACAP
WRS
WRST
ww
XAD
y
yr
Y
Yb
YOSE
Zn
Zr
USGS Colorado Water Science Center
USGS National Research Program (Trace Element Environmental Analytical
Chemistry Project)
USGS Wisconsin Water Science Center
vanadium
vitellogenin
tungsten
Western Airborne Contaminants Assessment Project
Willamette Research Station (U.S. EPA Analytical Laboratory in Corvallis)
Wrangell-St. Elias National Park and Preserve
wet weight
resin (Amberlite XAD) for passive air sampling devices
year
yr
yttrium
ytterbium
Yosemite National Park
zinc
zirconium
XXII
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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Executive Summary
Chapter 1. Introduction
The Western Airborne Contaminants Assessment Project (WACAP) was initiated to determine
the risk from airborne contaminants to ecosystems and food webs in western national parks of
the United States. From 2002 through 2007, WACAP researchers conducted analysis of the
concentrations and biological effects of airborne contaminants in air, snow, water, sediments,
lichens, conifer needles, and fish in watersheds in each of eight core parks in the western United
States, including Alaska (Figure 1):
1. Noatak National Preserve (NOAT)
2. Gates of the Arctic National Park and Preserve (GAAR)
3. Denali National Park and Preserve (DENA)
4. Olympic National Park (OLYM)
5. Mount Rainier National Park (MORA)
6. Glacier National Park (GLAC)
7. Rocky Mountain National Park (ROMO)
8. Sequoia and Kings Canyon National Parks (SEKI)
The parks included 6 west coast and Alaska parks (NOAT, GAAR, DENA, OLYM, MORA, and
SEKI) and 2 parks in the Rocky Mountains (ROMO and GLAC). We selected two sites/lakes for
sampling in each park—with the exception of NO AT and GAAR, where we sampled one site in
each, as the parks are adjacent—for a total of 14 sites.
Semi-volatile organic compounds (SOCs) and heavy metals were the primary focus of the study.
The SOCs fall into four general classes: current-use pesticides (CUPs), North American historic-
use pesticides (HUPs), industrial/urban use compounds (lUCs), and combustion byproducts. The
primary heavy metal of concern is mercury (Hg).
The seven ecosystem components selected for analysis (air, snow, water, sediments, lichens,
conifer needles, and fish) were chosen for several reasons. Concentrations of contaminants in air
can readily be compared among sites both within this study and with sites from other studies. In
many of the high altitude and high latitude sites studied, snow represents a potentially major
pathway for input of contaminants to ecosystems. Lake water samples provide an overview of
watershed chemical and physical characteristics that help interpret the contaminants data. Lake
bottom sediments show historical patterns of change over time in contaminant deposition.
Vegetation samples are used to determine spatial gradients of contaminants, and also provide
data on direct uptake of contaminants that accumulate in ecosystems through litterfall. Fish
bioaccumulate contaminants in their tissues, resulting in toxic effects in the fish themselves, and
in birds, animals, and humans who consume the fish.
WACAP researchers evaluated selected contaminant concentrations in samples from multiple
ecosystem components specifically to determine the origin of airborne contaminants and whether
these sources are local, regional, or global. In addition, air flow patterns to parks were analyzed
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT E-1
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m
m
m
7)
oo
O
70
z
m
O
O
m
m
z
H
Tl
O
m
O
m
X
m
O
c
m
Site Type
All media sampled at
lake sites in core parks
£ Vegetation only sampling sites
(in core parks, in addition to the
lake sites)
£&& Snow only sampling sites
(in core parks, sites outside
of lake sites)
A Air sampling sites
EPA Ecoregions-Level 1
ARCTIC CORDILLERA
GREAT PLAINS
H MARINE WEST COAST FOREST
MEDITERRANEAN CALIFORNIA
HI NORTH AMERICAN DESERTS
•• NORTHERN FORESTS
|B1 NORTHWESTERN FORESTED MOUNTAINS
SOUTHERN SEMI-ARID HIGHLANDS
H TAIGA
^H TEMPERATE SIERRAS
•• TROPICAL DRV FORESTS
•I TUNDRA
0 500 1,000
2,000
] Kilometers
3,000
Figure 1. WACAP Sites Mapped on North American Shaded Relief Map and EPA Level 1 Ecoregions (Biomes). See
Table 1-4 for key to national park abbreviations. Vegetation-only sampling sites in core parks designate sites used in elevational
transect in addition to lake sites. Snow-only sampling sites in core parks designate alternate sampling locations when lake sites
could not be reached safely.
-------
EXECUTIVE SUMMARY
through a process known as back-trajectory analysis, to assist in understanding potential sources
of contaminants to parks.
The specific objectives that guided the development of WACAP were:
1. Determine if contaminants are present in western national parks.
2. If contaminants are present, determine where they are accumulating (geographically and by
elevation).
3. If contaminants are present, determine which ones pose an ecological threat.
4. Determine which indicators appear to be the most useful for assessing contamination.
5. If contaminants are present, determine the source of the air masses most likely to have
transported contaminants to the national park sites.
In addition to the 8 core parks sampled, WACAP identified 12 secondary parks (or monuments,
preserves, or forests) for expanded spatial and environmental assessment (Figure 1). These
locations were identified for collection of samples from three ecosystem components: air,
lichens, and conifer needles.
1. Bandelier National Monument (BAND)
2. Big Bend National Park (BIBE)
3. Crater Lake National Park (CRLA)
4. Glacier Bay National Park and Preserve (GLBA)
5. Great Sand Dunes National Park and Preserve (GRSA)
6. Grand Teton National Park (GRTE)
7. Katmai National Park and Preserve (KATM)
8. Lassen Volcanic National Park (LAVO)
9. North Cascades National Park (NOCA)
10. Stikine-LeConte Wilderness, Tongass National Forest (STLE)
11. Wrangell-St.Elias National Park and Preserve (WRST)
12. Yosemite National Park (YOSE)
At all core and secondary parks, vegetation was sampled over an elevational gradient (including
core park target watersheds), and passive air sampling devices (PASDs) were deployed for one
year.
The WACAP study was designed as a screening study to assess contaminant concentrations
across large-scale spatial gradients and temporal scales relevant to western national parks. Future
related work, if conducted, might address additional concerns, for example, clarifying the various
temporal and spatial dimensions of contaminant pathways and defining and documenting the
extent and magnitude of specific ecological effects.
The US Environmental Protection Agency (USEPA), US Geological Survey (USGS), US Forest
Service (USFS), Oregon State University, and University of Washington worked in partnership
with the National Park Service (NPS) on this assessment. Information acquired through this
project is intended to enhance scientific understanding of the global fate, transport, and associ-
ated ecological impacts on sensitive ecosystems of airborne contaminants in western parks. It
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT E-3
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EXECUTIVE SUMMARY
will also help the NFS determine what actions are needed to further understand, mitigate, or
communicate impacts of potential effects of contaminants in national parks.
Chapter 2. Park Summaries
Park-specific summaries for the 8 core WACAP parks and the 12 secondary parks provide a
quick but in-depth graphical and written overview of the key results from the sites within the
parks and show how key variables associated with these sites compare with each other and
among results from other parks. Summaries identify key findings specific to each park.
Chapter 3. Contaminants Studied and Methods Used
WACAP researchers measured over 100 different SOCs spanning a wide range of volatility,
water solubility, and hydrophobicity, as well as persistence in the environment (Figure 2). Table
3-3 in the body of the report lists the SOCs, including abbreviation, chemical class, and regula-
tory status. Figure 3-1 provides a summary of the 70 SOCs (excluding PBDEs in fish and
sediment) found at detectable levels in WACAP snow, water, vegetation, lake sediment, and/or
fish. The SOC physio-chemical properties have been used to interpret the atmospheric transport,
deposition, and accumulation of these compounds to the ecosystems assessed in WACAP.
Finally, some of the SOCs measured in WACAP have been classified as persistent, bioaccumu-
lative, and toxic (PBT) chemicals by the USEPA. These PBT chemicals include benzo(a)pyrene,
aldrin, dieldrin, chlordane, DDT, ODD, DDE, hexachlorobenzene, mirex, and polychlorinated
biphenyls (PCBs). As with SOCs, the metals chosen for measurement in WACAP media were
selected because they serve as markers for a variety of different sources. These include anthropo-
genic sources such as coal combustion, petroleum combustion, industrial emissions, agriculture,
medical waste, incineration, and automotive sources, as well as natural sources such as sea
aerosols, volcanic deposits, and minerals. Mercury is a metal of particular concern because of its
detrimental neurological effects, as well as other effects, on humans, fish, and other organisms,
and it is classified by USEPA as a PBT chemical.
Because of the remote locations of the WACAP sites, atmospheric transport modeling was an
integral part of understanding how the contaminants were transported to the sites. We modeled
atmospheric transport via back-trajectory cluster analysis on three different time scales for each
of the WACAP core parks. A back-trajectory represents a meteorological calculation of the path
that an individual air particle has traveled over a specific time period. By grouping similar
trajectories into clusters, we obtained information about the routes of contaminant transport, as
well as the climatology for each park.
WACAP researchers assessed snowpack contaminants by sampling the seasonal snowpack from
at least 1 site in or near the 14 WACAP core park watersheds during each of the 3 years of the
study, in order to analyze inter-annual variability of contaminant loading.
We used PASDs to (1) obtain a measure of SOCs in ambient air by means of a simple, standard-
ized technology to compare loadings between parks and across geographic and elevational
gradients, (2) compare PASD and vegetation concentrations, and (3) compare ambient air SOC
concentrations in WACAP parks to ambient air concentrations at other national and international
locations measured with the same PASD design. In total, 37 PASDs were strategically deployed
E-4 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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EXECUTIVE SUMMARY
Lake water
Snow ,
Sediment
I
Lichen
i
Conifer
Needles
i i i i
Fish
O)
c
'(/>
TO
0>
b
c
PCB187
PCB183
PBDE 183
PCB118
PCB153
p,p'DDE
PBDE 99
PCB138
Retene
cis-Nonachlor
trans-Chi ordane
trans-Nonach lor
PBDE 100
Be nzo (a) anthracene
cis-Chi ordane
Chrysene + Triphenylene
Hexachlorobenzene
Dieldrin
Pyrene
Chlorpyrifbs
Endosulfan II
Endosulfan I
Phenanthrene
Dacthal
a-HCH
g-HCH
Endosulfan sulfate
SOC Groups
^H Endosulfans
HCHs
^M Dacthal
— PAHs
^^ Chlorpyrifbs
Dieldrin
0.0001 0.001 0.01 0.1 1 10 100 1000 10000
SOCs (pg/g ww)
Hexachlorobenzene
Chlordanes
PBDEs
PCBs
DDTs
Figure 2. Mean SOC Concentrations (pg/g ww) in Lake Water, Snow, Sediments, Lichens, Conifer
Needles, and Fish from Emerald Lake (SEKI). SOCs are ordered by increasing Kow, or decreasing
polarity and solubility in water, color-coded by group. SOC concentrations were 3 to 7 orders of
magnitude higher in sediments and biota relative to snow and water. SOC concentrations in water,
snow, and vegetation, but not sediments and fish, generally decreased with decreasing polarity.
Compared to vegetation, fish were better accumulators of PCBs and dieldrin and poorer accumulators of
PAHs, endosulfans, HCHs, dacthal, and chlorpyrifos. If no data are shown, all samples were below
detection limits; PBDEs were measured in sediments and fish only.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
E-5
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EXECUTIVE SUMMARY
in core and secondary WACAP parks. Multiple PASDs were deployed in the eight core WACAP
parks and two of the secondary parks to sample target watersheds and to obtain data along
elevational gradients.
We conducted vegetation sampling to (1) determine types and concentrations of SOCs that
accumulate in vegetation in each WACAP park, (2) compare individual SOC concentrations
within and across parks, especially along latitudinal and elevational gradients, to test for a cold
fractionation effect, (3) evaluate metal and nutrient concentrations in lichens in relation to known
ranges for lichens at other sites across the western United States, (4) determine the relationship
between environmental factors such as geographical location, proximity to urban-industrial and
agricultural areas, nitrogen concentrations in ambient particulates, and lichen nitrogen and sulfur
content with SOC concentrations in vegetation, and (5) estimate total concentrations of SOCs in
conifer needles at WACAP sites as a way of evaluating potential SOC inputs to watersheds via
litterfall.
We collected lake water samples from each catchment during the ice-free summer season to
characterize the condition of the WACAP lakes by assessing the chemical and physical
characteristics of water quality, including trophic state, chemical contamination, and
acidification status. Analytes included pH, alkalinity, specific conductance, dissolved organic
carbon, dissolved inorganic carbon, chlorophyll-a, total nitrogen, total phosphorus, and major
cations and anions.
We collected lake sediment cores to provide information about the accumulation and sources of
contaminants in the 14 WACAP catchments during the last -150 years. We dated cores and
analyzed sections for SOCs, mercury, metals, total carbon, total organic carbon, and total
inorganic carbon. In addition, we assessed spheroidal carbonaceous particles (SCPs) in sediment
because they serve as unambiguous indicators of deposition from industrial combustion of fossil
fuels, and offer clues as to the source fuel type.
Fish were used as the key bioaccumulators of SOC, Hg, and metal exposure because they are
continually immersed in the lake and provide an indication of impacts to the food web. Fish,
particularly top predators, are bioindicators of contaminant exposure because they accumulate
organic and metal contaminants through their diet. Piscivorous birds and mammals, including
humans, bioaccumulate contaminants when they consume fish. Selected biomarkers analyzed for
effects on fish condition and health included macrophage aggregates (MA), plasma vitellogenin
(Vtg), 11-ketotestosterone, testosterone, estradiol, and gonad, kidney, liver, spleen, and gill
histopathology. Contaminant concentrations and fish health analyses were assessed for each fish,
allowing a direct correlation of SOC, Hg, and metal concentrations to fish health parameters.
A small number of samples from moose liver and muscle tissue were collected in order to
explore the potential for the bioaccumulation of contaminants through the terrestrial food web.
Tissues from a total of three moose donated by hunters in Denali National Park and Preserve in
2004 and 2005 were analyzed for mercury, other metals, and SOCs.
Chapter 4. Contaminant Distribution
Spatial patterns of the contaminants found in greatest general abundance within each contamin-
ant category (SOCs, mercury, trace metals, SCPs, nutrients) are as follows.
E-6 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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EXECUTIVE SUMMARY
Total SOC concentrations in snow were highest in the Rocky Mountains (GLAC and ROMO)
and California (SEKI) parks. This general pattern was often repeated for other ecosystem
components. The highest SOC concentrations in vegetation were measured at SEKI, GLAC,
YOSE, and GRSA. At parks with the highest SOC concentrations in vegetation, the total SOC
concentration was dominated by CUP residues, notably endosulfans and dacthal.
Lichen concentrations of PCBs and many pesticides increased with elevation at most of the
WACAP parks for which there were sufficient data, suggesting that these compounds are
undergoing cold fractionation. Concentrations of poly cyclic aromatic hydrocarbons (PAHs)
decreased with increasing elevation at most parks, suggesting an association with wildfires at
lower, more heavily wooded elevations. Nitrogen concentrations in lichens from SEKI, GLAC,
BAND, and BIBE were elevated, indicating enhanced nitrogen deposition in these parks. Lichen
sulfur concentrations indicated enhanced sulfur deposition at SEKI and GLAC.
Fish whole-body lake mean and individual fish concentrations for dieldrin and Sum DDTs
(DDT, ODD, and DDE) are shown in Figure 3, along with contaminant health thresholds for
humans and piscivorous wildlife. Concentrations of dieldrin in fish (notably at ROMO, SEKI,
and GLAC) were significantly elevated compared with those in fish from similar Canadian
studies. DDT concentrations in fish from SEKI, GLAC, and ROMO were higher than those
reported for many fish elsewhere in the world, including fish from sites in Africa, where DDT is
used for mosquito control. Concentrations of the industrial flame retardant compound poly-
brominated diphenyl ether (PBDE) in WACAP fish were approximately 3 times higher than
concentrations in fish from similar alpine environments in Europe, and concentrations of CUPS
in WACAP fish were 2-9 times lower. All WACAP fish had lower PCB, HCH, and HCB
concentrations than fish in some recent surveys conducted in other locations of atmospheric
contaminants and reported in the literature. Mercury concentrations in fish (Figure 4) from this
study were compared with concentrations published in the literature for fish in other areas. In
general, mercury concentrations in trout from the parks in this study were lower than those
reported for trout in lakes in the Midwest and Northeast United States. However, mercury
concentrations were higher in WACAP fish than in some species of fish from northern lakes in
Canada and from mountain and sub-Arctic ecosystems in Europe. See Table 5-1 (pages 5-6
through 5-10) in the body of the WACAP report for comparisons of WACAP fish contaminant
concentrations to those found in other studies.
At WACAP parks in the conterminous 48 states, strong correlations were found between CUP
concentrations in snow and vegetation and percent cropland within 150 km. Concentrations of
the CUPs chlordanes, dacthal, and endosulfans in lichens and conifer needles, DDTs in conifer
needles, and PAHs in lichens correlated well with agricultural intensity, indicating that most
CUP concentrations in these parks are probably attributable to regional agricultural sources.
Pesticide deposition in the Alaska parks is attributed to long-range trans-Pacific transport,
because there are no significant regional pesticide sources nearby (Figure 5).
Where banned (historic-use) contaminants are found in park ecosystems, concentrations
significantly higher than those found in the Alaska parks probably indicate that re-volatilization
of persistent compounds is occurring from regional or local soils. Information about probable
sources or source areas of atmospherically transported contaminants into parks was assessed
from many varied types of data, including atmospheric transport pathways, contaminant spatial
patterns, and groupings of types of contaminants.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT E-7
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EXECUTIVE SUMMARY
(A) Whole Fish Dioldrin
D)
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(B) Whole Fish Sum DDTs
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I
O
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EXECUTIVE SUMMARY
10
Whole Fish Total Mercury
Human 185 ng/g
River Otter 100 ng/g
Mink 70 ng/g
Kingfisher 30 ng/g
^^.A^^tP ° C^^.^nP^*&,£>'
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0
0
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Figure 4. Fish Whole-Body Lake Mean (bars) and Individual Fish (symbols) Total Mercury and
Contaminant Health Thresholds for Various Biota. The mean ng/g total Hg in fish at NOAT exceeds
the human contaminant threshold, while some fish at Matcharak Lake (GAAR), PJ and Hoh Lakes
(OLYM), LP19 (MORA), and Pear Lake (SEKI) exceed the human contaminant threshold. The mean ng/g
Hg concentration in fish at all parks exceeds the kingfisher contaminant threshold, and the mean at 7
lakes exceeds all wildlife thresholds—Burial Lake (NOAT), Matcharak Lake (GAAR), Wonder Lake
(DENA), PJ and Hoh Lakes (OLYM), LP19 (MORA), and Pear Lake (SEKI). The human threshold is
300 ng/g wet weight (USEPA, 2001), and is based on methyl-Hg in the fillet for a general population of
adults with a body weight of 70 kg and 0.0175 kg fish intake per day. 95-100% of Hg in fish is methyl-Hg
(Bloom, 1992), and 300 ng/g in the fillet is equivalent to 185 ng/g ww whole body methyl-Hg (Peterson et
al., 2007). Contaminant health thresholds in piscivorous animals (wildlife) are based on 100% fish in the
diet for whole body total Hg, as determined by Lazorchak et al. (2003). Data are plotted on a Iog10 scale;
the y-axis starts at 10 ng/g.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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EXECUTIVE SUMMARY
B
Figure 5.1-, 5-, and 10-Day Cluster Plots for DENA. Clusters are sorted shortest to longest, A-F. Bars
represent the percent of trajectories in each cluster out of 2,922 total (1998-2005). Light blue = winter;
light green = spring; dark green = summer; orange = autumn. The dark blue dot is the percent of total
precipitation for which each cluster is responsible.
E-10
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EXECUTIVE SUMMARY
In GLAC, PAH concentrations in snow, sediment, and vegetation in the Snyder Lake watershed
were higher than those in the Oldman Lake watershed and 10 to 100 times higher than those in
all other WACAP parks. Several lines of evidence point to the aluminum smelter in Columbia
Falls, Montana, as the most likely major source of these elevated PAHs to Snyder Lake.
At ROMO, higher SOC and Hg deposition in snow was found in the Mills Lake watershed (on
the east side of the Continental Divide), than in Lone Pine Lake (on the west side), possibly
because the Continental Divide serves as a topographic barrier for transport of SOCs and Hg
from agricultural and populated areas on the east side of ROMO to the west side. SOC
concentrations in air (PASDs), conifer needles, and fish show no clear evidence of an east side
enhancement.
Sediment cores provide a historical record of contaminant deposition over the past -150 years.
The temporal records from sediment cores indicate that in nearly all parks, Hg deposition
increased in the twentieth century because of anthropogenic sources. In many parks, mercury
deposition fluxes have declined somewhat, although in other parks the Hg flux appears to still be
increasing. This finding reflects a complex array of decreasing regional sources, combined with
increasing global contributions and watershed influences on sediment records.
Lead (Pb), cadmium (Cd), and SCPs in sediment indicate regional fossil fuel combustion
sources. SCPs clearly show the build-up from industrial sources in lakes in the conterminous 48
states during the late twentieth century. In recent decades, Pb, Cd, and SCPs have declined
substantially, reflecting source reductions related to the Clean Air Act and regulation of lead in
gasoline. Lead concentrations in lichens at SEKI and MORA have decreased 5- to 6-fold since
the 1980s.
In the Alaska lakes, SCPs in sediment were non-detectable and Pb and Cd showed little sign of a
twentieth century increase. Only the Hg flux showed a consistent increase in the Alaska lake
sediments, reflecting a primary contribution from global sources.
Chapter 5. Biological and Ecological Effects
WACAP assessed the impacts and/or effects of contaminants on biota and ecosystems in a
variety of ways. Key results include the following.
Bioaccumulation of SOCs in vegetation appears to occur over time. Second-year needles
contained approximately triple the concentrations of contaminants in first-year needles. The
amount of contaminant stored in vegetation that eventually contributes to accumulation of SOCs
in forest litter-fall and soils is likely to be dependent upon forest productivity (annual above-
ground biomass production), tree species, and proximity to contaminant sources.
We observed biomagnification throughout park ecosystems. Concentrations of SOCs were 5 to 7
orders of magnitude higher in fish than in snow, water, and the PASD monitors indicating air
concentrations. Concentrations of SOCs were 3 to 5 orders of magnitude higher in fish tissue
than in sediments. Vegetation tended to accumulate more PAHs, CUPs, and HCHs, whereas fish
accumulated more PCBs, chlordanes, DDTs, and dieldrin. SOCs in vegetation and air (PASD
monitors) were expected to show similar patterns; however, this was not the case, possibly
because each medium absorbs different types of SOCs with varying efficiencies.
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EXECUTIVE SUMMARY
In this study, WACAP researchers assessed both fish-condition biomarkers and concentrations of
contaminants in fish tissue. Most fish appeared normal during field necropsies. Macrophage
aggregates (MAs; an immune system response, Figure 6) generally increased with mercury
concentrations and age in brook, rainbow, and cutthroat trout. Vitellogenin (Vtg) concentrations
in male fish are widely used as a biomarker for environmental estrogen exposure. Compounds
such as dieldrin, DDT, PDBEs, PCBs, PAHs, endosulfan, and methoxychlor (among others) are
known or suspected endocrine disrupting contaminants. Although sample sizes in this study were
very small, significant correlations between contaminants in fish tissue and Vtg concentrations in
male fish were found in the two core WACAP lakes sampled in ROMO in 2003. Two additional
lakes in ROMO, sampled as part of a separate study, contained fish with high concentrations of
Vtg; however, contaminants analysis of these fish has not yet been conducted. Two male fish
from GLAC and one male fish from MORA also displayed elevated concentrations of Vtg. The
fish from Oldman Lake (GLAC) with high Vtg concentrations also contained the highest
concentrations of DDT of any of the fish sampled in the project.
Intersex, the presence of both male and female reproductive structures in the same animal, is also
a commonly used biomarker of estrogen-like chemical exposure. Intersex fish were found in
ROMO and GLAC lakes. In this study, four levels of intersex condition were characterized, both
for current samples collected in WACAP, and for historic samples obtained for comparison from
museums. The data show that the number of sites with intersex fish has increased since the late
1800s. In the current WACAP samples, 8 of 117 fish in the Rocky Mountains were identified as
intersex and none of the 90 samples collected at other parks in the west were intersex. Six of 11
water bodies sampled in ROMO contained intersex fish. Sample size in the project was low, and
WACAP was not designed to fully characterize the extent and range of intersex fish in remote
sites in the west. However, based on the initial sampling conducted here, it appears that intersex
condition in fish from remote areas might be concentrated in the Rocky Mountains. Further
investigation is warranted (a variety of potential hypotheses are discussed in the report).
In addition, the severity of abnormalities observed in the intersex fish from ROMO was greater
than in any historical samples, displaying category 4 gonad abnormalities, low androgen and
estrogen levels, and elevated levels of Vtg. The three intersex fish that were also sampled for
contaminants contained high levels of endocrine disrupting contaminants such as dieldrin, DDT,
chlordanes, and PCBs. However, whether contaminants are causing the intersex condition in fish
cannot be definitively determined with this small dataset.
Mercury concentrations increased with fish age in all fish species up to approximately 15 years
of age. Fish older than 15 years had less mercury. Several hypotheses could account for this
finding and are discussed in more detail in Chapter 5 of the report. Fish mercury concentrations
in WACAP were highest in Burial Lake (NOAT), with the mean Hg concentration exceeding the
USEPA contaminant health threshold for human consumption (see Figure 4). The other Arctic
lake in this study, Matcharak (GAAR), also contains fish with elevated concentrations, with
some fish exceeding human contaminant health thresholds. Because mercury concentrations in
snow, sediment, and vegetation were found to be low in these two parks, it is likely that in-lake
biological processes, including fish age, Hg methylation rate, watershed biogeochemical
characteristics, and food web efficiency influence the higher rates of bioaccumulation in Alaska
lakes.
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EXECUTIVE SUMMARY
Figure 6. Representative Hematoxylin-Eosin Stained Brook Trout Organs Showing the Relative
Difference between Fish with Very Few or No Macrophage Aggregates (MAs) and Extensive
Accumulations of MAs (a-f) and Outlined High Magnification Hepatic MAs (g-i). Bars = 50 jjrn,
(a) Kidney with a few MAs; (b) Kidney with extensive MAs; (c) Spleen with a few MAs; (d) Spleen with
extensive MAs; (e) Liver with no MAs; (f) Liver with extensive MAs; (g) High magnification of liver MAs
corresponding to MAs (arrows) in (f); (h) 2X magnification of the MA corresponding to arrow 1 in (g); (i) 2X
magnification of the MA corresponding to arrow 2 in (g). The outlined areas in (g) through (i) are the
computer output of delineated MAs in the liver based on pigment selection by the computer program.
Modified from Schwindt et al. (2006).
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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EXECUTIVE SUMMARY
Current contaminant concentrations in 136 fish from 14 lakes in the 8 core WACAP parks were
compared to the USEPA's Guidance for Assessing Chemical Contaminant Data for Use in Fish
Advisories. The contaminant health threshold is the point at which a 70-kg person who consumes
17.5 g offish per day (2.3 servings per month; recreational fish consumption) or 142 g per day
(19 servings per month; subsistence fish consumption) increases lifetime risk of developing
cancer by 1 in 100,000, or significantly increases risk of chronic (non-cancer) disease. Most
contaminant concentrations in fish fell below these thresholds. However over half (77 of 136) of
the individual fish from 11 of the 14 lakes analyzed carried concentrations of dieldrin and/or
p,p,'-DDE that exceeded contaminant health thresholds for subsistence fishing (Figure 3). The
lake average fish dieldrin and/or p,p,'-DDE concentrations exceeded subsistence fishing thres-
holds in nine of the lakes and recreational fishing thresholds in none of the lakes, although five
individual fish exceeded dieldrin thresholds for recreational fish consumption in Mills and Lone
Pine lakes (ROMO), Pear Lake (SEKI), and Oldman Lake (GLAC). No other SOC concentra-
tions measured in fish from the eight core WACAP parks exceeded human contaminant health
thresholds. Of the other compounds detected in >50% offish (chlorpyrofos, dacthal, endosulfans,
methoxychlor, mirex, HCB, a-HCH, g-HCH, chlordanes, heptachlor epoxide, and PBDEs), all
were 1 to 7 orders of magnitude below the human contaminant health thresholds.
We assessed impacts of contaminants on aquatic food chains by comparing fish contaminant
concentrations with published contaminant health threshold for impacts to mink, river otter, and
belted kingfishers. At numerous sites, mean concentrations for mercury in WACAP fish were
above thresholds for potential negative health effects on the aforementioned wildlife (Figure 4).
Contaminant health thresholds for PCBs for wildlife (banned from production and use in the
United States in 1979) were not exceeded. DDT production ceased in the United States in 1972.
Some fish at the two sites in SEKI, and the mean concentration offish in Oldman Lake in
GLAC, had concentrations of the sum of DDTs above the threshold for negative health effects
for kingfishers (Figure 3B). The concentrations of chlordane, once a broad-use pesticide used to
control underground termites, were below thresholds for wildlife, except that one fish in Oldman
Lake in GLAC exceeded the threshold for kingfishers. A suspected carcinogen and endocrine
disrupter, chlordane was banned in the United States in 1983. The acutely toxic pesticide dieldrin
was banned for agricultural use in the United States in 1974 and for most other uses in 1987. The
highest dieldrin concentrations in fish in this study were found in ROMO. Dieldrin was produced
in nearby Denver, Colorado, from 1952 to 1973. Mean concentrations of dieldrin in fish at all
WACAP sites were below the contaminant health thresholds for wildlife (Figure 3A).
We analyzed moose tissue samples for SOCs and metals in Alaska parks with the intent of
exploring linkages between the Alaska food web and humans engaging in subsistence hunting.
However, tissue samples from only three animals were collected, all in DENA. Few of the target
SOC compounds were detected in the moose liver or muscle tissues analyzed. The generally low
detection frequencies and the absence of any major patterns among SOC compound groups,
among individual moose, or between moose tissue types suggest that these moose were not
biomagnifying SOCs to a concentration of concern. Metals concentrations were low, and at the
deficiency level for copper, which decrease adsorption of iron in the blood. Compared to the
sparse data available from other studies for metals in moose tissue, the WACAP samples were
generally lower in cadmium, copper, and zinc.
E-14 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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EXECUTIVE SUMMARY
Chapter 6. Recommendations and Conclusions
The report concludes with recommendations to the National Park Service that respond to the
question, "What did you learn in this project that could help guide or focus future work within
the NFS on contaminants in parks in the western United States?" These recommendations are
specific to the original five project objectives. Also included is a list of additional research
questions posed by WACAP scientists. These questions define fertile areas for future research
into processes, mechanisms, and ecological interactions of contaminants in western ecosystems.
In addition, broad conclusions answering the questions posed by the five project objectives are
discussed.
Contaminants were found in all WACAP lakes. In some cases, the concentrations in fish were
found to exceed important human and wildlife thresholds. It might be perceived that the two
lakes per park that WACAP examined are somehow outliers and that they do not represent the
total population of lakes within parks. From a strictly statistical perspective, these lakes are not
representative of the population of lakes. However, the lakes were selected to provide "clean"
and unambiguous signals of atmospherically deposited contaminants and in no way were they
selected to provide the highest or lowest contaminant concentrations. For future work,
researchers might choose to consider implementing a robust statistical sampling design for
specific parks that would provide a quantitative estimate of the contaminant condition of all lakes
in the population.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT E-15
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EXECUTIVE SUMMARY
E-16
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 1
Introduction
1.1 Background
Transport and deposition of atmospheric contaminants has been recognized as a possible threat
to aquatic and terrestrial ecosystems for several decades. However, it was not until the 1970s and
1980s that the potential for significant regional-scale ecological impacts of long-range transport
of contaminants, particularly the acidic precursors of acidic deposition, was recognized (Likens
et al, 1979) and later documented and quantified across multiple spatial scales (Linthurst et al.,
1986). In this case, it was demonstrated that SOX and NOX, byproducts of combustion, were
transported thousands of kilometers in the atmosphere, transformed to acids, and deposited via
precipitation on sensitive ecosystems that lacked sufficient buffering capacity to neutralize the
acid (Galloway and Cowling, 1978). In addition, metals were recognized as another class of
contaminants associated with the combustion of fossil fuels that could be transported great
distances in the atmosphere (Galloway et al., 1982).
Once the concept of trans-boundary airborne
contaminants had been demonstrated with
acidic precipitation, numerous other airborne
contaminant threats to ecosystems and the
humans that depend upon them were identified
(Perry et al., 1999). The lack of local or
watershed sources of contaminants confirmed
that the impacts of long-range atmospheric
transport of contaminants threatened many
remote ecosystems (Barrie et al., 1992;
MacDonald et al., 2000). It is now well known
that metals, particularly mercury and lead, are
emitted by human activities and can be
transported short and long distances from their sources to be deposited, retained, and, in some
instances, bioaccumulated within distant ecosystems. Similarly, a vast array of persistent organic
pollutants (POPs) and semi-volatile organic compounds (SOCs) are recognized as having the
potential to be transported by the atmosphere (Simonich and Kites, 1995; Muir et al., 1996; Li et
al., 1998; Van Drooge et al., 2002). These compounds are derived only from human activities
and many persist in the environment for long periods of time (Gubala et al., 1995; Fernandez et
al., 2000; Helm et al., 2002).
Recent studies have pointed out the atmospheric linkage between air masses traversing Eurasia
and arriving in North America (Welch et al., 1991; Wania and Mackay, 1996; Jaffe et al., 1999).
Although few studies have measured persistent and bioaccumulating toxics in these air masses,
model output suggests that they are likely to contain a variety of contaminants (Perry et al., 1999;
Koziol and Pudykiewicz, 2001). Recent studies by authors of this report (Jaffe, Simonich, and
others) have demonstrated some key linkages between air masses arriving on the west coast of
North America and Eurasian industrial and agricultural sources (Jaffe et al., 2005; Weiss-Penzias
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 1. INTRODUCTION
et al, in press). Many of the tracers used by air monitors to identify trans-Pacific air masses (e.g.,
CO, aerosols, Os) are directly related to human activities and combustion sources. However,
from an ecological perspective, it is very complicated to track these short lived and/or highly
reactive tracers through ecosystem compartments where they are not retained as SOCs and
metals are. This is particularly true in remote locations.
There is ample evidence of regional as well as long-
range sources of atmospheric contamination to
remote ecosystems in the western United States, but
there is scant evidence in any published source that a
threat to ecosystems is being realized. This is
particularly true over large spatial scales. One of the
major problems is that the atmospheric scientists
who have identified the long-range transport across
the Pacific Ocean from Eurasia have little
information about deposition of inorganic and
organic contaminants in these air masses. One of the
few and most convincing publications regarding this
issue describes the concentration of POPs in snow
sampled in the Canadian Rocky Mountains during
the spring of 1995 and 1996 (Blais et al., 1998). The
data in this publication suggest that there is good
evidence that high-elevation ecosystems are at risk
with respect to SOCs for two primary reasons: (1)
long-range transport of contaminants possibly being
deposited with the annual snow pack and (2) cold
fractionation of the lighter SOCs, resulting in
migrations of these and other compounds to the higher (i.e., colder) alpine areas (Wania and
Mackay, 1996). Cold fractionation appears to function at latitudinal as well as elevational
gradients, putting northern ecosystems are risk. A recent publication documents the cold-
trapping of POPs by vegetation in mountains in western Canada (Davidson et al., 2003).
Certainly there is sufficient scientific evidence to ponder the question of risk to high-latitude
ecosystems from airborne contaminants in the western United States. The information to date,
from various disciplines, published and reported independently, has generated considerable
concern regarding the risk to western ecosystems, prompting the US Environmental Protection
Agency (USEPA) to convene the First International Conference on Trans-Pacific Transport of
Atmospheric Contaminants in Seattle, Washington, in July 2000. The meeting was attended by
over 100 experts, representing disciplines that included energy and emissions, atmospheric
sciences, marine sciences, biogeochemistry, biological sciences, and international environmental
policy. This workshop culminated in a consensus statement published in Science (Wilkening et
al., 2000). Three of the conclusions from this conference most pertinent to this undertaking are
worth noting here:
• The nature, magnitude, and spatial distribution of the effects of airborne chemicals
transported in the Pacific region, including changes in variability, are largely unknown.
1-2
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 1. INTRODUCTION
• Long-range transport could have significant impacts on the chemistry of the troposphere
above the Pacific Ocean, and of the ocean itself, and on contaminant concentrations in
terrestrial and aquatic ecosystems.
• Some airborne chemicals, especially organochlorines and mercury, have the potential to enter
food webs and biomagnify, thereby increasing the toxicological risk to top predators,
including humans.
These conclusions were applied to a broad suite of contaminants, including organic
contaminants, heavy metals (including mercury), and radionuclides.
1.2 Approach
In 2001, the National Park Service (NPS) convened the first of two workshops at which experts
discussed how to identify a scientific approach that could be followed to quantify the risk from
airborne contaminants to the national parks in the western United States. This action followed
the legal mandate described in the National Park Service Organic Act (1916) that created the
NPS. This federal legislation required protection of the national parks for perpetuity,
".. .unimpaired for the enjoyment of future generations." The Clean Air Act augmented this
responsibility in 1977 by defining specific goals, objectives, and mechanisms for protecting air
quality in major parks and preventing
"significant deterioration" of air quality.
Not only are the national parks widely
distributed, but many of them have
considerable elevation ranges, possibly
predisposing these high-elevation
locations, because of their cold alpine
climates, to become long-term sinks for
some classes of contaminants. Moreover,
high-latitude national parks in Alaska are
also perceived to be at risk of becoming
sinks for air pollution, given their cold
climates and the trans-continental air
masses to which they are exposed.
The Western Airborne Contaminants Assessment Project (WACAP) was initiated by the NPS as
a direct result of these workshops. In communication with a broad range of NPS personnel, the
WACAP goal was finalized:
To assess the deposition of airborne contaminants in western national parks,
providing regional and local information on exposure, accumulation, impacts, and
probable sources.
At this early stage, WACAP conceptually incorporated an integrated, interdisciplinary, multi-
scalar scientific approach to the problem (Figure 1-1). The project was initiated, and principal
investigators with expertise in a broad range of disciplines were organized to develop a research
plan that was peer reviewed by an international scientific review panel, revised, published
(USEPA, 2003), and implemented in 2003. The WACAP report authors are shown in Table 1-1.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 1. INTRODUCTION
Snow
Annual Flux
North
I America
Lake
' Sediments
Chronology
Lake
Dissolved and
Particulate
Contaminants
Fish
Contaminants vs. Age,
Condition Factors,
Response Factors
Lichen
N, S, Metals
and SOCs
Conifer Needles
and Lichen
Intensify Spatial
Coverage of SOCs -
More National Parks
Moose
Subsistence Link
WACAP Indicators and Conceptual Diagram
Figure 1-1. WACAP Conceptual Diagram of Airborne Contaminant Assessment Approach
Because very little was known about the contaminant concentrations and potential impacts in any
of the western national parks at that time, an inventory of baseline SOCs, metals, and nutrient
contaminants across various ecosystem components (i.e., snow, water, vegetation, fish, and
sediment) was selected as the approach to be taken (Table 1-2). "Impacts" in the goal statement
refers to evidence of accumulation in the food web—particularly animals—and does not go so
far as to attempt to establish "effects," such as reproductive or lethal responses.
The specific objectives that guided the development of WACAP were also thoroughly examined
and approved by both the scientific team and the NFS:
1. Determine if contaminants are present in western national parks.
2. If contaminants are present, determine where they are accumulating (geographically and by
elevation).
3. If contaminants are present, determine which ones pose a potential ecological threat.
4. Determine which indicators appear to be the most useful to assess contamination.
5. If contaminants are present, determine the source of the air masses most likely to have
transported contaminants to the national parks sites.
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CHAPTER 1. INTRODUCTION
Table 1-1. WACAP Report Authors
Name and Affiliation
Project Components
Email
Luke Ackerman
OSU, Corvallis, OR
Tamara Blett
N PS-Air Resources Division
Lakewood, CO
Don Campbell
USGS, Denver, CO
Marilyn Erway
Dynamac Corp., Corvallis, OR
Linda Geiser
US Forest Service Pacific NW Region Air
Program, Corvallis, OR
Will Hafner
University of Washington-Bothell
Kim Hageman
OSU, Corvallis, OR
Dan Jaffe
University of Washington-Bothell
Michael Kent
Director, Center for Fish Disease Research,
OSU, Corvallis, OR
Dixon Landers
USEPA, Corvallis, OR
Neil Rose
University College London, UK
Carl Schreck
Oregon Cooperative Fish and Wildlife
Research Unit, Dept. of Fisheries and
Wildlife, USGS and OSU, Corvallis, OR
Jill Schrlau
OSU, Corvallis, OR
Adam Schwindt
OSU, Corvallis, OR
Staci Simonich
OSU, Corvallis, OR
Howard Taylor
USGS, Boulder, CO
Sascha Usenko
OSU, Corvallis, OR
Fish organic contaminants luke.ackerman@fda.hhs.gov
Project management
Snow
tamara_blett@nps.gov
dhcampbe@usgs.gov
Logistics, database, QA, erway.marilyn@epa.gov
sediment, water
Vegetation
lgeiser@fs.fed.us
Atmospheric Modeling and whafner@uwb.edu
Graphics
Snow
Atmosphere
Fish pathology
khageman@chemistry.otago.ac.nz
djaffe@u.washington.edu
michael.kent@orst.edu
Project Director, sediment landers.dixon@epa.gov
Sediment SCPs
Fish physiology
nrose@geog.ucl.ac.uk
carl. sch reck@orst. ed u
Vegetation organic schrlauj@onid.orst.edu
contaminants
Fish and Fish Sampling ar.schwindt@gmail.com
Organic contaminants
Metals
Sediment and water
organic contaminants
staci.simonich@orst.edu
hetaylor@usgs.gov
sascha_usenko@baylor.edu
1 NPS = National Park Service, USGS = US Geological Survey, USDA-FS = US Dept. of Agriculture Forest Service,
OSU = Oregon State University, USEPA = US Environmental Protection Agency
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
1-5
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CHAPTER 1. INTRODUCTION
Table 1-2. Ecosystem Components Sampled for WACAP
Media
Frequency of Sampling
Purpose
Snow
Fish
Water
Lake Sediment
Vegetation
Air
Moose
Atmospheric
Transport
Annually in the spring in each of
the 14 sites in the 8 core parks
Once in each of the 14 sites in
the 8 core parks
Once in each of the 14 sites in
the 8 core parks
Once in each of the 14 sites in
the 8 core parks
Once as elevation transects in
the 8 core parks and 12
secondary parks
Deployed PASDs (passive air
sampling devices) for
approximately one year in most
core and secondary parks
Once from several Alaska sites
Models developed based on 5
years of data for each park
Direct measure of annual atmospheric loading;
snow is 90% of annual precipitation in many
alpine sites
Direct measure of food web impacts and food
web bioaccumulation/biomagnification
Measure of hydrophilic current-use chemicals
and baseline water chemistry
Provides historic trends (-150 yrs) of
contaminant loading to watershed
Direct measure (conifer and lichens) of food
web bioaccumulation of nitrogen, sulfur,
mercury and other metals
Measure (lichens at core parks) of ecosystem
exposure for SOCs; large number for statistical
comparisons within and among sites, parks
and elevations at a broad spatial scale
Estimate of airborne exposure of SOCs at a
site over a period of time
Direct measure of subsistence food resources
Back trajectory models identify likely sources of
contaminants
1.3
One of the most difficult issues was identifying which national parks should be included in the
spatial design. We initially determined that we could include six parks, based on the estimated
budget available for the effort, assuming that each park would have two intensive sites (i.e.,
lakes). However, with additional funding committed from "fee demo" sources, we were able to
include 8 parks and 14 core sites. These sites, and the lakes in particular, were perceived to be
natural precipitation/deposition collectors, representing some of the most remote, undisturbed
remaining landscapes in the western United States. Contaminants collected at these sites were
presumed not to be derived from local sources or historic land uses within the parks, given their
National Park designation. However, we recognized the potential for the proximal influence of
local and regional atmospheric contaminant sources and planned to interpret results with this in
mind. We decided which parks to include based on the physical locations and characteristics of
the parks rather than any other criteria. Through an iterative process, we identified indicators of
1-6
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 1. INTRODUCTION
interest, determined the number of sites in each park, set criteria for the attributes of sites within
parks, and consulted with experts at each park, all with consideration for the overall spatial
design and acknowledgment of budget realities.
It was clear from the outset that a strong spatial design
containing enough sites to provide ample statistical
power for hypothesis testing was outside the budget
and scope of this project. Moreover, given the
absolute lack of information of any kind regarding the
impacts of airborne contaminants on the systems of
interest, it seemed unwarranted and premature to
pursue such a design, even if funding had been
available. Rather, we decided that the first question
that needed to be answered across large-scale spatial
gradients appropriate to the western national parks
was, "Is there a problem with respect to contaminants
in the parks?" It was decided that if this policy
question could be answered in a rigorous way by
WACAP, then future work, if warranted, could be
designed to deal with a more detailed secondary set of
objectives based on the results of WACAP. These
secondary objectives might include resolving the
various temporal and spatial dimensions of
contaminant pathways and defining and documenting
specific ecological effects.
Because trans-Pacific air masses moving generally from west to east affect the west coast of the
North American mainland from Alaska to California (Bailey et al., 2000; Husar et al., 2001), we
selected a series of national parks ranging from Arctic Alaska south to California. Our intent was
to identify a core set of western national parks along a north-south latitudinal gradient that could
be affected by air masses moving across the Pacific Ocean. We wanted to accomplish this while
recognizing that air masses originating in North America are also of potential interest and should
be considered. We also wanted to include some parks in the interior of the western United States
that could be affected by trans-Pacific air masses but that might be influenced more by regional
air masses. We selected six west coast parks (NOAT, GAAR, DENA, OLYM, MORA, and
SEKI) and two interior parks (GLAC and ROMO), for a total of eight core parks where all media
would be sampled and evaluated. GAAR is juxtaposed to NO AT, thus we selected one site in
each. As a result, 14 sites/lakes are associated with the core parks. Table 1-3 lists the locations of
the lake sites in the core parks.
To provide better coverage, we identified 12 additional (secondary) parks where vegetation
samples for SOC analyses would be collected. Vegetation samples from multiple sites repre-
senting a range of elevations were taken from both the core and secondary parks (Table 1-4) to
provide a better understanding of contaminant deposition in the west. In 2005, we also deployed
passive air sampling devices (PASDs) for one year in both core parks and secondary parks as a
means of further linking our spatial interpretations.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
1-7
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CHAPTER 1. INTRODUCTION
Table 1-3. WACAP Sites in Core Parks. All media were sampled, including water, snow, air (PASDs),
vegetation (conifer needles and lichens), fish, and sediments. Parks are listed alphabetically by park code.
Park
Code
DENA
DENA
GAAR
GLAC
GLAC
MORA
MORA
NO AT
OLYM
OLYM
ROMO
ROMO
SEKI
SEKI
Park1
Denali NP and
Preserve
Denali NP and
Preserve
Gates of the Arctic
NP and Preserve
Glacier NP
Glacier NP
Mt. Rainier NP
Mt. Rainier NP
Noatak National
Preserve
Olympic NP
Olympic NP
Rocky Mountain NP
Rocky Mountain NP
Sequoia and Kings
Canyon NP
Sequoia and Kings
Canyon NP
1NP = National Park, 2dd= decimal
State
Alaska
Alaska
Alaska
Montana
Montana
Washington
Washington
Alaska
Washington
Washington
Colorado
Colorado
California
California
degrees; 3from
Lake Site
McLeod
Wonder
Matcharak
Oldman
Snyder
Golden
LP19
Burial
Hoh
PJ
Lone Pine
Mills
Emerald
Pear
Latitude2
(dd)
63.38
63.48
67.75
48.50
48.62
46.89
46.82
68.43
47.90
47.95
40.22
40.29
36.58
36.60
drg (digital raster graphic), m
Longitude2
(dd)
-151.07
-150.88
-156.21
-113.46
-113.79
-121.90
-121.89
-159.18
-123.79
-123.42
-105.73
-105.64
-118.67
-118.67
= meter
Lake
Elevation3
(m)
564
605
502
2026
1597
1369
1372
430
1380
1384
3018
3030
2810
2908
Year
Sampled
2004
2004
2004
2005
2005
2005
2005
2004
2005
2005
2003
2003
2003
2003
The selection of the core set of national parks provides a group of 8 parks ranging over 30
degrees of latitude with 2 pairs of parks (one coastal and one interior) rather closely linked at
about the same latitude (OLYM and GLAC; SEKI and ROMO). This spatial arrangement, along
with the location of the secondary parks, is depicted in Figure 1-2, in association with EPA Level
1 Ecoregions (http://www.epa.gov/bioiweb 1/html/usecoregions.html).
The dominant factor influencing the deposition and accumulation of SOCs in the ecosystem is
temperature. This is especially true for those contaminants that demonstrate cold fractionation.
Figure 1-3 depicts the mean annual air temperature one could expect at each of the WACAP sites
in the eight core national parks. Temperature data were estimated from the nearest and most
representative locations with long-term meteorological data. In some cases, a small correction
was made to account for the difference in altitude between the meteorological and lake site (D.
Jaffe, pers. comm., University of Washington, Bothel). It is useful to notice that there is general
agreement in temperature for all of the sites in the conterminous United States, and that the four
sites in Alaska, although lower in elevation, are significantly colder.
1-8
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 1. INTRODUCTION
Table 1-4. Vegetation WACAP Sites in Core and Secondary Parks (SOCs in conifer needles, lichens,
and PASDs1). Parks are listed alphabetically by park code, with core parks in bold.
Park Code Park2
BAND
BIBE
CRLA
DENA
GAAR
GLAC
GLBA
GRSA
GRTE
KATM
LAVO
MORA
NOAT
NOCA
OLYM
ROMO
SEKI
STLE
WRST
YOSE
Bandelier National Monument
Big Bend NP
Crater Lake NP
Denali NPP
Gates of the Arctic NPP
Glacier NP
Glacier Bay NPP
Great Sand Dunes NPP
Grand Teton NP
Katmai NPP
Lassen Volcanic NP
Mt. Rainier NP
Noatak National Preserve
North Cascades NP
Olympic NP
Rocky Mountain NP
Sequoia & Kings Canyon
NPs
Stikine-LeConte Wilderness,
Tongass NF
Wrangell-St. Elias NPP
Yosemite NP
No. of
Vegetation Minimum Maximum
Sampling Elevation Elevation
State Sites of Sites (m) of Sites (m)
New Mexico
Texas
Oregon
Alaska
Alaska
Montana
Alaska
Colorado
Wyoming
Alaska
California
Washington
Alaska
Washington
Washington
Colorado
California
Alaska
Alaska
California
5
5
5
6
1
5
4
5
5
6
5
5
3
5
5
6
11
5
6
5
1854
560
1798
221
505
961
8
2469
2073
36
1829
654
227
198
137
2560
427
1
7
661
2926
2316
2713
1753
505
2024
625
3338
3048
1112
2713
1809
675
1600
1850
3451
2911
1064
1421
3048
No. of Air
Sampling Year
Sites Sampled
1
4
1
2
1
2
1
1
1
1
1
2
1
1
2
5
4
4
1
1
2005
2005
2005
2004
2004
2004
2005
2005
2005
2005
2005
2004
2004
2005
2004
2004
2003 &
2004
2005
2005
2005
1 PASDs = passive air sampling devices, 2NP = National Park, NF = National Forest, NPP = National Park and Preserve
1.4 Site Selection Within Parks
Within each of the core national parks, we selected two catchments containing lakes (i.e., sites)
that met the following pre-established criteria.
• Catchment is small, typical of the catchments found in the park (elevation, soils, vegetation,
aspect, etc.).
• Catchment contains a lake (>5 m deep; larger than ~0.8 hectares in surface area).
• Lake contains reproducing fish populations (preferably salmonids).
• No anadromous fish reach the lake.
• Lake is without major inlets, outlets, or glaciers in the catchment.
• Lake bathymetry is acceptable for sediment core analysis.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
1-9
-------
O
o
m
co
m
71
DO
O
71
m
O
O
CO
>
CO
CO
m
CO
CO
S
m
Site Type
All media sampled at
lake sites in core parks
f Vegetation only sampling sites
(in core parks, in addition to the
lake sites)
l^ Snow only sampling sites
(in core parks, sites outside
of lake sites)
A Air sampling sites
EPA Ecoregions-Level 1
ARCTIC CORDILLERA
GREAT PLAINS
^H MARINE WEST COAST FOREST
MEDITERRANEAN CALIFORNIA
•I NORTH AMERICAN DESERTS
•I NORTHERN FORESTS
JBI NORTHWESTERN FORESTED MOUNTAINS
SOUTHERN SEMI-ARID HIGHLANDS
TAIGA
TEMPERATE SIERRAS
TROPICAL DRY FORESTS
TUNDRA
m
7)
O
o
c
o
] Kilometers
500 1,000
2.000
3,000
Tl
71
O
m
O
Figure 1-2. WACAP Sites Mapped on North American Shaded Relief Map and EPA Level 1 Ecoregions (Biomes). See
Table 1-4 for key to national park abbreviations. Vegetation-only sampling sites in core parks designate sites used in elevational
transect in addition to lake sites. Snow-only sampling sites in core parks designate alternate sampling locations when lake sites
could not be reached safely.
-------
CHAPTER 1. INTRODUCTION
Safe access is possible by available means in late spring and summer.
Gill netting offish is acceptable.
Catchments are located within the seasonally persistent, non-melting snowpack development
for the park.
Both catchments are located in the same general quadrant within the park.
Mean Annual Air Temperature,
Latitude, and Longitude
of WACAP Sites
Figure 1-3. Relationships among Latitude, Longitude, and Mean Annual Temperature in the 8
National Parks and 14 Sites Sampled in WACAP.
Variability with respect to biogeophysical setting (e.g., lake and basin morphometry, vegetation,
fish, etc.) was large among candidate catchments within and among parks as the WACAP
research team set out to select appropriate sites. Given the large geographic scale of WACAP,
several major ecological regions were included in the final selection (see Figure 1-2). We
selected sites within parks that were located at similar elevations and generally in the same type
of biogeophysical setting, where possible.
Candidate sites were evaluated by Dr. Dan Jaffe (WACAP atmospheric science lead) prior to
final selection to maximize the possibility that atmospheric transport pathways between sites in
the same park would be similar, based on available deposition and atmospheric data available
only at a fairly coarse scale. The two exceptions to this strategy are in ROMO and GLAC, where
the selected lakes are at almost the same elevation but on opposite sides of the Continental
Divide.
Our final catchment selections in each core park represent "elevation duplicates," in the sense
that they are located at approximately the same elevation. Table 1-5 summarizes the attributes of
the selected catchments/lakes for each core park. Appendix 1A contains more detailed tables of
site physical and chemical characteristics for both the lake catchments in the core parks and the
vegetation sites in the secondary parks. Chapter 2 provides maps showing the locations of the
selected sites within each park, along with bathymetric maps of the lakes. In addition, Chapter 2
summarizes much of this site information, as well as the analytical results graphically for each
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
1-11
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CHAPTER 1. INTRODUCTION
site within each park within the context of the overall range of conditions found in all core
WACAP parks.
Figure 1-4 depicts the relationships among WACAP indicators and the broader contaminant
pathways, sources, sinks, and ecosystem components. The diagram is not all inclusive; it shows
key components addressed by WACAP and some of the components and contaminant pathways
that were not evaluated. The diagram should assist the unfamiliar reader with some of the
complexity that WACAP examined and the ecological position and interrelationships among key
areas of investigation.
Table 1-5. WACAP Lake Sites: Selected Physical and Surface (1 m) Chemical Characteristics1
Collected during WACAP Site Visits According to Methods Listed in Chapter 3. Parks are listed by
latitude, from north to south.
Park
Code
NOAT
GAAR
DENA
DENA
GLAC
GLAC
OLYM
OLYM
MORA
MORA
ROMO
ROMO
SEKI
SEKI
Lake Name
(Site)
Burial
Matcharak
Wonder
McLeod
Snyder
Oldman
PJ
Hoh
Golden
LP19
Mills
Lone Pine
Pear
Emerald
Lake
Surface
Area2 (ha)
65.5
300.7
265.6
35.9
2.6
18.2
0.8
7.7
6.6
1.8
6.1
4.9
7.3
2.5
Watershed
Area
(ha) Fish Species
264.9
2388.3
3212.4
236.8
303.7
230.3
56.2
43.9
106.1
44.9
1208.9
1830.0
142.0
121.3
Lake trout
Lake trout
Lake trout
Burbot
Round
whitefish
Westslope
Cutthroat trout
Yellowstone
River
Cutthroat trout
Brook trout
Brook trout
Brook trout
Brook trout
Rainbow
Cutthroat trout
Brook trout
Brook trout
Brook trout
PH
7.57
8.31
8.18
7.24
6.42
8.24
8.14
7.52
6.47
6.63
6.61
6.67
6.10
6.22
Specific
Cond.
(uS/cm)
35.08
248.10
190.10
8.41
16.80
159.10
127.40
63.69
10.08
10.72
12.04
14.02
4.02
5.42
ANC
(ueq/L)
272.98
1967.03
1693.60
51.02
162.08
1573.73
1092.95
512.45
69.05
80.14
50.81
91.52
23.99
26.34
DOC Total P Chl-a
(mg/L) (ug/L) (ug/L)
3.32
4.71
2.10
2.25
0.65
0.70
1.05
0.74
1.88
1.37
1.55
1.74
0.82
0.94
9.1
1.1
0.5
1.0
2.7
0.6
2.8
1.2
0.6
0.9
3.3
2.7
0.6
1.5
0.81
0.96
0.49
0.61
4.73
0.77
1.77
0.83
0.35
0.60
3.02
1.95
0.64
0.62
1 Specific Cond.= Specific Conductance; ANC = acid neutralizing capacity, DOC= dissolved organic carbon, Total P = total
phosphorus, Chi a = chlorophyll a.
2ha = hectare.
1-12
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 1. INTRODUCTION
Linkages and Interrelationships Among WACAP Ecosystem Components
Indicators in shaded boxes represent WACAP components
measured for contaminants
Other sources of
contaminants:
rain, gaseous
Contaminants
delivered in snow
Loss of contaminants from
snow to gaseous phase
SOCs in
conifer needles
Contaminants in
annual snow pack
Contaminants delivered
from snow to watershed
dissolved in or
carried by water
Contaminants
adsorbed to soil
Contaminants
delivered to
the take
Piscivorous
Biota
Contaminants
bioaccu mutated
in fish
Contaminants
dissolved in
water
(hydrophilic)
Particulate phase
contaminants in
water
(hydrophobic)
Contaminants
delivered to
the sediments
Figure 1-4. Linkages among Major WACAP and Ecosystem Components, Contaminant Pools, and
Pathways. Colored components indicate those investigated for contaminants as part of WACAP.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
1-13
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CHAPTER 1. INTRODUCTION
1 .5 Measurements and Contaminants
A wide variety of measurements could be used to provide information regarding the degree to
which airborne contaminants have become entrained in national park ecosystems. Similarly, a
large group of contaminants could be measured. One of the early WACAP design tasks was to
winnow the expansive list of possible measurements, as well as contaminants, down to a
manageable and affordable number. In doing this, we frequently referred back to the WACAP
goal and objectives to ensure that selected indicators collectively fulfilled broad, and in some
cases, multiple, purposes. Moreover, a secondary concern was to select indicators that would
compare to other similar ongoing and historic studies (e.g., the European Union EMERGE
program; Livingstone, 2005) regarding contaminant impacts in remote alpine and arctic
locations. Chapter 3 in this report identifies the contaminants of interest and discusses the
methods used in their analyses.
1.6 Timeline, Implementation, and Reporting
The 6-year WACAP began in fiscal year 2002 (October 2001 through September 2002) and
continued through fiscal year 2007. Year 1 was a pilot year devoted to design, organization,
funding for principal investigators, and methods development for the project. Some methods
development continued into fiscal year 2003. Fieldwork and associated laboratory work were
conducted during fiscal years 2003, 2004, and 2005. The final two years, 2006-2007, were
devoted to finishing analytical work, analyzing data, writing the final report, preparing and
publishing the final WACAP database, and publishing the results in the peer literature. Table 1-6
depicts the sequencing and timing of field collections acquired as part of WACAP.
Table 1-6. WACAP Timeline and Site Sampling Strategy.
Year
Activity
Yearl
(2002)
Year 2
(2003)
YearS
(2004)
Year 4
(2005)
YearS
(2006)
Year 6
(2007)
Design, organization, methods development, written research plan, peer review, quality
assurance plan
Annual snow
sampling
All 8 parks (14
sites)
All 8 parks (14
sites)
All 8 parks (14
sites)
Intensive Study Year
(fish, water, sediment)
SEKI, ROMO
(4 sites)
NOAT, DENA, and GAAR (4
sites)
OLYM, MORA, and GLAC (6
sites)
Vegetation/Air Sampling
Pilot study to choose target
vegetation & needle age
Lichen and conifer needle
sampling in 8 core parks (14
sites)
Lichen and conifer needle
sampling in 12 secondary parks
(61 sites); PASD installation in
all parks
Moose
Sampling
Moose from
DENA
Moose from
DENA
Data analyses, synthesis, publications, PASD retrieval in all parks
Final NPS report, final database
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CHAPTER 1. INTRODUCTION
1.7 Data Management and Quality Assurance/Quality Control
Data management occurred at several levels throughout the project. Laboratory analyses were
summarized, along with all QA/QC results at the batch level. This data management activity was
guided by the QAPP (Quality Assurance Project Plan available on the WACAP web site:
http://www2.nature.nps.gov/air/Studies/air_toxics/wacap.cfm. In this document, we use the term
"media" to describe the major environmental compartments we analyzed (e.g., snow, water,
fish). Batches of data were combined
for each medium/indicator and
forwarded to the appropriate principal
investigators. Copies of these data were
maintained by the analytical
laboratories and a copy was sent to the
WACAP coordination group at EPA
(Corvallis). The coordination group
confirmed laboratory QA/QC
procedures and worked with each
laboratory group to verify and validate
the data on a batch-by-batch basis. The
overall objective was to incorporate
these data, along with metadata derived
from a variety of sources (e.g.,
reconnaissance, field work, principal investigators, laboratories, park resources), into a working,
integrated database. Modeled and GIS-derived data were also entered into this database. The
database was combined into a final master database of the WACAP data in 2007. The final
database is intended as a final repository for the data resulting from the work conducted during
WACAP and will contain all data and associated QA/QC information. The final database will be
published as a peer reviewed EPA report and made available to the public via several
government website locations. It will also be stored permanently in NPS and USEPA searchable
data archival systems. The general website address for the NPS Data Store is
http://science.nature.nps.gov/nrdata/ and the general website address for the EPA location is
http ://www. epa.gov/nheerl/wacap/.
We participated in four levels of reporting for the WACAP data:
1. Professional papers (journal articles, dissertations, theses) generated by individual principal
investigators
2. Synthesis journal articles prepared by various combinations of principal investigators
3. Annual information summaries prepared as NPS brochures
4. Final report
In addition, principal investigators and other key WACAP personnel have been active in their
disciplinary societies by making oral presentations at annual national and international meetings.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
1-15
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CHAPTER 1. INTRODUCTION
1.8 WACAP Direction and Funding
WACAP was administered and funded primarily by the Air Resources Division (ARD) of the
NFS, directed by Ms. Christine Shaver, in Lakewood, Colorado. Funding was derived from a
variety of sources (base funding, competitive, and fee-demo) within ARD and NFS that varied
among years and funding cycles. In this context, interagency agreements between the NFS and
the US Geological Survey (USGS), the USEPA, and the US Forest Service (USFS) facilitated
funding. Supplemental funding with in-kind services was provided by these federal agencies, the
Oregon Cooperative Fish and Wildlife Research Unit and the Center for Fish Disease Research,
OSU. NFS funding for university participants was accomplished through the NFS Cooperative
Ecosystem Studies Unit (CESU) Cooperative Agreements process. In addition, several principal
investigators have sought and received funding from a variety of sources external to the NFS to
supplement funding they receive from ARD/NPS. These funds have been targeted to support
graduate students, supply research equipment, and provide supplemental technical support. The
USGS has contributed additional funding for the snow contaminants work. The success of
WACAP depended upon continued funding from the NFS and its collaborators at annual levels
sufficient to support the core WACAP efforts described in the research plan (USEPA, 2003).
Funding was forthcoming and WACAP proceeded to completion as planned.
1.9 Organization of this Report
This final WACAP interpretive report is organized into six major chapters. An overview of each
chapter and the name of the chapter organizer follow.
Chapter 1. Introduction (Dixon Landers)
Chapter 1 (this chapter) provides the background, goals, objectives, approach, and design
considerations for WACAP.
Chapter 2. Park Summaries (Dixon Landers)
The park-by-park summaries provide a quick but in-depth graphical overview of the key results
from the two sites within each core park and show how key variables associated with these sites
compare with each other and among results from all other parks. The two-page graphical
summary for each park is followed by a one-page written summary that identifies key findings
for the sites and the specific park, along with major differences and similarities among the
various measurements and the group of WACAP parks as a whole.
Chapter 3. Contaminants Studied and Methods Used (Staci Simonich)
In this chapter, all contaminants are identified and discussed regarding their occurrence in the
environment and the methods used to sample, extract, and quantify them in the various media, or
components, of the ecosystems sampled.
Chapter 4. Contaminant Distribution (Dan Jaffe)
This chapter interprets results of the WACAP effort for all components spatially, vertically, and
temporally. Media-specific spatial results are evaluated across the geographic area encompassing
the entire project. Vertical evaluations are limited to vegetation results among both core and
secondary parks and among all contaminants. Snow and sediment analyses are used to evaluate
inter-annual variation (snow) and decennial resolution and trends (sediment).
1-16 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 1. INTRODUCTION
Chapter 5. Biological and Ecological Effects (Linda Geiser)
In this chapter, a variety of results that lend themselves to evaluation of the impacts and effects
of contaminants in various media are examined. For fish, accepted pathological and physio-
logical indicators of contaminant exposure are related to contaminant concentrations. Inferences
on reproductive and overall health are made based on these relationships.
Chapter 6. Conclusions and Recommendations
Chapter 6 contains recommendations to
the National Park Service that respond to
the question, "What did you learn in this
project that could help guide or focus
future work within the NFS on
contaminants in western U.S. parks?"
These recommendations are specific to
the original five project objectives. Also
included is a list of additional research
questions posed by WACAP scientists.
These questions define fertile areas for
future research into processes,
mechanisms, and ecological interactions
of contaminants in western ecosystems.
Volume II. Appendices
The appendices provide supplemental information about the WACAP findings.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
1-17
-------
CHAPTER 1. INTRODUCTION
1-18
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2
Park Summaries
Introduction
This chapter contains summary information for the eight core parks (pages 2-4 to 2-31) and the twelve
secondary parks (pages 2-34 to 2-45). Information for the core parks includes a one-page written
summary organized by media (air, snow, vegetation, fish, and sediment), and a two-page graphical
summary of the results. The key to these two-page graphical summaries is on pages 2-2 and 2-3. The
air and vegetation summary results from the secondary parks begin on page 2-32, with the key to the
one-page graphical summaries for these parks on page 2-33.
Core Parks
The park summaries that follow in this chapter have been prepared to provide the reader with an
overview of selected contaminant results for each core WACAP national park. The descriptions for
the Arctic parks, GAAR and NO AT, with one lake site each, have been combined into one summary.
These summaries contain a considerable amount of information, but do not represent all data and
information available for the parks. The two-page key for the core parks provides explicit detail
regarding each block of information the reader will encounter on the two-page graphic summaries and
is intended to guide the reader through the summaries. Summaries for all core parks are presented in
the same format. The summaries are designed so that the two lake sites within each park can easily be
compared and the relative position of these sites within the context of all WACAP core parks can also
be visualized. The reader is encouraged to consult the other chapters of this report for more detailed
information on the full range of WACAP results and their interpretation.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
2-1
-------
Key to Park Summaries: Site Characteristics
KEY
The Park Summaries that follow In this chapter have been prepared to provide the reader with a summary of selected
contaminant results for each core WACAP national park. A set of reduced summaries for the secondary parks that contain
only vegetation results follows the core park summaries, with a separate key. The Arctic parks GAAR and NOAT, with one
lake site each, have been combined Into one summary. These summaries contain a considerable amount of Information,
but do not represent all data and Information available for the parks. This two-page Key provides explicit detail regarding
each block of Information the reader will encounter on the two-page graphical summaries and Is Intended to guide the
reader through the summaries. Summaries for all parks are presented In the same format. The summaries are designed so
the two lake sites within each park can easily be compared and the relative position of these sites within the context of all
WACAP core parks can be visualized. The reader Is encouraged to consult the other chapters of this report for more
detailed Information on the full range of WACAP results and their Interpretation.
• Park and Lake Setting •
The purpose of the Park Summaries Is to
provide an explicit location In the
WACAP report where contaminant and
other key Information can be easily
compared between the two sites within
each core park and among all of the core
park sites.
In this figure, the boundary of the park Is
shown as well as the boundary of each
watershed. All this is overlain on a
shaded relief map to give the reader
some perspective of the topography
surrounding these locations.
Mills Lake
Location: 40.29N 105.84W
Elevation: 3029 m
In each of these site locations, the name of
each lake Is given with some of the very basic
Information that defines the location of the
site and other Important characteristics of the
lake. We have used metric units: m = meters,
ha = hectare.
A digital Image of the lake and Its watershed Is
provided to give the reader an appreciation of
the steepness of the landscape, geology, and
vegetative cover of the watershed.
A depth map (bathymetrlc map) of each lake
Is provided. The depth contours are shaded
with the deepest depth the darkest colors.
The depth Increments shown In the inset key
are In meters (m). One meter equals 3.28 feet.
• Atmospheric Transport •
This figure shows calculated "back
trajectory" clusters derived from
thousands of computer simula-
tions that represent daily air mass
movements over an eight-year
time period closely associated with
the WACAP sampling period.
This figure shows the seasonally and
precipitation of the six, one-day clusters
shown to the left. The top of each bar
graph represents the percent of trajecto-
ries (or days from the eight-year period)
that are in the given clusters. The different
colors on the bars are the seasonal contri-
bution of these days and the blue circles
are the percent of total precipitation for
which each cluster is responsible.
• Physical and Chemical Characteristics •
TO,! I I
space displays physical in
mical characteristic
:icsofthe ake site
This
depicted above. The chemical characteristics are from measurements on
epilimnetic (i.e., near surface) water samples from the lakes. The value for
each site or lake is shown as a colored circle. The yellow bar graph behind
dots represents the total range for the specific variable among all core
.CAP parks. The line in the bar represents the median value among all
ks. Note that each group has its own scale and in several instances the
scale is broken in order to represent the range of all sites. This approach
allows comparisons between sites in a park and permits comparisons
mo
the i
WAC
park
all core WACAP parks
JUT I HI I By I inii
2-2
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/airjtoxics/wacap.cfm
-------
Key to Park Summaries: Contaminant Summaries
• Snow Contaminant Fluxes •
• Whole Fish Contaminant Concentrations •
This figure shows the fluxes of the most
prevalent semi-volatile organic compounds*
(SOC) and five metals present in the annual
snow pack samples taken over the three
years of WACAP sampling. The different
sites sampled in the park are represented
by the colored dots. The three sample
years are indicated by the placement of
the dots in the width of the yellow bars:
Spring 2003 is on the left edge, Spring
2004 is in the middle, and Spring 2005 is
on the right edge. The yellow bar in back
of the dots represent the total range of
snow contaminant fluxes among all core
WACAP parks and the line on each bar
represents the median value for each
contaminant. Note the vertical axis is in
log units that range from 0.01 to 1,000,001
ng/m2/yr.
Whole fish concentrations in ng/g wet weight of the most preva-
lent semi-volatile organic compounds* (SOC) and five metals are
shown in this figure. The blue bars for contaminant concentra-
tions show the maximum, minimum, and median (horizontal
line) values of the five to ten fish analyzed from each lake. The
yellow bar behind the fish data represents the total range for
these contaminants among all core parks and the line in each
bar represents the median value for all parks. Note the vertical
axis is in log units that range from 0.0001 to 100,000 ng/g.
fd I— frl Mi—i II. Hi—i RH
on tarn i nan t health thresholds cited in the literature are shown
>n this graph for selected SOCs and mercury for humans and
piscivorous mammals and birds. Human thresholds for SOCs
(discussed in Section 5.4.3) are based on consumption levels for
recreational and subsistence fishers. The human threshold for
mercury (discussed in Section 5.4.1) is based on a consumption
rate similar to the values for recreational fishers, and was
converted from a fillet to whole-fish basis. The piscivorous
mammal threshold (discussed in Section 5.4.2) is an average of
the thresholds for otter and mink (Lazorchak et al., 2003).
*SOC groupings by compound class are listed in Table 4-1.
• Vegetation Contaminant Concentrations • • Sediment Organic Contaminant Fluxes •
The concentrations of the most prevalent
semi-volatile organic compounds* (SOC) and
total mercury in lichen and two-year old
conifer needles are shown for core WACAP
sites in this figure. Conifers were sampled
along an elevation gradient shown by the
colored dots on the right, above each
contaminant name. The left-side shows the
median value for a bulk lichen sample taken
near the lake site. Lichens differ from conifer
needles in that they cannot be aged and
usually are expected to be more than two
years old. The yellow bars behind the dots
show the total range of contaminants among
all core WACAP parks for the respective
plants and the line in the bars represents the
median value. Note these values are on a log
scale - spanning a very broad range of
concentrations from 0.001 to 1,000,000 ng/g
(lipid weight for SOCs, dry weight for mercury).
1
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ACAP allocated appro
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Sediment Contaminant Fluxes
These figures are sediment profiles for each site. The top of the graph represents the surface of the sediment core;
the dates on the vertical axis are derived from 210Pb dating. Each point on a graph represents results of an analysis
of a sediment slice having an average date represented by the circle.
Spheroidal Carbonaceous Particles (SCP) are
microscopic "fly ash" materials formed only by high
temperature combustion associated with fossil fuel
(coal and oil) combustion. They are expressed as
number (no.) per unit area per year and are excellent
indicators of local or regional sources of human in-
dustrial activities. Total Organic Carbon, in mass per
unit area peryear, is a component of the sedimentrecord
derived from in-lake photosynthesis production or
watershed sources and typically decreases with depth
as a result of biogenic processes in the sediment.
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metals in lake sediments for each of the two
re shown here. The units are expressed as
Enrichment from historical (pre-industrial)
>und values near -1880, The results have
Iormalized" to titanium, which removes much
noise in the profiles related to watershed
>es (e.g., weathering, avalanches). These
show the recent history of metal deposition
lake system with respect to background.
www.nature.nps.gov/air/Studies/air_toxics/wacap.cfm
Western Airborne Contaminants Assessment Project
2-3
-------
CHAPTER 2. PARK SUMMARIES
Noatak National Preserve
and Gates of the Arctic National Park and Preserve
2-4
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2. PARK SUMMARIES
Summary: Noatak National Preserve
and Gates of the Arctic National Park and Preserve
Burial and Matcharak lakes both have small watersheds, contributing to long hydraulic residence
times. Burial Lake's surface area and volume are considerably smaller, but it had the highest total
phosphorus of all WACAP lakes. Both lakes had fairly high dissolved organic carbon, an important
factor in mercury methylation in lake systems. Acid neutralizing capacity of both lakes was high.
Air
The primary SOCs detected in air were HCB and a-HCH, both historic-use pesticides known to be
distributed by cold fractionation. Low concentrations of endosulfans chlordanes, g-HCH, and PAHs
were also detected.
Snow
Mercury flux to the snowpack at Burial and Matcharak lakes was low compared to that at the other
parks. SOCs varied considerably among collection sites and inter-annually. Compared to values at the
other parks, SOC flux was low for dacthal and chlorpyrifos and mid to high for endosulfans and
a-HCH.
Vegetation
No conifers were present at these Arctic sites, so we collected only lichens. Here we observed the
lowest concentrations of SOCs, nutrients, and toxic metals, including Hg, among the parks. Concen-
trations approached detection limits for many SOCs. However, we detected dacthal, endosulfans,
HCB, a-HCH, PCB153, and the PAHs retene, CHR/TRI, and FLA. Compared to values at other parks,
concentrations of many rare and trace elements were relatively high at Matcharak Lake. High mineral
content in regional lithology is the likely source.
Fish
Numerous parasites (worms) were found in the overall normal lake trout from both lakes. Fish
analyzed were the oldest in WACAP, with maximum ages of 33 and 41 years for fish analyzed for
SOC and metals, respectively. Spleen macrophage aggregates were positively related to mercury in
fish less than 15 years of age from Burial Lake. Concentrations of historic-use SOCs in fish were
generally mid-range compared with those at all other sites, whereas current-use SOCs were some of
the lowest measured in fish. The median dieldrin concentration in Burial Lake, as well as dieldrin
concentrations in some individual Matcharak Lake fish, exceeded contaminant health thresholds for
subsistence fishers. Mercury concentrations were high, indicating high mercury methylation and
bioaccumulation in NO AT and GAAR. Mercury concentrations exceeded thresholds for wildlife
health, and the median mercury concentration in Burial Lake and in some fish in Matcharak lake
exceeded the human contaminant health threshold.
Sediment
Many of the SOCs were below detection limits in the sediment profiles for both lakes. In addition,
SCPs were not present. Mercury percent enrichment profiles were generally very low, but showed
similar increasing trends from about 1875 in each lake. This pattern reflects the general increase in the
global background of Hg in the atmosphere caused by human activities, largely coal burning and
smelting.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 2-5
-------
Noatak National Preserve and
Gates of the Arctic National Park and Preserve: Site Characteristics
NOAT, GAAR
Burial Lake
Location: 68.43N 159.18W
Elevation: 429.8m
Maximum Depth: 24A m
Surface Area: 65.5 ha
Watershed Area: 264.9 ha
Matcharak Lake
Location: 67.75N 156.21W
Elevation: 502.3 m
Maximum Depth: 20.4 m
Surface Area: 300.7 ha
Watershed Area: 2388.3 ha
• Atmospheric Transport •
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• Physical and Chemical Characteristics •
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Noatak National Preserve and
Gates of the Arctic National Park and Preserve: Contaminant Summaries
• Snow Contaminant Fluxes •
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www. nature, nps.go v/air/Studies/air_toxics/wacap. cfm
Western Airborne Contaminants Assessment Project
2-7
-------
CHAPTER 2. PARK SUMMARIES
Denali National Park and Preserve
2-8
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2. PARK SUMMARIES
Summary: Denali National Park and Preserve
Wonder Lake and McLeod Lake were very different from one another in most physical characteristics, as
well as in many chemical parameters. Wonder Lake is a deep, large lake with high pH, specific conduc-
tance, acid neutralizing capacity, and sulfate concentration. McLeod Lake, by contrast, has very low
specific conductance and acid neutralizing capacity. Both are characterized by fairly small watersheds.
Air
Similar to SOCs at sites in the Arctic, the primary SOCs detected in air were HCB and a-HCH, both
historic-use pesticides. In addition, low concentrations of endosulfans, chlordanes, g-HCH, and PAHs
were detected.
Snow
Contaminant deposition fluxes in snow for DENA were among the lowest in all the parks, with low
concentrations and shallow snowpacks. Among the DENA snowpack samples, the Kahiltna site had the
highest deposition fluxes of most contaminants. Concentrations were similar to those in the other samples
in DENA, but greater snow water equivalent at this site caused contaminant fluxes to be higher than those
measured at the lower elevation sites. This pattern is typical in mountains and other environments where
large precipitation gradients are present. These results demonstrate that contaminant fluxes measured in
snowpack at a single site might not be representative of an entire park.
Vegetation
After NO AT and GAAR, DENA had the lowest concentrations of SOCs, nutrients, metals, and mercury in
vegetation among the parks. Concentrations were low for agricultural chemicals and PCBs, but higher for
PAHs. The pesticides detected were HCB, endosulfans, a-HCH, and dacthal, and all increased with
elevation. The dominant PAHs were retene and CHR/TRI, possibly attributable to wildfire, and decreased
with increasing elevation.
Fish
Fish historic-use SOC concentrations were in the mid to high range among parks for selected compounds
and among the lowest measured for most current-use SOCs. Median dieldrin concentrations in Wonder
Lake fish and in some individual fish in McLeod Lake exceeded contaminant health thresholds for
subsistence fishers. Median mercury concentrations in both lakes exceeded contaminant health thresholds
for piscivorous birds (kingfishers), and Wonder Lake also exceeded contaminant health thresholds for
mammals (otter and mink). Spleen macrophage aggregates were significantly higher in Wonder Lake fish
than those in lake trout from NO AT and GAAR. The reasons for this finding are unknown. Macrophage
aggregates were positively related to mercury concentrations in Wonder Lake, a pattern that was observed
for most of the lakes. Very few fish were available from McLeod Lake, despite two sampling efforts (2004
and 2005). All fish appeared reproductively normal.
Sediment
Sediment fluxes of most of the SOCs found in other lakes were below detection in the DENA sediment
profiles. PCBs were present, but at low concentrations—about the same order of magnitude as in the other
Alaska lake sediments. Wonder Lake showed distinct and similar percent enrichment increases from at
least 1920 to the surface for both mercury and lead, probably as a result of increasing global background
concentrations. McLeod Lake sediments did not show a similar trend. No SCPs were found in either
sediment profile.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 2-9
-------
Denali National Park and Preserve: Site Characteristics
McLeodLake
Watershed
Wonder Lake
Location: 63.48N 150.88W
Elevation: 605.0 m
Maximum Depth: 70.0 m
Surface Area: 265.6 ha
Watershed Area: 3212.4 ha
McLeod Lake
Location: 63.38N 151.07W
Elevation: 563.9 m
Maximum Depth: 13.5 m
Surface Area: 35.9 ha
Watershed Area: 236.8 ha
One-Day Cl
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Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxicsAwacap.cfm
-------
Denali National Park and Preserve: Contaminant Summaries
• Snow Contaminant Fluxes •
• Whole Fish Contaminant Concentrations •
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www.nature.nps.gov/air/Studies/air_toxics/wacap.cfm
Western Airborne Contaminants Assessment Project
2-11
-------
CHAPTER 2. PARK SUMMARIES
Glacier National Park
2-12
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2. PARK SUMMARIES
Summary: Glacier National Park
Oldman and Snyder lakes share many physical characteristics. However, Oldman Lake has a much greater volume
and greater maximum depth, as well as greater specific conductance, pH, and acid neutralizing capacity. Snyder
Lake is more productive, with higher total phosphorus, nitrogen, and chlorophyll-a. At GLAC, air, vegetation, and
snow had among the highest concentrations for current-use pesticides, compared with these media at the other parks.
The source of these compounds probably was regional agriculture within a few hundred kilometers of the park.
Air
Compared to measurements at the other parks, high concentrations of SOCs detected in air include PAHs, dacthal,
endosulfans, HCB, a-HCH, and g-HCH. Low concentrations of chlordanes and PCBs were also detected. Concentra-
tions at Oldman Lake, east of the Continental Divide, were higher than those at Snyder Lake, west of the Continental
Divide.
Snow
Snow water equivalents, contaminant concentrations (except PAHs), and contaminant flux to the snowpack in
GLAC were similar to those at the other parks. For PAHs, the concentrations and fluxes at Snyder Lake were
substantially higher than those at Oldman Lake. Mercury flux to the snowpack was near average among parks, but
fish concentrations of mercury were below average, indicating low rates of mercury methylation and bioaccumula-
tion, similar to rates at ROMO. SOC concentrations in snow varied considerably among the sites sampled. However,
within the same year, the range for all contaminants in GLAC was typically within an order of magnitude. PAH
concentrations in snow at Snyder Lake were always higher than at the other sites, and among the highest at all parks.
Vegetation
Numbers and concentrations of PAHs detected were highest at GLAC than at other parks. Proximity to an aluminum
smelter suggests a local source of PAHs contributing to the high concentrations. Other SOCs (endosulfans, dacthal,
DDTs, g-HCH, a-HCH, HCB, triallate, chlorpyrifos, and PCBs) were in the mid to upper ranges compared to those
at other parks. Dacthal, endosulfans, HCB, a-HCH, chlorpyrifos, DDTs, PCBs, and PAHs were higher on the west
side of the park, attributable to precipitation and temperature. Triallate, chlorpyrifos, and g-HCH were higher on the
east side of the park, probably because of agricultural intensity. Enhanced nitrogen and sulfur deposition related to
regional agricultural intensity is of concern. Many rare but not highly toxic elements were higher in lichen at GLAC
than in lichen at other parks. Because forest productivity is high, pesticides scrubbed from the air by vegetation
probably contribute significant contaminant loads to the ecosystem via canopy through-fall and needle litter-fall.
Fish
Pesticide concentrations (dacthal, g-HCH, HCB, dieldrin, and chlordanes) in fish in Oldman Lake were higher than
those in Snyder Lake, possibly related to agricultural intensity. One fish from Oldman Lake exceeded contaminant
health thresholds for piscivorous birds (kingfishers) for chlordanes, and the median concentration of DDTs from
Oldman Lake exceeded the contaminant health thresholds for piscivorous birds. Fish in both lakes exceeded king-
fisher thresholds for Hg. Lake average dieldrin and p,p'-DDE fish concentrations in Oldman Lake exceeded
contaminant health thresholds for subsistence fishers. Dieldrin concentration in one fish from Oldman Lake
exceeded the contaminant health threshold for recreational fishers. Mercury increased with increasing age offish in
Snyder Lake. Kidney and/or spleen macrophage aggregates were significantly related to mercury and age at both
lakes. All fish appeared reproductively normal, but elevated concentrations of estrogen-responsive protein were
found in males from both lakes. One intersex male was found at Oldman Lake. These data suggest endocrine
disruption.
Sediment
SOC profiles are consistent with the first usage of these chemicals in the United States, but most have not decreased
since use ceased. Snyder Lake profiles generally show greater contaminant flux than Oldman Lake profiles. PAHs in
Snyder Lake indicate some decline in the recent sediments since approximately 1990. Lead, cadmium, and mercury
profiles increase from approximately 1875 and decrease beginning in the 1960s. These profiles suggest a common
historic source that might have been affected by reductions in emissions related to the Clean Air Act. This relation-
ship is supported by the pattern observed in SCPs.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 2-13
-------
Glacier National Park: Site Characteristics
Snyder Lake
Location: 48.62N 113.79W
Elevation: 1597.2m
Maximum Depth: 3.5 m
Surface Area: 2.6 ha
Watershed Area: 303.7 ha
¥ One-Day Clusters (Back-Trajectories)
Oldman Lake
Location: 48.50N 113.46W
Elevation: 2025.7 m
Maximum Depth: 17.0 m
Surface Area: 18.2 ha
Watershed Area: 230.3 ha
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2-14
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxicsAwacap.cfm
-------
Glacier National Park: Contaminant Summaries
• Snow Contaminant Fluxes •
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www.nature.nps.gov/air/Studies/air_toxics/wacap.cfm
Western Airborne Contaminants Assessment Project
2-15
-------
CHAPTER 2. PARK SUMMARIES
Olympic National Park
2-16
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2. PARK SUMMARIES
Summary: Olympic National Park
The two OLYM lakes, Hoh and PJ, were similar in many ways, both physically and chemically. However,
PJ Lake was clearly more productive, with higher total phosphorus, chlorophyll-a, pH, and specific con-
ductance. PJ Lake had smaller mean and maximum depths and was frequently affected by avalanches that
brought trees and other debris into the lake.
Air
The primary SOCs detected in air were endosulfans, HCB, and a-HCH. Low concentrations of PAHs,
PCBs, g-HCH, trifluralin, dacthal, and chlordanes were also detected. SOC concentrations at Hoh Lake on
the west side of the park and PJ Lake on the east side were nearly identical.
Snow
Unusually warm conditions with heavy mid-winter rains occurred during the study period (2002-2005).
Because mid-winter rain or snowmelt can wash contaminants out of the snow, and ancillary data indicated
substantial loss of water from the snowpack prior to spring sampling in 2003 and 2005, snowpack samples
were collected in 2004 only. Two sites near PJ Lake had fairly high mercury fluxes in the 2004 snowpack,
whereas mercury deposition flux in the Hoh Lake snowpack was somewhat less. These results were
surprising, given that there are few known local or regional upwind sources. One possible explanation is
that deposition from regional sources to the east can reach OLYM on easterly airflows.
Vegetation
Like those for MORA, SOC and Hg concentrations in vegetation were at mid to upper ranges compared to
concentrations at other parks. PAHs were the dominant SOCs detected. Other SOCs were endosulfans, a-
HCH, HCBs, and dacthal, and concentrations of these SOCs varied substantially. We observed low
concentrations of chlorpyrifos, trifluralin, and PCBs. Nutrients and other metals in vegetation were within
expected ranges. Because forest productivity is high, pesticides scrubbed from the air by the vegetation
probably contribute significant contaminant loads to the ecosystem via canopy through-fall and needle
litter-fall.
Fish
Concentrations of SOCs in OLYM were generally among the lowest for dieldrin, mirex, and chlordanes,
and average for other pesticides. Fish mercury concentrations were among the highest of all parks,
exceeding contaminant health thresholds for piscivorous mammals (otter, mink) and birds (kingfishers),
and some fish from both lakes exceeded the human contaminant health threshold. Mercury and
macrophage aggregates increased with increasing age offish in both lakes. Spleen and kidney macrophage
aggregates were also positively related to mercury in both lakes. All fish appeared reproductively normal.
Sediment
Sediment profiles for SOCs in both lakes were generally below detection limits (except for PAH and
PCB). Mercury, cadmium, and lead show increasing percent enrichment toward the surface (present time)
beginning in the late 1800s, and stabilize at the surface at fairly high percent enrichment values. This
relationship suggests a possible common source. SCPs showed a historic peak in both lakes around 1950
and generally decreased toward the surface.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 2-17
-------
Olympic National Park: Site Characteristics
PJ Lake
Location: 47.95N 123.42W
Elevation: 1383.8m
Maximum Depth: 6.4 m
Surface Area: 0.8 ha
Watershed Area: 56.2 ha
On
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Location: 47.90N 123.79W
Elevation: 1379.2m
Maximum Depth: 14.9 m
Surface Area: 7.7 ha
Watershed Area: 43.9 ha
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• Physical and Chemical Characteristics •
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Olympic National Park: Contaminant Summaries
• Snow Contaminant Fluxes •
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www.nature.nps.gov/air/Studies/air_toxicsA/vacap.cfm
Western Airborne Contaminants Assessment Project
2-19
-------
CHAPTER 2. PARK SUMMARIES
Mount Rainier National Park
2-20
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2. PARK SUMMARIES
Summary: Mount Rainier National Park
Lake sites in MORA are very closely matched in both physical and chemical aspects. Golden Lake
and LP19 are typical small sub-alpine lakes with low productivity, low conductivity, low nutrients,
and small watersheds.
Air
The primary SOCs detected in air were the endosulfans, HCB, and a-HCH. Low concentrations of
g-HCH, trifiuralin, dacthal, and chlordanes were also observed.
Snow
Average winter precipitation rates at MORA are the highest among the parks, so contamination fluxes
are moderate to high, even though snow concentrations are mid-range for the parks. Contaminant
fluxes were fairly low in 2005, reflecting shallow snow accumulation and low snow water equivalent
that year.
Vegetation
SOC and Hg concentrations in vegetation were at or well above the concentrations observed at the
other parks. Dominant SOCs were PAHs, endosulfans, a-HCH, HCB, and dacthal. Detectable but low
concentrations of chlorpyrifos, dieldrin, DDTs, and PCBs were also observed. Chlorpyrifos, dacthal,
endosulfans, HCBs, HCHs, chlordanes, DDTs, and PCBs increased with elevation. PAHs, dominated
by CHR/TRI, PHE, and retene, decreased with increasing elevation. Nutrients and metals were within
expected ranges. Because forest productivity is high, pesticides scrubbed from the air by vegetation
probably contribute significant contaminant loads to the ecosystem via canopy through-fall and needle
litter-fall.
Fish
Contaminant concentrations in fish were generally mid-range, except for PBDEs in Golden Lake fish,
which were the highest among all fish at all lakes. The median dieldrin concentration offish in Golden
Lake and some individual fish in LP19 exceeded contaminant health thresholds for subsistence fishers.
Mercury concentrations in all fish from both lakes exceeded contaminant health thresholds for birds
(kingfishers), and some fish exceeded thresholds for piscivorous mammals (otter, mink). Mercury
concentrations in some fish from LP19 exceeded contaminant health thresholds for humans. These
mercury values indicate favorable conditions for methylation and subsequent bioaccumulation of
mercury. Mercury and macrophage aggregates increased with increasing age offish in LP19, but not in
Golden Lake. Spleen and kidney macrophage aggregates were positively related to mercury at LP19,
but only kidney macrophage aggregates were related to mercury at Golden Lake. All fish appeared
normal reproductively, although one male from Golden Lake had elevated concentrations of estrogen-
responsive protein in the blood.
Sediment
Many of the sediment SOCs were below detection limits. When they were present, the two lake
profiles showed some similarities. PAHs and PCBs showed the highest sediment fluxes. Mercury and
lead had both increased since about 1900, suggesting a common source. Mercury showed a rapid
percent enrichment near the surface (present time) of both lakes. The source of this increase is unknown,
but global warming, increased global background, and/or trans-Pacific sources could be responsible.
SCP profiles declined towards the surface, and did not correspond to changes in metal profiles.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 2-21
-------
Mount Rainier National Park: Site Characteristics
Golden Lake
Location: 46.89N 121.90W
Elevation: 1368.6m
Maximum Depth: 23.9 m
Surface Area: 6.6 ha
Watershed Area: 106.1 ha
Depth
(meters)
LP19
Location: 46.82N 121.89W
Elevation: 1371.6m
Maximum Depth: 12.1 m
Surface Area: 1.8 ha
Watershed Area: 44.9 ha
• Atmospheric Transport •
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2-22
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/airjtoxics/wacap.cfm
-------
Mount Rainier National Park: Contaminant Summaries
• Snow Contaminant Fluxes •
• Whole Fish Contaminant Concentrations •
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www.nature.nps.gov/air/Studies/air_toxics/wacap.cfm
Western Airborne Contaminants Assessment Project
2-23
-------
CHAPTER 2. PARK SUMMARIES
Rocky Mountain National Park
2-24
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2. PARK SUMMARIES
Summary: Rocky Mountain National Park
Mills and Lone Pine lakes are characterized by low specific conductance and acid neutralizing capacity, typical
of many sub-alpine lakes. Compared to lake surface area, their watershed areas are among the largest of all the
lakes. Total nitrogen is fairly high at Mills Lake. For many SOCs, snow and sediment fluxes were higher at
Mills Lake, on the eastern slope of the Continental Divide, where there is greater potential for transport from
local and regional agricultural sources than for Lone Pine Lake on the western slope.
Air
There were four air monitors on the west side and one monitor on the east side of the Continental Divide. East-
side concentrations for those SOCs detected by this method were similar to west-side concentrations, indicating
no obvious east-west differences. The primary SOCs detected in air were PAHs, dacthal, endosulfans, HCB,
a-HCH, and g-HCH. Low concentrations of the PCBs, chlordanes, and trifluralin were also detected.
Snow
Snowpack deposition fluxes of endosulfans and dacthal were high in ROMO compared to fluxes at most other
parks, and fish concentrations of these compounds were high as well. Mercury deposition fluxes in the snow-
pack were high relative to those at other parks; however, fish mercury was low, indicating low rates of mercury
methylation and bioaccumulation. Contaminant fluxes measured in the snowpack do not account for atmos-
pheric deposition during summer rains. However, summer precipitation is higher in ROMO than in most other
parks, and rainfall concentrations of many contaminants are also high, indicating that a larger significant source
of contaminant deposition was unmeasured. Deposition fluxes of dieldrin in the snowpack were also consis-
tently higher at Mills Lake than at sites to the west, suggesting re-emission from contaminated soils to the east
(dieldrin was manufactured in Denver) and subsequent transport on upslope airflow.
Vegetation
Unlike concentrations in sediments and snow, SOC concentrations in vegetation were in the low to median
ranges compared to those at other parks and not different on east and west sides of the Continental Divide.
SOCs detected in vegetation were PAHs (mostly CHR/TRI, retene, PHE, and ANT), endosulfans, g-HCH,
a-HCH, dacthal, HCB, chlorpyrifos, DDTs, and PCBs. Lichen concentrations indicate enhanced nitrogen and
sulfur deposition; metals were within expected ranges for remote sites.
Fish
Mercury and macrophage aggregates increased with increasing age of fish in both lakes, although mercury was
fairly low. Spleen and kidney macrophage aggregates were also positively related to mercury in both lakes.
Endosulfans and dacthal were fairly high. Additional lakes (9 total) were sampled as part of a related NPS
study and elevated estrogen-responsive protein was found in males from four of the nine lakes. Poorly
developed testes and/or intersex male trout were also found in five of the nine lakes sampled. These data
suggest that endocrine and reproductive disruption is occurring in several park lakes. Dieldrin concentrations in
all fish exceeded contaminant health thresholds for subsistence fishers and some fish from both lakes exceeded
thresholds for recreational fishers. Mercury concentrations in some fish exceeded contaminant health thresholds
for piscivorous mammals (otter or mink) and/or birds (kingfishers) at both lakes.
Sediment
Lake sediment profiles indicate that fluxes of most current-use pesticides, historic-use pesticides, and urban
chemicals have steadily increased since their use in the USA began and no widespread decrease in flux or
enrichment has occurred. In Lone Pine Lake, lead, cadmium, and mercury show a similar historic increase in
the lake sediments beginning around 1875 that could be related to a common source, such as metal mining and
smelting. Mills Lake shows similarity in the profiles for these metals beginning later, around 1915, but the two
systems show similar mercury enrichment. All three metals have decreased in recent times.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 2-25
-------
Rocky Mountain National Park: Site Characteristics
Mills Lake
Location: 40.29N 105.64W
Elevation: 3029.7 m
Maximum Depth: 9.0 m
Surface Area: 6.1 ha
Watershed Area: 1208.9 ha
Lone Pine Lake
Location: 40.22N 105.73W
Elevation: 3017.5m
Maximum Depth: 9.7 m
Surface Area: 4.9 ha
Watershed Area: 1830.0 ha
• Atmospheric Transport •
•One-Day Clusters (Back-Trajectories)
c '•
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Seasonally and Precipitation
I by Trajectory
CH % Autumn
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2-26
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxicsAwacap.cfm
-------
Rocky Mountain National Park: Contaminant Summaries
• Snow Contaminant Fluxes •
• Whole Fish Contaminant Concentrations •
104-
10-1-
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D Current-Use Pesticides D Historic-Use Pesticides D Combustion By-products D Industrial Compounds D Metals D Current-Use Chemical
• Vegetation Contaminant Concentrations •
• Sediment Organic Contaminant Fluxes •
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0 100 0 100 200 300 0 100 200 300 0 100 200
Percent Enrichment
www. na fore. nps.gov/air/Studies/air_toxicsAwacap. cfm
Western Airborne Contaminants Assessment Project
2-27
-------
CHAPTER 2. PARK SUMMARIES
Sequoia and Kings Canyon National Parks
2-28
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 2. PARK SUMMARIES
Summary: Sequoia and Kings Canyon National Parks
Emerald and Pear lakes are similar physically and chemically, although Emerald Lake is a bit shallower and has
slightly higher total phosphorus and nitrogen. Compared to the other sites, they are among the most dilute,
poorly buffered (i.e., have low acid neutralizing capacity), and oligotrophic (low productivity) systems. At
SEKI, air, vegetation, and snow had among the highest concentrations for current-use pesticides, compared
with these media in the other parks. The source of these compounds could be regional agriculture within a few
hundred kilometers of the park.
Air
SOCs detected in air were trifluralin, dacthal, endosulfans, chlorpyrifos, and g-HCH, all of which are current-
use pesticides. In addition, HCB, a-HCH, dieldrin, PCBs, and PAHs were detected. Most SOC concentrations
in air ranked high relative to those in other parks and more SOCs were detected in SEKI than in other parks.
Snow
Atmospheric deposition in SEKI is dominated by deep snowpacks with high snow water equivalent. Concen-
trations of many current-use pesticides and historic-use pesticides were high, producing high deposition fluxes
in the snow. In contrast, with few local or regional sources of mercury emissions upwind, mercury concentra-
tions in the snow were generally low, producing only moderate fluxes of mercury deposition. Summers are
generally quite dry in SEKI, providing less opportunity for wet deposition of contaminants in rainfall than
wetter summers at parks in the Rocky Mountains.
Vegetation
SOCs, Hg, and nutrient concentrations in SEKI vegetation were in the median to highest ranges among the
parks, attributable partly to intensive regional agriculture. SOCs detected in vegetation were PAHs (mostly
retene, CHR/TRI, PHE, FLO, FLA, and PYR), endosulfans, dacthal, DDTs, chlorpyrifos, HCB, g-HCH,
dieldrin, a-HCH, and PCBs. Lichen concentrations indicate enhanced nitrogen and sulfur deposition.
Concentrations of endosulfan, dacthal, HCH, HCB, and chlorpyrifos in lichens increased with elevation.
Because forest productivity is high, pesticides scrubbed from the air by vegetation probably contribute
significant contaminant loads to the ecosystem via canopy through-fall and needle litter-fall.
Fish
Mercury and macrophage aggregates increased with increasing fish age in both lakes. Spleen and kidney
macrophage aggregates were positively related to mercury at Pear Lake, but only kidney macrophage aggre-
gates were so related at Emerald Lake. All fish appeared normal reproductively. Current-use SOC concentra-
tions in fish were among the highest measured. Lake average dieldrin and individual fish p,p'-DDE concentra-
tions in both lakes exceeded contaminant health thresholds for subsistence fishers; the dieldrin concentration in
one fish in Pear Lake exceeded the threshold for recreational fishers. In at least one fish from each lake,
contaminant health thresholds for mercury and DDTs were exceeded for one or more piscivores (otter, mink,
kingfishers). Two fish from Pear Lake exceeded the human contaminant health threshold for mercury.
Sediment
SOC flux profiles are very similar in both lakes, and SOCs appear after being registered for use in the USA.
DDTs and chlordanes decrease after being banned in the USA, but PCBs are still accumulating. Mercury began
to increase in both lakes in the late 1800s, and lead began to increase around 1900. Mercury profiles are similar
in both lakes, in that they tend to stabilize, noisily, at about 100% enrichment. Lead and cadmium profiles are
similar in Pear Lake, both peaking in the 1970s and decreasing toward the surface (present time). SCPs were
first detected in the late 1800s, but the patterns in both lakes are not closely associated with metal flux profiles,
suggesting that major high temperature combustion sources were not the primary historic source of metals to
the sediments.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 2-29
-------
Sequoia and Kings Canyon National Parks: Site Characteristics
SEKI
-TIB
TEakeWatefsHed
5 j ^SHip
AC/m/s Canyon portion of SEKI not shown.
Pear Lake
Location: 36.60N 118.67W
Elevation: 2907.8 m
Maximum Depth: 27.0 m
Surface Area: 7.3 ha
Watershed Area: 142.0 ha
Emerald Lake
Location: 36.58N 118.67W
Elevation: 2810.3m
Maximum Depth: 10.0 m
Surface Area: 2.5 ha
Watershed Area: 121.3 ha
One-Day Clusters (Back-Trajectories)
• Atmospheric Transport •
45
Seasonally and Precipitation
by Trajectory
Average annual precip-
itation during 8-year
period = 68 cm/yr.
• Physical and Chemical Characteristics •
3000-
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2000-
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2-30
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxicsAwacap.cfm
-------
Sequoia and Kings Canyon National Parks: Contaminant Summaries
• Snow Contaminant Fluxes •
• Whole Fish Contaminant Concentrations •
105-
104-
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D Current-Use Pesticides D Historic-Use Pesticides D Combustion By-products D Industrial Compounds D Metals D Current-Use Chemical
• Vegetation Contaminant Concentrations •
• Sediment Organic Contaminant Fluxes •
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. Spheroidal .
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Particles (x103)
50 100 150 200 0 5 10
no/m2/yr g/m2/yr
Focusing Factor-Corrected Flux
0 50 0 100 200 0 100 200 0
Percent Enrichment
www.nature.nps.gov/air/Studies/air_toxicsA/vacap.cfm
Western Airborne Contaminants Assessment Project
2-31
-------
CHAPTER 2. PARK SUMMARIES
Secondary Parks
The pages that follow have been prepared to provide the reader with a summary of contaminant results
for air and vegetation sampling in each secondary WACAP park. The word park, as used here,
encompasses federally managed lands, including national parks, monuments, preserves, and wilder-
ness. The objectives and design for sampling in the secondary parks are described this report in
Section 1.3, Park Selection, Section 3.4.3, Air, and Section 3.4.4, Vegetation.
The one-page key on page 2-33 provides explicit detail regarding each block of information the reader
will encounter. Summaries for all secondary parks are presented in the same format. The summaries
are designed so that the location and contaminant concentrations at the four to six sampling sites
within each park and across all parks can easily be visualized and compared. The reader is encouraged
to consult the other chapters of this report and the primary park summaries for more information on
the full range of WACAP results and their interpretation.
2-32
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
Key to Secondary Parks: Summary
Park
Loc,
Elev
Ave
Ave,
Air'.
Con
L/cft
KEY
The pages that follow have been prepared to provide the reader with a summary of contaminant
results for air and vegetation sampling In each secondary WACAP park. The word park, as used
here, encompasses federally managed lands, Including national parks, monuments, preserves,
and wilderness. This one-page key provides explicit detail regarding each block of Information
the reader will encounter. Summaries for all secondary parks are presented In the same format.
The summaries are designed so that the location and contaminant concentrations at the four to
six sampling sites within each park and across all parks can be easily visualized and compared.
The reader Is encouraged to consult the other chapters of this report and the primary park
summaries for more Information on the full range of WACAP results and their Interpretation.
GRSA4
Carbonate Iv
Location: 3'
Elevation: 3
Ave. Ann. T
Ave. Ann. P
Air Sample;
Conifer Pin
Lichen: Xan
Site Photos
A photograph of the environs at each site Is provided to give the
reader an appreciation of the vegetative cover, steepness of the
landscape, climate, and geology. The name of the sampling site
Is given with some very basic Information: a short verbal
description of the site location, latitude and longitude In
decimal degrees, average annual temperature and precipitation
estimated by the PRISM model, whether or not air was sampled
at the site, and the scientific names of the conifer and lichen
vegetation sampled at the site. Units follow the metric system:
m = meters, cm = centimeters, °C = degrees Centigrade. N, W =
north, and west.
Vegetation Summary Statements
This text block summarizes the results of the laboratory
analysis of vegetation samples and highlights the most
Important findings. These Include: the SOCs detected
and their concentrations In nanograms per gram conifer
needle llpld or lichen llpld (bdl indicates values below
detection limit); differences between concentrations
in needles versus lichens, if important; effects of
elevation on concentrations of SOCs in lichens; ranking
of SOC concentrations in vegetation relative to vegeta-
tion in other WACAP parks; concentrations of nitrogen
and sulfur (nutrients), mercury and other toxic metals in
lichens relative to known or expected background ranges;
and ecological implications of, or concerns indicated by,
the results.
US Map
This Inset shows the
location of the park
(yellow dot) In
western North
America relative to
other WACAP
secondary parks
(brown dots).
Park Relief Map
In this figure, the boundary of the park
and the location of the vegetation and
air sampling sites are overlaid on a
shaded relief map to give the reader
some perspective of the topography
surrounding the sites.
20 Kilometers
Air Summary Statements
This text block summarizes the results of the laboratory analysis
of the passive air sampling devices (PASDs) and highlights the
most important findings. These include the location of the
monitors in the parks, the SOCs detected and their concentra-
tions in picograms per gram XAD resin (dry weight), within-park
differences in SOC concentrations if multiple samplers were
deployed, and how park SOC concentrations ranked relative to
other WACAP parks.
Vegetation Contaminant Concentrations
The concentrations of the most prevalent semi-volatile organic
compounds (SOCs) in lichens and 2-year-old conifer needles are
shown for WACAP sites in the figure. Conifers and lichens were
sampled along an elevational gradient. Concentrations at each site are
represented by the shaded circles above each contaminant name. The
middle horizontal line within each background bar behind the circles
shows the median value for all WACAP sites across all parks; the top
and bottom horizontal edges of the background bars show the
maximum and minimum concentrations across all WACAP sites.
Brown and green bars indicate lichen and conifer needle concentra-
tions, respectively. These values are on a log scale - spanning a very
broad range of concentrations from 0.001 to 1,000,000 ng SOC per
gram of lipid in lichens or needles. When sample concentrations were
below detection limits, the circle representing the site was placed at
one-half the estimated detection limit and the circle is open. Circle
shading intensity darkens with increasing elevation. SOCs are
grouped by current-use pesticides (endosulfans, chlorpyrifos,
dacthal), historic-use pesticides (g-HCH, a-HCH, HCB), polycyclic
aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs).
Metals were not analyzed in vegetation samples at secondary parks.
www.nature.nps.gov/air/Studies/airjtoxicsAwacap.cfm
Western Airborne Contaminants Assessment Project
2-33
-------
Wrangell - St. Elias National Park and Preserve: Summary
WRST
WRST1
Kageets Pt at Icy Bay
Location: 60.05N 141.31W
Elevation: 7 m
Ave. Ann. Temp: 3.1 °C
Ave. Ann. Precip: 312 cm
Air Sampler: No
Conifer: Picea sitchensis
Lichen: Platismatia glauca,
Hypogymnia apinnata
WPSTJ
' '
*•
,
0 50 100 200 Kilometers
i i I i i i I
Air Summary
• The air sampler, at WRST3 in the Crystalline Hills, near
McCarthy, Alaska, had the lowest number of detected
SOCs among the 20 WACAP parks.
• Only PAHs (221 pg/g dry XAD, a mid-range level
compared with concentrations in other WACAP parks)
and low concentrations of g-HCH (18 pg/g dry XAD)
were detected.
105
104
1
2 S S I
idi| 6/6u) oos
EH Lichen *6oxes denote range
CH Conifer of values for all parks.
•8
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Pesticides
• WRST5
• WRST4
OWRST3
OWRST2
OWRST1
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° Below Detection Limit
O
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Industrial
Compounds
WRST2
Chintina and Copper R confluence
Location: 61.52N 144.40W
Elevation: 219 m
Ave. /Inn. Temp: -1.9°C
Ave. /Inn. Precip: 31 cm
Air Sampler: No
Conifer: Picea glauca
Lichen: None
WRST4
Bonanza Mine Trail 1020m
Location: 61 .SON 142.87W
Elevation: 1020 m
Ave. Ann. Temp: -2.2°C
Ave. Ann. Precip: 85 cm
Air Sampler: No
Conifer: Picea glauca
Lichen: None
Vegetation Summary
WRST3
Crystalline Hills Trail
Location: 61.39N 143.60W
Elevation: 648 m
Ave. Ann. Temp: -1.7°C
Ave. Ann. Precip: 62 cm
Air Sampler: Yes
Conifer: Picea glauca
Lichen: Hypogymnia physodes
WRST5
Bonanza Mine Trail 1421m
Location: 61 .SON 142.84W
Elevation: 1421 m
Ave. Ann. Temp: -2.7°C
Ave. Ann. Precip: 127 cm
Air Sampler: No
Conifer: Picea glauca
Lichen: Flavocetraria cucullata,
Cladina arbuscula
• Among the 20 WACAP parks, SOCs in vegetation from interior
WRST (sites 2-5) were at or below the median, or were not detected.
• Dominant SOCs were PAHs (bdl -130 ng/g lipid in conifers,
38-3660 in lichens), HCB (3-11 in conifers, 9-150 in lichens),
a-HCH (2-54), and g-HCH (1-36).
• Small amounts of PCBs (<5 ng/g lipid), chlorpyrifos (<1), dacthal,
and chlordanes (<7) were also detected.
• Highest concentrations of g-HCH and chlordanes in lichens and
conifer needles, and highest concentrations of endosulfans,
HCB, a-HCH, dacthal, PCBs, and PAHs in lichens were observed
at the high precipitation, marine site at Icy Bay (WRST1).
• Nitrogen concentrations in lichens were within background
ranges, indicating that nitrogen deposition is not elevated.
2-34
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxics/wacap.cfm
-------
Glacier Bay National Park: Summary
GLBA1
Beartrack Cove
Location: 58.60N 135.88W
Elevation: 8 m
Ave. Ann. Temp: 4°C
Ave. Ann. Precip: 261 cm
Air Sampler: Yes
Conifer: Picea sitchensis
Lichen: Platismatia glauca
GLBA2
Beartrack Mtn footslopes
Location: 58.61 N 135.88W
Elevation: 168 m
Ave. Ann. Temp: 4°C
Ave. Ann. Precip: 261 cm
Air Sampler: No
Conifer: Picea sitchensis
Lichen: Sphaerophorus globosus
GLBA3
Beartrack Mtn glacial trim line
Location: 58.61 N 135.87W
Elevation: 457 m
Ave. Ann. Temp: 4°C
Ave. Ann. Precip: 261 cm
Air Sampler: No
Conifer: Picea sitchensis
Lichen: Sphaerophorus globosus
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GLBA4
Beartrack Mtn treeline
Location: 58.61 N 1035.87W
Elevation: 625 m
Ave. Ann. Temp: 4°C
Ave. Ann. Precip: 261 cm
Air Sampler: No
Conifer: Picea sitchensis
Lichen: Alectoria sarmentosa
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Vegetation Summary
• The dominant SOCs detected in lichens were PAHs
(110-2780 ng/g lipid), endosulfans (21-115), HCB (17-84)
and a-HCH (20-41); the dominant SOCs detected in conifer
needles were g-HCH (1-53), HCB (7-9), and a-HCH (4-8).
• Concentrations of HCBs and a-HCH (HUPs) in lichens and
g-HCH (a CUP) in conifers ranked very high compared to
concentrations at other WACAP parks.
• Other SOCs detected in vegetation were low concentrations of
dacthal (<2.5), chlordanes (0.3-10), and PCBs (3-13).
• Although pesticide concentrations and compounds detected
essentially replicate STLE in both lichens and conifer
needles, with respect to PAHs, GLBA had a higher propor-
tion of 4-5 ring PAHs and more retene than STLE to the
south and WRST to the north, pointing to a local source.
• As at other parks, PAH concentrations decreased with
increasing elevation. Pesticides and PCBs that were
observed to increase with elevation in other parks did not
increase at GLBA. A possible explanation is that a very good
accumulator, Platismatia glauca, was collected at sea level
and the poorest accumulator, Alectoria sarmentosa, was
collected at the highest elevation.
• Lichen nitrogen concentrations were elevated at sea level,
but concentrations at higher elevations were within
species-specific background ranges expected for southeast-
ern Alaska and remote sites in the Pacific Northwest.
Air Summary
• The air sampler was near sea level at GLBA1,
Beartrack Cove.
• Low concentrations of CUPs trifluralin (1.4 pg/g
dry XAD) were detected.
• Overall, SOC concentrations in air at GLBA ranked
very low compared to those at other WACAP parks.
Vegetation Contaminant Concentrations
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Western Airborne Contaminants Assessment Project
2-35
-------
Katmai National Park and Preserve: Summary
KATM
KATM1
3 Forks Overlook Road 2 km
Location: 58.55N 155.78W
Elevation: 36 m
Ave. Ann. Temp: 2.2°C
Ave. Ann. Precip: 50 cm
Air Sampler: No
Conifer: Pinus glauca
Lichen: Hypogymnia physodes
KATM4
Dumpling Mtn Trail at 563 m
Location: 58.57N 155.84W
Elevation: 563 m
Ave. Ann. Temp: 1.4°C
Ave. Ann. Precip: 68 cm
Air Sampler: No
Conifer: Pinus glauca
Lichen: Flavocetraria cucullata
KATM2
Dumpling Mtn Trail at 183 m
Location: 58.57N 155.79W
Elevation: 213 m
Ave. Ann. Temp: 1.9°C
Ave. Ann. Precip: 54 cm
Air Sampler: No
Conifer: Pinus glauca
Lichen: Hypogymnia physodes
KATM5
Dumpling Mtn summit
Location: 58.58N 155.86W
Elevation: 724 m
Ave. Ann. Temp: 1.4°C
Ave. Ann. Precip: 68 cm
Air Sampler: No
Conifer: Pinus glauca
Lichen: Flavocetraria cucullata
KATM3
Dumpling Mtn Trail at 366 m
Location: 58.57N 155.80W
Elevation: 370 m
Ave. Ann. Temp: 1.9°C
Ave. Ann. Precip: 54 cm
Air Sampler: Yes
Conifer: Pinus glauca
Lichen: Hypogymnia physodes
KATM6
Mt. Katolinat
Location: 58.47N 155.49W
Elevation: 1112m
Ave. Ann. Temp: 0.1 °C
Ave. Ann. Precip: 83 cm
Air Sampler: No
Conifer: None
Lichen: Flavocetraria cucullata
Air Summary
• The air sampler was at KATM3.
• Concentrations of HUPs HCB (1260 pg/g dry XAD),
a-HCH (340), and CUP g-HCH (57) were among the
highest values recorded from WACAP parks.
• Endosulfans (61 pg/g dry XAD), trifluralin (1), and
chlordanes (14) were also detected, but concentrations
were low compared to those at other WACAP parks.
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Vegetation Summary
• Concentrations in vegetation of all SOCs except HCB were
at or below the median for WACAP parks; in general, KATM
was one of the least contaminated WACAP parks.
• Dominant SOCs in KATM vegetation were PAHs (bdl-583
ng/g lipid), endosulfans (<3 in conifers, 5-47 in lichens),
HCB (5-35), and a-HCH (3-13).
• Low concentrations of chlorpyrifos and dacthal (<1 ng/g lipid),
g-HCH, chlordanes (<5), and PCBs (<4) were also detected.
• Small increases in spruce needle concentrations of endo-
sulfans, dacthal, a-HCH, and HCB were observed with
increasing elevation from 36 to 724 m; lichens also showed
this trend when the tundra lichen, Flavocetraria cucullata,
collected at the top three elevations, and the epiphyte,
Hypogymnia physodes, a better accumulator, collected at
the lowest three elevations, were considered separately.
• Lichen nitrogen concentrations were within background
ranges, indicating that nitrogen deposition is not elevated.
2-36
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxicsAwacap.cfm
-------
Stikine-LeConte Wilderness, Tongass National Forest: Summary
STLE1
Bussey Creek outlet in Icy Bay
Location: 56.79N 132.51W
Elevation: 1 m
Ave. Ann. Temp: 4.5°C
Ave. Ann. Precip: 318 cm
Air Sampler: Yes
Conifer: Picea sitchensis
Lichen: Platismatia glauca,
Alectoria sarmentosa
STLE2
Bussey Creek ridge line
Location: 56.80N 132.53W
Elevation: 254 m
Ave. Ann. Temp: 3.6°C
Ave. Ann. Precip: 378 cm
Air Sampler: Yes
Conifer: Picea sitchensis
Lichen: Platismatia glauca,
Lobaria oregana
STLE4
0.4 km NW of Bussey Lake
Location: 56.83N 132.57W
Elevation: 815 m
Ave. Ann. Temp: 2.7°C
Ave. Ann. Precip: 488 cm
Air Sampler: Yes
Conifer: Picea sitchensis
Lichen: Platismatia glauca,
Alecton'a sarmentosa
STLE5
Thunder Mtn summit
Location: 56.82N 132.61 W
Elevation: 1064 m
Ave. Ann. Temp: 3.6°C
Ave. Ann. Precip: 431 cm
Air Sampler: No
Conifer: Picea sitchensis
Lichen: Platismatia glauca,
Cladina arbuscula
STLE3
Muskeg bench over Bussey Creek
Location: 56.81 N 132.54W
Elevation: 567 m
Ave. Ann. Temp: 3.6°C
Ave. Ann. Precip: 378 cm
Air Sampler: No
Conifer: Picea sitchensis
Lichen: Alectoria sarmentosa
Vegetation Summary
• Among the 20 parks, vegetation samples from STLE were
at or below the median for CUPs, PCBs, and PAHs, and at
or above the median for HUPs; this pattern was also
observed at other high precipitation sites along coastal
southeastern Alaska (i.e., WRST1, GLBAall).
• Dominant SOCs were PAHs (bdl-2162 ng/g lipid), endosul-
fans (<3 in conifers, 5-272 in lichens), a-HCH (3-110), HCB
(5-100), and g-HCH (1-42); low concentrations of chlorpyri-
fos (<1), dacthal (0.2-14), and chlordanes (0.3-13) were
also detected.
• Significant increases in pesticide concentrations and
decreases in PAH concentrations in lichens with increasing
elevation were discernible when the poorest (Alectoria
sarmentosa) and best (Platismatia glauca) accumulators,
sampled at alternating sites, were considered separately.
• Because needle productivity (kg/ha/yr) is high, the ecologi-
cal effects of cumulative SOCs contributed by needle
litter-fall are a potential concern.
• Nitrogen concentrations in lichens were within background
ranges, indicating that nitrogen deposition is not elevated.
,
STLE
Air Summary
• Air was sampled at STLE1, 2, and 4.
• SOCs that increased with elevation in vegetation also
increased in air. These were current-use endosulfans (17-
96 pg/g dry XAD) and g-HCH (4-38), and the HUPs HCB
(490-1150), a-HCH (100-390), and chlordanes (10-21).
• Other SOCs were not detected or were near instrument
detection limits (trifluralin and dacthal, <2 pg/g dry XAD).
• All SOC concentrations ranked low at the lowest eleva-
tion; concentrations at the highest elevation ranked
moderate (g-HCH, chlordanes) to high (HCB, a-HCH)
relative to concentrations at other WACAP parks.
Vegetation Contaminant Concentrations
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www. na fore. nps.gov/air/Studies/air_toxics/wacap. cfm
Western Airborne Contaminants Assessment Project
2-37
-------
North Cascades National Park: Summary
NOCA
NOCA1
Thorton Creek
Location: 48.65N 121.31W
Elevation: 198 m
Ave. /Inn. Temp: 8.6°C
/Ive. /Inn. Precip: 198 cm
Air Sampler: No
Conifer: Pseudotsuga menziesii
Lichen: Alectoria sarmentosa
10 20
40 Kilometers
Air Summary
• Air was sampled at NOCA5.
• SOCs detected, in order by decreasing concentration, were
PAHs (1521 pg/g dry XAD), HCB (910), endosulfans (492),
a-HCH (200), heptachlor (150), chlorpyrifos (110),
dacthal (91), chlordanes (63), g-HCH (32), and
trifluralin (13); PCBs, dieldrin and DDTs were not detected.
• NOCA was the only park in which heptachlor was
detected; concentrations of PAHs, CUPs chlorpyrifos,
trifluralin, and endosulfans, and HUPs HCB, a-HCH,
and chlordanes ranked well above the medians for the
20 WACAP parks.
105
104
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Lower S slope Mt Triumph
Location: 48.64N 121.34W
Elevation: 614 m
Ave. Ann. Temp: 8.6°C
Ave. Ann. Precip: 196 cm
Air Sampler: No
Conifer: Tsuga heterophylla
Lichen: Platismatia glauca
NOCA4
Upper SE slope Mt Triumph
Location: 48.67N 121.32W
Elevation: 1228 m
Ave. Ann. Temp: 8.7°C
Ave. Ann. Precip: 198 cm
Air Sampler: No
Conifer: Abies amabilis
Lichen: Alectoria sarmentosa
NOCA3
SE slope Mt Triumph
Location: 48.66N 121.33W
Elevation: 945 m
Ave. Ann. Temp: 8.3°C
Ave. Ann. Precip: 222 cm
Air Sampler: No
Conifer: Abies amabilis
Lichen: Alectoria sarmentosa
NOCA5
S ridge Trappers Peak near treeline
Location: 48.68N 121.32W
Elevation: 1600 m
Ave. Ann. Temp: 8.2°C
Ave. Ann. Precip: 194 cm
Air Sampler: Yes
Conifer: Abies amabilis
Lichen: Alectoria samientosa
Vegetation Summary
• Among samples from the 20 WACAP parks, vegetation
samples from NOCA were at or above medians for all SOCs.
Dominant SOCs were PAHs (bdl-7773 ng/g lipid), endosul-
fans (24-355), dacthal (3-34), HCB (8-60), a-HCH (6-49),
and g-HCH (2-11).
• Low concentrations of trifluralin (<0.2), chlorpyrifos (3-8),
chlordanes (1-6), DDTs (< 7), and PCBs (< 6) were also detected.
• Total SOC concentrations were similar to those in other
Pacific Northwest parks (CRLA, MORA, OLYM). Pesticide
and PCS concentrations in the lichen, Alectoria sarmentosa,
increased with elevation.
• Because needle productivity (kg/ha/yr) is high, the ecological
effects of cumulative SOCs contributed by needle litter-fall
are a potential concern.
• Nitrogen concentrations in lichens were within background
ranges, indicating that nitrogen deposition is not elevated.
2-38
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxicsAwacap.cfm
-------
Grand Teton National Park: Summary
GRTE1
Lupine Meadows
Location: 43.73N 110.74W
Elevation: 2073 m
Ave. Ann. Temp: 2.2°C
Ave. Ann. Precip: 69 cm
Air Sampler: No
Conifer: Pinus contorta
Lichen: Usnea
GRTE4
0.5 km E of Sunrise Lake
Location: 43.73N 110.77W
Elevation: 2804 m
Ave. Ann. Temp: 1.1 °C
Ave. Ann. Precip: 103 cm
Air Sampler: No
Conifer: Pinus flexilis
Lichen: None
GRTE2
Bradley Lake
Location: 43.73N 110.76W
Elevation: 2362 m
A ve. Ann. Temp: 2.2°C
Ave. Ann. Precip: 79 cm
Air Sampler: No
Conifer: Abies lasiocarpa
Lichen: Letharia vulpina
GRTE5
S rim above Amphitheater Lake
Location: 43.13N 110.78W
Elevation: 3048 m
Ave. Ann. Temp: 2.2°C
Ave. Ann. Precip: 68 cm
Air Sampler: Yes
Conifer: Pinus albicaulis
Lichen: None
.
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GRTE
GRTE3
Midslope Amphitheater Lake
and valley floor
Location: 43.73N 110.77W
Elevation: 2591 m
Ave. Ann. Temp: 1.1 °C
A ve. Ann. Precip: 102 cm
Air Sampler: No
Conifer: Pinus flexilis
Lichen: None
Vegetation Summary
• As in other parks of the conterminous 48 states, the
dominant SOCs in vegetation were PAHs (bdl-931 ng/g
lipid) and CUPs endosulfans (3-165) and dacthal (5-50).
• Compared with other WACAP parks, lichen SOC
concentrations were at or above the median; conifer
needle concentrations were at or below medians, except
at GRTE2, where fir was collected, with concentrations
at or above the median. (Pine, collected at other GRTE
sites, tends to accumulate 2-1 Ox lower SOC concentra-
tions than to other WACAP conifers.)
• All other SOCs detected in WACAP vegetation were also
detected at GRTE: trifluralin (< 1 ng/g lipid), triallate (< 6),
chlorpyrifos (1-5), HCB (4-17), a-HCH (2-15), g-HCH
(1-9), chlordanes (0.1-6), dieldrin (< 2), DDTs (12-20),
andPCBs(0.1-4).
• Elevation effects were not observed in lichens; there
were only two sites, with different species. The two
highest conifer sites often had lowest SOC concentra-
tions, possibly related to extended snow burial in winter.
• Lichen nitrogen concentrations were within background
ranges, indicating that nitrogen deposition is not elevated.
0 5 10 15 20 Kilometers
I i I i I i I i I
Air Summary
• The air sampler was at GRTE5.
• Concentrations of detected SOCs were above
medians for the 20 parks [CUPs dacthal
(390 pg/g dry XAD), endosulfans (359), and
g-HCH (44); HUPs HCB (840), a-HCH (140),
and chlordanes (19)]; PAHs were low (12).
Vegetation Contaminant Concentrations
10s
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CH Conifer of values for all parks.
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www. na fore. nps.gov/air/Studies/air_toxics/wacap. cfm
Western Airborne Contaminants Assessment Project
2-39
-------
Crater Lake National Park: Summary
CRLA
CRLA1
Lodgepole picnic area
Location: 42.84N 122.15W
Elevation: 1798 m
Ave. Ann. Temp: 4.2°C
Ave. Ann. Precip: 152 cm
Air Sampler: No
Conifer: Abies magnifica
Lichen: Letharia vulpina
CRLA4
Mt. Scott Trail 1.6 km
Location: 42.92N 122.03W
Elevation: 2423 m
Ave. Ann. Temp: 3.5°C
Ave. Ann. Precip: 108cm
Air Sampler: No
Conifer: Pinus albicaulis
Lichen: Letharia vulpina
CRLA5
Mt. Scott Summit
Location: 42.92N 122.02W
Elevation: 27 Km
Ave. Ann. Temp: 3.5°C
Ave. Ann. Precip: 108 cm
Air Sampler: Yes
Conifer: Pinus albicaulis
Lichen: None
0 5 10
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20 Kilometers
_i |
Location: 42.88N 122.19W
Elevation: 1859 m
Ave. Ann. Temp: 3.5°C
Ave. Ann. Precip: 155 cm
Air Sampler: No
Conifer: Abies concolor
Lichen: Letharia vulpina
CRLA3
Lightning Sprgs Trail near Rim Drive
Location: 42.93N 122.18W
Elevation: 2043 m
Ave. Ann. Temp: 3.4°C
Ave. Ann. Precip: 158 cm
Air Sampler: No
Conifer: Abies magnifica
Lichen: Letharia vulpina
Air Summary
• The air sampler was deployed at CRLA5.
• Compared with other WACAP parks, concentrations of
all SOCs detected were moderate [CUPs dacthal (160
pg/g dry XAD), endosulfans (467), and g-HCH (36), and
HUPs a-HCH (230), chlordanes (17), and PAHs (721)]
to high [HCB (920)].
• No elevational or east-west patterns were observed.
Vegetation Contaminant Concentrations
105
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Vegetation Summary
• The CUPs endosulfans (3-486 ng/g lipid) and dacthal
(5-150) comprised most of the pesticide burden in
vegetation; other dominant SOCs were PAHs (bdl-3825)
and the HUPs HCB (5-45), a-HCH (4-34), chlordanes
(3-33), and DDT (5-17). Low concentrations of g-HCH
(1-11), trifluralin (<0.4), chlorpyrifos (2-10), and PCBs
(1-16) were also detected.
• Pesticide concentrations were at or above the median
relative to other WACAP parks, except for consistently
low values in conifers at the two highest sites, possibly
related to deep snow burial in winter.
• Concentrations of endosulfans, dacthal, HCB, HCHs,
and PCBs increased in lichens with elevation, most by
an order of magnitude or more.
• Lichen nitrogen concentrations were within background
ranges for remote sites in the Pacific Northwest,
indicating that nitrogen deposition is not elevated.
2-40
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/airjtoxics/wacap.cfm
-------
Lassen Volcanic National Park: Summary
LAVO1
Sunflower Flat
Location: 40.56N 121.53W
Elevation: 1829m
Ave. Ann. Temp: 7.4°C
Ave. Ann. Precip: 123 cm
Air Sampler: No
Conifer: Abies concolor
Lichen: Letharia vulpina
LAVO2
Chaos Crags Trail 2.4 km
Location: 40.53N 121.53W
E/evafion:2012m
Ave. Ann. Temp: 6.6°C
Ave. Ann. Precip: 168 cm
Air Sampler: No
Conifer: Abies concolor
Lichen: Letharia vulpina
LAVO3
Ridge Lake Basin
Location: 40.46N 121.54W
Elevation: 2271 m
Ave. Ann. Temp: 4.1 °C
Ave. Ann. Precip: 267 cm
Air Sampler: No
Conifer: Abies magnifica
Lichen: Letharia columbiana
LAVO4
Broke-off Top Mtn Trail 3.2 km
Location: 40.44N 121.56W
Elevation: 2499 m
Ave. Ann. Temp:4.8°C
Ave. Ann. Precip: 235 cm
Air Sampler: No
Conifer: Abies magnifica
Lichen: Letharia vulpina
LAVO5
Broke-off Top Mtn summit
Location: 40.45N 121.57W
E/evafion:2713m
Ave. Ann. Temp: 4.8°C
Ave. Ann. Precip: 235 cm
Air Sampler: Yes
Conifer: Abies magnifica
Lichen: Letharia vulpina
Vegetation Summary
• The same genera (wolf-lichen and true fir) were collected
at all sites.
• SOC concentrations in LAVO vegetation were close to,
above, or well above the median for the WACAP parks.
• The dominant SOCs were PAHs (bdl-562 ng/g lipid),
endosulfans (83-177), dacthal (40-110), DDTs (5-32), and
HCB (8-19). Proportions of PAHs were similar to those in
southern Oregon and other California parks.
• Low concentrations of chlorpyrifos (<3 ng/g lipid), a-HCH
(5-24), g-HCH (2-8), chlordanes (5-14), dieldrin (1-6), and
PCBs (1-6) were also detected.
• Increases in endosulfans, chlorpyrifos, and dacthal, and
decreases in PAH concentrations in lichens were observed
with increasing elevation.
• Lichen nitrogen concentrations were within background
ranges, indicating that nitrogen deposition is not elevated.
LAVO
Air Summary
• Air was sampled at LAVO5.
• Compared with other WACAP parks, concentrations
of all SOCs detected, except PAHs (321 pg/g dry
XAD), were above the median [CUPs trifluralin (5),
dacthal (380), g-HCH (30), and endosulfans (363); and
HUPs HCB (840), a-HCH (150), and chlordanes (34)].
Vegetation Contaminant Concentrations
10s
104
3 I | » 1 1
ldl| B/Bu) OQS
CD Lichen *6oxes denote range
CD Conifer of values for all parks.
8
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^ ^ ^
www.nature.nps.gov/air/Studies/airjtoxicsAwacap.cfm
Western Airborne Contaminants Assessment Project
2-41
-------
Yosemite National Park: Summary
YOSE
YOSE1
Hwy 140 park boundary
Location: 37.68N 119.75W
Elevation: 661 m
Ave. Ann. Temp: 12.1°C
Ave. Ann. Precip: 87 cm
Air Sampler: No
Conifer: Pinus sabiniana
Lichen: Xanthoparmelia
/Piriji'l
#%&*$
0510 20 30 40 Kilometers
YOSE2
Turtleback Dome
Location: 37.72N 119.68W
Elevation: 1433m
Ave. Ann. Temp: 10.7°C
Ave. Ann. Precip: 92 cm
Air Sampler: No
Conifer: Pinus ponderosa
Lichen: Letharia vulpina
YOSE4
Lewis Creek at Cony Crags
Location: 37.75N 119.36W
Elevation: 2713m
Ave. Ann. Temp: 4.2°C
Ave. Ann. Precip: 112 cm
Air Sampler: No
Conifer: Pinus contorta
Lichen: None
YOSE3
Nevada Falls
Location: 37.72N 119.53W
Elevation: 1829 m
Ave. Ann. Temp: 10.3°C
Ave. Ann. Precip: 104 cm
Air Sampler: No
Conifer: Pinus lambertiana
Lichen: Letharia vulpina
YOSE5
Lewis-Gallison Creek confluence
Location: 37.77N 119.34W
Elevation: 3048 m
Ave. Ann. Temp: 3.1 °C
Ave. Ann. Precip: 109 cm
Air Sampler: Yes
Conifer: Pinus contorta
Lichen: None
Air Summary
• Air was sampled at YOSE5.
• Concentrations of all SOCs detected, except PAHs
(144 pg/g dry XAD) and dacthal (360), ranked above
medians for the 20 parks. Other CUPs detected were
g-HCH (33) and endosulfans (413); HUPs detected
were a-HCH (120) and chlordanes (30).
105
104
101
10°
n Lichen *6oxes denote range
CH Conifer of values lor all parks.
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Vegetation Summary
• The dominant SOCs were PAHs (511-19120 ng/g lipid), the
CUPs endosulfans (9-474), dacthal (30-350), and chlorpyrifos
(4-31), and the HUPs DDTs (10-72) and HCB (3-32), all of which
were at or well above medians for the 20 WACAP parks. Low
concentrations of trifluralins (<4), g-HCH (1-9), and PCBs (0.4-9)
were also detected.
• SOC concentrations were about 10x higher in lichens than conifers.
• A strong elevational effect was observed in lichens: concentra-
tions of endosulfans, dacthal, HCHs, and PCBs increased by
one-half to one order of magnitude, from 660 to 1830 m.
• Pine, the only conifer genus sampled in YOSE, appears to be a
poor accumulator of SOCs compared with spruce, fir, and
hemlock sampled in other west coast parks; had these species
been collected, total pesticides would probably have ranked
intermediate compared with concentrations in LAVO and SEKI,
as did the lichen data.
• Lichen nitrogen concentrations were at or slightly above upper-
most Pacific Northwest background ranges, indicating potential
enhancement of depositional nitrogen.
2-42
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/airjtoxics/wacap.cfm
-------
Great Sand Dunes National Park and Preserve: Summary
GRSA1
Park headquarters
Location: 37.73N 105.53W
Elevation: 2469 m
Ave. Ann. Temp: 5.3°C
Ave. Ann. Precip: 25 cm
Air Sampler: No
Conifer: Pin us edulis
Lichen: None
GRSA4
Carbonate Mtn sideslope
Location: 37.72N 105.47W
E/evafion:3109m
Ave. Ann. Temp: 4.3°C
Ave. Ann. Precip: 48 cm
Air Sampler: No
Conifer: Pin us flexilis
Lichen: Xanthoparmelia
GRSA2
Mosca Pass Trail midpoint
Location: 37.73N 105.49W
Elevation: 2774 m
Ave. Ann. Temp: 4.3°C
Ave. Ann. Precip: 48 cm
Air Sampler: No
Conifer: Pin us edulis
Lichen: Xanthoparmelia
GRSA5
Carbonate Mtn peak
Location: 37.71N 105.47W
Elevation: 3338 m
Ave. Ann. Temp: 4.3°C
Ave. Ann. Precip: 52 cm
Air Sampler: Yes
Conifer: Pin us flexilis
Lichen: None
GRSA3
Mosca Pass
Location: 37.73N 105.46W
Elevation: 2941 m
Ave. Ann. Temp:3.9°C
Ave. Ann. Precip: 60 cm
Air Sampler: No
Conifer: Pin us flexilis
Lichen: None
Vegetation Summary
• The dominant SOCs detected in vegetation were PAHs,
especially 4-5 ring compounds (bdl-1149 ng/g lipid), endosul-
fans (3-710, 10x higher in lichens than conifers), and dacthal
(3-240, 10x higher in lichens), DDTs (10-94), HCB(1-67),
a-HCH (1-38), chlordanes (0.2-32), and g-HCH (5-17); low
concentrations of PCBs (1-13) were detected.
• Lichen concentrations were generally higher than conifer
needle concentrations by at least an order of magnitude.
• Although SOC concentrations in conifer needles from
GRSAwere comparable to those at BAND and BIBE,
where the same target genus (pine) was collected, SOC
concentrations in lichens were disproportionately high at
GRSA compared to ROMO, BAND, and BIBE even though
the same rock-dwelling lichen, Xanthoparmelia, was
collected there.
• Field notes and the park website indicate that the two sites
where lichens were collected were very windy. It is possible
that a disproportionately high absorption of SOCs from soil
particulates contributed to high lichen SOC concentrations.
• Lichen nitrogen concentrations were within background
ranges, indicating that nitrogen deposition is not elevated.
GRSA
0510 20 Kilometers
! I I I I I I I I
Air Summary
• The air sampler was at GRSA5.
• Compared with concentrations at other WACAP parks,
concentrations of all SOCs detected were moderate
[dacthal (300 pg/g dry XAD), endosulfans (353),
HCB (580), a-HCH (120), and chlordanes (15)] to high
[g-HCH (73) and PAHs (1471)].
105
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EH Conifer of values for all parks.
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www.nature.nps.gov/air/Studies/airjtoxicsAwacap.cfm
Western Airborne Contaminants Assessment Project
2-43
-------
Bandelier National Monument: Summary
BAND
BAND1
Burro Tr above Lummis Canyon
Location: 35.73N 106.27W
Elevation: 1854 m
Ave.Ann. Temp: 10.5°C
Ave. Ann. Precip: 35 cm
Air Sampler: No
Conifer: Pinus edulis
Lichen: Xanthoparmelia
Air Summary
• Compared with concentrations at the 20 WACAP
parks, concentrations of all pesticides detected in the
air sampler at BANDS were near the median.
• Pesticides detected were the CUPs [endosulfans (494
pg/g dry XAD), dacthal (150), g-HCH (37), trifluralin (9)],
and HUPs [HCB (750), a-HCH (110), chlordanes (19)];
PAHS were low (246).
Vegetation Contaminant Concentrations
10s
104
01 103-
!5 102
Q.
f 1»1-
C
8 10°
(0
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10-2
n Lichen *6oxes denote range
CH Conifer of values for all parks.
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BAND2
NW of Juniper Campground
Location: 35.80N 106.28W
Elevation: 2076 m
Ave. Ann. Temp: 9.9°C
Ave. Ann. Precip: 41 cm
Air Sampler: No
Conifer: Pinus edulis
Lichen: Usnea
BAND4
Lower SW slope of Cerro Grande
Location: 35.83N 106.39W
Elevation: 2576 m
Ave. Ann. Temp: 6.4°C
Ave. Ann. Precip: 58 cm
Air Sampler: No
Conifer: Pinus ponderosa
Lichen: Usnea
BAND3
Lower E slopes of Frijoles Park
Location: 35.82N 106.36W
Elevation: 2348 m
Ave. Ann. Temp: 8.TC
Ave. Ann. Precip: 51 cm
Air Sampler: No
Conifer: Pinus ponderosa
Lichen: Xanthoparmelia
BANDS
Saddle SW of Cerro Grande Peak
Location: 35.86N 106.42W
Elevation: 2926 m
Ave.Ann. Temp:5.4°C
Ave. Ann. Precip: 67 cm
Air Sampler: Yes
Conifer: Pinus ponderosa
Lichen: Usnea
Vegetation Summary
or
• Concentrations of CUPs [endosulfans (2-256 ng/g lipid),
dacthal (2-56), chlorpyrifos (<5)] were at or above medians
and concentrations of HUPs [HCB and a-HCHs (1-17)] were
at or below medians for the 20 WACAP parks.
• Other SOCs detected were trifluralin (<0.5), DDTs (3-41),
and PAHs(bdl-1180).
• Pine is a poor accumulator of SOCs compared to other
conifers and lichens, explaining the large range in
concentrations observed within individual SOCs.
• Pesticides and PCBs increased and PAHs decreased in
lichens with elevation.
• Abundance of nitrophytic lichens and elevated nitrogen
concentrations in Xanthoparmelia and Usnea, relative to
clean sites in the Pacific Northwest and northern Rockies,
indicate enhanced nitrogen deposition.
2-44
Western Airborne Contaminants Assessment Project
www.nature.nps.gov/air/Studies/air_toxicsAwacap.cfm
-------
Big Bend National Park: Summary
BIBE1
Rio Grande Village
Location: 29.19N 102.97W
Elevation: 560 m
Ave. Ann. Temp: 21 °C
Ave. Ann. Precip: 26 cm
Air Sampler: Yes
Conifer: None
Lichen: None
BIBE2
Water Tank near Panther Jet
Location: 29.31 N 103.18W
Elevation: 1067m
Ave. Ann. Temp: 18.6°C
Ave. Ann. Precip: 37 cm
Air Sampler: Yes
Conifer: None
Lichen: None
BIBE4
Pinnacles Campground
Location: 29.25N 103.30W
Elevation: 1920 m
Ave. Ann. Temp: 16.7°C
Ave. Ann. Precip: 51 cm
Air Sampler: Yes
Conifer: Pinus cembroides
Lichen: Usnea
BIBE5
Emory Peak
Location: 29.25N 103.30W
Elevation: 2316 m
Ave. Ann. Temp: 16.7°C
Ave. Ann. Precip: 50 cm
Air Sampler: Yes
Conifer: Pinus cembroides
Lichen: Usnea
BIBE3
Panther Pass
Location: 29.29N 103.28W
Elevation: 1608m
Ave. Ann. Temp: 17.5°C
Ave. Ann. Precip: 46 cm
Air Sampler: No
Conifer: Pinus cembroides
Lichen: None
Vegetation Summary
• Compared with other parks, concentrations in lichens
and conifer needles were at or slightly above medians
for CUPs, and at or below medians for HUPs, PAHs,
and PCBs.
• Pine, the only conifer available at BIBE, is a poorer
accumulator of SOCs than the spruce, fir, and hemlock
collected in the northern Rockies, Pacific Coast, and
Alaska parks.
• SOCs detected were CUPs [chlorpyrifos (<2 ng/g lipid),
dacthal (1-14), endosulfans (9-255, up to 25x higher in
lichens than pine), g-HCH], HUPs [HCB, a-HCH,
chlordanes (<5), DDE (8-17)], PCBs (<3), and PAHs
(bdl-668, up to 50x higher in lichens than pine).
• Lichen nitrogen concentrations and abundance of
nitrophytic lichens at sites indicate enhanced nitrogen
deposition; IMPROVE data indicate ammonium sulfate
could be the main culprit.
0 10 20
40 Kilometers
I
Air Summary
• Air was sampled at four sites from the Rio Grande to Emory
Peak (BIBE1, BIBE2, BIBE4, and BIBE5).
• Dramatic differences among sites in annual precipitation,
humidity, vegetation cover, and airborne soil particulates might
have masked elevation effects on pesticide concentrations in
air, although PAHs decreased markedly with increasing
elevation from moderate to low concentrations (2091, 388,
11, and 11 pg/gdryXAD).
• Endosulfans (472-1096), HCB (460-1260), and DDE (n.d. to
43) ranked high compared with other WACAP parks.
• Dacthal (67-390), a-HCHs (56-150), g-HCH (13-39), and
chlordanes (16-37) ranked below or near the median;
very low concentrations (3-5) of trifluralin were detected.
Vegetation Contaminant Concentrations
10s
Q.
SOC (ng
i o c
m-3
CH Lichen *6oxes denote range
dConifer of values for all parks.
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www.nature.nps.gov/air/Studies/airjtoxicsAwacap.cfm
Western Airborne Contaminants Assessment Project
2-45
-------
CHAPTER 2. PARK SUMMARIES
2-46
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 3
Contaminants Studied and Methods Used
3.1 Introduction
This chapter introduces the organic and inorganic contaminants measured in the WACAP
environmental media and describes the methods used to collect and analyze these media. Table
3-1 lists the analytes measured in each environmental medium, along with the abbreviation for
the responsible laboratory for each medium and analyte. Table 3-2 provides the full identification
and contact information for each laboratory. Appendix 3A summarizes the sampling and analysis
plan for each environmental medium and lists the years in which each medium was sampled in
each park. Details of the methods and data quality control are provided in Appendix 3B and in
the peer-reviewed literature cited throughout this chapter.
Table 3-1. Analytical Laboratories by Media and Analyte. Abbreviations for laboratories, along with
contact information, are provided in Table 3-2.
Analyses
SOC
Hg
Metals
Major ions/
Nutrients/
Physical
SCP*
Particulate C
andN
Sediment
Dating
Fish Physio/
Path*
Snow
SEC
USGS-
wwsc
USGS-NRP
Boulder
USGS-
CWSC
ECRC
CBL
NA
NA
Sediment
SEC
WRS
USGS-NRP
Boulder
WRS
ECRC
NA
ERRC
NA
Fish
SEC
WRS
USGS-NRP
Boulder
NA
NA
NA
NA
OSU-Fish
Vegetation
SEC
WRS
USGS-NRP
Boulder
UMNRAL
NA
NA
NA
NA
Air*
SEC
NA*
NA
NA
NA
NA
NA
NA
Water
SEC
NA
NA
WRS
NA
NA
NA
NA
Moose
SEC
WRS
USGS-NRP
Boulder
NA
NA
NA
NA
NA
*Air = measured by SOC concentrations in Amberlite XAD-2 resin-filled passive air sampling devices (PASDs).
NA = not applicable; SCP = spheroidal carbonaceous particle; Fish Physio/Path = Fish physiology and pathology
assessments.
3.2 Contaminants Studied
3.2.1 Semi-Volatile Organic Compounds (SOCs)
Over 100 different semi-volatile organic compounds (SOCs) were measured in WACAP. Table
3-3 lists the SOCs measured in WACAP in four separate categories: North American current-use
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
3-1
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
pesticides (CUPs), North American historic-use pesticides (HUPs), combustion byproducts, and
industrial/urban use compounds (lUCs). Table 3-3 also provides the common names of the
SOCs, the abbreviations for the SOCs used throughout this report, the compound chemical class,
and the use or source of the SOC. When available, the year of the first product registration in the
United States, as well as the 2006 regulatory status of these compounds in the United States,
Canada, China, Korea, and Japan, are also given in Table 3-3. Regulatory status is at the federal
level only. State regulations can be more strict. It is also important to know that many of the
banned or restricted SOCs went though prolonged phase-out periods that took several years to
complete.
Table 3-2. WACAP Analytical Laboratories.
Laboratory
Abbreviation
Laboratory
Address
Contact Information
SEC
WRS
USGS-NRP
Boulder
OSU-Fish
USGS-WWSC
USGS-CWSC
CBL
UMNRAL
ERRC
ECRC
Simonich
Environmental
Chemistry Laboratory
Willamette Research
Station Analytical
Laboratory
Trace Element
Environmental
Analytical Chemistry
Project
OSU Kent Laboratory
USGS Wisconsin
Water Science Center
Mercury Research
Laboratory
USGS Colorado
Water Science Center
Alpine Hydrological
Research Team
Chesapeake
Biological Laboratory,
University of
Maryland
University of
Minnesota Research
Analytical Laboratory
Environmental
Radioactivity
Research Centre
Environmental
Change Research
Centre
1161 Agricultural and Life
Sciences
Dept. of Environmental and
Molecular Toxicology
Oregon State University
Corvallis, OR 97331
U.S. EPA
200 SW 35th Street
Corvallis, OR 97333
U.S Geological Survey, WRD
National Research Program
3215 Marine St., Suite E-127
Boulder, CO 80303
Dept. of Microbiology
220 Nash Hall
Oregon State University
Corvallis, OR 97331
USGS Water Resources Division
8505 Research Way
Middleton, Wl 53562
USGS-WRD, Colorado
Denver Federal Center
MS-415, Bldg. 53
Lakewood, CO 80225
Chesapeake Biological Laboratory
Center for Environmental and
Estuarine Studies
1 Williams St.;PO Box 38
Solomons, MD 20688
University of Minnesota
Rm. 135 Crops Research Bldg.
1902 Dudley Ave.
StPaul, MN 55108-6089
The University of Liverpool
Liverpool, UK
L69 3BX
University College London
Pearson Building, Gower Street
London UKWC1E6BT
Prof. Staci Simonich
541-737-9194
staci.simonich@orst.edu
Dr. Dixon Landers
541-754-4427
Landers.Dixon@epa.gov
Dr. Howard Taylor
303-541-3007
hetaylor@usgs.gov
Prof. Michael Kent
541-737-8652
michael.kent@orst.edu
Dr. David Krabbenhoft
608-821-3843
dpkrabbe@usgs.gov
M. Alisa Mast
303-236-4882
mamast@usgs.gov
Carl. F. Zimmermann
410-326-7252
carlz@cbl.umces.edu
Dr. Roger Eliason
612-625-3101
ral@soils.umn.edu
Prof. Peter Appleby
+44(0)151 7944020
Appleby@liv.ac.uk
Prof. Neil Rose
+44 (0) 20 7679 0543
nrose@geog.ucl.ac.uk
3-2
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-3. Semi-Volatile Organic Compounds (SOCs) Measured in WACAP.
Federal Regulatory Status 2007*
Compound Name
Current-Use Pesticides
Acetochlor
Alachlor
Metolachlor
Propachlor
Endosulfan 1
Endosulfan II
Endosulfan sulfate
Ethion
Malathion
Methyl parathion
Parathion
Diazinon
Chlorpyrifos
Chlorpyrifos oxon
EPTC
Pebulate
Triallate
Atrazine
Prometon
Simazine
Cyanazine
Dacthal
Trifluralin
Abbreviation
(CUPs)
ACLR
ALCLR
MCLR
PCLR
ENDOI
ENDO II
ENDOS
ETHN
MTHN
M-PTHN
PTHN
DIAZ
CLPYR
CLPYR O
EPTC
PBLT
TRLTE
ATRZ
PMTN
SIMZ
CYAZ
DCPA
TFLN
Chemical Class
Chloroamide
Chloroamide
Chloroamide
Chloroamide
Organochlorine Sulfide
Organochlorine Sulfide
Organochlorine Sulfide
Phosphorothioate
Phosphorothioate
Phosphorothioate
Phosphorothioate
Phosphorothioate
Phosphorothioate
Phosphorothioate
Thiocarbamate
Thiocarbamate
Thiocarbamate
Triazine
Triazine
Triazine
Triazine
ChloroPhthalate
Dinitroaniline
Use/Source
Herbicide
Herbicide
Herbicide
Herbicide
Insecticide
Insecticide
Degradation Product
Insecticide
Insecticide
Insecticide
Insecticide
Insecticide
Insecticide
Degradation Product
Herbicide
Herbicide
Herbicide
Herbicide
Herbicide
Herbicide
Herbicide
Herbicide
Herbicide
First
U.S.
Usage
1994
1969
1976
1964
1954
1954
NA
1958
1954
1956
1965
NA
1968
1961
1961
1958
1955
1963
U.S.
A
A
R
A
A
A
NA
B2004
A
A
B
A
R
NA
A
R
A
R
A
A
B
A
A
Canada China Korea Japan
A
A
A
NA NA NA NA
B2000
A
A R
B 2003 R
A
A
NA NA NA NA
A
B2003
A
A
A
B2004
A
A
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
3-3
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-3. Semi-Volatile Organic Compounds (SOCs) Measured in WACAP.
Federal Regulatory Status 2007*
Compound Name
Etridiazole
Metribuzin
y-HCH (Lindane)
Historic-Use Pesticides
a-HCH
P-HCH
5-HCH
Hexachlorobenzene
Aldrin
Dieldrin
Endrin
Endrin aldehyde
Chlordane, trans
Chlordane, cis
Nonachlor, trans
Nonachlor, cis
Chlordane, oxy
Heptachlor
Heptachlor epoxide
Methoxychlor
p,p'-DDT
Abbreviation
ETDZL
MBZN
g-HCH or
gHCH
(HUPs)
a-HCH or
aHCH
b-HCH or
bHCH
d-HCH or
dHCH
HCB
Aldrin
Dieldrin
Endrin
Endrin A
t-CLDN
c-CLDN
t-NCLR
c-NCLR
o-CLDN
HCLR
HCLRE
MXCLR
pp-DDT
Chemical Class
Terrazole
Triazinone
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Chlorobenzene
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Use/Source
Fungicide
Herbicide
Insecticide
Insecticide
Insecticide
Insecticide
Fungicide
Insecticide
Insecticide
Insecticide
Degradation Product
Insecticide
Insecticide
Impurity/Insecticide
Impurity/Insecticide
Degradation Product
Insecticide
Degradation Product
Insecticide
Insecticide
First
U.S.
Usage
1963
1973
1948
1948
1948
1948
1945
1949
1949
1949
NA
1948
1948
1948
1948
NA
1952
NA
1948
1942
U.S.
A
A
R
B 1978
B 1978
B 1978
B 1984
B 1987
B1987
B1991
NA
B 1988
B 1988
B1988
B1988
NA
B 1988
NA
R
B1972
Canada
A
A
B2004
B 1971
B 1971
B1971
B1972
B1990
B 1990
B 1990
NA
B1995
B1995
B 1995
B 1995
NA
B1985
NA
B2005
B 1989
China
R
B
1983
B
1983
B
1983
N
N
N
NA
R
R
R
R
NA
B
NA
B
Korea
B1979
B1979
B1979
B1979
B 1971
B 1971
B 1971
NA
B
B
B
B
NA
B1979
NA
B1973
Japan
B 1975
B 1975
B 1975
NA
R
R
R
R
NA
R
NA
B 1981
3-4
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-3. Semi-Volatile
Organic Compounds (SOCs) Measured in WACAP.
Federal Regulatory Status 2007*
Compound Name
o,p'-DDT
p,p'-DDD
o,p'-DDD
p,p'-DDE
o,p'-DDE
Mi rex
Abbreviation
op-DDT
pp-DDD
op-DDD
pp-DDE
op-DDE
Mi rex
Chemical Class
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Organochlorine
Use/Source
Insecticide
Degradation Product
Degradation Product
Degradation Product
Degradation Product
Insecticide
First
U.S.
Usage
1942
NA
NA
NA
NA
1959
U.S.
B 1972
NA
NA
NA
NA
B 1978
Canada
B1989
NA
NA
NA
NA
B1978
China
1983
B
1983
NA
NA
NA
NA
R
Korea
B1973
NA
NA
NA
NA
Japan
B1981
NA
NA
NA
NA
Combustion Byproducts
Acenaphthylene
Acenaphthene
Fluorene
Phenanthrene
Anthracene
Fluoranthene
Pyrene
Benzo(a)anthracene
Chrysene+Triphenylene
Benzo(b)fluoranthene
Benzo(k)fluoranthene
Benzo(e)pyrene
Benzo(a)pyrene
lndeno(1 ,2,3-cd)pyrene
Dibenz(a,h)anthracene
ACY
ACE
FLO
PHE
ANT
FLA
PYR
B[a]A
CHR/TRI
B[b]F
B[k]F
B[e]P
B[a]P
l[123-cd]p
D[ah]A
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
PAH
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
Combustion
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
3-5
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-3. Semi-Volatile Organic Compounds (SOCs) Measured in WACAP.
Federal Regulatory Status 2007*
Compound Name Abbreviation
Benzo(ghi)perylene B[ghi]P
Retene Retene
Chemical Class
PAH
PAH
Use/Source
Combustion
Combustion
First
U.S.
Usage
NA
NA
U.S.
NA
NA
Canada
NA
NA
China Korea
NA NA
NA NA
Japan
NA
NA
Industrial/Urban Use Compounds (IBCs)
PCB 74 PCB74
PCS 101 PCB101
PCB 118 PCB118
PCB 138 PCB138
PCB 153 PCB153
PCB 183 PCB183
PCB 187 PCB187
mono-PBDEs (1, 2, 3)
di-PBDEs (7, 8, 10, 11, 12, 13, 15)
tri-PBDEs (17, 25, 28, 30, 32, 33, 35, 37)
tetra-PBDEs (47, 49, 66, 71, 75, 77)
penta-PBDEs (89, 99, 100, 116, 118, 126)
hexa-PBDEs (138, 153, 154, 155, 166)
hepta-PBDEs(181, 183, 190)
PCB
PCB
PCB
PCB
PCB
PCB
PCB
PBDE
PBDE
PBDE
PBDE
PBDE
PBDE
PBDE
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Flame Retardant
Flame Retardant
Flame Retardant
Flame Retardant
Flame Retardant
Flame Retardant
Flame Retardant
1929
1929
1929
1929
1929
1929
1929
1977
1977
1977
1977
1977
1977
1977
B 1977
B 1977
B 1977
B 1977
B1977
B 1977
B1977
R
B1977
B1977
B1977
B 1977
B 1977
B 1977
B 1977
N
N
N
N
N
N
N
B 1972
B1972
B1972
B1972
B1972
B1972
B1972
*Regulatory status legend: A = current active use, B = banned for use, R = some restricted uses, N = not likely used in the given country, and NA = not
applicable (Primbs et al., 2007; UNEP, 2002; Breivik et al., 2002; USEPA, 2007c; Pesticide Action Network; Purdue University, National Pesticide Information
Retrieval System; Environment Canada, EDDENet). The SOC ban year is included if this information is available.
3-6
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
We selected the list of SOCs in Table 3-3 by evaluating the existing scientific literature to
determine which SOCs have been shown to undergo atmospheric transport and deposition to
remote ecosystems, including high latitude and high elevation ecosystems. In addition, the SOCs
measured in WACAP span a wide range of volatility, water solubility, and hydrophobicity, as
well as persistence in the environment. The SOC physico-chemical properties have been used to
interpret the atmospheric transport, deposition, and accumulation of these compounds to the
ecosystems assessed in WACAP. Finally, some of the SOCs measured in WACAP are classified
as persistent, bioaccumulative, and toxic (PBT) chemicals by the USEPA. These PBT chemicals
include aldrin, benzo(a)pyrene, chlordane, DDT, ODD, DDE, hexachlorobenzene, mirex, and
PCBs.
Not all of the SOCs measured in WACAP were consistently detected in all WACAP media and
national parks. Figure 3-1 summarizes the current status of SOC contamination in all WACAP
parks and media. The figure shows the SOCs that were detected in at least one WACAP environ-
mental medium. The horizontal bars represent the percentage of all WACAP samples that had an
SOC concentration above the estimated detection limit.
Figure 3-1 also highlights some of the key SOCs that are the focus of this report. Total endosul-
fans (sum of ENDO I, ENDO II, and ENDO S), g-HCH, chlorpyrifos (CLPYR), and dacthal
(DCPA) were among the most commonly detected CUPs and are markers for recent agricultural
sources. a-HCH, hexachlorobenzene (HCB), dieldrin, total DDTs (sum of p,p'-DDT, o,p'-DDT,
p,p'-DDD, o,p'-DDD, p,p'-DDE, and o,p'-DDE), and total chlordanes (sum of t-CLDN, c-CLDN,
t-NCLR, c-NCLR, and o-CLDN) were among the most commonly detected HUPs and are
markers for historic agricultural sources. The polycyclic aromatic hydrocarbons (PAHs) are
markers for combustion sources. Finally, the polybrominated diphenyl ether (PBDE) flame
retardants and the polychlorinated biphenyls (PCBs) are markers for industrial/urban sources.
3,2,2
Mercury, an element found in the earth's crust, is a common component of coal and ore rich in
minerals. In nature, the mineral cinnabar (HgS, mercury sulfide) occurs in concentrated deposits
and has been used as the primary source of commercially mined mercury since Roman times.
When coal is burned or ores are smelted, mercury enters the atmosphere. In 2000, as much as
two-thirds of the total anthropogenic emissions world-wide (ca. 2,190 tons of Hg) was from the
combustion of fossil fuels (Pacyna et al., 2006), mostly coal. On a global scale, Hg emissions
increased from 1,881 tons in 1990 to 2,235 tons in 1995, then decreased only slightly in 2000 to
approximately 2,190 tons. It is estimated that over the last 100 years, anthropogenic Hg has
accounted for approximately 70% of the total atmospheric deposition of mercury at the location
of the Upper Freemont Glacier (4,100 m, Wyoming) in the western United States, with the
remainder coming from geologic (e.g., weathering of the lithosphere, volcanoes) and biogenic
sources (Schuster et al., 2002). This estimate is consistent with the analysis of Wiener et al.
(2006), who determined that anthropogenic Hg inputs from atmospheric deposition to Voyageurs
National Park (Minnesota, USA) accounted for 63% ± 13% of the mercury accumulated in the
park's lake sediments during the twentieth century.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 3-7
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
.2
1
.a
-Q
<
o
o
V)
ACLR -
ALCLR -
MCLR -
PCLR -
ENDOI
ENDO II
ENDOS
ETHN
MTHN -
M-PTHN -
PTHN-
DIAZ-
CLPYR -
CLPYR O -
PBLT-
TRLTE -
ATRZ -
PMTN
SIMZ -
CYAZ-
DCPA-
TFLN -
g-HCH -
a-HCH -
b-HCH -
d-HCH -
HCB -
Aldrin -
Dieldrin -
Endrin
Endrin A -
t-CLDN
C-CLDN
t-NCLR -
C-NCLR -
o-CLDN -
HCLR -
HCLR E -
MXCLR -
pp-DDT -
op-DDT -
pp-DDD -
op-DDD -
pp-DDE -
op-DDE -
Mirex -
ACY-
ACE •
FLO-
PHE -
ANT
FLA-
PYR -
B[a]A-
CHR/TRI -
B[b]F -
B[k]F
B[e]P
B[a]P -
l(123-cdjp -
D[ah]A -
B[ghi]P -
Retene -
PCB74 -
PCB101 -
PCB118 -
PCB138 -
PCB153 -
PCB183-
PCB187
O O O O
CM "sr to co
oooo
CM •q-
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Among the world's nations, the United States was the sixth greatest emitter of Hg to the
atmosphere in 2000, with 109.2 tons (5% of the total annual world emissions). China topped the
global 2000 list of Hg emitters with 604.7 tons (28% of world total; Pacyna et al, 2006). USEPA
estimates that about one-fourth of the mercury emitted in the United States is deposited in the
United States, while the remaining 75% enters the atmospheric component of the global mercury
cycle where it can reside for up to 2 years, circling the earth approximately every 3 weeks.
Mercury is a contaminant of concern because of its detrimental neurological effects, as well as
other effects, on humans, fish, and other organisms; it is classified by USEPA as a PBT chemical
(Wiener et al., 2003). Mercury concentrations in the atmosphere have greatly increased because
of the greater use of fossil fuels (particularly coal) since industrialization, and because of the ease
with which mercury is distributed globally through the atmospheric mercury cycle. Moreover,
once mercury has been deposited to a watershed and finds its way into aquatic systems, it can be
methylated by reducing bacteria and, only while in this form, incorporated into aquatic food
webs where it can be biomagnified, accumulating in the top predators of the aquatic food web at
concentrations 10 to 1,000 times greater than in the water itself. These top aquatic predators
often are targeted food sources of terrestrial wildlife and humans, particularly subsistence fishers
and terrestrial wildlife, adding another final step to the biomagnification pattern of Hg.
3,2,3
As with SOCs, the metals chosen for measurement in WACAP media were selected because they
serve as markers for a variety of different sources (Table 3-4). These include anthropogenic
sources such as coal combustion, petroleum combustion, industrial emissions, agricultural,
medical waste, incineration, and automotive sources, as well as biogenic sources such as sea
aerosols, volcanic deposits, and minerals.
Table 3-4. Environmentally Significant Metals.
Coal Combustion
Aluminum
Antimony
Arsenic
Barium
Beryllium
Boron
Cadmium
Chromium
Cobalt
Copper
Gallium
Iron
Lead
Manganese
Mercury
Molybdenum
Nickel
Al
Sb
As
Ba
Be
B
Cd
Cr
Co
Cu
Ga
Fe
Pb
Mn
Hg
Mo
Ni
Selenium
Thallium
Vanadium
Zinc
Zirconium
Sea Aerosols
Boron
Calcium
Magnesium
Sodium
Strontium
Volcanic
Aluminum
Arsenic
Bismuth
Cadmium
Iron
Manganese
Se
Tl
V
Zn
Zr
B
Ca
Mg
Na
Sr
Al
As
Bi
Cd
Fe
Mn
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
3-9
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-4. Environmentally Significant Metals (continued).
Volcanic (continued)
Nickel
Vanadium
Petroleum Combustion
Arsenic
Barium
Beryllium
Cadmium
Chromium
Copper
Lead
Manganese
Molybdenum
Nickel
Selenium
Vanadium
Zinc
Industrial (nonferrous metal
Cadmium
Chromium
Copper
Lead
Manganese
Vanadium
Zinc
Agricultural
Arsenic
Mercury
Selenium
Zinc
Waste Incineration
Cadmium
Copper
Lead
Mercury
Zinc
Ni
V
As
Ba
Be
Cd
Cr
Cu
Pb
Mn
Mo
Ni
Se
V
Zn
production)
Cd
Cr
Cu
Pb
Mn
V
Zn
As
Hg
Se
Zn
Cd
Cu
Pb
Hg
Zn
Automotive
Barium
Cadmium
Lead
Nickel
Mineral (Earth's crust)
Aluminum
Barium
Calcium
Cerium
Cesium
Holmium
Iron
Lanthanum
Lithium
Magnesium
Manganese
Mercury
Neodymium
Praseodymium
Rhenium
Rubidium
Samarium
Sodium
Strontium
Tellurium
Terbium
Thulium
Thulium
Tungsten
Uranium
Vanadium
Yttrium
Ytterbium
Zirconium
Medical
Neodymium
Ba
Cd
Pb
Ni
Al
Ba
Ca
Ce
Cs
Ho
Fe
La
Li
Mg
Mn
Hg
Nd
Pr
Re
Rb
Sm
Na
Sr
Te
Tb
Tm
Tm
W
U
V
Y
Yb
Zr
Nd
3.3 Data Quality Summary
The WACAP Quality Assurance (QA) Project Plan (Western Airborne Contaminants
Assessment Project, 2004), completed in May 2004, outlines the quality assurance and quality
3-10
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
control objectives and procedures for WACAP. It establishes methods for assessing data quality
for each analyte and media type, and includes analysis of replicate samples, surrogate spikes,
field and laboratory blanks, and SRMs when available. WACAP data quality is described
primarily by precision of replicate analyses, accuracy as indicated by difference from SRM or
recoveries of surrogate spikes, and detection limits. Appendix 3B provides detailed results of
these indicators of data quality. The following subsections summarize the data quality of the
major WACAP contaminants studied.
3.3.1 SOC Data Quality
All SOC analyses were conducted at the Simonich Environmental Chemistry Laboratory at
Oregon State University. Table 3-5 summarizes SOC data quality, with means of estimated
detection limits (EDLs), surrogate recoveries, replicate sample analyses, and percent difference
of the SRMs for sediment and fish. EDLs were calculated for each compound by the approach
described in Method 8280A (USEPA, 1996), and are listed by media in the tables in Appendix
3B. For statistical comparisons, concentrations less than the EDL were replaced by
concentrations representing one-half of the EDL (Antweiler, R.C., and H.E. Taylor, written
communications), following the guidelines listed in Section 3.5.1.
Target analyte loss was corrected via target analyte-to-surrogate response ratios in calibration
curves. Laboratory blanks were generated by the use of designated extraction disks spiked with
surrogate solution, after which all elution and clean-up steps were followed. Method blanks
consisted of sodium sulfate and were taken through the entire analytical method. Reported SOC
concentrations were blank-subtracted, according to the laboratory blank, and then recovery
corrected. Concentrations are not reported for cases in which the mass in the laboratory blank
exceeded 33% of that in the sample.
SRMS were available for sediment and fish analyses. Baltimore Harbor sediment certified refer-
ence material (NIST SRM #1941b) was analyzed for 27 certified compounds, with percent dif-
ference from the certified values ranging from 0 to 55.4% difference and a mean of 16.8%. Fish
certified reference material (NIST SRM #1946) was analyzed for 31 certified compounds, with
percent difference from the certified values ranging from 0 to 30% difference and a mean of 7%.
3.3.2 Mercury Data Quality
Total mercury on unfiltered snow samples was measured at the USGS Wisconsin Water Science
Center Mercury Research Laboratory (USGS-WWSC) by cold vapor atomic fluorescence
spectrometry (Olson and DeWild, 1999). The detection limit was 0.04 ng/L. In addition, methyl
mercury was measured on the 2005 unfiltered snow samples. Field replicate samples were
analyzed at the USGS-WWSC laboratory for total mercury. Percent relative standard deviation
of field replicates was higher for total mercury than for most constituents, because much of the
snowpack mercury is associated with particulates. Particulates vary substantially at scales of a
meter or less, which is typical of the spacing between replicate samples. Additional replicate
samples were analyzed in the in the USGS Trace Element Environmental Analytical Chemistry
Research (USGS-NRP Boulder) Laboratory in Boulder, Colorado, by cold vapor atomic
fluorescence spectrophotometry, with a detection limit of 0.4 ng/L (Roth, 1994). The relation of
the values between the two laboratories is described by the equation:
(USGS-WWSC lab values) = 1.2(USGS-NRP Boulder lab values) + 0.64 (n = 28, R2 = 0.71) [3-1]
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 3-11
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-5. Summary of Data Quality Indicators for SOCs by Media.1
Media
Snow
Air
Lichen
Conifer
needles
Lake water
Sediment
Fish
Units2
pg/L
ng/g dw
XAD
ng/g lipid
ng/g dw
P9/L
ng/g dw
pg/g ww
Estimated Detection
Limit3
Range Mean
0.20 to 125 22
0.00 to 0.2 0.03
0.01 to 54.3 4.6
0.01 to 72.3 5.7
0.5 to 385 13
0.1 to 204.7 23.8
0.2 to 920 78.7
Method Recoveries Replicate Sample
Iniections
Range
28.1 to 206.2%
20.9 to 21 0.0%
31. 3 to 139.5%
24.6 to 97.3%
24. 7 to 158.8%
20.9 to 136%
31. 4 to 98.3%
Mean Mean %RSD
68.3% 3.5
93.7% 49.5
73.9% 18.9
73.2% 19.4
99.0% 26.5
60.3% 6.2
61.4% 23.4
Mean
"/..Difference of NIST
NIST SRM SRM
na Na
na Na
na Na
na Na
na Na
16.8 1941b
7 1946
1 Detailed recovery and EDLs for moose were not conducted because there were so few moose samples. However, the SOC
recoveries and EDLs were similar to those for fish.
2dw = dry weight, ww = wet weight, lipid = lipid weight; XAD = Amberlite XAD-2 styrene divinylbenze resin beads 460 urn in
diameter.
3 Estimated detection limits determined by U.S. EPA Method 8280A.
3-12
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
The slightly higher values from the Wisconsin laboratory might have resulted from more
complete oxidation of the mercury in the method used by the Wisconsin laboratory compared to
the Colorado laboratory's methods.
Total mercury in lichen, sediment, fish, and moose was measured at the WRS Analytical Labora-
tory by combustion atomic absorption spectrophotometry (CAAS) with a LECO AMA254
Mercury Analyzer (LECO, St. Joseph, Michigan, USA) according to EPA method 7473
(USEPA, 1998). This method uses thermal decomposition, catalytic removal of interference,
collection onto a gold alloy amalgamate, and thermal release of Hg from the amalgamate. Hg
released from the amalgamate is measured by atomic absorption spectrophotometry (254 nm)
with a high voltage (2 kV) mercury lamp. Each sample was analyzed in duplicate, with the
average of the duplicates reported as the sample concentration. Samples were rerun if the percent
relative standard deviation (%RSD) of the duplicates was greater than 15%.
Table 3-6 summarizes the data quality of the mercury analyses, with method detection limits,
mean precision of replicate samples, and mean percent differences of standard reference
materials (SRMs). Method detection limits were determined according to Taylor (1987), with
repeated analyses of a low concentration sample. SRMS were available for snow, lichens,
sediment, and fish from the National Research Council of Canada (NRCC), the National Institute
of Standards and Technology (NIST), and the Standards, Measurements, and Testing Program of
the European Commission (ECSMTP). Moose samples were analyzed with the fish SRMs.
Table 3-6. Summary of Data Quality Indicators for Mercury by Media.1
Media
Snow
Units2
ng/L
Method
Detection
Limit3
0.04
%RSD from
Replicate
Samples SRM4
29.45 NIST 3133
Mean
%Difference of
SRM
0.51
Lichen
Sediment
Fish
ng/g
ww
ng/g
dw
ng/g
ww
3.3
6.0
(Diluted to theoretical
value of 5 ng/L)
4.5 ECSMTP Lichen CRM 482
(480 ± 20 ng/g)
5.3 NRCC Marine Sediment: PACS-2
(3040 ± 200 ng/g)
NRCC Marine Sediment: MESS-3
(91 ± 9 ng/g)
6.6 NRCC DORM-2 dogfish
(4640 ± 260 ng/g)
NIST 2976 Mussel Tissue
(61.0 ±3.6 ng/g)
-3.5
3.8
2.9
3.3
-7.9
1 Method detection limits and precision from replicate samples were not determined for moose because there
were so few moose samples (6 over 2 years). Moose samples were analyzed with the same SRMs used for
fish analyses.
2ww = wet weight, dw = dry weight
3Method detection limits determined as described in Taylor (1987)
4NRCC= National Research Council of Canada; NIST= National Institute of Standards and Technology;
ECSMTP = Standards, Measurements, and Testing Program of the European Commission.
5 %RSD values for snow are from the analysis of replicate samples collected at the field sites, while the %RSD
values for the other media are from analysis of samples split into replicates in the laboratory.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
3-13
-------
CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
3.3.3 Data
All trace metals analyses were conducted at the USGS-NRP Boulder (Trace Element Environ-
mental Analytical Chemistry Project, National Research Program) Laboratory. Quality control at
this laboratory involves the systematic analysis of blanks, replicates, SRMs, and spike addition
samples. All sample measurements were made at least in triplicate, with the mean value reported
as the sample concentration. Table 3-7 summarizes the data quality of metal analyses, with mean
method detection limits, median precision of replicate samples, and mean percent differences
from analyses of SRMs for Cd, Cu, Ni, Pb, V, and Zn.
Table 3-7. Summary of Data Quality Indicators for Metals by Media.1
Media
Snow
Lichen
Sediment
Fish Fillet
Fish Liver
Media
Snow
Lichen
Sediment
Fish Fillet
Fish Liver
Units
ug/L
M9/9
M9/9
M9/9
M9/9
Units
%RSD
%RSD
%RSD
%RSD
%RSD
Mean Method
Cd
0.003
0.01
0.01
0.005
0.01
Median Precision
Cd
13.1
2.7
2.6
14.5
2.3
Detection Limits2
Cu
0.01
0.2
0.1
0.03
0.1
of Replicate
Cu
9.2
2.4
1.6
1.7
2.1
Ni
0.008
0.1
0.06
0.06
0.06
Samples
Ni
11.4
9.7
1.7
15.2
39.4
Pb
0.008
0.04
0.03
0.01
0.009
Pb
19
1.9
1.2
11.9
12.2
V
0.02
0.1
0.4
0.05
0.05
V
59.8
3.2
2.1
18.9
10.3
Zn
0.1
0.9
0.7
0.4
0.4
Zn
22.7
4.6
1.7
1.5
2.8
Mean %D if fere nee of SRMs3
Media
Snow
Lichen
Sediment
Fish Fillet
Fish Liver
Units
% Difference
% Difference
% Difference
% Difference
% Difference
Cd
0.3
8.6
-2.1
-4.0
2.23
Cu
-1.8
7.9
0.8
7.1
11.4
Ni
0.6
-1.2
2.7
16.8
-7.2
Pb
-2.2
14.2
4.2
10.4
15.9
V
-0.2
33.3
1.0
na
-0.7
Zn
1.6
9.5
0.4
5.5
10.2
1 Results for moose meat and moose liver are not included here, but are provided in the Quality Assurance/Quality
Control Report for Trace and Major Elements with the database;
na = not available; %RSD = percent relative standard deviation.
2 Median value of detection limits are provided for sediment. Method detection limits calculated as described in
Taylor (2001).
3 One to five SRMs were analyzed for each metal for each media. The mean value of the percent differences for all
SRMs is presented here.
3-14
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Detection limits for each metal vary slightly, depending on specific analysis conditions for each
analytical run (Taylor, 2001). Appendix 3B provides the mean detection limits, by media, for all
the metals analyzed. Actual calculated detection limits are listed in the database. For statistical
comparisons, concentrations less than the detection limits were replaced by concentrations
representing one-half of the detection limit (Antweiler and Taylor, written communications),
following the guidelines listed in Section 3.5.1. Details, results, and figures describing the data
quality of all metals analyses are provided in the Quality Assurance/Quality Control Report for
Trace and Major Elements, included with the documentation for the WACAP database.
3.4 Methods Used
3.4.1 Air Modeling
Because of the remote locations of the WACAP sites, atmospheric transport modeling was an
integral part of understanding how the contaminants were transported to the sites. Atmospheric
transport was modeled via back-trajectory cluster analysis on three different time scales for each
of the WACAP core parks. A back-trajectory represents a meteorological calculation of the path
that an individual air particle has traveled over a specific time period. By grouping similar
trajectories into clusters, we obtained information about the routes of contaminant transport, as
well as the climatology for each park.
Using the National Oceanic and Atmospheric Administration's (NOAA) HYSPLIT model and
the National Centers for Environmental Prediction (NCEP) meteorological grids (Draxler and
Hess, 2004), we calculated back trajectories for each WACAP core park daily, from 1998
through 2005, for 1-, 5-, and 10-day durations, making a total of 2,922 trajectories for each
WACAP park and duration, and a total of 21 sets of trajectories. Figure 3-2 shows all the 2,922
1-day back-trajectories for MORA. The individual points are the hourly locations of each
trajectory.
\ r
i X
r ->. /
Figure 3-2. All 2,922 One-Day Back-Trajectories for MORA. The figure on the left (1) shows 2,922
individual one-day trajectories for MORA. The figure on the right (2) shows cluster means (dots), standard
deviations (ellipsoid shapes), and member trajectories for clusters A and F.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
3-15
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
The goals of cluster analysis were to group the trajectories so as to minimize the variability of
trajectories within a cluster, and maximize the variability among clusters (Owen, 2003; Hafner et
al., 2007). Using a non-hierarchical clustering algorithm (meaning the number of clusters are
pre-determined), we separated each set of 2,922 trajectories into 6 clusters consisting of 55 to
1,125 member trajectories. Clusters are graphically represented by the cluster mean and standard
deviation, which are the average position of all trajectories in that cluster, and the standard
deviation of the trajectories about the cluster mean, respectively. In each set, the clusters were
labeled alphabetically, with the shortest being A and the longest F. The graph on the right in
Figure 3-2 shows an example of the mean and standard deviation for clusters A and F from the
1-day cluster results at MORA (member trajectories of these clusters are included).
Daily precipitation totals from nearby metrological stations were applied to the member trajec-
tories from each cluster. Using these data, we were able to calculate the sum of precipitation for
which each cluster was responsible, and normalize this amount to total precipitation. This rela-
tive amount of precipitation per cluster is a useful metric for determining the pathways of wet
deposition. Table 3-8 lists the starting locations and altitudes of the trajectories, as well as the
locations and names of the precipitation stations.
Table 3-8. Starting Locations for Back Trajectories and Precipitation Data Used for Cluster
Analysis. Latitude and longitude are in decimal degrees, altitude is in meters above ground level.
Trajectory Starting
WACAP Site
NOAT and GAAR
DENA
GLAC
OLYM
MORA
ROMO
SEKI
Latitude
68.0
63.3
48.5
47.9
46.9
40.3
36.6
Longitude
-158.5
-151.3
-113.5
-123.5
-121.9
-105.6
-118.7
Locations
Altitude
0
0
580
1100
1100
600
700
Precipitation Data
Settles WBAN
Den417CASTNET
Flattop Mountain Snotel
Mount Crag Snotel
Paradise Snotel
Lake Irene Snotel
Virginia Lakes Ridge Snotel
Distance
from
WACAP
Site
320 km SE
125 KM NE
43 km NW
38 km SE
18kmSE
23 km NW
171 kmNW
3.4.2.1 Snowpack Sample Collection
The objective was to sample the seasonal snowpack from at least one site in or near the WACAP
sampling watersheds during each of the 3 years of the study, in order to assess inter-annual
variability of contaminant loading. This objective was generally met, with a few exceptions
caused by either poor snow conditions or consideration for safety of field crews.
All snow sampling tools were pre-cleaned in the laboratory with high-purity deionized water,
and stored in sealed polyethylene bags. Teflon™ bags for organics aliquots were pre-rinsed with
ethyl acetate, followed by a 1:1 mixture of hexane and acetone; Teflon™ bags for inorganics
aliquots were pre-rinsed with high-purity deionized water. Each sample bag was sealed in 2
ziploc bags, and groups of these were stored in large polyethelene bags.
3-16
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Snowpack samples were collected in small forest clearings or open areas, near the time of annual
maximum snow accumulation but before the onset of spring snowmelt (Ingersoll et al., 2001). A
snowpit was dug from snow surface to ground surface, and physical properties of the snowpack
were measured, including snow grain type and size, hardness, temperature, and density. Snow
density was measured with a volumetric cutter (250 or 1,000 cc) inserted into the snowpit wall,
tared, and weighed on a portable electronic balance. Density was measured at 10-cm intervals in
shallow snowpacks, or at an interval that provided at least 10 measurements in deeper
snowpacks. We calculated snow water equivalent (SWE) by multiplying the average density by
total snow depth. Later, we calculated fluxes of contaminants in the snowpack by multiplying
concentrations by SWE.
Snowpits were dug with steel and ordinary polycarbonate shovels used for avalanche safety, then
the pre-cleaned polycarbonate shovels and scoops were used to create a fresh face in the snowpit.
A vertical column of snow was cut from the pit face and placed in pre-cleaned Teflon™ bags.
The vertical column integrated snow that accumulated throughout the snowfall period. Snow
samples were collected carefully to prevent contamination. The top 5 cm and bottom 10 cm of
snow from the pit face were excluded from the sample to reduce the possibility of contamina-
tion. The sealed Teflon™ sample bags were placed in thin black polyethylene bags to exclude
light, then in clean heavy-duty polyethylene bags for protection. Samples were frozen within 12
hours on dry ice to minimize chemical and biological reactivity during transport and shipping.
Frozen samples were sent by overnight
express shipping service to laboratories at
USGS-CWSC (inorganic fraction) and
Oregon State University (organic fraction).
Two sub-samples were collected from each
snowpit: one sample for analysis of
inorganic constituents, including major
ions, nutrients, dissolved organic carbon,
trace metals, mercury, and particulate
matter; and one sample for analysis of
organic contaminants (see Appendix 3A).
Each sample for inorganic analysis was
collected in a single Teflon™ bag
containing approximately 6 liters of snow,
yielding about 2 liters of meltwater. Each sample for organic analyses consisted of six large,
solvent-rinsed Teflon™ bags containing a total of approximately 150 liters of snow, yielding
about 50 liters of meltwater.
3.4.2.2 Analytical Methods for Major Ions and Nutrients in Snow
Snow samples for inorganic analyses, including analyses for major ions, nutrients, and trace
metals, were processed in the USGS Colorado Water Science Center (CWSC) Laboratory
according to protocols established for related projects (Ingersoll, 2001). To melt the samples,
Teflon™ collection bags were placed in clean polyethylene buckets at room temperature for
approximately 12 hours. Buckets were placed on a shaker table to homogenize the distribution of
fine particulate matter. Sample aliquots were drawn through a small hole in the top of the sealed
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
3-17
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
bag. The sample was drawn through a tube connected to a peristaltic pump. Sample aliquots
were then distributed to various laboratories for analysis.
Calcium (Ca), magnesium (Mg), potassium (K), sodium (Na) and silicon (dissolved SiCh) were
determined on an acidified (0.4% by volume ultra-high-purity nitric acid) filtered (0.45 um)
aliquot by direct calibration inductively coupled plasma-atomic emission spectrophotometry
(ICP-AES), with a Perkin Elmer model 3300DV multi-channel emission spectrometer
(Garbarino and Taylor, 1979; Boss and Fredeen, 1999; Mitko and Bebek, 1999, 2000). Samples
were introduced into the spectrometer via a Teflon™ parallel path nebulizer.
Ammonium (NH4+), potassium (K), sodium (Na), nitrate (NOs ), chloride (Cl ), and sulfate
(SC>4 ~) concentrations were determined, within 2 weeks of melting, on a filtered (0.45 um)
refrigerated aliquot by ion chromatography (Fishman, 1993). Detection limits were less than
0.5 ueq/L for all major ions.
Specific conductance, pH, and alkalinity were determined on an unfiltered, chilled aliquot.
Specific conductance was measured with a platinum electrode; pH was measured with a
combination glass electrode designed for low-ionic strength waters; and alkalinity was
determined by automatic titration and Gran calculation. Dissolved organic carbon (DOC)
concentration was determined on a filtered, chilled aliquot stored in a pre-combusted glass bottle.
DOC determinations were by infrared detection with a detection limit of 0.5 mg/L.
An aliquot for particulate carbon and nitrogen analysis was filtered through a glass-fiber filter,
which was shipped to the University of Maryland Chesapeake Biological Laboratory in
Solomons, Maryland. The filter was combusted and the products of combustion were analyzed
by thermal conductivity detector (http://www.cbl.umces.edu/nasl/index.htm; USEPA, 1997).
3.4.2.3 Analytical Methods for SOCs in Snow
Snowpack samples were stored at -20°C. At the time of analysis, samples were removed from
the freezer and allowed to melt in the dark, without heat, for ~36 hrs in sealed Teflon™ bags.
Once a sample was melted, a methanol solution containing isotopically labeled SOCs, for use as
recovery surrogates, was spiked into the sample (Usenko et al., 2005; Hageman et al., 2006). The
SOCs were extracted from melted snow with solid-phase extraction disks (combination of
hydrophobic and hydrophilic 1-g divinylbenzene Speedisks™ from Mallinckrodt Baker,
Phillipsburg, New Jersey) (Usenko et al., 2005; Hageman et al., 2006). No effort was made to
analyze dissolved-phase SOCs and SOCs sorbed on particulate matter separately because their
phase distribution in the snowpack is not maintained when the snow melts. Thus, the snow
concentrations reported in this report are for total SOC concentrations in snow (i.e., the sum of
SOCs in the dissolved and sorbed phases).
Gel permeation and silica gel adsorption chromatography were performed to remove media
interferants (Usenko et al., 2005; Hageman et al., 2006). The final extract was spiked with an
ethyl acetate solution containing four isotopically labeled internal standards. Analyte separation,
detection, and identification were performed on Agilent (Palo Alto, California) 6890N gas
chromatographs equipped with Agilent DB-5MS 30 m x 0.25 mm x 0.25 um columns and
5943N mass selective detectors (Usenko et al., 2005; Hageman et al., 2006). Approximately one-
half of the target analytes were quantified by means of electron impact (El) ionization, whereas
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
the other half were quantified by means of electron capture negative ionization. EDLs and
method recoveries for specific SOCs are provided in Table 3B-1 in Appendix 3B.
3.4.2.4 Analytical Methods for Metals in Snow
Total mercury was analyzed on whole-water samples (unfiltered) by oxidation, purge and trap,
and cold vapor atomic fluorescence spectrometry in the USGS Wisconsin Water Science Center
Mercury Laboratory (Olson and DeWild, 1999). For the 2003 and 2004 field seasons,
subsamples for other trace-metals were processed for both total (whole-water) and dissolved
(filtered) determinations. For 2005 and 2006 field seasons, only filtered samples were analyzed.
The aliquot for analysis of dissolved constituents was filtered through a 0.4-um, pore-size
polycarbonate membrane filter. After filtration, the sample was preserved by acidification to 1%
by volume with concentrated ultra-high-purity nitric acid. The nitric acid was purified in the
laboratory by double distillation (Kuehner et al., 1972).
The aliquot for analysis of whole-water constituents was preserved by the addition of 2 mL of
concentrated ultra-high-purity nitric acid to 250 mL of sample, and then subjected to a modified
in-bottle digestion with 5 mL of concentrated ultra-high-purity hydrochloric acid per 200 mL of
sample in a water bath at "near boiling" conditions (Garbarino and Hoffman, 1999). Following
digestion, the samples were filtered as described in subsection 3.4.2.2 to remove undissolved
particulate.
Metals present at trace concentration levels, including aluminum (Al), antimony (Sb), arsenic
(As), boron (B), beryllium (Be), barium (Ba), bismuth (Bi), cadmium (Cd), cerium (Ce), cesium
(Cs), chromium (Cr), cobalt (Co), copper (Cu), dysprosium (Dy), erbium (Er), europium (Eu),
gadolinium (Gd), holmium (Ho), lanthanum (La), lithium (Li), manganese (Mn), molybdenum
(Mo), neodymium (Nd), nickel (Ni), lead (Pb), praseodymium (Pr), rhenium (Re), rubidium
(Rb), samarium (Sm), selenium (Se), strontium (Sr), tellurium (Te), terbium (Tb), thallium (Tl),
thulium (Tm), tungsten (W), uranium (U), vanadium (V), ytterbium (Yb), yttrium (Y), zinc (Zn)
and zirconium (Zr), were determined by a multi-element inductively coupled plasma-mass
spectrometric (ICP-MS) method (Garbarino and Taylor, 1995; Taylor, 2001).
These determinations were performed with a Perkin Elmer model Elan 6000 mass spectrometer.
Aerosols of nitric acid preserved sample solutions were introduced into the spectrometer with a
Teflon™ parallel path nebulizer. Multiple internal standards (indium, iridium, and rhodium)
were used to normalize the system for drift. Detection limits for metals in snow samples are
listed in Table 3B-2 in Appendix 3B.
3.4.3.1 Passive Air Sampling Device (PASD) Deployment
Passive air sampling devices (PASDs) were used to (1) obtain a measure of SOCs in ambient air
by means of a simple, standardized technology to compare loadings between parks and across
geographic and elevational gradients, (2) compare PASD and vegetation concentrations, and (3)
compare ambient air SOC concentrations in WACAP parks to ambient air concentrations at other
national and international locations measured with the same PASD design.
In total, 37 PASDs were strategically deployed in all core and secondary WACAP parks.
Multiple PASDs were deployed in the eight core WACAP parks and in two secondary parks to
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 3-19
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
sample target watersheds and to obtain data along elevational gradients (see Table 3-9). All but
four of the PASDs (two in SEKI and two in ROMO) were co-located with WACAP vegetation
sampling sites.
The PASD design followed Wania et al. (2003). Each PASD consisted of a stainless steel wire
mesh cylinder (1.6 cm in diameter, 20 cm long) filled with 460-|am-diameter styrene divinyl-
benze resin beads (Amberlite XAD-2), suspended in an aluminum stove pipe housing with an
aluminum cap to prevent wetting of the cylinder from precipitation (see Figure 3-3a). The PASD
housing was open at the bottom to allow air circulation, but covered with chicken wire to prevent
incursion by small mammals or birds. The housing had been previously spray-painted green to
camouflage the device and baked at 66°C for 2 hours to off-gas SOCs.
Freshly loaded resin cartridges were mailed to field offices in air-tight stainless steel containers
by overnight mail a few days before field deployment. Cartridges were exposed and assembled
in their housing at field sites and hung from exposed tree branches with stainless steel wire or
nylon rope (Figure 3-3b). In WACAP park sites with few or no trees (GAAR, NO AT, CRLA),
the PASDs were attached to structures (e.g., Figure 3-3c). All 37 PASD samplers were installed
in summer 2005 and retrieved 1 year later (±2 weeks). A small temperature data logger was hung
inside the PASD housing to record mean temperature at hourly intervals. To retrieve a PASD,
field personnel disassembled it at the field site, placed the resin cartridge into the air-tight
cylinder, and mailed it back to the Simonich laboratory, where it was stored at -40°C until
analysis.
Table 3-9. Summary of Passive Air Sampling Device Distribution among WACAP Parks.
No. of Monitors Park
1 GAAR
NOAT
BAND, CRLA,
GLBA,GRSA, GRTE,
KATM, LAVO, NOCA,
WRST, YOSE
2 GLAC
MORA
OLYM
DENA
4 BIBE
SEKI
STLE
5 ROMO
Lake Watersheds
Matcharak
Burial
Snyder & Oldman
LP1 9 & Golden
PJ & Hoh
Wonder
Emerald
Mills & Lone Pine
Elevation(s) (m)
505
388
2926,2713,
8, 3338, 3048
370,2713, 1600
648, 3048
1609,2036
1372, 1369
1392, 1433
564, 686
560, 1067,2316,2713
658,670,2300,2816
0,254,567,815
2560, 2720, 3018, 3042, 3536
3-20
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Figure 3-3. Passive Sampler: (a) Hooking resin cartridge to underside of housing cap, (b)
Deployment from an exposed tree branch in the Stikine-LeConte Wilderness, and (c) Deployment
from a fire tower in Crater Lake National Park.
3.4.3.2 Analytical Methods for SOCs in PASDs
Before deployment, the sampling resin (Amberlite XAD-2) was cleaned by means of a high
temperature and high pressure extraction method (pressurized liquid extraction). The XAD-2 was
spiked with d-hexachlorocyclohexane (d-HCH) and polychlorinated biphenyl PCB 166 before
shipping, to track SOC volatilization from the PASDs during shipping and deployment (Wania et
al., 2003). Once collected, the XAD-2 was removed from its sampling tube and spiked with 14
isotopically labeled surrogates and extracted by means of pressurized liquid extraction (see
Tables 3B-3 and 3B-4 in Appendix 3B). The extract was concentrated and spiked with iso-
topically labeled internal standards. The extract was analyzed for the SOCs listed in Table 3-3 by
means of gas chromatographic mass spectrometry. The PASD concentrations were surrogate
recovery corrected and travel blank subtracted. EDLs and method recoveries for specific SOCs
are provided in Table 3B-4 in Appendix 3B.
3.4.4 Vegetation
3.4.4.1 Vegetation Sample Collection
The vegetation sampling objectives were to (1) determine
which SOCs accumulate in vegetation in each WACAP park
and their respective concentrations, (2) compare individual
SOC concentrations within and across parks, especially
along latitudinal and elevational gradients, to test for a cold
fractionation effect (higher concentrations in colder sites,
i.e., higher latitudes and elevations), (3) evaluate metal and
nutrient burdens in lichens in relation to known ranges for
clean sites and accumulation of SOCs, (4) determine the
relationship between environmental factors such as
geographical location, proximity to urban-industrial and
agricultural areas, nitrogen concentrations in ambient
particulates, and lichen nitrogen content with SOC
concentrations in vegetation, and (5) make rough estimates
of total burdens of SOCs in conifer needles at WACAP sites
in g/ha as a way of evaluating SOC inputs to watersheds via
litterfall.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
3.4.4.1.1 Vegetation Selection
Conifer needles were chosen as the primary form of
vegetation for SOC analysis because samples repre-
senting a defined exposure period (second-year needles)
could be collected. In addition, coniferous trees were a
dominant component of the vegetation at all WACAP
parks, excluding the arctic (NOAT, GAAR). Conifer
needles have been previously used to study SOCs in
North American high elevation and high latitude
ecosystems (Howe et al., 2004; Davidson et al., 2003).
Lichens were collected from the core parks for SOC,
mercury, metals, nitrogen (N), and sulfur (S) analyses,
and from the secondary parks for N and SOC analyses.
Lichens have been used extensively for N, S, and metals
analysis, plus their use allowed sampling of treeless sites
(tundra and alpine ecosystems). In addition, they gener-
ally had higher concentrations of SOCs than conifer
needles collected from the same sites, facilitating
detection of site-to-site differences.
3.4.4.1.2 General Sampling Strategy
The general sampling strategy was to collect two forms of vegetation, when available, at each
site: second year needles from one species of conifer and multiple thalli representing the on-site
population of one species of lichen. A sample consisted of > 150 g dry weight (dw) of conifer
needles or > 40 g dw of lichens. Collection sites were ~ 1 ha in size. Five collection sites were
selected within each park, evenly spaced between the lowest and highest vegetated elevations,
and included the target watersheds. Conifer needle and epiphytic lichen samples were sampled
from a minimum of 8 trees, but usually > 20 trees from the 1-ha collection site. Rock lichens
(Xanthoparmelia) and tundra lichens (Flavocetraria cucullata and Masonhalea richardsonii)
were sampled from a minimum of eight rocks or ground patches, respectively. To obtain field
replicates, the 1 ha area was resampled, with collection from different trees, rocks, or ground
patches.
3.4.4.1.3 Site Selection
Prior to the field season, resource specialists at each park were consulted to pre-select collection
sites in order to minimize the number of species needed to sample across all elevations (sites)
within each vegetation type (needles and lichens). Collection sites emphasized the west side of
each park to increase the probability of detecting trans-Pacific contaminants. Although sites were
not located along linear transects, an effort was made to keep all sites in the same quadrant of the
park. A total of 20 species of conifer needles and 16 species of lichens were collected across the
WACAP parks (Table 3-10). Of the 354 samples collected, 302 were analyzed for SOCs, 157
lichen samples were analyzed for total N, and 52 lichen samples were analyzed for S and metals,
including mercury.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-10. Vegetation Sample Summary. See Appendix 1A-3 for scientific names of the species
sampled at each site.
Sample
Type
Conifer
needles
Genera
Fir, spruce, pine, Douglas-fir,
hemlock
Genus
Count
5
Species
Count
19
Samples
for SOC
Analysis
157
Samples
for N but
not S or
Metals
Analysis
o
Samples
for N, S
and Metals
Analysis
o
Lichens
Total
Count
Alectoha, Bryoria, Cladina,
Flavocetraria, Hypogymnia, Lethaha,
Lobaria, Masonhalea, Platismatia,
Sphaerophorus, Thamnolia, Usnea,
Xanthoparm elia
13
18
16
35
143
296
105
105
52
52
3.4.4.1.4 Differences in Sampling Strategy between Core and Secondary Parks
The sampling strategy differed slightly between core and secondary WACAP parks. At the core
parks (NOAT, GAAR, DENA, OLYM, MORA, SEKI, ROMO, GLAC), the study design called
for collection of three replicate samples of conifer needles and whole lichen thalli from one
species of conifer and two species of lichens at each of five elevations in each park (3 repetitions
x 3 species x 5 elevations = maximum of 45 samples/park). Every effort was made to use the
same species across elevations. However, in parks with large elevational gradients, this was not
possible. Also, in treeless areas, only lichens could be collected, and in very dry locations, only
conifers could be collected. Conifer needles were analyzed for SOCs, one species of lichen was
analyzed for SOCs, and both species of lichen were analyzed for mercury, metals, N, and S. Core
park vegetation samples were collected during summer 2004.
At the secondary parks (BAND, BIBE, CRLA, GLBA, GRSA, GRTE, KATM, LAVO, NOCA,
STLE, WRST, YOSE), the study design called for the collection of one sample each of needles
and lichen thalli from one species of conifer and one species of lichen at each of five elevations
(2 species x 5 elevations = maximum of 10 samples/park). Ten percent of the samples were
replicated. Secondary park vegetation samples were collected during summer 2005.
3.4.4.1.5 Field Protocols for Vegetation Sampling
The WACAP Research Plan (USEPA, 2003)
provides details of the field protocol for
vegetation sampling. Briefly, field personnel
established collection site centers, staying within
the 1-ha area, and collected samples into 2-liter,
Silverpac metalized polyester bags (Ampac
Flexibles, Product # 602B-IM, 5305 Parkdale Dr.,
St. Louis Park, MN 55416). The bags were pre-
tested in the laboratory to ensure they would not
contribute to sample SOC or metal concentra-
tions. Conifer branches were clipped at the first-
and second-year terminal bud scars with solvent-
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
washed hand-pruners; lichens were collected by hand, by personnel wearing disposable latex or
nitrile gloves. Filled bags were weighed, sealed with laboratory tape, and placed inside two
zipper-locking plastic bags (see Figure 3B-1 in Appendix 3B for field photographs). Samples
were kept cold in freezers or packed with crushed ice in plastic coolers, depending on the
remoteness of the field site, in order to prevent decay. At the conclusion of sampling within each
park, a 2-5 day process, samples were shipped in coolers filled with crushed ice in overnight or
second-day mail to the Simonich laboratory. At the laboratory, samples were stored at -40° C
until analysis.
On-site observations made by field personnel included location coordinates of the sampling site
center; elevation; slope; aspect; cover of dominant trees, shrubs, and herbs; size of the sampling
area; landform; exposure; and canopy cover. Notes were made on vegetation condition and
potential local sources of SOC, nutrient, or metals contamination. See Appendix 3B for complete
site data, sample records, and a sample field data record sheet.
3.4.4.2 Analytical Methods for SOCs in Vegetation
Frozen 2-year-old conifer needles and lichen were ground with a Buchi mixer with ceramic
knives. A ground sub-sample of-10-20 g [wet weight (ww)] was mixed with sodium sulfate,
spiked with isotopically labeled surrogates, and extracted at a high temperature and pressure with
dichloromethane. The extract was purified with water extractions and solid phase extraction with
silica. Conifer needles required gel-permeation chromatography (GPC) as an extra clean-up step.
The extract was concentrated to 300 uL, spiked with isotopically labeled internal standards, and
measured for the SOCs listed in Table 3-3 by means of gas chromatography mass spectrometry
(GC/MS). EDLs and method recoveries for specific SOCs are provided in Tables 3B-7 and 3B-8
in Appendix 3B.
3.4.4.3 Analytical Methods for Metals in Lichen
Lichen samples for metals, total nitrogen
and sulfur, and mercury were collected from
11 sites in 7 core parks in 2004. No lichens
were available for collection from Pear Lake
in SEKI, and the lichen collected from the
two sites in ROMO were on rock substrate
with potential for interference from soil
contamination.
These lichen samples were prepared for
analyses at the USGS-Boulder laboratory.
The samples were rinsed with deionized
water to remove potential surface
contamination from soils. Each sample was
finely chopped with a ceramic knife,
subsampled, and freeze-dried to remove residual moisture. After drying, samples were
pulverized to a fine powder. Subsamples of the dried, ground lichens were then collected for
metals, total nitrogen and sulfur, and mercury analyses.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
The subsamples for metals analyses were digested with ultra-high-purity nitric acid in a closed
Teflon™ container in a microwave oven (Barber et al., 2003). Following dissolution, the
samples were analyzed in triplicate for metals present at high concentrations, including calcium
(Ca), iron (Fe), magnesium (Mg), potassium (K), and sodium (Na) by inductively coupled
plasma-atomic emission spectrophotometry ICP-AES.
Trace metals present at low concentrations were determined by a multi-element inductively
coupled plasma-mass spectrometric method (Garbarino and Taylor, 1995; Taylor, 2001) with a
Perkin Elmer model Elan 6000 mass spectrometer. Typical detection limits for metals analyzed
in lichen are listed in Table 3B-9 in Appendix 3B.
3.4.4.4 Analytical Methods for Total Nitrogen and Sulfur in Lichen
The subsamples of the dried, ground lichen samples for total N and S analyses were shipped to
the University of Minnesota Research Analytical Laboratory. Nitrogen was measured by the
Dumas total combustion method (Simone et al., 1994; Matejovic, 1995), with a LECO Model
FP-528 nitrogen analyzer. Samples of 150 to 200 mg were combusted in an C^-rich atmosphere
at 859°C. A 3-mL aliquot of the cooled combustion material was integrated into a He carrier
stream and passed through a hot copper column to remove C>2 and convert NOX to N2. N2 was
measured with a thermal conductivity cell, which displayed the result as % N. The batch size was
30 samples.
For sulfur analysis, a 0.100-0.150-g sample was weighed into a ceramic boat, covered with
tungsten oxide Corn-Cat Accelerator and dry combusted in an C^-rich atmosphere at 1,350°C.
Total % S was determined by infrared absorption of evolved sulfur dioxide on a LECO Model
No. S144-DR Sulfur Determinator. The batch size was 45 samples.
Total percent dry weight nitrogen and sulfur concentrations in all samples were above method
detection limits, which was 0.01% for both elements. See Appendix 3B for information on the
quality control checks used and analysis of sources of variability.
3.4.4.5 Analytical Methods for Mercury in Lichen
The subsamples of the dried, ground lichen samples for mercury analyses were shipped to the
WRS Analytical Laboratory. Total mercury was measured with a LECO® AMA254 Mercury
Analyzer in accordance with EPA method 7473 (USEPA, 1998).
3.4.5.1 Lake Water Sample Collection
Lake water samples were collected from each catchment during the ice-free summer season to
characterize the condition of the WACAP lakes by assessing the chemical and physical
characteristics of water quality, including trophic state, chemical contamination, and
acidification status. Analytes included pH, alkalinity, specific conductance, dissolved organic
carbon, dissolved inorganic carbon, chlorophyll-a, total nitrogen, total phosphorus, and major
cations and anions. Samples were collected at a depth of 1 m, from the deepest area of the lake,
with a 2-L Kemmerer sampler, and stored in a 4-L cubitainer. Syringe samples were collected
from a port in the Kemmerer for closed-system analyses of pH and dissolved inorganic carbon.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Twenty-five to 50 liters of lake water
were sampled, filtered, and extracted for
SOCs in situ with an Infiltrex 100
submersible pump (Axys, B.C., Canada)
(Usenko et al, 2005). The Infiltrex 100
contained a l-(j,m glass fiber filter (OFF)
(14.2 cm diameter) to remove SOCs
sorbed to particulate matter followed by
a modified Speedisk to extract SOCs
dissolved in the aqueous phase. Field
blanks consisting of a OFF and
modified Speedisk were taken during
sample collection and placed in the
Infiltrex 100, but not submerged in or
exposed to lake water (Usenko et al.,
2005). The blank OFF and modified Speedisk were removed from the Infiltrex 100 and treated
identically to the OFF and modified Speedisk used for sampling. After the in situ extraction, the
OFF was removed from the Infiltrex 100 and stored in a 40-mL clean glass vial. The modified
Speedisk was also removed from the Infiltrex 100 and resealed with a Teflon™ cap and a
polypropylene syringe needle cap and stored in a clean polypropylene jar. The OFF and modified
Speedisk were placed on dry ice and stored in coolers in the field and during overnight transport
to the Simonich laboratory. Once in the laboratory, the OFF and modified Speedisk were stored
at-12°C.
3.4.5.2 Analytical Methods for SOCs in Lake Water
For lake water SOC analysis, analytes were eluted from the modified Speedisk with ethyl acetate
(EA), dichloromethane (DCM), and DCM:EA, and the OFF was extracted with a pressurized
liquid extraction (Usenko et al., 2005). A modified Speedisk and OFF were spiked directly
before elution/extraction with 15 (J.L of 10 ng/(j,L isotope labeled surrogate-EA solution (Usenko
et al., 2005). Eluants from both the Speedisk and the OFF were dried separately with sodium
sulfate. Extracts were concentrated and then purified on a 20-g silica solid phase extraction
cartridge. The lake water extracts were analyzed for the SOCs listed in Table 3-3 by GC/MS, by
means of both El ionization and electron capture negative ionization (ECNI) (Usenko et al.,
2005). The analytical method was validated for efficiency with triplicate spike and recovery
experiments over modified Speedisks with 50-L samples of reverse osmosis water (Usenko et al.,
2005). Method recoveries for specific SOCs are provided in Table 3B-11 in Appendix 3B.
3.4.5.3 Analytical Methods for Inorganic Compounds in Lake Water
Water samples were collected and analyzed following the water chemistry protocols from the
Environmental Monitoring and Assessment Program's Surface Water (EMAP-SW) group
(Chaloud and Peck, 1994). A portion of the sample from the cubitainer was filtered with a hand
pump through a glass fiber filter for chlorophyll analyses. The cubitainer, syringes, and
chlorophyll filters were stored on ice in a cooler, and shipped via overnight FedEx as soon as
possible after collection to the WRS Analytical Laboratory. These water samples were collected
on the last day at each lake site to minimize the holding times.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
3.4.6 Sediment
3.4.6.1 Sediment Sampling
Lake sediment cores were collected
to provide information about the
accumulation and sources of
contaminants in the WACAP
catchments during the last -150
years. Cores were collected from the
profundal areas of each lake with a
UWITEC gravity corer fitted with a
Plexiglas tube with an 86-mm
internal diameter. Cores were
sectioned in the field the same day
samples were collected, and were
stored in 250-mL, solvent-rinsed
glass jars. The core was extruded
and sliced in 0.5-cm intervals (12-18
g ww) from 0 to 10 cm, then 1.0-cm
intervals (30-40 g ww) from 10 cm to the bottom of the core. Sediment samples were shipped
overnight in 50-L coolers with ice-packs to the WRS Analytical Laboratory where they were
stored at 4°C until physical and elemental analyses were conducted.
Each wet sediment interval was split in the laboratory, with approximately 12 cm3 removed for
inorganic analyses [dating, percent moisture, spheroidal carbonaceous particles (SCPs), mercury,
carbon, and metals) and the remaining wet sediment, approximately 17 cm3, was stored at -20°C
for analysis of SOCs by the Simonich Laboratory. Each core was processed beginning with the
bottom intervals and proceeding to the top intervals to ensure that the cleanest samples were
processed first. Each interval was homogenized to a uniform color and texture with a Teflon™
spatula before the inorganic subsample was removed.
The inorganic subsample was freeze-dried and percent moisture determined. A 0.15-g subsample
was removed for SCP analyses, and the remaining dried sediment was lightly ground with a
mortar and pestle. Dried, ground sediment from 10 to 12 intervals from each core was used to
determine the dating chronology to ensure that the stratigraphy of the core was intact, i.e., the
layers of sediment were deposited in chronological order and had not been disturbed. The main
dating technique used was the 210Pb method, but the artificial radionuclides 137Cs and 241Am
were also used, with the peak in fallout of these radionuclides reached in 1963 (Appleby et al.,
1986; Appleby et al., 1991). The radionuclide analysis was performed at the Environmental
Radioactivity Research Centre at the University of Liverpool by direct gamma assay, with an
Ortec HPGe GWL series well-type coaxial low background intrinsic germanium detector. Two
models were used to determine the sediment core chronologies: the CRS (constant rate of
supply) and the CIC (constant initial concentration) (Appleby, 2001).
210
'Pb
Two cores were collected and sectioned from each lake, and if the dating results from the
primary core indicated the stratigraphy was not intact, the second core was dated. The core with
the most acceptable chronology was used for the other analyses, including SOCs, mercury,
metals, SCPs, total carbon (TC), total organic carbon (TOC), and, by difference, total inorganic
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
carbon (TIC). At five sites, the amount of sediment material in the primary core was limited, so
SCP analyses were conducted on the secondary core. Metal analyses were conducted on both
cores from five sites (LP19, Golden, Hoh, PJ, and Snyder) to provide additional information
about the secondary cores.
3.4.6.2 Lake Sediment Focusing Factors
Correcting for the redistribution of particulate matter in lake sediment is important because
lipophilic SOCs and metals sorb to particulate matter. The erosion and accumulation of
particulate matter can also result in the erosion and accumulation of SOCs and metals from one
area of the lake to another. For examination of the spatial and temporal trends of SOCs and
metals in multiple cores, all SOC and metal sediment concentrations (ng/g dry wt) were
multiplied by the mass sedimentation rate (g/cm2/y) and normalized to the unitless focusing
factors to arrive at the focus-corrected flux (ng/m2/yr or ug/m2/yr).
Focusing factors (FF) were calculated for each sediment core in order to correct for differences
in sediment focusing among the WACAP lake sites. Sediment focusing describes the redistribu-
tion of particulate matter throughout the lake (Likens and Davis, 1975).
210Pb Inventory [3-2]
210Pb Fallout
In equation [3-2], 210Pb inventory is derived by plotting unsupported 210Pb against the mass
sedimentation accumulation rate (Zhu and Kites, 2005). The 210Pb atmospheric fallout was
modeled from ice cores, soil samples, and atmospheric collectors near sampling sites. 210Pb
fallout values from Alaska and the Yukon were used for DENA, GAAR, and NO AT; values
from Seattle, Washington, were used for OLYM and MORA; values from mid-California were
used for SEKI; and values for Colorado were used for ROMO and GLAC (Granstein and
Turekian, 1986; Carpenter et al, 1984; Monaghan, 1989; Monaghan and Holdworth, 1990;
Nevissi, 1985). 210Pb atmospheric fallout can vary over short time periods; however, over the
time frame of these sediment cores (<150 years), the 210Pb atmospheric fallout is considered to
be fairly constant (Appleby et al., 1986; Appleby, 2001). In areas of a lake where the particulate
matter is accumulating, the FF is greater than one. In areas of a lake where the particulate matter
is eroding, the FF is less than one. The FFs of the WACAP lake sediment cores ranged from 0.78
to 4.55.
3.4.6.3 Measurement of Spheroidal Carbonaceous Particles (SCPs) in Lake Sediments
Fossil fuels are burned at high temperatures to produce heat and power for electricity generation
and other industries. At temperatures of up to 1,750°C (Commission on Energy and the Environ-
ment, 1981) and at a rate of heating approaching 104°C/s (Lightman and Street, 1983), the drop-
lets, or pulverized grains of fuel, are efficiently burned, even though they remain in the furnace
only for a matter of seconds. The products of this combustion are porous spheroids of mainly
elemental carbon (Goldberg, 1985) and fused inorganic spheres formed from the mineral com-
ponent of the original fuel (Raask, 1984). These SCPs (Figure 3-4) and inorganic ash spheres
(lASs) are collectively known as fly-ash, the term used to describe the particulate matter within
emitted flue-gases.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Figure 3-4. Scanning Electron Micrograph of a Spheroidal Carbonaceous Particle (SCP). Photo:
Neil Rose.
SCPs are not produced from wood, biomass, or charcoal combustion, and hence have no natural
sources. Therefore, they are unambiguous indicators of deposition from industrial combustion of
fossil fuels. Their use as markers in sediments and other depositional sinks is enhanced by their
easily identifiable morphology and, because of their elemental carbon composition, by their
relatively simple extraction from the sediment matrix. In addition, the elemental composition of
SCPs can give clues as to their source fuel type (Rose et al, 1996).
Composed of elemental carbon, and thus physically fragile, SCPs are resistant to chemical
attack. Strong reagents were therefore used to remove unwanted fractions of sediment without
doing physical damage to the SCPs. The method used for the WACAP sediment samples has
been previously described (Rose, 1994). Briefly, the dried, unground sediment subsample was
digested in polytetrafluoroethylene (PTFE) tubes in a water bath. Unwanted sediment fractions
were removed by sequential chemical attack, with nitric acid (HNOs), hydrofluoric acid (HF),
and hydrochloric acid (HC1) used to remove organic, siliceous, and carbonate material,
respectively. A starting sediment mass of 0.15 g was reduced to less than 0.001 g by this
technique, thus removing more than 99.3% of the sediment (Rose, 1994).
The sediment digestion procedure results in a suspension of mainly carbonaceous material in
water. A known fraction of this suspension is evaporated onto a microscope covers lip and
mounted onto a slide using Naphrax (a low refractive index mountant). SCPs on the entire
coverslip are then counted at 400x magnification under a light microscope. SCPs are positively
identified with reference to the criteria of morphology, color, and porosity. The number of SCPs
counted is then converted to a concentration in units of "number of SCPs per gram dry mass of
sediment," or gDM"1. Reference sediment material of known SCP concentration and sediment
blanks were analyzed in all sediment sample digestions for quality assurance/quality control.
3.4.6.4 Analytical Methods for SOCs in Lake Sediments
Sediment samples for SOC analysis were allowed to thaw in sealed glass jars, ground with
sodium sulfate, and extracted with a pressurized liquid extraction. Sediment samples were spiked
directly before extraction with 15 (J.L of 10-ng/(j,L isotope labeled surrogate-EA solution.
Interferences were removed from the sediment extract with a 20-g silica solid phase extraction
cartridge and GPC. The sediment extracts were analyzed for the SOCs listed in Table 3-3 by
GC/MS, with both El ionization and ECNI. EDLs, recoveries, and percent difference of the
sediment SRM for specific SOCs are listed in Table 3B-13 in Appendix 3B.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
3.4.6.5 Analytical Methods for Mercury in Lake Sediments
Total mercury was measured on freeze-dried, ground sediment samples at the WRS Analytical
Laboratory with a LECO® AMA254 Mercury Analyzer in accordance with EPA method 7473
(USEPA, 1998).
3.4.6.6 Analytical Methods for Carbon in Lake Sediments
Total carbon was measured on untreated, freeze-dried, ground sediment samples with a Carlo
Erba 1108A CN analyzer. TOC was determined by measuring TC on samples after acid
treatment to remove carbonates (i.e., inorganic carbon). The TC remaining after treatment is
attributed to organic carbon. Total inorganic carbon (TIC) was determined by the difference
between TC and TOC. The mean precision of analytical duplicate samples was 2.2 percent
relative standard deviation, and the mean percent difference from certified reference materials
was 2.6%.
3.4.6.7 Analytical Methods for Metals in Lake Sediments
Subsamples of freeze-dried sediment intervals were dissolved by ultra-high-purity HNOs, HC1,
(Kuehner et al., 1972), and HF acid digestion in a closed Teflon™ container in a microwave
oven (Roth et al., 1997; Hart et al., 2005). Excess fluoride and chloride were removed by
successive evaporation to dryness followed by reconstitution with 1% (volume) ultra-high-purity
HNOs acid. Following dissolution, the samples were analyzed in triplicate for metals present at
higher concentrations, including calcium (Ca,) iron (Fe), magnesium (Mg), potassium (K), and
sodium (Na) by ICP-AES. Trace metals present at lower concentrations (listed in Section
3.4.2.4) were determined by multi-element inductively coupled plasma-mass spectrometric
method (Garbarino and Taylor, 1995; Taylor, 2001) with a Perkin Elmer model Elan 6000 mass
spectrometer. Typical detection limits for the metals analyzed are listed in Table 3B-14 in
Appendix 3B.
3,4,7 Fish
3.4.7.1 Fish Sampling
Fish were used as the vertebrate indicators of SOC, Hg, and metal exposure because they are
continually immersed in the lake and provide an indication of impacts to the food web.
Contaminant and fish health analyses were performed on the same fish, allowing a direct
correlation of SOC, Hg, and metal body burdens to fish health parameters. Where possible, lakes
were chosen based on the known presence of salmonid fishes. We sampled 15 fish for SOCs, Hg,
and physiological analyses and 5-10 different fish for trace metals analysis. The objective was to
provide a wide age distribution and an even sex ratio. Fish were captured primarily by single-line
angling, but gillnets and set-lines were also used in DENA. Some fish remained in gillnets for up
to 4 hours because of the large distance between the nets. The species captured are listed in Table
3-11.
In terms of the trophic levels of the fish, we presume that lake trout (Salvelinus namaycush) eat
other fish when possible. However, at the time of sampling, gastropods dominated the stomach
contents. It is also presumed that the burbot (Lota lota) are piscivorous. The remaining fish we
assumed to be insect- or planktivorous.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Table 3-11. Species of Fish Captured.
Park
Lake
Common Name
Scientific Name
NOAT
GAAR
DENA
DENA
GLAC
GLAC
OLYM
OLYM
MORA
MORA
ROMO
ROMO
SEKI
SEKI
Burial Lake trout
Matcharak Lake trout
Wonder Lake trout
McLeod Round Whitefish, Burbot
Snyder Westslope cutthroat
Oldman Yellowstone cutthroat
PJ Brook trout
Hoh Brook trout
Golden Brook trout
LP19 Brook trout
Mills Rainbow trout
Lone Pine Brook trout
Pear Brook trout
Emerald Brook trout
Salvelinus namaycush
Salvelinus namaycush
Salvelinus namaycuch
Prosopium cylindraceum, Lota lota
Oncorhynchus dark! lewisi
Oncorhynchus clarki bouvieri
Salvelinus fontinalis
Salvelinus fontinalis
Salvelinus fontinalis
Salvelinus fontinalis
Oncorhynchus mykiss
Salvelinus fontinalis
Salvelinus fontinalis
Salvelinus fontinalis
In general, each lake was sampled once during the months of July, August, and September, from
2003 to 2005. After the fish had been captured, they were placed in mesh bags in the lake near
the shoreline and held for up to 1 hour prior to sampling. Fish were killed with a blow to the
head and placed on acetone rinsed and combusted aluminum foil for the necropsy. The necropsy
took 15 minutes and included an assessment of
any gross internal or external abnormalities.
Sex was determined by visual inspection of the
gonads and confirmed by histology. Fork
length and mass were recorded. Blood was
collected by heparinized syringe and trans-
ferred to tubes placed in ice-cold water.
Plasma was separated within 15 minutes with
a hand-driven centrifuge mounted on a custom
machined tri-pod anchored to the ground by
guy-lines. Plasma was stored immediately on
dry ice and then at -80°C at the laboratory.
Small pieces of posterior kidney, liver, spleen,
gill, and gonad samples were removed and fixed in 10% buffered formalin for histological
examination. Sagittal otoliths were removed and stored in 70% ethyl-alcohol for age
determination. Stomach and gut contents were removed and preserved with 70% ethyl-alcohol.
After the necropsy, the fish were wrapped in the same foil on which the organ sampling occurred
or were transferred to metalized bags, then double bagged in Ziplocs and frozen on dry ice. The
samples were received frozen solid, with approximately 5 kg of dry ice remaining, and
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
transferred to -20°C. Because of a power outage, some of the fish captured in 2005 thawed, but
remained cool, and were refrozen within 12 hours. For fish collected only for elemental analysis,
the fish sex was determined visually and otoliths were removed in order to age the fish.
3.4.7.2 Fish Health Analytical Methods
In the field, length was used as a surrogate for age; however, fish from these environments are
often stunted. Therefore, very old fish could be as large as some younger fish. Overall we could
not definitively assemble the age distribution until the fish were aged in the laboratory. The fish
were aged in the fish pathology laboratory at Oregon State University, Department of Micro-
biology. We counted a dark and a light ring as 1 year on hand-ground otoliths using transmitted
light microscopy. Otoliths were read from two to five times. If agreement in years was reached
after two readings, then the age was recorded. However, several required three to five readings
before agreement was reached. Brook, rainbow, and cutthroat trout were aged following Hall
(1991) and the lake trout were aged following Simoneau et al. (2000).
Posterior kidney, liver, spleen, gill, and gonad were processed for routine histological
examination according to standard procedures, sectioned, and stained with hematoxylin and
eosin. Using compound light microscopy, we examined all sections for histopathological
changes (e.g., lesions, intersex) and quantification of macrophage aggregates (Schwindt et al.,
2006). Blood plasma vitellogenin was determined following Schwindt et al. (2007), except that
the male fish plasma was diluted 100X, and for all analytes, the secondary antibody concentration
was 1:5,000. The detection limit (DL) was 3.9 ng/mL and was determined by visual inspection of
the linear portion of the standard curve. The intra- and inter-assay RSDs were 6% and 16%,
respectively, and recovery was 95-100%.
For the sex steroids, steroids were extracted from the plasma following the method of Fitzpatrick
et al. (1986). Plasma testosterone, 11-ketotestosterone, and 17B-estradiol concentrations were
measured by radioimmunoassay (RIA), as described by Sower and Schreck (1982) and modified
by Feist et al. (1990). All steroid assay results were corrected for recovery. The DL ranged from
1.25 to 3.12 pg/tube, depending on the assay. Intra-assay RSD was < 7%, inter-assay RSD was
11-12%, and extraction efficiency ranged from 64% to 97%, depending on the assay. For all
blood analytes, concentrations were determined in the fish physiology laboratory at Oregon State
University, Department of Fisheries and Wildlife. Samples were assayed in duplicate, and
concentrations were validated by determining that serial dilutions were parallel to standard
curves. Values that were below the DL were reported and analyzed as one-half the DL.
3.4.7.3 Analytical Methods for SOCs in Whole Fish
Whole fish carcasses were homogenized while frozen in a stainless steel food processor with
liquid nitrogen at the WRS Analytical Laboratory. A subsample of the fish homogenate was
collected for Hg analyses, and the remaining homogenate was used for SOC analyses. Twenty
gram subsamples were spiked with isotopically labeled recovery surrogates and the analytes
were extracted with organic solvent and pressurized liquid extraction. Lipid in each fish was
measured by evaporating the solvent from a portion of the extract and weighing the remaining
lipid. The extracts were cleaned with silica solid phase extraction, gel-permeation chroma-
tography (GPC), and concentrated to 0.3 mL. Fish extracts were spiked with isotopically labeled
internal standards and the SOCs listed in Table 3-3 were quantified by GC/MS analysis.
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3.4.7.4 Analytical Methods for Mercury in Fish Tissue
Total mercury was analyzed on a subsample of the whole body homogenate at the WRS
Analytical Laboratory with a LECO® AMA254 Mercury Analyzer in accordance with EPA
method 7473 (USEPA, 1998). The fish tissue homogenate samples were stored at -6° C, and
were allowed to thaw before analysis. The freezing process causes homogenate to separate into
phases, so the homogenate was mixed before removing subsamples to be placed in the
instrument sampling containers.
3.4.7.5 Analytical Methods for Metals in Fish Tissue
Five to ten fish were collected for trace metal analysis, and were shipped frozen, on dry ice, to
the USGS-NRP Boulder laboratory for processing at the end of each year's sampling. Fish tissue
was sampled from two parts of each fish specimen, the fillet tissue and the liver tissue. The skin
and bony material were removed from the fillet tissue, and the fillet tissue was finely chopped
with a ceramic knife, subsampled, and freeze-dried to remove residual moisture. After drying,
the fillet samples were pulverized to a fine powder. Liver samples were carefully dissected from
the fish entrails, manually homogenized, and freeze-dried. Subsamples of both the fillet and the
liver samples were digested with ultra-high-purity nitric acid in a closed Teflon™ container in a
microwave oven (Barber et. al., 2003). Following dissolution, the samples were analyzed in
triplicate for metals present at higher concentrations, including Ca, Fe, Mg, K, and Na, using the
ICP-AES methods described in Section 3.4.2.2. Trace metals present at lower concentrations
(listed in Section 3.4.2.4) were determined by an ICP-MS method (Garbarino and Taylor, 1995;
Taylor, 2001).
Typical detection limits for the metals analyzed in fillet samples are listed in Table 3B-16 in
Appendix 3B, with detection limits for metals in liver samples in Table 3B-17 in Appendix 3B.
Because the sample size for livers was generally small, detection limits were often dramatically
poorer than those observed for fillet samples. Although detection limits listed in Appendix 3B
are based upon 0.1-g sample size, as a result of variability in the size of the fish specimens, liver
sample sizes actually varied between 0.04 and 0.11 g.
3.4.8 Moose
3.4.8.1 Moose Tissue Sampling
Samples from moose were collected in order to understand the
potential for the bioaccumulation of contaminants through the
terrestrial food web. Samples from three moose donated by hunters
in DENA were analyzed for mercury, metals, and SOCs. Sampling
kits were provided to hunters in the park, with instructions to collect
a three-pound sample of meat from the shoulder or rump area, a
three-pound sample of the liver, and one incisor tooth to determine
the animal's age. Meat and liver samples were provided from two
moose collected in 2004, and from one moose in 2005. The exterior
layers of each sample were removed, and a portion was sent to the
Taylor laboratory for metals analyses. The remaining portions of
both the meat and liver (approximately half of the original sample)
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
were ground with liquid nitrogen in a stainless steel food processor for mercury and SOC
analyses.
3.4.8.2 Analytical Methods for SOCs in Moose Tissue
A portion of the moose homogenate was ground with sodium sulfate and spiked with isotopically
labeled recovery surrogates before extraction with dichloromethane (DCM) and pressurized
liquid extraction. Sodium sulfate was added to the extract and it was cooled overnight at -18 °C.
The extract was brought to 500-mL volume with DCM and a 10-mL fraction was taken for
gravimetric lipid determination. The remaining extract was reduced in volume and purified with
silica gel columns and gel permeation chromatography. The extract was measured for the SOCs
listed in Table 3-3 by GC/MS analysis.
3.4.8.3 Analytical Methods for Mercury in Moose Tissue
Total mercury was measured on the ground samples wi
at the WRS Analytical Laboratory in accordance with EPA method 7473 (USEPA, 1998).
Total mercury was measured on the ground samples with a LECO® AMA254 Mercury Analyzer
3.4.8.4 Analytical Methods for Metals in Moose Tissue
Moose tissue from the meat and liver samples was finely chopped with a ceramic knife, sub-
sampled, and freeze-dried to remove residual moisture. After drying, samples were pulverized to
a fine powder. Subsamples were digested with ultra-high-purity nitric acid in a closed Teflon™
container in a microwave oven similar to that used for fish tissue (Barber et al, 2003). Following
dissolution, the samples were analyzed in triplicate for metals present at higher concentrations,
including Ca, Fe, Mg, K, and Na, by methods described in Section 3.4.2.2. Trace metals present
at lower concentrations (listed in Section 3.4.2.4) were determined by an ICP-MS technique
previously described (Section 3.4.2.4). Typical detection limits for the metals analyzed are listed
in Table 3B-18 in Appendix 3B.
3.4.9
The following additional data were assembled for use with back trajectory calculations, fish
physiological marker data, and other environmental and physical variables measured at the
sample collection sites (e.g., geographic coordinates, elevation, and habitat characteristics) to
interpret and predict SOC, nutrient and metal accumulation in the WACAP media.
3.4.9.1 Climate Data
PRISM (Parameter-Elevation Regressions on Independent Slopes Model), developed at Oregon
State University (http://www.ocs.orst.edu), uses point measurements of climate data and a digital
elevation model to generate estimates of annual and monthly climatic variables. Climate
estimates are converted to a horizontal grid and are compatible for use with Geographic
Information Systems (GIS).
3.4.9.1.1 Individual Years
Annual and monthly means for total precipitation (cm), maximum daily temperature (°C), and
minimum daily temperature (°C) were obtained for the individual years 2002 through 2005 from
the Climate Data Source (http://www.climatesource.com) for all WACAP target lake, snow, and
vegetation sampling sites, excluding Alaska (no data available). Grid cell size was 2 km.
3-34 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
3.4.9.1.2 Long-Term Averages
Thirty-year (1971-2000) annual and monthly means for total precipitation (cm), daily maximum
temperature (°C), and daily minimum temperature (°C) were obtained for all WACAP target
lake, snow, and vegetation sampling sites. Grid cell size was 800 m for the lower 48 states and
2 km for Alaska. Twenty-year (1971-1990) means for annual, January, and August daily
temperature (°C), relative humidity (%), dew point temperature (°C), and number of days with
measurable precipitation were obtained for target lake and vegetation sampling sites. Resolution
was 2 km for the conterminous 48 states; only mean temperatures and mean precipitation were
available for Alaska; grid cell size was 4 km. We calculated values by overlaying site coord-
inates (in decimal degrees, accurate to 4 decimal places) on climate grids obtained from
http://www.ocs.orst.edu/prism via GIS.
3.4.9.2 Radial Population Estimates
We calculated population estimates for the core and secondary parks using radial distances of 25,
75, 150, and 300 km. To compare human population with SOC concentrations, we needed both a
consistent method and a consistent population data set.
LandScan, created by the Oak Ridge National Laboratory's (ORNL) Global Population Project,
is a worldwide human population database on a 30- by 30-second (30" x 30", or approximately
0.84 km x 0.84 km) latitude/longitude grid. Census counts form the basis of the LandScan
population estimates, with the population being further distributed based on nighttime lights,
proximity to roads, land cover and slope, and other data sets. The LandScan database compiled
in 2002 was used for this project; see http://www.ornl.gov/landscan; Hafner et al. (2005).
The LandScan data for North America were downloaded in the form of a raster dataset, and
ArcGIS 9.0 (ESRI, Redlands, California) was used to calculate populations for each site. The
raster dataset was projected via the North America Albers Equal Area projection, with an output
grid size of 841.002833 meters. Using the same projection, separate point feature classes were
created for each of the WACAP sites. Within each feature class was a field with an assigned
value of 1. Each point was buffered with radii of 25, 75, 150, and 300 km. The respective radial
buffers were converted to raster datasets with an output grid size matching the size of the
population grid. With reference to the times function in raster math, the radial raster datasets
were multiplied by the population grid, resulting in radial raster data containing the appropriate
population values. To get the total radial population, data were exported and summed.
3.4.9.3 Radial Agriculture Estimates
Agricultural estimates for the core and secondary parks were calculated at a radial distance of
150 km using a dataset compiled from the 2002 United States Census of Agriculture and the
2001 Canadian Census of Agriculture:
http://www.nass.usda.gov/Census_of_Agriculture/index.asp
http://www.statcan.ca/english/agcensus2001 /index.htm
For the United States, the total area of cropland per county was used, and for Canada, the amount
of land in crops (excluding Christmas trees) per census division was used. All units of area were
converted to square meters, and the census data were added to the attribute table for all US
counties and the provinces of Alberta and British Columbia.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
Operating under the assumption that no crops are grown on national forest and national park
lands, park areas were erased from the county/province map. With the parks removed, county
areas were recalculated. Dividing cropland area by the recalculated county area gave the percent
of agriculture in each county, or agricultural intensity, minus parks (Hageman et al., 2006).
The agricultural intensity map was projected with reference to the North America Albers Equal
Area projection. As was done for the population calculation, a separate point feature class was
created for each of the WACAP sites. Each point was buffered at 150 km. The clip function was
used to remove the buffer area from the agricultural intensity map. In doing so, some counties
were cut in half. The area of each county within the clipped buffer area was recalculated and
multiplied by the agricultural intensity, resulting in cropland area for each county or part of a
county within the buffer. The total cropland within the buffer was summed, and divided by the
total area of the county.
3.4.9.4 Ambient Ammonium Nitrate and Ammonium Sulfate in Fine Participates
The Interagency Monitoring of Protected Visual Environments (IMPROVE) program
(http://vista.cira.colostate.edu/improve) is a cooperative effort to aid the protection of visibility in
156 national parks and wilderness areas. An IMPROVE site is operating at each WACAP park in
the conterminous 48 states and at DENA and STLE in Alaska. Each IMPROVE site deploys an
aerosol sampler to measure speciated fine aerosols for a 24-hour period every third day. Mean
annual ammonium nitrate (|j,g/m3) and ammonium sulfate (|j,g/m3) in ambient particulates
< 2.5 (am diameter for the years 1998-2004 were downloaded from the website:
http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx
Annual data from 1998-2004 that met QA/QC standards (Guidance for Tracking Progress under
the Regional Haze Rule (http://www.battelle.org/projects/epa-environment/default.htm) were
averaged to produce a single value per park. There were at least 5 years of data for all monitors
except SEKI (1999-2001 and 2004 only), OLYM (2002-2004 only), NOCA (2001-2002 only),
and STLE (no data yet). Sulfate and nitrate IMPROVE data were used as a measure of nitrogen
and sulfur availability in the parks.
3.4.9.5 Ammonia Emissions Density
Projected 2001 county ammonia emissions density data (tons/square mile), based on the USEPA
1999 National Emissions Inventory database (USEPA, 2007b), was obtained for the county in
which each site was located, and all adjacent counties (http://www.epa.gov/air/data/geosel.html).
Most emissions estimates are supplied to USEPA by state environmental agencies. Some
estimates are for individual sources, such as factories, and some estimates are county totals for
classes of sources, such as vehicles. Emissions estimates for individual sources are based on their
normal operating schedules, and take into account the effects of installed pollution control equip-
ment and of regulatory restrictions on operating conditions. Because most ammonia emissions
are related to agriculture, ammonia emissions data were tested in correlations with other agri-
cultural indicators, such as agricultural intensity, as a measure of local agricultural activity.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
3.5 Data Handling and Statistical Analysis Methods Used
3.5.1 Data Handling of Contaminant Concentrations Below the Detection Limit
Throughout this report, the following rules were applied to contaminant concentrations below the
method or estimated detection limits used in data analyses:
1. If more than 70% of the measured concentrations were above the detection limit, a value of
one-half of the detection limit was substituted for below detection limit values (Antweiler
and Taylor, written communications).
2. If 50-70% of measured concentrations were above the detection limit, a value of one-half of
the detection limit was substituted for below detection limit values, and the resulting
averages were noted with superscript "1".
3. If less then 50% of the measured concentrations were above the detection limit, a value of
one-half of the detection limit was substituted for below detection limit values, and the
resulting averages were noted with superscript "2".
4. In calculating a compound class sum concentration (e.g., Sendosulfans), if a compound
concentration in the sum was below the detection limit, a value of one-half of the detection
limit was substituted. If more than 50% of the total value of the compound class sum was
made up of values below the detection limit, the entire sum was flagged as below the
detection limit for consideration in steps 1 through 3.
3.5.2 Evidence and Magnitude of the Cold Fractionation Effect
One of the objectives of WACAP was to attain a better understanding of latitudinal and eleva-
tional influences on SOC concentrations in WACAP ecosystems, i.e., to look for evidence of
increased SOC concentrations at the colder temperatures associated with increased latitudes and
elevations. Specifically, we wanted to (1) identify which SOCs showed the cold fractionation
effect and (2) quantify the magnitude of the concentration enhancement by SOC and by park in
vegetation, within the vegetated zone of each park. Because accurate estimates of temperature at
WACAP sites were not available, we used elevation as a within-park surrogate for temperature.
Vegetation was chosen over other WACAP media for this analysis because it is a biotic media in
direct contact with the atmosphere. In addition, SOC concentration data were available from 3-5
different site elevations within most of the core and secondary parks.
Exploration of the vegetation and snow SOC
concentration data indicated that park proximity to
regional sources influences SOC concentrations
much more than latitude. For example, current-use
pesticides were not detected or were much lower in
concentration in the Alaska parks than in the other
parks, even though the Alaska parks are at higher
latitudes and experience colder temperatures year-
round. The vegetation species sampled and the
amount of precipitation also contributed to the SOC
concentration variability observed between and
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
within parks. Because of these complexities, we did not attempt to demonstrate a latitudinal
effect with vegetation SOC concentrations.
Early in our data interpretation, we observed that conifer needle SOC concentrations in many of
the parks increased across the lowest 2-3 elevations but then leveled off or decreased in the top
2-3 highest elevations. Re-examination of field notes and discussions with field observers
revealed that the conifer needles at some sites (sampled according to protocol with handclippers
by field personnel standing on the ground) were probably buried under snow for variable periods
of time during the winter months, especially at the higher elevation sites. The duration of snow
burial was unknown and is likely to have resulted in decreased conifer needle exposure to SOCs
in the atmosphere and, ultimately, decreased conifer needle SOC concentrations at these sites. In
contrast, epiphytic lichens do not survive snow burial and, therefore, were collected only above
the lichen snow-line visible on tree trunks or from litterfall. This factor made epiphytic lichens a
more suitable media for analysis of potential elevation trends.
In summary, elevation analyses were restricted to lichen SOC data that met the following
criteria:
1. The same lichen species was collected from at least three different elevations within the park.
2. Samples of the same species came from the same geographic quadrant in the park, i.e., they
were exposed to similar pollution sources and weather patterns.
3. Only SOCs for which data were above detection limits in at least 50% of samples were
tested.
4. Only epiphytic lichens, exposed to the air all year round, were used. Two exceptions to this
criterion were (1) KATM, where adequate data from both tundra and epiphytic lichens were
available, and could be compared, and (2) DENA, for which no other vegetation type was
available.
Samples from arctic parks (NOAT, GAAR) were not included in analyses of elevational trends
because most of the SOCs were below detection limits, the elevation gradient was small (450 m
from lowest to highest site), and arctic parks experience frequent temperature inversions, which
contradicts the assumption that higher elevations would be associated with colder temperatures.
Multiple regression analysis was used to identify statistically significant trends in lichen SOC
concentration with elevation. The statistical software package used was S-Plus 2000 (Mathsoft.
1999. Data Analysis Products Division, Seattle Washington). Models for each SOC were first
constructed using Park and Elevation as explanatory variables. The regression model was:
Y = po + pi; Park + p2 Elevation [3-3]
Where:
Y = the mean of the response variable (SOC concentration)
Po = the coefficient for the intercept
Pii = the coefficient for the z'th Park
P2 = the coefficient for site Elevation
This model assumes equal slopes for all parks and tests for an overall trend of SOC concentration
over elevation. This analysis answers the question: "After accounting for differences between
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
parks, is there a trend in SOC concentration over elevation?" Differences between parks must be
accounted for because SOC concentrations differ between parks because of proximity to sources
and varying application rates of SOCs near the parks. This analysis takes these differences into
account by assuming they are additive (i.e., the average SOC concentration difference between
parks is the same at all elevations) and estimates an average relationship between compound
concentration and elevation. Summary results of the regression analyses are reported in Chapter
4 (Table 4-3); model details are reported in Appendix 4A.1.
Within each park, a simple regression analysis was used to investigate the trend in SOC
concentration over elevation. The regression model was:
Y = po + pi Elevation [3-4]
Where:
Y = the mean of the response variable (SOC concentration)
Po = the coefficient for the intercept
PI = the coefficient for site Elevation (the slope of the compound concentration trend in
the Park)
This model estimates the trend of SOC concentration over elevation within each park. This
analysis answers the question "What are the trends of SOC concentration over elevation within
each park?" The results of the regression interaction analyses are reported in Chapter 4 (Table 4-
4). Only those parks that met the data criteria for analysis were analyzed.
Residual plots, plots of the residuals of the regression versus the fitted values, were used to
assess the need for transformations of the response variable (SOC concentration) to stabilize the
variance (Ramsey and Schafer, 1997). Two transformations were used, the natural logarithm and
the square root, depending on the patterns of the residual plots. The results of log-transformed
data are reported as the percent change in SOC concentration over 500 m elevation. The results
of square root-transformed data were back-transformed and are reported as the increment of SOC
concentration change over 500 m elevation.
Outliers were identified using Cook's Distance to determine the overall influence of a data point
on the regression (Neter et al, 1990). Regressions were run with and without the outliers to
assess their influence on the overall statistical significance of the model and on the model
coefficients. Influential data points were identified for further investigation (see Appendix
4A.11, elevation regressions); however, no outliers were removed from the final models.
3.5.3 Comparison of Park and Site Means for SOC and Element Concentrations in
Vegetation and Air
The Tukey-Kramer multiple means comparison test (Ramsey and Schafer, 1997) was used to
provide statistical evidence of significantly different mean concentrations of SOCs and elements
in vegetation or air samples, between parks. This multiple comparison procedure controls for
family-wise error rate. For example, suppose that we want to compare the mean concentration of
a target SOC in each of four parks, i.e., park 1 vs. park 2, 1 vs. 3, 1 vs. 4, 2 vs. 3, 2 vs. 4, and 3
vs. 4. Such a set of comparisons is called a family. If we use a T-test to compare each pair of
parks at a certain significance level (a), then the probability of Type I error (incorrect rejection of
the null hypothesis of equality of means) is guaranteed equal to a for any single pair-wise
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
comparison, but not for the whole family. The Tukey-Kramer test, based on the studentized
range distribution (standardized maximum difference between the means) is adjusted for
unbalanced designs (i.e., unequal number of samples from each site) and controls for family-wise
error rate. The statistical software package JMP (JMP, Version 5. SAS Institute Inc., Gary, North
Carolina, 1989-2002) was used to perform these analyses. Although the SOC data were not
normally distributed, T-tests are robust to non-normality of data when sample sizes are similar
(see Ramsey and Schafer, 1997, Chapter 3.2).
Concentrations of elements (N, S, Hg, Pb, and Cd) and some SOCs (dacthal, endosulfan 1,
endosulfan sulfate, a-HCH, and HCB) exceeded EDLs in all samples. The concentrations of the
remaining SOCs were below EDLs in some samples. When SOC concentrations were not above
the EDLs within a park, the park was not compared to other parks. When at least one vegetation
or air sample from a park was above the EDL, half of the EDL was substituted for any sample
below the EDL and the park was included in comparison tests.
3.5.4 Correlations
Correlations summarize the strength of relationships between variables and provide a measure of
the predictive potential of any variable for another. Correlations between contaminants in vege-
tation and environmental variables such as regional agricultural intensity, ambient particulate
nitrogen, and population density were calculated with the statistical software package JMP (JMP,
Version 5. SAS Institute Inc., Gary, North Carolina, 1989-2002). Because some of the data were
half EDLs and concentrations of some compounds encompassed several orders of magnitude
across parks, many of the data did not meet the normality assumptions for a parametric analysis.
Therefore, a non-parametric measure of association, Spearman's Rho, was used. Spearman's
Rho is a correlation coefficient computed on the ranks of the data values instead of on the values
themselves, by the formula for Pearson's (parametric) correlation (Sokal and Rohlf, 1981).
Concentrations of some elements (N, S, Hg, Pb, and Cd) and some SOCs (dacthal, endosulfan 1,
endosulfan sulfate, a-HCH, and HCB) exceeded EDLs in all samples. The concentrations of the
remaining SOCs were below EDLs in some samples. When no samples were above detection
limits within a park, the park was not included in the correlation. When at least one sample from
a park was above the EDL, then half of the EDL was substituted for park values below the EDL,
and the park was included in comparison tests. Significance probabilities were also calculated
and reported.
3.5.5 Paired T-tests
Paired T-tests were used to test for significant differences in mean SOC concentrations between
lichens and conifers. Individual SOC data for up to 69 matched pairs of lichens and conifer
needles collected at the same sites were used. The matched pairs platform in the statistical
software package, JMP (JMP, Version 5. SAS Institute Inc., Cary, North Carolina, 1989-2002)
was used to compare means between the two response columns (lichen SOC concentrations vs.
conifer SOC concentration) by means of a paired Student's T-test.
Starting from the master WACAP vegetation database, all sites without both lichen and conifer
samples were deleted. If all samples within a vegetation type (i.e., lichens or conifer needles) at a
site had SOC concentrations below the EDLs , then no data were used for that vegetation type at
that site. Otherwise, half of the EDLs were used for all samples where SOC concentrations were
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
less than the EDL within a site and vegetation type. Within-site field replicate SOC concentra-
tions were averaged before comparisons were made between sites. At the nine sites where more
than one species was collected within a vegetation type, the species SOC concentrations were
averaged after the field replicate SOC concentrations were averaged. After these steps, 69 sites
remained from which both lichens and conifer needles had been collected and SOCs measured
above the EDLs. After averaging as described, there was one concentration value for conifer
needles and one concentration value for lichens for each SOC, at each site. A matched pairs test
was carried out to test for significant differences in concentrations between lichens and conifer
needles for each SOC, across the 69 sites. The SOCs tested were: trifiuralin, triallate,
chlorpyrifos, methoxychlor, dacthal, endosulfans, HCB, a-HCH, g-HCH, dieldrin, DDTs,
chlordanes, PCBs, and PAHs. The standard error used was a pooled estimate of variance. For
any given SOC, the number of sites for each vegetation type is not always equal, because at
some sites, all samples of a vegetation type had SOC concentrations less than the EDLs.
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CHAPTER 3. CONTAMINANTS STUDIED AND METHODS USED
3-42
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CHAPTER 4
Contaminant Distribution
4.1 Introduction
This chapter is organized by contaminant category, beginning with semi-volatile organic com-
pounds (SOCs), and followed by mercury, trace metals, spheroidal carbonaceous particles
(SCPs), and finally nutrients. Atmospheric transport is also addressed. The SOCs include
current- and historic-use pesticides (CUPs and HUPs), and industrial compounds such as
polychlorinated biphenyls (PCBs) and combustion tracers (polycyclic aromatic hydrocarbons;
PAHs). Within each section, data are discussed by medium—snow, air, vegetation, fish, and
lake sediments. Lake water concentrations were generally very low and often below detection
limits (see Figure 3-1 in Chapter 3). As such, lake water data are not reported in this chapter.
The contaminants found in greatest general abundance within each category are discussed with
respect to both their spatial and their temporal distributions. Spatial distributions include the
horizontal differences in contaminants among parks, as well as the elevational gradients.
Whenever possible, study-wide data are displayed in order of decreasing latitude. Throughout
each section, source attribution of individual contaminants is discussed. A separate section
details the principal airsheds of each park.
4.2 Semi-Volatile Organic Compounds (SOCs)
For all results reported in this chapter, SOC compounds are grouped by compound classes, as
listed in Table 4-1.
Table 4-1. Compound Groupings Used in Chapter 4.
Term Used Definition
PCBs1 or "sum PCBs" Sum of 5 PCS congeners (118, 138, 153, 183, 187)
PBDEs1 or "sum PBDEs" Sum of 5 PBDE congeners (47, 99, 100, 153, 154)
PAHs1 or "sum PAHs" Sum of 16 PAHs (fluorene, anthracene, phenanthrene,
fluoranthene, pyrene, retene, benzo(a)anthracene,
chrysene/triphenylene, benzo(b)fluoranthene,
benzo(k)fluoranthene, benzo(e)pyrene, benzo(a)pyrene,
indeno(1,2,3-cd)pyrene, dibenz(a,h)anthracene, and
benzo(ghi)perylene)
Endosulfans or "sum Endosulfans" Sum of 3 endosulfans (I, II and sulfate)
Chlordanes or "sum Chlordanes" Sum of 4 chlordanes (cis- and trans-chlordane, cis- and trans-
nonachlor, excludes oxy-chlordane)
DDTs or "sum DDTs" Sum of p,p'-DDT, o,p'-DDT, p,p'-DDE, o,p'-DDE, p,p'DDD, and o,p'-
DDD
Total Pesticide Sum of endosulfans, chlorpyrifos, dacthal, HCB, g-HCH, a-HCH,
dieldrin, DDTs, and chlordanes
PCBs = polychlorinated biphenyls; PBDEs = polybrominated diphenyl ethers; PAHs = polycyclic aromatic hydrocarbons
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-1
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Figures 4-1 to 4-6 show key SOC compounds across all WACAP parks and in each medium—
snow, air, lichen, conifer, fish, and surficial sediment (top-most sediment layer). The plots
present average (arithmetic mean) concentrations or fluxes and their standard deviations.
Polybrominated diphenyl ethers (PBDEs) were measured only in sediment and fish samples.
Sections 4.2.1 to 4.2.5 discuss each medium separately. To minimize species differences, SOC
concentrations in fish and lichens are reported per gram of lipid, rather than weight. Data are
often presented on a log scale.
4.2.1 in
Figures 4-1 to 4-5 show both concentrations and fluxes of SOCs in snow. Fluxes, in ng/m2/yr,
represent the total SOC input to the watershed from the accumulated springtime snowpack. The
fluxes are calculated by multiplying the SOC concentration in the melted snow water (ng/L) by
the accumulated amount of snow at the time of sampling, in liters water per m2 per year (this is
simply related to the height of the snowpack and its density or water equivalence). In general,
CUPs, including endosulfans, chlorpyrifos, and dacthal, were detected in more than 90% of the
snow samples (see Figure 4-1). Both a- and g-HCH (hexachlorocyclohexane) show similar
spatial trends for snow (see Figure 4-2). NO AT and GAAR have higher a- and g-HCH fluxes
than DENA, and the fluxes in the sites in the conterminous 48 states fall into a close range
9
between 40 and 90 ng/m /yr. Within this range, g-HCH at GLAC has the highest flux and
concentration. Hexachlorobenzene (HCB), dieldrin, and sum chlordanes are frequently detected
in snow, even in the Alaska parks (see Figure 4-3). NO AT and GAAR have the highest measured
snow concentrations for HCB and sum chlordane, at 0.023 and 0.057 ng/L, respectively.
Within each WACAP watershed, snow samples were collected at the same site for 3 consecutive
years, providing important insight into the variability associated with contaminant deposition via
snow. Samples collected at the same site at the same time (site replicates) show less than 20%
relative standard deviation (RSD) in concentrations. Within one park, for the same year, the
inter-site variability was 40-60%. Combined, inter-site and inter-annual variability within a park
was much larger, at 80-120% RSD, for parks in the conterminous 48 states. For the Alaska parks,
variability was even higher, approaching 140% RSD for the 3 years of sampling. The higher
variability in snow concentrations and deposition in Alaska probably reflects the lower
concentrations and lower snow amounts. The high variability among sampling years within a
park highlights the importance of using same-year data to make inter-park comparisons and the
importance of multi-year sampling to understand contaminant inputs to the park ecosystems.
Figure 4-7 shows the distribution of pesticides in snow observed at SEKI and MORA in the 3
years of sampling. For these parks, the concentration of pesticides in snow showed substantial
year-to-year variability. For example, total pesticide concentrations at SEKI ranged from a low
of 3.5 ng/g to a high of 10 ng/g. At MORA, total pesticides ranged from 0.38 ng/g to 0.63 ng/g.
Despite this result, the pattern of SOCs at each park is clearly different from the other and is
consistent for each park from year to year. At MORA, endosulfans have the highest concentra-
tion in all 3 years, whereas at SEKI, dacthal is highest in all 3 years. So, despite significant year-
to-year variations in total concentrations, the pattern of pesticides deposited to each park is
consistent for all 3 years of the WACAP study. This finding implies that the sources of pesticides
to each park do not change significantly from year to year. It also implies that the sources
influencing MORA and SEKI are different, given the different patterns of pesticides deposited.
The year-to-year variation in snowpack concentration and flux implies that the SOC inputs to the
ecosystem, via annual snowpack, vary substantially from year to year.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Air
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Figure 4-1. Current-Use Pesticides (CUPs): Average Concentrations and Fluxes of Sum
Endosulfans, Chlorpyrifos, and Dacthal across Parks and Media. Snow data include fluxes in blue
and concentrations in gray. Sediment data are reported as focusing factor-corrected (FF) flux for surficial
sediment (top-most sediment layer) only. Conifer samples were not collected at NOAT and GAAR. If no
label is present at the top of a bar, the component was detected in at least 70% of the samples. Below
detection limit (BDL) values were replaced with half the estimated detection limit (EDL). 1 indicates that
the analyte was detected in 50-70% of the samples and BDL values were replaced with half the EDL. 2
indicates that the analyte was detected in <50% of the samples and the value on the graph is half the
EDL. N = no data.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-3
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Air
•innnn
1 UUUU ~i
'-
1000 :
-
100 =
I 10 =
1 =
0.1 -=
:
1000 -=
'-_
100 :
I 10 :
ra
1 ,
;
0.1 =
001 -
Q
•51
Q.
T I
JT
pr
I
j-TX
I X
pr
T
Lichen Conifer Snow Fish Sediment
T3
:
*
5.
S>
0)
c
]T
2
f
-r~
S
1
T Tj
IT
r
X
I
T
73
^5.
1
C
1
1
X
N
1
I
Ti
N
X
fei
3., .^
^ 6
o, S
x ^b
_3 C
,
•5. ^
-E? ™
? S
LJ_
u_
T?
LL
2
22 I
22. JT
Srfl"
n?
2
ffli i
|||
In
(I
.
f
Figure 4-2. HCHs: Average Concentrations and Fluxes of a-HCH and g-HCH across Parks and
Media. Snow data include fluxes in blue and concentrations in gray. Sediment data are reported as
focusing factor-corrected (FF) flux for surficial sediment only. Conifer samples were not collected at
NOAT and GAAR. 1, 2, and N codes are the same as for Figure 4-1.
4-4
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
IUUUU ;
1000 !
100 =
DO
0 10 7
I ;
1 1
0.1 -,
mnnn
IUUUU i
1000 =
100 |
c :
I 10 i
Q :
1 !
0.1 -=
mnnn
IUUUU "
1000 =
Chlordanes
-i O
0 0
1 ,
0.1 T
n n-i
f} fi
rrfifi
Q
.
'Q. c^
5 t
O C
c ^
Ix E
pL LL T
XTT "; 2i2[Ij
-rM 2 I T
1 j2 1
xlr T
inn £H xS
i^n FT^n
r n
Figure 4-3. Historic-Use Pesticides (HUPs): Average Concentrations and Fluxes of HCB, Dieldrin,
and Sum Chlordanes across Parks and Media. Snow data include fluxes in blue and concentrations in
gray. Sediment data are reported as focusing factor-corrected (FF) flux for surficial sediment only. Conifer
samples were not collected at NOAT and GAAR. 1, 2, and N codes are the same as for Figure 4-1.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-5
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
10000
1000
100
1
0.1
Air
Lichen
Conifer
Snow
Fish
Sediment
Q
5
^O>
01
2
N
2
2
2
I
r
1
•S1
Ol
C
2
n
II
pc"
2
r
^
,Cp
D)
C
1
ill
I
N
i
j;
l£
^
c
>
LT
1
I
; £
?"
; CO
! C
- 1
T
[
- -
pr
I
I
I
r
i
^Q.
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CO
C
1
fflr
I
'
[
L
L
>i
•ks
=
3)
z
^
3
L
Ifi
lU
Figure 4-4. PCBs: Average Concentrations and Fluxes of Sum PCBs across Parks and Media.
Snow data include fluxes in blue and concentrations in gray. Sediment data are reported as focusing
factor-corrected (FF) flux for surficial sediment only. Conifer samples were not collected at NOAT and
GAAR. 1, 2, and N codes are the same as for Figure 4-1.
Lichen
Conifer
Snow
Sediment
uuuuuu i
100000 |
10000 i
, 1000 i
: 100 i
10 !
1 5
n 1
•a
'o
™
-S1 ?
D)
C
2
1
T
I
pr
-i
•a
'S.
•S1
a>
2
N
pr
ll
±
>, ^
CN O
ET ^
5>
r
i
i_
s
Li.
I
I
1 m
~
c
c
2
I
I
J
i)
T
I
T
I
z
i
>,
CN^
E -
| T
LL
LL
T
^
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Fish
Sediment
1000 =
100 1
-in
UJ IU =
Q :
DQ
°- 1J
0.1 =
n n-i
2
"S>
T
T
JL
T
T
I
If
2
2
T!
2
I
_L
i
T
r
CN^
E
c
X
U-
U_
A,
^*
A,
^
Figure 4-6. PBDEs: Average Concentrations and Fluxes of Sum PBDEs in Fish and Sediments
across Parks. Sediment data are reported as focusing factor-corrected (FF) flux for surficial sediment
only. 1, 2, and N codes are the same as for Figure 4-1.
70
60
50
40
I 30
TO
? 20
S
o 10
o
ts
t 70
0
1 6°
1 50
40
30
20
10
[
Sequoia
J
rf"
|
Mt. Rainier
p
|
HI
^— 2003 (10 ng/L)
i 1 2004 (3.5 ng/L)
i i 2005 (8.3 ng/L)
L
^— 2003 (0.39 ng/L)
i 1 2004 (0.63 ng/L)
I 1 2005 (0.38 ng/L)
• Nru , -
" c n x x m .g «
SSsooo^g
S o^II1^™
Figure 4-7. Annual Percent of Total Concentration in Snow for Current- and Historic-Use
Pesticides at SEKI and MORA (after Hageman et al., 2006).
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-7
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
4.2.2 SOCsinAir
As described in Chapter 3, PASDs (passive air sampling devices) were deployed at both core and
secondary parks for one year (± 2 weeks) to give a qualitative picture of atmospheric contamina-
tion. Following collection, the PASDs were analyzed to indicate regional patterns of SOC
concentrations in ambient air.
The SOCs detected in air were similar to those detected in other media for each park, e.g., snow
and vegetation (Figure 3-1 in Chapter 3); however, the concentrations of SOCs were generally
much lower in the PASD sampling material, XAD (pg/g dry XAD), compared with concentra-
tions in vegetation (ng/g lipid lichens or conifer needles; see Figures 4-1 to 4-4). Current-use
pesticides, especially dacthal and endosulfans, were highest in parks in the conterminous 48
states (Figure 4-8), notably at SEKI. Of the historic-use pesticides, HCB and chlordanes
appeared similar across all parks, whereas HCHs were as high in some Alaska parks as they were
in parks in the conterminous 48 states (Figure 4-8). A few SOCs were detected primarily in the
air of only one or two parks: chlorpyrifos (almost entirely as the degradation product
Chlorpyrifos-oxon) atNOCA and SEKI; PCBs at SEKI; and p,p'-DDE at CRLA, SEKI, and
BIBE (Figure 4-8). The fact that these contaminants occur at only a few parks suggests a
regional source.
Simple linear regression of individual SOCs by latitude (Figure 4-9) yielded convincing evidence
(p < 0.003) that concentrations of a-HCH increase and that concentrations of dacthal, endosul-
fans, and chlordanes decrease in the air with increasing latitude. There was suggestive evidence
(p < 0.1) that PAHs and g-HCH also decrease with increasing latitude; p,p'-DDE was detected
only at a few sites, all in the conterminous 48 states. The latitudinal increase of a-HCH in air in
North America has been reported previously (Shen et al., 2005; Simonich and Kites, 1995) and is
generally attributable to greater fractionation and lower revolatilization of this compound at
colder temperatures. In Europe, HCB has been shown to increase with latitude (Meijer et al.,
2003); however, this result was not observed in the WACAP data.
4.2.3 SOCs in Vegetation
4.2.3.1 Spatial Patterns of SOCs in Vegetation
4.2.3.1.1 Regional Patterns of Pesticide Accumulation in Lichens
Average total pesticide concentrations (Figure 4-10) in lichens were lowest (-5-10 ng/g lichen
lipid) in parks in the Arctic and interior Alaska (GAAR, NO AT, and DENA), and increased
southward with decreasing latitude. Mean park total pesticide concentrations were -100 ng/g
lipid in southern coastal Alaska (KATM, WRST, GLBA, and STLE) and were up to two orders
of magnitude higher than concentrations in parks in the Arctic and interior Alaska (500-1,000
ng/g lipid) in some parks in the conterminous 48 states, notably YOSE, SEKI, GLAC, and
GRSA. Pesticide concentrations in much of the central and southern Rockies (GRTE, ROMO,
BAND, and BIBE) were comparable to the concentrations in the Pacific Northwest (200-300
ng /g lichen lipid). Means concentration comparisons are provided in Table 4-2.
4-8 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Q
<
X
£•
•a
O)
30-
25-
20-
15-
10-
5-
0
Q 350
X 300
£ 250
^ 200
^ 150
O 100
A 50
0
U lhlil.Hl
^o^K^>m
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
1250-
1000-
" 750-
500-
CQ
O 250-
0
•5
~
<
X
150^
CT iooH
O)
(/)
CD
l±-J^>£
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
900-
Q
£ 700H
_O5
~O5
500-
CO
CO
Q
300-
100-
-100-
Dacthal pgTg dry XAD = 653.56-10.43 Latitude
rsq adj = 0.246
F ratio = 11.12; p> F =0.0023
N =32
25 30 35 40 45 50 55 60 65 70
Latitude
LJ £UUU
^2000-
T3
0>1500-
05
Q.
w 1000-
co
3 500-
to
o
c 0-
LiJ
• Endosulfans pg/g dry XAD =
1561.96-24.81 Latitude
rsq adj = 0.332
F Ratio = 17.93; p > F = 0.0002
• N =35
. •
•~~- ~^.
• * ^*^B-^_ •
! " !*^
r-^i^
25 30 35 40 45 50 55 60 65 70
Latitude
M-UU
Q 350-
X300-
^250-
T3
0,200-
§M50-
x 100-
x 50-
co o-
-50-
a-HCH pg/g dryXAD = -17.94+3.55 Latitude
rsq fldj = 0.222
F ratio = 10.43; p > F =.0029
N = 3*
• • ^^-~~
m
• _ ^^-~—~~~^
* m\^^ * * *
M~~~~~~*M ' • •
• m *
•
I I I I I I
1 OU
§125-
X
^100-
T3
O) —1 r-
-^ 75-
O5
Q.
x 50-
o
-p
i 25-
0)
0-
g-HCH pg/g dry XAD = 63.24 - 0.62 Latitude
r sq adj = 0.048
F ratio = 2.72; p > F = 0.1085
N = 35
•
g
•
,
^~^^ — r ^ • .
* — ___j^
• " " • " i
•
ii i i i i ii
25 30 35 40 45 50 55 60 65 70 25 30 35 40 45 50 55 60 65 70
Latitude Latitude
Q
x 150-
•a
O5
^100-
Q.
(/)
CD
-g 50-
_o
0 n
f
Chlordanes pg/g dryXAD=84.41-1.20 Latitude
Rsq adj = 0.111
F ratio = 5.25; p > F = 0.0284
1 N =35
•
^~~~ "
1 -I | *\^ ^i^*» f
3500
3000-
2500-
w 2000-
5 1500-
n
°- 1000-
500-
o-
cnn
PAHs pg/g dry XAD = 987.1 5-1 1 .91 Latitude
• r sq adj = 0.007
F ratio = 1.24; p > F = 0.2743
N = 35
•
. *
•
_______^ • •
' ^~^~ -"~.
• . • ^>^
• ••••• • ••
25 30 35 40 45 50 55 60 65 70
Latitude
25 30 35 40 45 50 55 60 65 70
Latitude
Figure 4-9. Simple Linear Regression of Individual SOCs Determined in the XAD Resin by Latitude.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-11
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
1000^
! SOD:
100-
•5 20-
I I
II I II I II I II I
IIII
£ £ S a £ a 5 £ £ a £ a .
ig:igigIgSgSgiig:i
_IU_|_|
-------
CHAPTER4. CONTAMINANT DISTRIBUTION
Table 4-2. Mean SOC Concentrations in Lichens and Conifer Needles (ng SOC/g lipid in vegetation) from Each WACAP Park. For each contaminant (i.e.,
within columns), concentrations that do not share a T-K letter are significantly different. The T-K letter indicates grouping derived from the Tukey-Kramer multiple
means comparison test, a = 0.05). Blank cells indicate that all samples were below laboratory detection limits.
Lichens Current-Use Pesticides
Park
Trifluralin Triallate Chlorpyrifos Dacthal
NOAT, GAAR 0.60 d
DENA
KATM
WRST
GLBA
STLE
NOCA
OLYM
MORA
CRLA
LAVO
YOSE
SEKI
GLAC
GRTE
ROMO
GRSA
BAND
BIBE
0.54 d
0.58 d
1.34d
1.51 a
0.60 b 0.60 b 6.09 d
0.1 7 a 5.20 b 5.20 b 15.44 d
0.89 a 1.57b 1.57b 12.91 d
6.00 a 6.00 b 13.21 d
4.37 b 4.37 b 57.63 bed
75.00 bed
1.88 a 19.83 a 19.83 a 204.67 a
0.94 a 19.33 a 19.33 a 169.75 ab
1.00 a 5.28 b 5.28 b 242.61 a
0.13 a 4.10 ab 4.10 ab 39.75 bed
9.00 bed
0.63 a 1 75.00 abc
0.51 a 5.10 ab 5.1 ab 39.80 cd
9.8d
Endosulfans
1.61 c
2.83 c
19.72c
25.21 c
60.70 c
76.66 c
11 9.30 c
11 7.92 c
101. 14c
205.38 be
101.98C
227.57 be
487.75 b
775.34 a
144.25 be
14.00 c
536.00 ab
1 38.35 c
190.00 be
Conifers Current-Use Pesticides
Park
DENA
KATM
WRST
GLBA
STLE
NOCA
OLYM
MORA
CRLA
LAVO
YOSE
SEKI
GLAC
GRTE
ROMO
GRSA
BAND
BIBE
Trifluralin Triallate Chlorpyrifos Dacthal
0.86 b 0.09 bed
0.63 b 0.26 d
0.61 b
2.35 b 0.10 d
0.81 b 0.44 d
9.36 bed
2.31 a 1.62b 3.36 d
1.68b 9.11 bed
0.27 a 21. 12 be
2.00 b 85.80 a
7.45 a 53.20 abed
2.48 b 66.42 ab
13. 73 a 2.39 b 58.20 abc
4. 13 a 1.01 b 10.35 bed
0.80 b 16.39 bed
4.08 cd
4.99 cd
1.63b 2. 17 bed
Endosulfans
0.69 be
1.68c
1.16c
1.63 be
1.66c
42.52 b
19.99 be
93.57 abc
42.27 be
1 36.28 ab
19.23 be
191. 72a
132.09 abc
8.01 be
15.12b
6.06 be
2.69 c
12.05 be
Historic-Use Pesticides
HCB
0.84 ab
3.56 b
25.17ab
50.06 ab
48.75 ab
40.86 ab
18.74ab
21.31 ab
9.27 ab
18.35 ab
10.90 ab
16.80 ab
13.33ab
55.06 a
11.85ab
1.83ab
65.50 ab
10.99 ab
3.45 ab
a-HCH
0.53 be
1.85c
9.65 abc
20.28 abc
28.00 abc
30.67 abc
12.92 abc
43.33 ab
20.84abc
14.98 abc
10.73 abc
6.93 abc
9.24 abc
45.00 a
9.00 abc
2.01 abc
32.00 abc
10.37 abc
2.50 abc
g-HCH
0.45 c
2.61 be
5.32 be
6.96 be
7.60 be
5.50 be
23.47 b
9.67 be
4.69 be
3.58 be
4.00 be
12. 16 be
65.06 a
5.30 be
0.49 be
1 1 .00 be
5.78 be
2.68 be
Chlordanes Dieldrin
0.03 e
2.42 cde
1.77de
4.91 cde
2.50 de
2. 19 cde
3.04 cde
5. 14 cde
12.04 bed
11.14bcde 3.14 a
13.85 abc
19.84 ab 8.01 a
9. 15 cde
4.51 cde 1.45 a
1.12 cde
28.25 a
7.433 cde
2.06 cde
DDTs
5.72 c
2.96 c
10.88 c
31. 08 be
34.80 be
159.95a
11 3.40 ab
16.50 be
71. 79 abc
29.98 be
12.40 be
Historic-Use Pesticides
HCB
4.53 a
7. 76 a
6.15a
7.40 a
9.09 a
26.00 a
20.80 a
23.83 a
21. 36 a
14.60 a
14.63 a
12.61 a
24.11 a
6.91 a
6.24 a
3.42 a
3.26 a
5.27 a
a-HCH
4.82 ab
4.44 ab
1.93b
5.38 ab
4.69 ab
31.80ab
34.20 a
34.87 a
16.58ab
16.80ab
6.30 ab
13.79ab
31.78ab
5.80 ab
6.17 ab
1.45b
2.22 b
2.47 ab
g-HCH
0.80 a
1.15a
7.42 a
32.76 a
16.63 a
5.74 a
5.38 a
6.71 a
3.71 a
4.28 a
6.99 a
27.45 a
0.80 a
8.36 a
Chlordanes Dieldrin
0.15 be
0.17 be
0.15 be
0.44 be
0.33 be
2.22 be
2.57 be
4.94 be 5.56 a
1 .45 be
6.00 b
3.15 be
13.81 a 3.83 a
2.28 be
0.36 be
1.12 be
0.09 c
0.30 be
DDTs
6.61 ab
2.13 b
4.85 ab
19.03 a
7.46 ab
1.51 b
CUPs
2.2 e
3.4 e
20.3 e
26.6 de
62.2 de
84.0 de
145.3 de
134.9de
126.4 de
271. 8 cde
177.0de
473.8 bed
697.1 ab
1029.5a
192.3bcde
23.0 cde
71 1.6 abc
188.9 de
199.8bcde
CUPs
1.6d
2.6 d
1.8d
4.1 d
2.9 d
51.9cd
27.3d
104.4 bed
63.7 bed
224.1 3b
79.9 bed
260.6 a
206.4 sbc
23.5 d
32.3d
10.1 d
7.7 d
15.9cd
Tota
HUPs
1.4c
5.9 c
39.9 c
77.4 be
88.6 be
81. 6 be
45.1 be
91. 2 be
47.9 be
60.9 be
70.6 be
76.4 be
222.5 a
287.73
48.6 be
5.5 be
208.5 3b
64.6 be
23.1 be
Tota
HUPs
10.3b
13.5b
15.7 b
46.0 3b
30.7 3b
72.4 3b
63.0 3b
78.0 3b
43.1 3b
46.5 3b
24.1 3b
70.1 3b
93.1 a
13.9b
23.4 3b
4.9 b
5.6 b
2.8 3b
Pesticides
%CUPs
61 sbcd
37 cd
34 e
26 e
41 de
51 de
76s
60 bed
73 3b
82 abc
71 3b
86s
76s
78s
80 3b
81 abc
77 ab
PCBs PAHs
0.03 c 3 b
87 b
1 .79 c 262 b
1.97 be 1258b
7. 08 abc 1264b
2.70 be 780 b
4.07 abc 1960b
7. 84 abc 217 5 b
6. 18 sbc 764 b
5.91 sbc 1103b
4.04 abc 315 b
5.49 abc 2096 b
6.48 sbc 81 4 b
9.39 a 72758 a
2.87 abc 571 b
0.42 abc 100 ab
11.60ab 667 b
75 ab 4.734 abc 190b
90 a
Pesticides
%CUPs
14h
16gh
10h
8h
9h
42 fe
30 cde
57 fg
60 de
83s
77 3b
79 3b
69 abc
63 bed
58 cd
68 abed
58 cd
85 abed
1 .97 abc 484 b
PCBs PAHs
0.56 ab 96b
0.13 b 59 b
40b
131b
0.18b 227b
1.66ab 2429b
2.33 3b 2462 b
3.89s 1955b
0.84 3b 826 b
1.31 ab 2170 b
1.10ab 3787 b
4.19a 3255 b
3.46 3b 20044 a
0.73 ab 186b
1 .04 ab 242 b
1.22ab 134b
1.08ab 221 b
0.12sb 20 b
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-13
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Pesticide concentrations in tundra lichens (Cladina arbuscula and Flavocetraria cucullatd)
collected at WRST and STLE appeared to be comparable to those in the non-buried lichen
epiphyte, Alectoria sarmentosa, indicating that some tundra lichen species accumulate
pesticides as well as some epiphtyes. Alectoria sarmentosa appears to be as poor an
accumulator among the epiphytic lichens as M. richardsonii is among the tundra lichens
(Figure 4-11; Appendix 4A.4).
7
1
1
I J
I
-r-
I
i
SAAR NOAT DENA KATM WRST 6LBA STLE NOCA OLYM MORA CRLA LAVO YOSE SEKI GLAC 6RTE ROMO GRSA BAND BIBE
Genus HMasonhalea
Jhypogymnia
]Alectoria
Jxanthoparmelia
Figure 4-11. Comparison of Total Pesticide Accumulation in Lichen Species by Park from North to
South along the Pacific Coast (GAAR to SEKI) and from North to South in the Rocky Mountains
(GLAC to BIBE). Pesticide concentrations generally increased from north to south along the Pacific
Coast and decreased from north to south in the Rocky Mountains (with the exception of GRSA, where
intensive local agriculture might have influenced accumulation). Some lichens are better accumulators
than others; of the tundra lichens, Flavocetraria accumulated more pesticides than Masonhalea; of the
epiphytes, Platismatia accumulated more pesticides than Alectoria. Standardizing species can reduce
noise in contaminant data.
In conclusion, inter-species differences in accumulation rates, and possibly snow burial, appear
to account to some extent for low concentrations of pesticides in lichens from northern Alaska
parks compared to those in lichens from parks in coastal Alaska and many conterminous 48
states. However, in any analysis disregarding (Table 4-2) or accounting for species differences
(Appendix 4A.2), lichens from GLAC, SEKI, and sometimes YOSE and GRSA had higher
pesticide concentrations than lichens in other parks.
4.2.3.1.2 Regional Patterns of Pesticide Accumulation in Conifer Needles
Regional patterns of pesticide accumulation in conifer needles were similar to patterns in lichens,
except that the difference between samples from parks with lowest and highest concentrations
was only about one order of magnitude (Appendices 4A.4 and 4 A. 7). Parks in the Arctic are
largely treeless, and no conifer needles were collected there. The average total pesticide
concentration in conifer needles from DENA was 11 ng/g lipid in needles. Concentrations of
pesticides in conifer needles increased with southerly latitudes, maximizing in the Pacific
Northwest and California and in GLAC in the northern Rockies at 100-200 ng/g lipid. Average
total pesticide concentrations in conifer needles from parks of the central and southern Rockies
were comparable to those in the Canadian Rockies (Davidson et al., 2003; Davidson et al., 2004)
and coastal Alaska (20-30 ng/g lipid). Mean comparison tests disregarding (Table 4-2) or
4-14
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
accounting for species differences between parks (Appendix 4A. 5) support lichen data, in that
conifer needles from SEKI, GLAC, and YOSE were most likely to have higher concentrations of
SOCs than conifer needles in other parks.
The main discrepancy between vegetation sample types is the low concentration of total pesti-
cides in conifer needles, compared to lichens, in the central and southern Rockies. The genus
Finns, the only species collected in parks from the central and southern Rocky Mountains,
appears to accumulate lower concentrations of nearly all SOCs than other coniferous species in
parks (Appendix 4A.6). For example, if fir had been sampled instead of pine, SOC concentra-
tions in needles in the southern Rockies parks (GRSA, BAND, and BIBE) might have been
closer to concentrations in needles in the Pacific Northwest, as they were in lichens. It is also
possible that the drier, warmer climates of the southern Rockies affect lichen and conifer uptake
differently. Pine was also collected exclusively at YOSE, where needle SOC concentrations
might otherwise have been intermediate to LAVO and SEKI (see subsection 4.2.3.2.3). Finally,
it seems unlikely that snow burial can account for the magnitude of difference between northern
Alaska and other WACAP parks, because SOC concentrations in conifer needles at the lowest
elevations in DENA were substantially smaller than concentrations in samples from the highest
elevation sites in other parks, which presumably are also buried under snow for many months of
the year.
4.2.3.1.3 General Observations Regarding Pesticides in WACAP Vegetation
The current-use pesticides, endosulfans and dacthal, dominated total pesticide loading in
vegetation samples from the conterminous 48 states (Figures 4-12 and 4-13, Table 4-2). This
observation is consistent with the proximity of parks in the conterminous 48 states to large-scale
agriculture, compared with Alaska parks. Agricultural intensity within 150 km of a park is
strongly correlated with concentrations of these CUPs in vegetation. The Spearman's Rho
correlation for dacthal and agricultural intensity was 0.873 for conifer needles and 0.849 for
lichens; the Spearman's Rho correlation for endosulfan and agricultural intensity was 0.777 for
conifer needles and 0.743 for lichens (see Table 5-15 in Chapter 5). Average regional
concentrations of dacthal and endosulfans in lichens were 2 and 31 ng/g lipid, respectively, in
Alaska parks and 82 and 243 ng/g lipid, respectively, in parks of the conterminous 48 states.
Trifiuralin and triallate in conifer needles and chlorpyrifos in lichens were below detection limits
at all Alaska parks.
Historic-use pesticides, especially HCB, a-HCH, and g-HCH, comprised a larger fraction of the
total contaminant concentration in Alaska parks, compared to parks in the conterminous 48
states, and concentrations were similar across parks, varying about one order of magnitude
between lowest (NOAT, GAAR, DENA, ROMO, BIBE) and highest (GRSA, GLAC)
measurements in both types of vegetation (Figures 4-12 and 4-13, Table 4-2). Endosulfan and
dacthal comprised less than half of total pesticide concentrations in vegetation from Alaska
parks, in contrast to parks in the conterminous 48 states. Dieldrin and DDTs were not detected in
any Alaska parks in either conifer needles or lichens. Where they were detected in parks in the
conterminous 48 states, mean dieldrin concentrations were < 20 ng/g lipid; DDT ranged up to
110 and 160 ng/g lipid at SEKI and GLAC, respectively.
Agricultural intensity, nitrogen availability as indicated by ammonium nitrate in fine particulates
sampled by park IMPROVE monitors, and population density were positively correlated with
SOC concentrations in vegetation. Many of the SOCs were also correlated with each other (i.e.,
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-15
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
parks with high concentrations of one SOC tended to have high concentrations of other SOCs).
Agricultural intensity and population density were confounded (Spearman's Rho = 0.63 to 0.68),
probably because many major western cities are built in valleys and at lower elevations where
conditions are optimal for both habitation and agriculture (compare backgrounds of Figure 4-12
or 4-13 with Figure 4-19 later in this chapter). For lichens, stronger correlations were observed
between SOC concentrations and agricultural intensity, but for conifer needles, correlation
strength was comparable between agricultural intensity and population density. See Chapter 5 for
a table (Table 5-15) and a discussion of strongest correlations.
Lichen Pesticides
^A
| 300 ng/g hpid
% Agriculture
<5 ^^ <50
15
< 30 ^H < 100
Figure 4-12. Pesticide Concentrations (ng/g lipid) in Lichens from Core and Secondary WACAP
Parks Overlaid on a Map of Agricultural Intensity (US Department of Agriculture, National Agriculture
Statistics Service, 2002). Circle area is proportional to total pesticide concentration. Light to dark green
shading indicates increasing agricultural intensity. White shading indicates national forests or parks.
Current-use pesticides endosulfan and dacthal dominate pesticide concentrations in parks in the
conterminous United States, where most agriculture occurs. Historic-use pesticides comprise a relatively
larger fraction of total pesticide concentrations in Alaska. Sites outlined in black are the core parks.
Pesticide groups are endosulfans (ENDOs), chlorpyrifos (CLPYR), dacthal (DCPA), g-HCH and a-HCH
(gHCH and aHCH), HCB, and chlordanes (CLDNs).
4-16
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Conifer Pesticides
1QOng/glipid
ENDOs d aHCH
CLPYR | J HCB
DCPA Q ^] CLDNs
gHCH X No Data
\
Figure 4-13. Pesticide Concentrations (ng/g lipid) in Conifer Needles from Core and Secondary
WACAP Parks Overlaid on a Map of Agricultural Intensity (US Department of Agriculture, National
Agriculture Statistics Service, 2002). Circle area is proportional to total pesticide concentration. Light to
dark green shading indicates increasing agricultural intensity. White shading indicates national forests or
parks. Current-use pesticides endosulfan and dacthal dominate pesticide concentrations in parks in the
conterminous United States, where most agriculture occurs. Historic-use pesticides are relatively more
important in Alaska, although total contaminant concentrations are lower. Conifers were not present in
NOAT and GAAR. Sites outlined in black are the core parks. Pesticide coding is identical to that in Figure
4-12.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-17
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Concentrations of individual current-use pesticides in park vegetation appear to be markedly
influenced by local usage. Because different types of crops are grown in different parts of the
country, application rates (g/ha) of crop-specific insecticides and herbicides vary regionally
across the United States. A visual comparison among maps of application rates of chlorpyrifos
(Figure 4-14), dacthal (Figure 4-15), endosulfans (Figure 4-16), triallate (Figure 4-17), and
trifiuralin (Figure 4-18) in the western United States, with concentrations detected in vegetation,
shows good agreement, especially if back trajectories are considered. In vegetation, application
rate does not necessarily correspond with concentrations. For example, chlorpyrifos are applied
at higher rates than endosulfans and dacthal but concentrations in vegetation were fairly low.
PAHs. Total PAH concentrations were lowest in the Arctic (<10 ng/g lipid) and in parks in
central Alaska (<500 ng/g lipid), increasing in concentration and number of compounds with
decreasing latitude along the Pacific Coast from southeastern Alaska (<5,000) to southern
California (<20,000), peaking in GLAC (up to 200,000 ng/g lipid) and lower in the rest of the
Rockies (<1,100 ng/g lipid) (see also Figure 4-19). The number of PAH compounds detected
generally increased with total PAH concentration from 2 in the arctic to 17 in GLAC.
The PAHs detected in greatest concentrations (10 to 10,000 ng/g lipid) and in most or all parks
were fiuorene, phenanthrene, fiuoranthrene, pyrene, retene, chrysene/triphenylene, and benzo(a)
anthracene. The other PAHs detected in vegetation—acenaphthylene, acenapthene, anthraxcene,
benzo(b)fiuoranthene, benzo(a)pyrene, indeno(l,2,3-cd)pyrene, dibenz(a,h)anthraxcene, and
benzo(ghi)perylene—were detected in fewer parks, usually at concentrations < 100 ng/g lipid.
One exception is the west side of the Continental Divide in GLAC, where concentrations of these
PAHs were higher, but decreased with distance and elevation from Columbia Falls, Montana.
Total PAH concentrations in vegetation reported in this document could be overestimated
because of the co-elution of matrix interferences.
PCBs. Compared to the major herbicides and PAHs, concentrations of PCBs were very low in
both lichens and conifer needles, and no discernable regional patterns were observed, either in
total accumulation of PCBs or the relative proportions of PCBs detected (Table 4-2).
4.2.3.2 Effects of Species on SOC Concentrations in Vegetation
4.2.3.2.1 Conifer Needles vs. Lichens
The relative contribution of individual SOCs to the total contaminant concentration in vegetation
of the 20 WACAP parks was similar in conifer needles and lichens: PAHs > endosulfans >
dacthal > HCB and a-HCH (Table 4-2). However, SOC concentrations were usually higher in
lichens than in conifer needles, even after lipid normalization. Specifically, paired t-tests of
conifer vs. lichen SOC concentrations at 69 WACAP sites where both vegetation types were
collected provided evidence that mean concentrations of chlorpyrifos, dacthal, endosulfans,
HCB, chlordanes, DDTs, PCBs, and PAHs were 2.6 to 9.0 times higher in lichens than in conifer
needles (Prob > t< 0.05; see Appendix 4A.7). Only a-HCH and g-HCH concentrations did not
differ among the vegetation types, and no compounds were higher in conifer needles than in
lichens. Although there was no evidence that dieldrin, trifuralin, and triallate concentrations
differed between vegetation types, statistical power was low because only 3,1, and 7 of the 69
sites, respectively, had detectable concentrations of SOCs in both media.
4-18 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Average annual use of
active ingredient
(pounds par square mile of agricultural
land in county)
D no estimated use
D 0,001 to 0.088
[H O.OB9 to 0.411
] 0,412 to 1.189
Q 1-19 to 3.069
• >=3.07
CHLORPYRIFOS - insecticide
2002 estimated annual agricultural use
Crap*
com
CUflfMI
ftifaK&Nxy
wheat fwyain
dtnnfrut
acvfcs
peanut
aovteans
pecans
Total
pounds appted
3362B5-
671112
547472
525292
395831
324*52
309560
241666
23693S
201 603
Pocnnt
naUonaiiBa
40.B4
8,10
8.91
K&f
4.78
a£2
3.74
2.92
i.es
£43
s
£
5>
£. 15-
I i«:
s,
* 5-
5 „
.
I
]
I
J
I ,
GA*t MOAT DEIIAKATMV^STGLBA STLE IIOCA OLYMMORACRLA IAVO YOSE SEKI GLflC GRTE ROWH3GRSA BPHD BIBE
Figure 4-14. Uses and Estimated Application Intensity in 2002 of the Current-Use Insecticide
Chlorpyrifos in the Conterminous 48 States vs. Mean Concentration in Vegetation (ng
chlorpyrifos/g lipid conifer needles or lichens) from WACAP Parks. Chlorpyrifos were detected in
vegetation in all parks except NOAT and GAAR, but highest concentrations were observed in SEKI and
YOSE, close to the San Joaquin Valley in California, a particularly high use area. Error bars indicate one
standard error.
Source of Chlorpyrifos data:
http://ca.water.usgs.gov/pnsp/pesticide_use_maps/show_map.php?year=02&map=m6009.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-19
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Average annual use of
active ingredient
(pounds per square mile of agricultural
land in county)
D no estimated use
D 0.001 to 0.003
D 0.004 to 0.009
D 0.01 to 0.028
D 0.029 to 0.212
• >= 0.213
DC PA-herbicide
2002 estimated annual agricultural use
Crops
dry onions
broccoli
sod harvested
cabbage
cauliflower
squash
green onions
collards
dry beans
strawberries
Total
pounds applied
233665
94487
28200
25590
14367
1343S
7272
6920
6771
5905
Percent
national use
52.0B
21.04
6.28
5.70
3.20
2.99
1.62
1.54
1.51
1.31
100-
10
! !!!
GAAR NOAT DENA KATM WRST GLBA STLE NOCA OLYM MORACRLA LAVO YOSE SEKI GLAC GRTE ROMOGRSA BAND BIBE
Figure 4-15. Uses and Estimated Application Intensity in 2002 of the Current-Use Herbicide
Dacthal in the Conterminous 48 States vs. Mean Concentration in Vegetation (ng dacthal/g lipid
conifer needles or lichens) from WACAP Parks. Dacthal was detected in vegetation in all parks, but
the three parks with highest concentrations were YOSE and SEKI, downwind of the high-use San Joaquin
Valley in California, and GRSA, in Alamosa County, Colorado (red patch in southeast Colorado). GLAC
also had high concentrations; no data are available for Montana and Wyoming, but high-use areas in
western Washington and northern Idaho are upwind of GLAC. Error bars indicate one standard error.
Source of dacthal data:
http://ca.water.usgs.gov/pnsp/pesticide_use_maps/show_map.php?year=02&map=m1892.
4-20
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Average annual use of
active ingredient
(pounds per square mile of agricultural
land in county)
D no estimated use
D 0.001 to 0.005
D 0.006 to 0.018
D 0.019 to 0.064
D 0.065 to 0.259
• >=0.26
ENDOSULFAN - insecticide
2002 estimated annual agricultural use
Crops
cotton
tomatoes
potatoes
apples
tobacco
pears
cucumbers and pickles
lettuce
green beans
squash
Total
pounds applied
160060
88607
87452
62973
58016
43730
34370
33267
28323
28632
Percent
national use
20.32
11.25
11.10
7.99
7.36
5.55
4.36
4.22
3.67
3.63
Lichen Conifer
ifer ^*3
GAAR NOAT DENA KATM WRST GLBA STLE NOCA OLYM MORACRLA LAVO YOSE SEKI GLAC GRTE ROMOGRSA BAND BIBE
Figure 4-16. Uses and Estimated Application Intensity in 2002 of the Current-Use Insecticide
Endosulfan in the Conterminous 48 States vs. Mean Concentration in Vegetation (ng endosulfan/g
lipid conifer needles or lichens) from WACAP Parks. Endosulfans were detected in vegetation in all
parks. No use data are available for Montana, Wyoming, New Mexico, or Mexico, hindering interpretation
of results for GLAC, GRTE, BAND, and BIBE. But proximity to high-use areas appears to affect relative
concentrations of endosulfans in other parks (NOCA, OLYM, MORA, CRLA, LAVO, YOSE, SEKI, ROMO,
and GRSA). Error bars indicate one standard error.
Source of endosulfan data:
http://ca.water.usgs.gov/pnsp/pesticide_use_maps/show_map.php?year=02&map=m6019.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-21
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Average annual use of
active ingredient
(pounds per square mile of agricultural
land in county)
LJ no estimated use
D 0.001 to 0.287
D 0.288 to 1.558
D 1.559 to 3.321
D 3.322 to 5.962
• >= 5.963
TRIALLATE - herbicide
2002 estimated annual agricultural use
Crops
wheat for grain
barley tor grain
dry peas
green peas
Total
Pounds Applied
1399275
220316
37107
10966
Percent
National Use
83.91
13.21
2.23
0.66
9- 20
Q.
D) 15
^)
E
~s 10
1 5-
I-
n
1|
GAAR NOAT DENA KATM WRST GLBA STLE NOCA OLYM MORACRLA LAVO YOSE SEKI GLAC GRTE ROMOGRSA BAND BIBE
Figure 4-17. Uses and Estimated Application Intensity in 2002 of the Current-Use Herbicide
Triallate in the Conterminous 48 States vs. Mean Concentration in Vegetation (ng triallate/g lipid
conifer needles or lichens) from WACAP Parks. Triallate is used most intensively in the northern
states and was detected, correspondingly, only in vegetation from GLAC and GRTE. Error bars indicate
one standard error.
Source of triallate data:
http://ca.water.usgs.gov/pnsp/pesticide_use_maps/show_map.php?year=02&map=m1790.
4-22
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Average annual use of
active ingredient
(pounds per square mile of agricultural
land in county)
EH no estimated use
D 0.001 to 0.035
D 0.036 to 0.293
D 0.294 to 1.162
D 1.163 to 3.53
• >= 3.531
TRIFLURALIN - herbicide
2002 estimated annual agricultural use
Crops
soybeans
cotton
alfalfa hay
wheat for grain
sugarcane
sunflower seed
dry beans
tomatoes
sorghum
green beans
Total
Bounds aDDlled
2999382
2672127
1053635
715160
304406
274123
260349
108776
62492
58753
Percent
national use
33.69
30.02
11.95
8.03
3.42
3.08
2.92
1.22
0.70
0.66
Lichen
•o
Q.
ra
"Si
c
I
!
GAAR NOAT DENA KATM WRST GLBA STLE NOCA OLYM MORACRLA LAVO YOSE SEKI GLAC GRTE ROMOGRSA BAND BIBE
Figure 4-18. Uses and Estimated Application Intensity in 2002 of the Current-Use Herbicide
Trifluralin in the Conterminous 48 States vs. Mean Concentration in Vegetation (ng trifluralin/g
lipid conifer needles or lichens) from WACAP Parks. Trifluralin was detected in vegetation at low
concentrations in nine parks, primarily in lichens. Parks where it was detected are downwind of high-use
areas. Trifluralin was detected in OLYM (only at OLYM1) on the outskirts of Port Angeles, Washington.
Error bars indicate one standard error.
Source of trifluralin data:
http://ca.water.usgs.gov/pnsp/pesticide_use_maps/show_map.php?year=02&map=m1361.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-23
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Population per grid cell (~1 sq km)
Lichen PAHs
| | 2-3 ring PAH
4-5 ring PAH
Retene
2,000 ng/g lipid
Figure 4-19. Concentrations (ng/g lipid) of PAHs in Lichens from Core and Secondary WACAP
Parks Overlaid on a Map of Population Density. Circle area is proportional to total PAH concentration.
Total PAH concentrations were lowest in Arctic Parks (< 10 ng/g lipid) and parks in central Alaska (< 500
ng/g lipid), increasing in concentration with decreasing latitude along the Pacific Coast from southeastern
Alaska to southern California. Highest concentrations were in GLAC, where the black circle represents
the true size of the total PAH concentration. Sites outlined in black are the core parks.
4-24
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Possible explanations for greater SOC accumulations in lichens are:
1. Lichen material was on average older than conifer needles. Conifer needles were all sampled
in the summer of their second year, whereas lichen samples were collected to represent the
population on the site. Therefore, lichen samples, after homogenization in the laboratory, had
a longer exposure and accumulation period than conifer needle samples. This explanation is
valid only if SOCs in vegetation never equilibrate but continue to increase year after year;
there is limited literature to suggest otherwise (see discussion in Chapter 5).
2. Epiphytic lichens were not buried under snow in wintertime, whereas conifer needles at
higher elevation and higher latitude sites could be buried under snow from several weeks to
many months of the year, reducing total exposure compared to lichens. The "lichen line" on
trees demarcates the winter snow line; unlike conifer needles, epiphytic lichens usually do
not survive extended burial.
3. Some physiochemical properties between lichens and conifer needles differed (e.g., surface
chemistry or texture), predisposing lichens to more effectively accumulate some compounds
or some forms of compounds (e.g., particulate vs. gas phase forms).
The chief advantage of sampling conifer needles is that their age is known and therefore, unless
they are buried under snow in winter, their exposure period is also known. In contrast, lichen
concentrations represent average concentrations in the lichen population that was sampled at the
site, within which some individuals could be decades old and others only a few years old.
Because coniferous forests cover an extensive land area in western North America and because
the biomass of needles on a kg/ha basis is usually very much larger than lichen biomass, from an
ecological perspective, conifer needle data are likely to be more relevant than lichen data. To the
extent that contaminants concentrated in needles are deposited in litterfall or washed out in
leachates, conifer needles must play a greater role than lichens in transferring contaminants to
soils and soil organisms.
The chief advantage of sampling lichens is that their SOC concentrations are more likely to be
above detection limits (e.g., PAHs at WACAP sites in GLAC) which makes it easier to detect
differences between sites in mapping local contamination or elevation effects. In arctic and
alpine ecosystems, where coniferous trees are absent, lichens can be a dominant component of
the ecosystem and a good sampling choice. Analytically, the clean-up process was faster and
instrument output was more readily interpretable for lichens than for conifer needles.
4.2.3.2.2 Differences in SOC Accumulation between Lichen Species
One of the assumptions of WACAP was that, within biological media types (i.e., lichens, conifer
needles, or fish), differences between species could be minimized by lipid normalization.
Although WACAP was not designed to specifically test this assumption, at the conclusion of the
laboratory analyses, there were six sites (DENA5, WRST1, WRST5, OLYM5, and GLAC5)
where SOC concentrations had been determined in multiple replicates of more than one species
of lichen. A comparison of within-site means across species by one-way analysis of variance
provided evidence that lipid normalization (i.e., reporting SOC concentration on a gram lipid
basis) was largely successful in minimizing differences across species. Most comparisons
showed no differences between species. However, significant differences between some species
combinations for some SOCs did occur and the concentration differences were usually between
2- and 10-fold (Appendix 4A.1). For example, at WRST1, mean concentrations of HCBs and
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-25
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
PAHs were 2.5 times higher in the leafy epiphyte, Platismatia glauca, than in the leafy epiphyte,
Hypogymnia apinnata. At WRST5, concentrations of dacthal, endosulfans, HCBs, a-HCH, g-
HCH, chlordanes, PCBs, and PAHs were 5-17 times higher in P. glauca than in the pendant
epiphyte, Alectoria sarmentosa. At OLYM5, concentrations of dacthal, endosulfans, and PCBs
were 2.5-5 times higher in Bryoria, another pendant epiphyte, than in A. sarmentosa. Most
notably, of the leafy tundra lichens, SOC concentrations were 7-50 times higher in Flavocetraria
cucullata than in Masonhalea richardsonii for all SOCs except dacthal, which was not different.
Masonhalea appears to be a very poor accumulator of SOCs.
When SOCs differed between species, they were usually consistently higher in one species than
in another, but exceptions did occur. At GLAC5, concentrations of HCB, g-HCH (p > F = 0.06),
chlordanes, DDTs, PCBs, and PAHs were 2-10 times higher in the leafy epiphyte, Hypogymnia
physodes, than in the shrubby epiphyte, Letharia vulpine. However, L. vulpina had 2 times more
g-HCH than H. physodes, and it also had detectable concentrations of chlorpyrifos, which was
not detected in H. physodes at that site. Therefore, using the same species reduces error between
sites by up to an order of magnitude. Because error among field replicates of the same species is
fairly low (Appendix 4A.1), using the same species is an inexpensive way to improve detection
of between-site differences. Based on WACAP results, Masonhalea should be avoided in future
sample efforts. It is not a good accumulator of SOCs, nutrients, or metals, possibly because of its
dense, glossy surface. Flavocetraria cucullata and Cladina arbuscula are better accumulators
and could be comparable to epiphytes that are poor accumulators, such as Alectoria sarmentosa
(Appendix 4A. 8). Flavocetraria cucullata is readily recognizable and widespread in Alaska and
in some moist, alpine areas of the northern conterminous 48 states.
In general, epiphytes are the best choice for sampling, but in dry, treeless areas of the
conterminous 48 states, the rock lichen, Xanthoparmelia, appears to accumulate SOCs at
concentrations similar to those in the epiphyte, Usnea (Appendix 4A.9). However, a higher
proportion of total dry weight will be in soil mineral particles trapped between overlapping lobes
of the lichen, some of which can be very time consuming, if not impractical, to remove. In
addition, the burden of soil particulates in Xanthoparmelia might be magnified in windy or dusty
sites, which might have happened in GRSA, because overlapping lobes trap soil particulates as
the lichen grows.
4.2.3.2.3 Differences in SOC Accumulation between Conifer Species
The working assumption for the WACAP study design was that lipid normalization would
minimize inter-species differences in SOC accumulation among conifers, as with lichens and
fish. Unlike lichens (multiple species collected at some sites), only one species of conifer was
collected at each site. Because conifer needles did not show elevation trends within parks, even
when the same species were compared, mean SOC concentrations of different species from
different sites within parks were compared. These means comparison tests (Appendix 4A. 6)
indicated that spruce (Picea), true fir (Abies), and Douglas-fir (Pseudotsuga) had fairly similar
accumulation capacities, whereas western hemlock (Tsuga) was somewhat higher and pine
(Pinus) was substantially lower, compared to the firs and spruce. See Figure 4-20 for a visual
representation of these patterns.
4-26 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
tt
DENA KATM WRST GLBA STLE NOCA OLYM MORA CRLA LAVO YOSE SEKI GLAC GRTE ROMO GRSA BAND BIBE
Common name
spruce
fir
^Doug-fir
_Jpine
hemlock
Figure 4-20. Comparison of Total Pesticide Accumulation in Conifer Needles by Species and Park.
Although data were lipid normalized, intra-park differences among species indicate that some species are
better accumulators than others (e.g., spruce and fir are comparable, hemlock is a somewhat better
accumulator, and pine is the poorest accumulator). These differences might be attributable to needle
morphology (see text for discussion). Mean concentrations in YOSE, GRSA, BAND, and BIBE would
probably have been higher, had fir been sampled there instead of pine. Bar height indicates the mean;
error bars indicate one standard error.
Specifically, firs usually accumulated substantially higher concentrations of SOCs than did
pines, especially endosulfans (~10 x higher), dacthal (~2-5x higher), and historic-use HCHs and
HCB (~ 3-5x higher). Western hemlock SOC concentrations were higher than those in fir by 1/3-
to 3-fold. Douglas-fir was similar to true firs for most SOCs but was often 2-3 times lower in
endosulfans and dacthal. Spruce was similar to fir and hemlock but tended to be -5-10 times
lower in endosulfans. These differences make sense from a needle morphology and tree
architecture point of view (i.e., spruce, fir, and Douglas-fir needles have a similar flattened shape
and size, whereas hemlock needles are shorter and pine needles are round and longer. Compared
to fir and spruce needles, hemlock needles are more densely packed on the branches and pine
needles are more loosely packed. All these factors could affect air circulation and needle surface
area, and, together with differences in surface wax chemistry, affect absorption rates. Komp and
McLachlan (1997) and Collins et al. (2006) have discussed these and other factors leading to
interspecies variability. Figure 4-21 presents photos of the most commonly collected species of
fir, spruce, and Douglas-fir in WACAP, compared with western hemlock and the most
commonly collected pine.
The three genera that were most widely collected in national parks in the western United States
were spruce in Alaska, fir in the Pacific Northwest, California, and northern Rocky Mountains,
and pine in the southern Rocky Mountains. Although species differences after lipid normaliza-
tion were lower (usually none to 5-fold) than park-to-park differences (up to 10-fold), species
can nevertheless be an important source of error. To improve detection of differences among
sites or parks, future researchers might choose to limit collections to a single genus, or to collect
enough within-site replicates of multiple species to calculate a compensation factor for cross-
species comparisons.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-27
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Figure 4-21. Needles of (A) Subalpine Fir, (B) Sitka Spruce, (C) Douglas-fir, (D) Western Hemlock,
and (E) Lodgepole Pine. Similar morphology and arrangement on branches might help explain why SOC
concentrations were usually similar among fir, spruce, and Douglas-fir. Hemlock tended to have higher
concentrations of SOCs and pine had substantially lower concentrations of SOCs compared to spruce
and fir; differences in needle length and density might partially explain this effect. Photos ©Susan
McDougall (hemlock) and J.S. Peterson (all others) @ USDA-NRCS PLANTS database.
4.2.3.3 Elevational Gradients of SOCs in Vegetation
Several reasons explain why we might expect altitudinal gradients in SOC concentrations. First,
many contaminants are present primarily because of regional sources (see Section 4.2.6). For
such compounds, we would expect the greatest air concentrations, and therefore ecosystem
exposure, to occur at the lowest altitudes. For contaminants associated with trans-Pacific/Asian
sources (Jaffe et al., 2003; Killin et al., 2004), we would expect air concentrations and ecosystem
exposure to occur at altitudes greater than 2,000 meters, because of the greater occurrence of
transport at these altitudes (Jaffe et al., 2003). Finally, for contaminants that can undergo cold
fractionation, we would expect an altitude gradient with highest concentrations at the highest and
coldest elevations (Wania and Mackay, 1993; Blais et al., 1998; Simonich and Kites, 1995;
Davidson et al., 2003; Davidson et al., 2004). It is also likely that several of these processes can
operate simultaneously, thus complicating interpretation of the data.
Regression analysis of the combined WACAP lichen data provided strong evidence (p < 0.014)
that concentrations of PCBs and the pesticides chlorpyrifos, dacthal, endosulfans, HCB, a-HCH,
g-HCH, and chlordanes increased with elevation averaged across WACAP parks (Table 4-3).
There was suggestive evidence (p = 0.0875) that DDT concentrations also increased with
elevation.
4-28
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Table 4-3. Linear Regression Model Results. Data are from all WACAP parks that met
criteria*, for the fit of lichen SOC concentrations to increasing elevation after accounting for
differences between parks.
Estimated Ave. %
Change in Concentration
SOC Slope SE P-value R2 from 500 to 1,000 m
Chlorpyrifos
Dacthal
Endosulfans (sum)
Endosulfan I
Endosulfan II
Endosulfan sulfate
HCB
a-HCH
g-HCH
Chlordanes (sum)
Trans-Chlordane
Cis-Nonachlor
Trans-Nonachlor
DDTs (sum)
PCBs (sum)
PCS 118
PCB138
PCS 153
PCB183
PAHs (sum)
Fluorene
Phenanthrene
Retene
Chrysene/Triphene
Benzo(a)anthracene
0.0035
0.0010
0.0009
0.0009
0.0008
0.0009
0.0012
0.0013
0.0016
0.0009
0.0018
0.0009
0.0017
0.0004
0.0030
0.0006
0.0011
0.0006
0.0002
-0.0009
-0.0007
-0.0006
0.0015
-0.0005
-0.0014
0.0013
0.0002
0.0002
0.0001
0.0002
0.0002
0.0002
0.0002
0.0002
0.0002
0.0005
0.0002
0.0005
0.0002
0.0009
0.0002
0.0003
0.0002
0.0001
0.0002
0.0002
0.0001
0.0001
0.0002
0.0003
0.0117
0.0001
0.0001
0.0001
0.0011
0.0001
0.0001
0.0001
0.0001
0.0003
0.0005
0.0001
0.0007
0.0875
0.0009
0.0141
0.0007
0.0042
0.0049
0.0007
0.0001
0.0001
0.1443
0.0219
0.0001
0.5338
0.9654
0.9541
0.9454
0.9297
0.9541
0.9368
0.8793
0.8970
0.7878
0.7684
0.7595
0.6971
0.9321
0.6174
0.3786
0.6589
0.5345
0.4552
0.9374
0.9479
0.9754
0.4881
0.9271
0.8998
218
165
157
157
149
157
124
192
153
156
217
364
236
122
165
135
161
150
178
-64
-70
-74
n.s.
-78
-50
*Parks in models: BAND, CRLA, DENA, GLAC, KATM, LAVO, MORA, NOCA, SEKI
for concentration change represent the average across all parks in the models. See
Appendix 4A.11 for regression model details.
n.s. = not significant
, and STLE. Values
Chapter 3 and
By contrast, all the PAHs tested, except retene, decreased with elevation (p < 0.0219) (Table
4-3). The absolute change in ng/g lipid (non-transformed data) or percentage change (natural lo
transformed data) per 500 m increase in elevation varied by park, contaminant, and lichen
species within park (Table 4-4). The bar charts in Figure 4-22 and Appendix 4A. 10 show the
concentration increases (or decreases) from lowest to highest elevations within each park and
portray the magnitude and consistency of concentration changes across vegetated elevations of
the WACAP parks.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-29
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Table 4-4. Simple Linear Regression Results of Lichen SOCs on Park and Elevation.
increment in ng/g lipid (non-transformed data) or percent change (natural log-transformed
Compound
Chlorpyrifos
Dacthal
Endosulfans
Endosulfan 1
Endosulfan II
Endosulfansulfate
HCB
a-HCH
g-HCH
Chlordanes
t-Chlordane
c-Nonachlor
t-Nonachlor
DDTs
PCBs
PCS 118
PCB138
PCS 153
PCB183
PCS 187
PAHs
FLO
PHE
Retene
CHR/TRI
B(a)A
Values represent concentration changes per 500 m elevation
data) calculated for individual parks.
PARKS
BAND
ND
156%
201%
173%
192%
212%
0.81
192%
0.64
0.01
0.10
0.40
0.35
NS
1.25
NS
0.35
0.25
0.15
0.30
-19.2%
ND
ND
NS
-60.6%
-40.7%
CRLA
7.20
547%
386%
521%
426%
349%
10.24
406%
2.72
8.41
6.30
2.35
5.60
NS
11.3
NS
4.40
2.60
0.20
1.25
-74.1%
ND
-0.74
NS
-90.5%
-33.3%
DENA
ND
128%
223%
223%
ND
223%
1.69
300%
0.20
ND
ND
ND
ND
NS
ND
NS
ND
ND
ND
ND
-52.2%
ND
38.47
NS
-182.2%
-173.3%
GLAC
-1.40
212%
182%
173%
259%
182%
0.12
165%
2.10
1.69
2.70
1.55
3.25
NS
0.15
NS
0.05
0.20
0.00
0.30
-60.6%
-74%
-0.70
NS
-60.6%
-54.9%
KATMF1
ND
332%
192%
142%
ND
234%
0.49
165%
0.04
0.06
0.00
0.10
-0.20
NS
0.35
NS
0.10
0.20
0.00
0.00
-315.8%
ND
1.16
NS
-258.6%
ND
KATMH2
ND
105%
547%
234%
ND
605%
-0.42
-90%
0.06
1.96
0.70
0.60
1.20
NS
1.95
NS
0.50
0.60
0.05
0.30
-77.9%
-17.4%
-0.95
NS
-28.7%
ND
LAVO
ND
182%
173%
173%
182%
173%
0.12
173%
0.12
0.72
1.55
0.55
1.25
NS
1.95
NS
0.80
0.50
0.05
0.30
-23.5%
-30%
-0.33
NS
-5.0%
-36.8%
MORA
3.10
149%
192%
157%
149%
223%
0.64
300%
1.56
0.56
1.05
0.35
0.80
NS
4.85
246%
1.60
1.20
0.30
0.50
-86.1%
105%
1.00
NS
-81.87%
-19.2%
NOCA
1.40
165%
135%
122%
128%
142%
0.00
135%
0.09
0.00
0.05
0.10
-0.05
NS
0.75
NS
0.40
-0.10
0.05
0.10
-54.9%
-52%
-0.58
NS
-81.9%
-60.6%
SEKI
ND
128%
116%
142%
111%
111%
0.16
182%
1.00
0.16
0.90
0.50
0.95
NS
0.5
NS
0.15
0.10
0.05
0.15
-95.1%
-86%
-0.78
NS
-81.9%
-74.1%
STLEA3
ND
192%
157%
192%
100%
142%
0.06
192%
0.16
0.02
0.10
0.10
0.10
NS
0.8
NS
0.20
0.15
0.00
0.05
-30.1%
-44%
-0.50
NS
-22.3%
-9.07%
STLEP4
ND
191%
105%
128%
173%
100%
0.56
100%
0.06
-0.06
-0.50
-0.05
-0.75
NS
-0.25
NS
0.05
-0.15
-0.05
0.00
-81.9%
-74%
1.05
NS
-60.6%
-35.0%
Note: Negative trends indicate decreasing concentration with increasing elevation. See Methods chapter and Appendix 4A.11 for model details. ND = no data, all samples
below EDLs; NS = fitted line not a significantly better fit than the mean for this park ( p > 0.05).
1KATMF = KATM Flavocetraha cuculata. 2KATMH = KATM Hypogymnia physodes. 3STLEA = STLE Alectoria sarmentosa. 4STLEP = STLE Platismatia glauca.
4-30
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Dacthal
£2 Cs| 5 un m U_ U , ,
<.
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
In general, changes in SOC concentration per unit change in elevation were within the same
order of magnitude across parks (Table 4-4). Parks with similar percentage increases might have
different absolute increases in SOC concentrations per unit increase in elevation. For example,
endosulfan concentrations in the lichen Flavocetraria cucullata from KATM increased 192% per
500 m, with the lowest elevation averaging 0.13 ng/g lipid, whereas endosulfans in GLAC
lichens increased nearly the same percentage, 182%; however, samples from the lowest elevation
averaged ~ 500 ng/g lipid (Table 4-4, Figure 4-22). Doubling the latter concentration is more
likely to have adverse ecological effects, especially if the total quantities of contaminants
accumulated in vegetative biomass/ha are considered (see discussion in Chapter 5).
For the two parks, KATM and STLE, where two species were collected, the change in SOC
concentration per unit elevation increase was as variable across species as it was between parks
(Table 4-4). In KATM, we expected smaller SOC concentration changes in the tundra lichen,
Flavocetraria cucullata (KATMF), compared to the epiphytic lichen Hypogymniaphysodes
(KATMH). Presumably the ground lichen would be buried under snow more days per year,
whereas the epiphyte would be exposed to the air a greater number of days per year.
Concentrations of SOCs were consistently higher in H. physodes (Figure 4-22, Appendix 4A. 10),
whereas elevational differences were often larger inF. cucullata (Table 4-4). Similarly, in STLE,
Platismatia glauca (STLEP) appeared to be a better accumulator of SOCs than Alectoria
sarmentosa (STLEA) (Figure 4-22; Appendix 4A.10) but changes in concentration per unit
elevation increase were consistently larger for A. sarmentosa.
It is important not to over-interpret Table 4-4 because the slopes used to calculate percent change
within individual parks are based on only 3-5 elevations; also, in the secondary parks, only one
sample (three for the core parks) was collected per elevation. In contrast, the general models
shown in Table 4-3 were developed from the combined WACAP dataset, providing better
statistical power. Very high R-squared values and low standard errors for many compounds (e.g.,
dacthal, endosulfans, HCB, PAHs) indicate that concentrations of these important SOCs can be
predicted accurately from elevation with general models (see Appendix 4A. 11 for regression
model details). For most PCBs and pesticides, concentrations at 1,000 m were 150-220% of
concentrations at 500 m. PAHs were 50% to 80% lower at 1,000 m, compared with 500 m.
4,2,4
Figures 4-1 to 4-4 and 4-6 show the concentrations of SOCs in fish; the data are reported in ng/g
lipid. The dominant SOCs in fish were p,p'-DDE, dieldrin, PBDE 47, PBDE 99, PCB 153, PCB
138, dacthal, trans-nonachlor, HCB, and endosulfan sulfate. Dieldrin, p,p'-DDE, dacthal, and
endosulfan sulfate concentrations were highest in fish from SEKI, ROMO, and GLAC. PBDE
concentrations in fish across the WACAP parks (Figure 4-6) varied less than most other SOCs,
both within and between lakes, and were highest in MORA fish, and lowest in fish in the Alaska
national parks. Concentrations of the five major PCB congeners were comparable among fish
from Alaska and Pacific coast parks (SEKI, OLYM, and MORA), and lower in fish from the
Rocky Mountains (GLAC, ROMO) (Figure 4-4). Dacthal concentrations were highest in fish
from SEKI, followed by ROMO and GLAC (Figure 4-1), and lower in parks in the Pacific
Northwest (OLYM, MORA) and Alaska (DENA, NO AT). For most compounds, the variation in
fish SOC concentrations within lakes was as large as the variations between lakes.
4-32 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
When compared to similar fish species collected from high-elevation lakes throughout Europe
(Vives et al., 2004a), PBDE concentrations measured in fish in national parks in the western
United States were, on average, approximately three times higher in concentration, after
adjusting for differences between muscle and whole tissue concentrations (USEPA, 2000).
Concentrations of most historic-use SOCs (HCB, DDTs, and HCHs) in fish in the western
United States were comparable to or 2- to 9-fold lower than those in European mountain fish
(Vives et al., 2004b). Because the European mountain fish and the WACAP fish studied included
similar fish species collected from cold, oligotrophic lakes, within 3 years time, it is unlikely that
the observed differences in the SOC concentrations are a result offish accumulation differences
or rapidly changing SOC emissions. Even a rapid PBDE doubling time of 6 years could not
account for the 3-fold higher PBDE concentrations in fish in the western United States. This
finding suggests that fish in national parks in the western United States are exposed to higher
PBDE concentrations than similar European mountain fish, which is consistent with PBDE
concentrations measured in other North American and European environmental compartments
(Kites, 2004), and more recent European fish samples (Gallego et al., 2007).
Compared to fish collected from several alpine lakes in Canada (Demers et al., 2007), WACAP
fish were significantly lower in HCHs and chlordanes (only ~ V4 of the concentration) and
comparable in concentrations of DDTs and HCB; dieldrin concentrations were approximately 3
times higher. Because the fish sampled in Canada and the western United States were of similar
species and the lakes had similar productivity, these differences probably reflect differences in
SOC exposure. Other than these, there are few observations of broad-ranging mountain fish
DOC loads (particularly for CUPs) with which we can compare the WACAP fish data.
4.2.5
4.2.5.1 Spatial Distribution of SOCs in Sediments
Sediment cores provide information on the temporal changes of contaminant loadings in
WACAP parks over the last -150 years. Cores were collected from the lake sites in each core
park by means of the methods described in Section 3.4.6.1. Because sediment data reflect an
annual accumulation of material, they are reported as a flux (ng/m /yr), rather than a concentra-
tion. The sediment flux data have been corrected by lake "focusing factor," as described in
Section 3.4.6.2, which adjusts for differences in sediment accumulation for each watershed.
Figures 4-1 to 4-6 show the SOC fluxes in the WACAP lake surficial sediments corrected for
sediment focusing. In these figures, we used the most recent year of sediment data to represent
the surficial flux. Figures 4-23 to 4-29 show the temporal trends in SOC fluxes.
The most common CUPs detected in the surficial sediments were the endosulfans and dacthal.
Endosulfans have the highest recent sediment flux of all CUPs. GLAC, OLYM, and MORA all
had fluxes near 60 ng/m /yr. ROMO and SEKI were a factor of 5-7 higher at 290 and 420
ng/m2/yr, respectively. Endosulfans at NO AT, GAAR, and DENA were at or below detection
limits (see Figure 4-1). The most frequently detected groups of HUPs in the WACAP lake
surficial sediments were the chlordanes and dieldrin (see Figure 4-3). Chlordanes were detected
in every park, and concentrations were a factor of 40 higher at SEKI than at NOAT and GAAR.
Sum DDTs were frequently detected at ROMO and SEKI. Surficial sediment fluxes of DDTs at
these parks were 2,500 and 760 ng/m2/yr, respectively.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-33
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Historic-Use SOCs
Current-Use SOCs
Matcharak
Burial
024
i Trans-Chlordane
• Cis-Nonachlor
246
Trans-Nonachlor
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2000
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1900
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0 100 200
3 PBDE-47
• PBDE-153
300 100
PBDE-99
PBDE-154
200 300
^ PBDE-100
Focusing Factor-Corrected Flux (ng/rrT/yr)
Focusing Factor-Corrected Flux (ng/m2/yr)
Figure 4-23. Focusing Factor-Corrected Flux (ng/m2/yr) Profiles of Current- and Historic-Use SOCs
in Matcharak Lake and Burial Lake Sediment Cores at GAAR and NOAT. Solid lines ( ) indicate
US registered use dates, dashed lines ( ) indicate US restriction dates, and asterisks (*) indicate
below method detection limit.
4-34
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Historic-Use SOCs
Current-Use SOCs
McLeod
Wonder
2000
1975
1950
1925
1900
1875
E McLeod Wonder
2000
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1925
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PBDE-154
Focusing Factor-Corrected Flux (ng/m /yr)
Focusing Factor-Corrected Flux (ng/m /yr)
Figure 4-24. Focusing Factor-Corrected Flux (ng/m2/yr) Profiles of Current- and Historic-Use SOCs
in McLeod Lake and Wonder Lake Sediment Cores at DENA. Solid lines ( ) indicate US registered
use dates, dashed lines ( ) indicate US restriction dates, and asterisks (*)indicate below method
detection limit.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-35
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Historic-Use SOCs
Current-Use SOCs
Snyder
Oldman
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2000
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PBDE-154
5000 7500
=> PBDE-100
Focusing Factor-Corrected Flux (ng/m'/yr)
Focusing Factor-Corrected Flux (ng/m2/yr)
Figure 4-25. Focusing Factor-Corrected Flux (ng/m2/yr) Profiles of Current- and Historic-Use SOCs
in Snyder Lake and Oldman Lake Sediment Cores at GLAC. Solid lines ( ) indicate US registered
use dates, dashed lines ( ) indicate US restriction dates, and asterisks (*)indicate below method
detection limit.
4-36
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Historic-Use SOCs
Current-Use SOCs
A
2000
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is 1Q 1925
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TO 1950
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Historic-Use SOCs
Current-Use SOCs
LP19
Golden
2000
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0 15 30
• Trans-Chlordane
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0 1500 3000 4500 1500 3000 4500
^ PBDE-47 czzzz2 PBDE-99 i=^i PBDE-100
• PBDE-153 ^ PBDE-154
Focusing Factor-Corrected Flux (ng/m2/yr)
Figure 4-27. Focusing Factor-Corrected Flux (ng/m /yr) Profiles of Current- and Historic-Use SOCs
in LP19 and Golden Lake Sediment Cores at MORA. Solid lines ( ) indicate US registered use
dates, dashed lines ( ) indicate US restriction dates, and asterisks (*)indicate below method detection
limit.
4-38
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Historic-Use SOCs
Current-Use SOCs
Lone Pine
Mills
Lone Pine
Mills
9rtno
1975
£ 1925
1900
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Sf^~
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^
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) 5 10 15 20 5 10 15 20
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Lone Pine Mills
•
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) 20000 40000 60000 20000 40000 60000
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PBDE-153 — PBDE-154
Focusing Factor-Corrected Flux (ng/m'/yr)
Focusing Factor-Corrected Flux (ng/m /yr)
Figure 4-28. Focusing Factor-Corrected Flux (ng/m2/yr) Profiles of Current- and Historic-Use SOCs
in Lone Pine Lake and Mills Lake Sediment Cores at ROMO. Solid lines ( ) indicate US registered
use dates, dashed lines ( ) indicate US restriction dates, and asterisks (*)indicate below method
detection limit.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-39
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Historic-Use SOCs
Current-Use SOCs
Emerald
Pear
0 100 200 300
i Trans-Chlordane i=
i Cis-Nonachlor
100 200 300
Trans-Nonachlor
B
2000
1975
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) 100 200 300 100 200 300
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Emerald Pear
^™
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hr
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) 10000 20000 10000 20000
IDDDs i=i IDDEs
Emerald Pear
— =— =
•=
•x
F
2000
1975
1950
1925
1900
1875
(
G
2000
1975
1950
1925
1900
1875
Emerald Pear
^™
*
*
) 25 50 75 100 25 50 75 100
Dacthal
Emerald Pear
=
!=
*
0 10000 20000 30000 10000 20000 30000
^ IPAHs
H
2000
1975
1950
1925
1900
1875
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-
•X
Emerald
Pear
•7QOO
1975
IQ^D
1925
1900
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•=^=—
^™=™
I
2000
1975 •
-men
1925
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^ =
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^
^^™ — '
i*
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0 100200300400500
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=3 Endosulfan I
100200300400500
• Endosulfan II
0 100
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3 PCB-183
200
PCB-138
PCB-187
100 200
— PCB-153
0 2500
3 PBDE-47 cz
• PBDE-153 -
5000
PBDE-99
PBDE-154
2500 5000
>=i PBDE-100
Focusing Factor-Corrected Flux (ng/m /yr)
Focusing Factor-Corrected Flux (ng/m /yr)
Figure 4-29. Focusing Factor-Corrected Flux (ng/m2/yr) Profiles of Current- and Historic-Use SOCs
in Emerald Lake and Pear Lake Sediment Cores at SEKI. Solid lines ( ) indicate US registered use
dates, dashed lines ( ) indicate US restriction dates, and asterisks (*)indicate below method detection
limit.
4-40
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
PCBs were detected in all surficial sediments, and were highest at low latitude sites. The lowest
focusing factor-corrected fluxes were at DENA, NO AT, and GAAR. The highest fluxes were
found at SEKI and ROMO (see Figure 4-4).
PBDE fluxes were above the method detection limits in the surficial sediment only from ROMO.
Within ROMO, Mills Lake, located on the east side of the Continental Divide, had PBDE focus-
corrected flux an order of magnitude greater than Lone Pine Lake, which is located on the west
side of the Continental Divide. This finding is discussed further in Section 4.2.6.3.
PAHs had the highest surficial sediment flux of all SOCs in the WACAP sites. As it did for the
vegetation and snow samples, GLAC (Snyder Lake) had the highest surficial sediment PAH flux.
This finding is discussed further in Section 4.2.6.2.
4.2.5.2 Temporal Distribution of SOCs in Sediments
Lake sediment analysis can be an extremely useful tool for evaluating the history of contaminant
loading to a lake, but its utility is dependent upon several key assumptions: (1) the constituent of
interest is stable (i.e., the compound is persistent and does not move appreciably as a result of
diagenesis within the sediment) and (2) the sediment dating profile can be reasonably established
through appropriate isotopic dating techniques. The WACAP sediment analysis meets both of
these objectives for the target contaminants (both SOCs and selected elements). The fairly short
gravity cores that were obtained from each lake typically penetrated to a depth going back to at
least 1850. Some cores from lakes with very slow sedimentation rates penetrated much further
back in time. We collected two cores from each lake, and if the first core dated out well with the
radioisotope techniques, we accepted it for further analyses. If the first core could not be clearly
dated, or if other concerns were apparent, we examined the second core and then selected the
most appropriate of the two for further analysis. In all cases, one of the two cores displayed an
acceptable dating profile.
For SOCs, given the expense of the analytical measurement, we carefully selected the exact
slices of sediment for which we would perform analyses. These slices were selected and
analyzed incrementally so that we would end up with a profile that maximized depiction of
trends from pre-industrial times to the present. In general, we allocated approximately eight core
slices per core for a complete SOC analyses. The results of this work are displayed in Figures
4-23 to 4-29). Each figure shows the pair of lakes for a particular WACAP park side by side, and
the most prevalent SOCs are displayed by date. All figures use focus-corrected flux, so that
comparisons among the lakes can readily be made. The solid horizontal lines in each figure
represent the registration date for the specific SOC and the dashed lines represent the date of US
restriction, if appropriate. The asterisks (*) indicate values below the method detection limit.
In most cases, the sediment SOC profiles are as expected with regard to the history of SOC use
and regulation in the United States. Most do not appear in the sediment profiles until the date
(sediment depth) that they were registered. After US restriction, most SOC concentrations began
to decline; however, they have not reached zero because of revolatilization from various ecosys-
tem sinks (including agricultural soils) and subsequent atmospheric deposition. For those cases
in which the compound appears before the time of registration, we suspect that bioturbation
and/or diffusion of the chemical within the sediment profile are responsible.
An examination of all seven figures reveals three groupings, with respect to the overall flux of
SOCs to the lake sediments: high (ROMO, GLAC, SEKI), intermediate (OLYM, MORA), and
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-41
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
low (DENA, NO AT, GAAR). Sediment profiles of SOCs from SEKI and ROMO have the
highest flux for most SOCs. Most of the SOC profiles for these two parks show an increase in
specific SOCs near the time of registration. For SEKI lakes, there is often a decrease after the
time in which the use of the specific compounds was restricted in the United States. In the
ROMO lakes, there is good agreement within the sediment cores concerning the appearance of
the compounds with time of US registration, but there has been virtually no decrease in
deposition since use was restricted in the United States. This pattern might be a result of SOCs
re-volatilizing from historic sources and undergoing atmospheric transport and deposition to
remote ecosystems. A similar result was observed by Donald et al. (1999).
The profiles for specific SOCs from the paired lakes from each park generally show the same
temporal pattern; however, the magnitude of flux to the sediments is frequently quite different.
The differences in flux between sites in the same park could result from a variety of factors,
including actual differences in airborne concentrations and deposition, differences in
precipitation type and rate, differences in the way the watershed processes these compounds, and
differences in mobility and delivery from the watersheds to the lake sediments themselves.
Examining these issues for all sites is beyond the scope of WACAP, but will be addressed for
ROMO as part of this work (Usenko et al., in press).
Several of the SOC sediment profiles stand out with much higher concentrations. Endosulfans
and chlordanes are much higher at Emerald Lake (SEKI) and Mills Lake (ROMO), compared
with all other sediment profiles, even those for the other lake in the same park. PAHs are sig-
nificantly greater at Snyder Lake (GLAC) compared with all other lake profiles. This finding is
consistent with PAH results at GLAC in other media, and is discussed further in Section 4.2.6.2.
For other SOCs, some concentrations were below our detection limit. This was the case for the
entire profile of dieldrin, and for sum DDD and sum DDE, for all six lakes in MORA, OLYM,
and GLAC. PCBs were lower in the intermediate lakes than in the high group, but are detectable
throughout all profiles. Trends in PCB concentrations appeared to be stabilized or declining in
the intermediate lakes, whereas two of the four lakes in the high group clearly had the highest
PCB value at the surface of the core, which suggests a recent increase in flux of PCBs to the
highest group.
SOC fluxes were lowest at the sites in Alaska (DENA, NO AT, GAAR), throughout their
profiles. This is undoubtedly a result of the fact that these sites are long distances from major
urban, industrial, and agricultural activities, and that precipitation, an important vector of SOC
deposition, is generally lower at these sites. Chlordane and its major constituents, along with
PCBs, were detectable in all Alaska lake sediment profiles, but at very low concentrations.
The group of contaminants most abundant in the sediments differed from those most common in
snow and fish, primarily because of the different affinities SOCs have to partition among water,
particulates, and lipid. Snow typically contains hydrophilic compounds, and some hydrophobic
compounds can be associated with particulate deposition. Fish tend to accumulate compounds
that are lipophilic. Lake sediments are a mix of organic and inorganic particles. Therefore, the
contaminant histories in the sediments are influenced strongly by the watershed and lake
processes leading to sedimentation, and would not be expected to have the same dominant suite
of SOCs as snow and fish. It is difficult to compare SOC patterns across matrices; nonetheless,
the sediments give information on the temporal changes in exposure to these contaminants.
4-42 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Wonder Lake Burial Lake
4.2.6 Source Attribution for SOCs
4.2.6.1 Sources of Pesticides
Some of the pesticides detected in both annual snowpack and in surficial sediment in WACAP
lakes include dacthal, endosulfans, dieldrin, and chlordanes. SEKI, ROMO, and GLAC had the
highest pesticide concentrations in 3 years of annual snowpack measurements and surficial
sediment fluxes.
In an analysis of the first year of WACAP snow data (collected in 2003), Hageman et al. (2006)
found a correlation between regional agricultural intensity and the concentration of several
pesticides in WACAP snow, including both current-use and historic-use pesticides. They used
the linear relationship between percent of regional agriculture and log concentration to estimate
the fraction of each pesticide attributable to regional sources (within 75, 150, and 300 km). This
model assumes that the amount of pesticide present in the snow in the Alaska parks was entirely
from global sources and that the global contribution was constant at all parks. We have updated
this analysis to include all 3 years of WACAP snow data. Figure 4-30 shows the percentage of
pesticides related to regional sources, within 150 km of each park, by means of this method.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-43
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
100
90 -
80 -
70 -
60 -
s. 5
I 40 H
f, 30 -
S. 20 H
10 -
0
1
I
<$
AO-- \°
Current-use pesticides
Historic-use pesticides
Figure 4-30. Percentage of Total Pesticide Concentration Related to Regional Sources. Regional is
defined as within 150 km. Calculations use snow data collected from the springs of 2003-2005.
A small amount of agriculture occurs in Alaska, but for this analysis, the percentage of cropland
intensity is insignificant. For GLAC and SEKI, the calculated contribution attributable to
regional sources and transport is 80-100% for current-use pesticides, such as dacthal, total
chlorpyrifos, and endosulfans. Even for some HUPs, such as dieldrin, a-HCH, and HCB, the
pattern suggests a significant influence from historical regional agriculture. Presumably, this is a
result of revolatilization of persistent SOCs that were applied to soils before the United States
bans on agricultural usage.
Figures 4-12 and 4-13 show plots of SOC concentrations in lichens and conifer needles,
respectively, overlaid on a map of agricultural intensity. For both lichens and conifer needles, the
highest concentrations were measured in parks adjacent to the most intensive agricultural
regions. In addition, parks with highest SOC concentrations were dominated by CUPs, usually
endosulfans (see also Figure 4-31). At SEKI, the highest SOC concentrations in vegetation were
endosulfans, but dacthal was the dominant SOC in snow (see Figures 4-1 and 4-7). This finding
might reflect seasonality of sources, usage, or differences in uptake by snow and vegetation.
At parks with lowest overall concentrations of SOCs, HUPs made a larger contribution to total
pesticide concentration. Concentrations of HUPs in Alaska parks were not very different from
concentrations in parks in the conterminous 48 states. The results of Hageman et al. (2006)
suggest that even for HUPs, regional agricultural sources are a good predictor for concentration
in snow, presumably because the pesticide burden has revolatilized from soil and other sinks. For
HUPs, regional agriculture explains 20-80% of the snow concentrations, with presumably global
sources accounting for the remainder (see Figure 4-30).
4-44
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
400-
350-
=5-300-
0)
1)250H
ro
£200-
, ISO-
50-
| |Mean(Historic Use Pesticides)
QlVlean(Current Use Pesticides)
HMean(Total Pesticides)
v<->
HI
Q
o
Figure 4-31. Mean Concentrations of Historic-Use (HCB, HCHs, Chlordanes, DDT, Dieldrin) and
Current-Use (Trifluralin, Triallate, Chlorpyrifos, Dacthal, Endosulfans) Pesticides in Two-Year-Old
Conifer Needles from WACAP Parks. Parks are ordered, left to right, from north to south along the
Pacific Coast (DENA -^ SEKI), and from north to south in the Rocky Mountains (GLAC -^ BIBE). Current-
use pesticides were not detected often in Alaska parks, comprised about one-third to one-half the total
pesticide concentrations in northern Washington, and most of the pesticide burden elsewhere. Conifer
needles were not sampled in NOAT and GAAR. Total pesticide burdens (current use + historic use) were
highest in national parks of Washington, Oregon, California, and Montana.
Some specific pesticides in park vegetation could be influenced by regional and local application
rates. Because different types of crops are grown in different parts of the United States,
application rates of crop-specific insecticides and herbicides varies across the country.
Comparison of maps of application rates of chlorpyrifos, dacthal, endosulfans, triallate, and
trifluralin in the western United States (Figures 4-14 to 4-18, respectively) and concentrations
measured in vegetation suggest a relationship, especially if air mass back trajectories are
considered (see Section 4.5).
Pesticide application rate does not correspond to pesticide concentrations in vegetation. For
example, chlorpyrifos is applied at higher rates than endosulfans and dacthal, but concentrations
in vegetation were low, probably as a result of the differences in the physico-chemical properties
of these pesticides and their relative affinities to accumulate in vegetation. In addition, pesticide
use data is missing for some western states and counties, limiting our ability to identify source
regions.
4.2.6.2 Sources of PAHs at Glacier National Park
Previous studies have identified a relationship between PAH concentrations and proximity to
urban regions (Garban et al., 2002; Hafner et al., 2005). Figure 4-5 shows the concentration of
sum PAHs in vegetation, snow, and sediments. Concentrations of PAHs at GLAC were 1 to 2
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-45
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
orders of magnitude greater than at any other site, in these matrices. Figure 4-19 shows the
concentration of sum PAHs in lichen at core and secondary parks, overlaid on a plot of popula-
tion density. In contrast to the previous studies mentioned, a correlation with population is not
present in the WACAP data, in large part because of the high PAH concentrations and low
population density near GLAC.
However, the PAH concentrations in GLAC are not uniform across the park. Measurements in
the watershed (Snyder Lake) closest to Columbia Falls showed significantly higher PAH
concentrations. The sum PAH concentrations in snowpack, lichens, and surficial sediment were
a factor of 7.7, 32.8, and 5.3 greater, respectively, in the Snyder Lake catchment (west of the
Continental Divide) than in the Oldman Lake catchment (east of the Continental Divide). The
PAH concentrations at Snyder Lake were the highest among all WACAP sites, whereas concen-
trations at Oldman Lake were comparable with those at sites in other parks. Referring to Figure
4-25, we see also that the PAHs in the Snyder Lake sediment core increased substantially in the
early-mid 1950s. We believe the presence of high concentrations of PAHs at GLAC are related
to the aluminum smelter in Columbia Falls, Montana, which came on-line in 1955 (Usenko et al.,
in press).
The electric-powered aluminum smelter in Columbia Falls operates with Soderberg aluminum
smelting technology (Columbia Falls Aluminum Company, 2007). The smelter resides on the
Flathead River (west of the Continental Divide), approximately 10 km southwest of GLAC and
approximately 45 km southwest of Snyder Lake. Outflow from Snyder Lake forms a tributary of
the Flathead River. Aluminum smelters that use Soderberg technology are known emitters of
fluoride and PAHs, and can be significant local PAH sources in rural areas (Booth and Gribben,
2005; International Aluminum Institute, 2007). According to the USEPA, this specific smelter
has been releasing hydrogen fluoride to the atmosphere at a rate of ~65 tons per year from 1999
to 2004 and PAHs to the atmosphere at a rate of-14 tons per year from 1999 to 2005 (USEPA,
2007a). A previous study (National Park Service, 1998) suggests that fluoride, emitted to the
atmosphere from the smelter, undergoes atmospheric transport and deposition to Snyder Lake
catchment. These same upslope winds likely also transport PAHs from the aluminum smelter.
PAH ratios can be used to identify potential sources (Schauer et al., 2002; Yunker et al., 2002;
Killin et al., 2004). The ratio of indeno[l,2,3-cd]pyrene concentration to indeno[l,2,3-cd]pyrene
concentration + benzo[e]pyrene concentration (IcdP/(IcdP+BeP)) should remain fairly constant
from emission sources to deposition in the environment, because these PAHs are typically sorbed
to the particulate phase in the atmosphere and have similar physical and chemical properties. The
IcdP/(IcdP+BeP) ratio from gasoline combustion in motor vehicles is 0.74 (Schauer et al., 2002),
whereas the ratio from combustion of pine wood in a fireplace is 0.53 (Figure 4-32; Schauer et
al., 2001). Aluminum smelters that use Soderberg aluminum smelting technology have been
shown to emit an IcdP/(IcdP+BeP) ratio of 0.39 ( Booth and Gribben, 2005; Sanderson et al.,
2005).
4-46 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
Q.
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v
;\
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Gasoline
Motor
Pine Wood
Combustion
(Fireplace)
Aluminum
Smelter
^
Pre 1955
Post 1955
Figure 4-32. Fraction Ratios of lcdP/(lcdP+BeP) (average ± standard deviation) Calculated from
Snow, Lichen, and Pre- and Post-1955 Sediment in Snyder and Oldman Lake Catchments in GLAC
Compared to Measured Ratios. Measured ratios for gasoline motors from Schauer et al. (2002), pine
wood combustion from Schauer et al. (2001), and an aluminum smelter from Sanderson and Farant
(2005)." *" indicates values were below detection limits.
Figure 4-32 shows the measured IcdP/(IcdP+BeP) in snow, lichen, and sediment from the Snyder
Lake and Oldman Lake catchments. The IcdP/(IcdP+BeP) ratio measured in the 2003 seasonal
snowpack from Snyder Lake was 0.40, which closely matches the ratio previously seen for
Soderberg aluminum smelter emissions; the ratio in the 2003 snowpack from Oldman Lake was
0.49. In addition, although the concentrations of PAHs in Snyder Lake vary considerably, the
IcdP/(IcdP+BeP) ratio was fairly constant during the years of snow sampling.
The sum PAH flux in the Snyder Lake 2003-2004 snowpack was significantly lower than it was
in the 2002-2003 snowpack. From March 2003 until 2007, the aluminum smelter reduced
operations from 60% to 20% (Jamison, 2003). In 2002 (60% capacity), the aluminum smelter
released 15.1 tons of PAHs to the atmosphere and in 2004 (20% capacity), the plant released 5.0
tons of PAHs (USEPA, 2007a). Although it is difficult to compare quantitatively, because a
detailed timeline of smelter emissions is lacking, the reduction in PAH emissions is corroborated
by a decline in PAH concentrations in the snowpack over this timeframe. The 2003-2004
snowpack PAH concentrations measured were approximately one-third of the 2002-2003
concentrations, similar to the reported emission reduction. At the same time the IcdP/(IcdP+BeP)
ratios measured in the seasonal snowpack samples remained fairly constant from 2003 to 2004
(Figure 4-32), indicating that the smelter was still the dominant source of PAHs. BeP was not
detected in lichens from Oldman Lake catchment, so the IcdP/(IcdP+BeP) ratio could not be
calculated.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-47
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
In the sediment cores at Snyder Lake, the IcdP/(IcdP+BeP) ratio was 0.49 before 1955, when the
smelter came on line. From 1955 to the present, the IcdP/(IcdP+BeP) ratio has been fairly
constant in the Snyder Lake sediment core, with an average and standard deviation of 0.35 ±
0.05. The IcdP/(IcdP+BeP) ratio measured in the Snyder Lake surficial sediment was 0.38. In
Oldman Lake, IcdP was detected only in the 2005 (surficial sediment) and 1906 intervals. In
1906, the fraction ratio of IcdP/(IcdP+BeP) in the Oldman Lake sediment core was 0.66; in the
surficial sediment of Oldman Lake, the IcdP/(IcdP+BeP) was 0.45 (Figure 4-32). In addition, the
retene sediment flux over time was not significantly correlated with sum PAH, BeP, IcdP, or
BghiP flux over time in Snyder Lake (p > 0.05), suggesting biomass combustion was not a major
source of PAHs to the Snyder Lake catchment.
Taken together, we believe the data strongly suggest that the Snyder Lake watershed is
influenced by the Columbia Falls aluminum smelter. The sources of PAHs for the Oldman Lake
watershed are not as certain. The much lower concentrations and the IcdP/(IcdP+BeP) ratio are
not consistent with an influence from the smelter, and the concentrations overall differ little from
those in other parks.
4.2.6.3 Possible Urban/Regional Influences at Rocky Mountain National Park
At ROMO, a different issue emerges, related to observations at two sites across the Continental
Divide. Mills and Lone Pine lakes are only 10 km apart, but they are on different sides of the
Continental Divide. Mills Lake is on the east side and Lone Pine Lake is on the west side.
Considering the impact that the Continental Divide has, it is possible that these two lakes receive
different contaminant exposures.
Evidence of differences in atmospheric deposition within the Colorado Front Range has been
reported in a number of studies (Burns, 2003, and references therein). Deposition of NOs"
(nitrate) and NH4+ (ammonium) are greater on the east side of the Continental Divide than on the
west side during summer. These two contaminants are associated with urban and agricultural
sources. The elevated concentrations of these two contaminants might have resulted from
summer upslope winds, or transport from the eastern lowlands up the Front Range of the Rocky
Mountains, a wind pattern that is counter to the prevailing westerly winds (see Section 4.5).
Snow and sediment SOC data from ROMO suggest that these upslope winds might also be
important in transporting PAHs and agricultural SOCs in higher concentrations to Mills Lake
(Usenko et al., in press). Together, these matrices suggest year round differences in atmospheric
concentrations between the two lakes. Figure 4-28 shows the SOC sediment flux profile for Mills
and Lone Pine Lakes. For all compound classes, the sediment flux to Mills Lake was greater than
the flux to Lone Pine (Usenko et al., in press).
A similar trend is true for the snow flux of SOCs. Table 4-5 shows the ratio of SOC fluxes, and
concentrations, in the snowpack for Mills Lake compared with Lone Pine Lake for 2003 (Usenko
et al., in press). The Mills Lake flux is always higher by a factor of from 1.6 to 4.1. However,
much of the enhancement in snow flux at Mills Lake results from more snowfall in the Mills
Lake basin, rather than higher atmospheric concentrations of contaminants. At Mills Lake, the
2003 snow water equivalent (SWE) was 90 cm; for Lone Pine Lake, it was 40 cm. The enhance-
ment in snow concentrations between Mills and Lone Pine lakes is less than the fluxes. In fact,
for dacthal, chlordanes, and PAHs, the snow concentrations are the same or greater in Lone Pine
Lake than in Mills Lake (Usenko et al., in press).
4-48 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 4. CONTAMINANT DISTRIBUTION
SOC concentrations in air, conifer needles, and fish do not show a clear enhancement on the east
side of ROMO. Conifers, in particular, do not provide evidence that the east side of ROMO has
higher concentrations of pesticides than the west side. Although there is a suggestion of an east-
west difference in SOC deposition at ROMO, not all of the WACAP data provide clear evidence
for this effect. A more focused study, with many more sampling locations, would have to be
conducted to address this question.
Table 4-5. Comparison of SOC Data from Mills and Lone Pine Lakes.
The values in the table show the ratio of the Mills/Lone Pine Lake results for
snow fluxes and snow concentrations.
Endosulfans
Chlorpyrifos
Dacthal
g-HCH
a-HCH
HCB
Dieldrin
Chlordanes
PAHs
Mills to
Flux
3.2
NA
2.5
4.1
4.1
NA
3.7
1.6
2.2
Lone Pine Ratio
Concentration
1.4
NA
1.0
1.8
1.8
NA
1.6
0.7
0.9
4.3 Trace Metals, Including Mercury
Figure 4-33 shows mean total mercury (Hg) concentrations in lichens, snow, fish, and sediments
across all parks. Snow data are provided, both as concentrations and as fluxes. Hg concentrations
in lichens are highest in parks in the conterminous 48 states. The same is true for Hg snow
deposition fluxes. However, in fish, highest concentrations were found in samples taken in
NO AT and GAAR. Detailed discussion of Hg concentrations in each medium follows.
4.3.1 Mercury and Trace Metals in Snow
Total Hg was measured in 60 snowpack samples, with park median concentrations ranging from
0.94 ng/L in MORA to 4.1 ng/L in GLAC. Mercury concentrations showed considerable spatial
and temporal variability. Much of the Hg in snow is associated with particulate matter, which
shows much greater variability at all scales, compared to dissolved constituents in melted snow
(Turk et al., 2001). Hg was correlated with particulate carbon, and both were found at higher
concentrations in snow samples from forested sites compared with samples from open meadows.
In general, Hg concentrations in snow were lowest in the west coast parks, intermediate in the
Alaska parks, and highest in the Rocky Mountain parks (Figure 4-33). The exception was
OLYM, where concentrations and fluxes were fairly high. However, only a single year is
represented because of poor snowpack conditions in 2003 and 2005.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-49
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Lichen
Snow
Fish
Sediment
!
10000 =
1000 =
:
f 100 =
.
10 =
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.
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
4.3.2 Mercury and Particulate Carbon in Snow
We found a strong correlation (R2 = 0.63, p < .0001) between total mercury and particulate
carbon concentrations in the snowpack (Figure 4-34). The underlying mechanisms that control
^^^^^^^^^~ this relationship are uncertain. It is possible that mercury and particulate
Mercury in the carbon become associated in the atmosphere and are deposited to the
snowpack is snowpack together. Or they could be deposited separately and become
associated with associated within the snowpack. This association could provide a
particulate mechanism to bind atmospherically deposited reactive mercury and prevent
carbon ^s reduction and subsequent evasion to the atmosphere (LaLonde et al.,
^^^^^^^^^~ 2002). Thus, particulate carbon might act to sequester more of the
deposited Hg from the winter deposition period, increasing the net flux of Hg to the watershed
when the snowpack melts. Otherwise, Hg in the snowpack that is not bound to particulate carbon
might be lost through revolatilization to the atmosphere. Thus, the WACAP Hg concentrations
reflect complex biogeochemical cycling with natural and anthropogenic components.
At Lake Irene in ROMO, snowpack sampling was conducted at paired sites ^^^^^^^^^™
approximately 200 m apart. The forest site was in a forest clearing about Higher mercury
10 m in diameter on a northwest-facing 20-degree slope; the meadow site concentrations
was in an open meadow on a southeast-facing 5-degree slope (see photo). were round in
A single pair of samples (one from the forest site, one from the meadow ; e TOreSt than
site) was collected in 2002, 2004, and 2005, and two pairs were collected in an adjacent
in 2003. Unfiltered total Hg averaged 2.7 ng/L greater (28%) at the forest meadow.
site than at the meadow site for the five sample pairs. Particulate carbon ^^^^^^^^^™
was also higher at the forest site. Various processes could contribute to the difference. The
greater surface area and more favorable depositional substrate of the forest canopy relative to the
open snow-covered meadow probably enhances dry deposition of mercury and carbon in the
forest (St. Louis et al., 2001). The northerly slope and shading from the canopy would also
reduce solar radiation to the snowpack surface in the forest, limiting photo-reduction and evasion
of mercury from the snowpack. The combined effects of particulate carbon and forest canopy on
Hg concentrations in snow contribute to the large temporal and spatial variability in Hg
deposition at all scales, as discussed in Section 4.3.1.
4.3.3 Trace Metals in Vegetation
Fifty-two lichen samples were analyzed for 45 elements, excluding nitrogen, representing 6
species for the 8 core parks. In addition, 105 samples representing 13 lichen genera from all 20
^^^^^^^^^^^^^^ WACAP parks were analyzed for nitrogen. Because lichens differ in
^jwQroii m^tr^l^ w/oro
. . ' their elemental profiles, lichen element concentrations were
P® . considered separately by taxon (i.e., species when known, genus
background ranges in otherwise). Summary statistics for WACAP element concentrations
tho WAPAP narU-c
tne vvAiar parKS. by park and taxon are provided in Appendices 4A. 12 and 4A. 13.
To determine the enhancement of the nitrogen and sulfur nutrients and the toxic metals cadmium
(Cd), nickel (Ni), and lead (Pb) in lichens of WACAP parks, relative to other remote sites in the
western United States, we calculated the upper limit for the background range for public lands.
To do this, we queried the national databases for the National Park Service [NPElement
(Bennett, 2007)] and the US Forest Service [USFS Lichens and Air Quality Database (US Forest
Service, 2007)] for lichen element concentrations from Montana, Wyoming, Colorado, New
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-51
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CHAPTER 4. CONTAMINANT DISTRIBUTION
2003-2005 Snowpack
14
12
8 -
2 -
01234567
Particulate carbon, mg/L
Figure 4-34. Unfiltered Total Mercury vs. Particulate Carbon Concentrations for All WACAP
Snowpack Samples, 2003-2005.
Snow sampling crew digging a snow pit at the meadow site near Lake Irene in ROMO. The paired
forest site is in the background.
4-52
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Mexico, and Texas westward (excluding Hawaii), and all lichen species and elements targeted in
WACAP parks.
To augment comparison data for the Arctic lichens, Masonhalea richardsonii and Flavocetraria
cucullata, we added 2004 and 2005 data from the NFS Arctic Parks database from NO AT (Peter
Neitlich, pers. comm.) to the resulting database. This combined WACAP Western States Lichen
Element Database contains data for 7,953 lichen samples from 76 national forests, parks, and
other federally managed lands for 60 elements representing all WACAP target lichens and
includes the WACAP lichen element data. See Appendix 4A.15 for a list of public lands
encompassed in the database.
To specifically examine the relative enhancement of nitrogen, sulfur, mercury, cadmium, nickel,
and lead, we calculated the 0, 2.5, 10, 25, 50, 75, 90, 97.5 and 100% distribution quantiles by
lichen genus and element for the WACAP Western States Lichen Element Database, excluding
the WACAP data to determine background ranges that were independent of the WACAP data
(Appendix 4A. 14). Because not all parks are in pristine locations, we chose the 90% quantile,
rather than a higher quantile, as the upper limit for background values. This limit is arbitrary,
being based on a perusal of distribution histograms which indicate that values below the 90%
quantile tend to follow a normal distribution typical of a background population. Ideally,
comparisons at the species level would be desirable, but in this analysis, species in the WACAP
target genera examined were similar morphologically and had similar element profiles, justifying
assessment at the genus level. Also, comparing genera simplifies calculations by reducing the
number of comparisons.
The same lichens that were best accumulators of SOCs—Platismatia, Usnea, Xanthoparmelia,
and Hypogymnia—also accumulated larger concentrations of metals and nutrients, than did the
poorer SOC accumulators—Alectoria, Cladina, Flavocetraria, and Masonhalea (compare 50%
quantiles across species in Appendix 4 A. 14). This finding underscores the desirability of using a
single species where possible, choosing species with similar element profiles, or overlapping
target species sampling so that a system for comparison can be developed.
To evaluate how the samples in each park compared with background ranges for remote sites in
the western United States, we expressed each sample as a percentage of the background range by
dividing element concentrations of each WACAP sample by the upper limit of the background
range for the relevant lichen genus and element. Results are presented as percent enhancement in
Figure 4-35. The chief advantage of this approach is that it allows samples from different species
within and across parks to be compared.
Overall, metals were not noticeably elevated in any of the WACAP parks (Appendix 4 A. 12).
The GAAR site at Matcharak Lake had high concentrations of many of the rare earth elements
[dysprosium (Dy), erbium (Er), europium (Eu), gallium (Ga), gadolinium (Gd), holmium (Ho),
lanthanum (La), lutetium (Lu), neodymium (Nd), praseodymium (Pr), antimony (Sb), terbium
(Tb), thulium (Tm), uranium (U), yttrium (Y), ytterbium (Yb)], compared to other WACAP core
parks. Rare earth elements are typically associated with soils and probably represent local dust.
Evidence of this association is the rather high aluminum concentrations in the Flavocetraria
cucullata samples from GAAR. In general for lichen samples, the higher the aluminum and iron
concentrations the higher the concentrations of the rare earth elements. Of the elements identified
for comparison with other public lands of the western United States, mean concentrations of
cadmium, nickel, and lead were well under thresholds at all parks (Figure 4-35).
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-53
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CHAPTER 4. CONTAMINANT DISTRIBUTION
0.9-
0.8-
D.7-
D 6-
D 5-
'
•I.
3 g S =! g
I. .ll
g O
Figure 4-35. Comparison of Selected Elements in Lichens of WACAP Parks with Elements in Other
National Parks and Forests in Western North America. Thresholds are the 90th percentile of element
concentration distributions in lichens from remote sites. In general, lichen element concentrations indicate
that metals were within expected ranges, sulfur deposition was elevated in SEKI and GLAC, and nitrogen
deposition was elevated in SEKI, GLAC, BAND, BIBE. Error bars indicate one standard error.
Highest mercury concentrations in lichens among western parks in the NPElement database were
measured in samples from BIBE in 2002: 39% of Usnea samples and 25% of Xanthoparmelia
samples collected there in 2002 were above the 90% quantile of the background range for these
species. (Lichen samples were collected at all WACAP parks and are currently archived at
UMNRAL, but were analyzed only for sulfur, mercury, and metals at the core parks). Although
mercury concentrations are not higher in the WACAP core parks than we would expect for
western forests and parks, because the biomass of needles is so high on a per hectare basis, forest
fire is a significant source of mercury release into the atmosphere (e.g., Friedli et al., 2003).
Mercury is an emerging element in lichen biomonitoring of air quality, and historic data for
trends analysis is nearly non-existent. The oldest data in NPElement and the USFS Lichens and
Air Quality databases are from 1990 and 1995; only in recent years have researchers been more
systematic about including mercury in lichen biomonitoring studies (Bennett, 2007; US Forest
Service, 2007). On a global scale, mercury emissions are predicted to increase with increased
human population and concomitant development of coal resources for energy production,
especially in China. Continued monitoring of mercury in vegetation can assess the effects of
implementing emission controls vs. increased energy production.
In contrast to mercury, most lichen elemental concentration studies on public lands have
included measurements of lead and sulfur (Bennett, 2007; US Forest Service, 2007). Significant
reductions have been observed in study areas where re-measurements have occurred. For
example, lead concentrations inAlectoria sarmentosa collected in 1983 from Golden Lake in
4-54
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 4. CONTAMINANT DISTRIBUTION
MORA averaged 5.45 ± s.d. 2.62 ppm. WACAP samples of A. sarmentosa from Golden Lake
collected in 2005 averaged 1.29 ppm ± s.d. 0.12 ppm. In SEKI, 27 samples ofLetharia vulpina
sampled at 8 sites in 1984 had concentrations ranging from 3.11 to 24.12 ppm (average 8.58).
The 2004 WACAP samples of L. vulpina from Emerald Lake basin in SEKI ranged from 1.22 to
1.77 (average 1.39).
In summary, metal concentrations in WACAP core parks were within background ranges for
remote sites in the western United States. Mercury is likely to be the metal of highest concern
because it is re-released to the atmosphere during forest fires. Comparisons of WACAP data with
historic data in the NPS lichen element database, NPElement (Bennett, 2007), indicate that lead
is decreasing in MORA and SEKI. Although lichen sulfur and metal concentrations were not
analyzed in the secondary parks, historic data from NPElement indicates mercury concentrations
could be elevated in BIBE. Analysis of archived WACAP lichen samples could provide trends
data for some secondary WACAP parks and establish baselines for parks currently lacking lichen
data.
4.3.4 Mercury and Trace Metals in Fish
Unlike lipophilic SOCs (contaminants stored in lipids), Hg in fish is mainly in the form of
methyl-Hg, which accumulates in muscle tissue (Munthe et al., 2007). Although fish Hg
concentrations (shown in Figure 4-33) are measured in the
whole fish, the bulk of the Hg is tied up in muscle tissue. The complex processes
Incorporation of Hg into the food web and into fish tissue is that control mercury
generally limited by rates of methylation, which in turn can be accumulation in fish
limited by nutrient availability (St. Louis et al., 2004). There- include atmospheric
fore, some areas can have fairly high Hg deposition, but low deposition, methylation
methylation rates and hence low fish tissue Hg (Bloom, 1992). and bioconcentration in
This limitation probably explains the fish concentration data at the food web, and
ROMO, where Hg flux to the snow and sediments is fairly high, bioaccumulation as fish
yet fish Hg concentrations are fairly low (see Figure 4-33). Data a9e-
from NO AT and GAAR show the opposite pattern—low Hg in
snow and sediment fluxes, but high Hg concentrations in fish. On this basis, it appears that even
though atmospheric deposition is a primary source of Hg to these ecosystems, the linkage
between snow deposition and fish concentrations is weak. Other factors, as already mentioned,
must also be important in explaining the Hg uptake and bioaccumulation in fish. More attention
to this topic is given in Section 5.6 of Chapter 5.
In a comprehensive study at Voyageurs National Park in Minnesota, Wiener et al. (2006)
investigated the factors associated with Hg concentrations in fish. These authors concluded that
high dissolved sulfate, low lake water pH, and high organic carbon favored methyl-Hg
accumulation in the fish. Lake temperature has also been implicated in methylation (Schindler et
al., 1995; Lambertsson and Nilsson, 2006). These results indicate that we should not expect a
direct relationship between Hg concentrations in vegetation, snow, and fish in the WACAP
parks, and indeed we see no simple relationship.
For trace metals in fish, we analyzed concentrations in both fillets and livers. A comparison of
concentrations in the fillets with those in the livers showed significantly higher concentrations in
the livers. For Cd, Cu, Ni, Pb, and zinc (Zn), concentrations in the livers were elevated by factors
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-55
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
of 20, 60, 10, 900, and 230, respectively, compared to those in the fillets. On average, vanadium
(V) did not show an enhancement. Because the concentrations in the fillets are very low, and
close to the limit of detection for the various metals, small
differences in concentration are not easily observed, whereas these
changes are clearly indicated in the liver concentration data.
For all metals except V,
the liver compositional
analysis shows stronger
influence from
bioaccumulation than
did the corresponding
fillet tissue analysis.
Figure 4-36 shows the distribution of trace metals in the liver of
fish from the core WACAP parks. The highest concentrations of
lead in fish livers were observed in fish from Snyder Lake in
GLAC (Figure 4-36). The next highest concentrations were
observed for fish from Burial Lake (NOAT), Wonder Lake
(DENA), and Emerald Lake (SEKI). There was always a significant difference in lead
concentrations among lakes within a park, except at ROMO, where concentrations between Lone
Pine and Mills Lakes were similar.
The Cd concentration in fish liver was highest in Emerald Lake (SEKI), followed by similar
concentrations in Burial Lake (NOAT), Wonder Lake (DENA), Mills Lake (ROMO), and Pear
Lake (SEKI). However, the fish specimens sampled in Burial and Wonder lakes were
considerably older than specimens from the other lakes. Therefore, this observation could
include an age/bioaccumulation effect or a species effect.
1000 -
100 -
10 -
o
<±
1 -
0.1 -
0.01
Figure 4-36. Trace Metals in Fish Liver. Distribution of Cd, Cu, Pb, Ni, V, and Zn average concentra-
tions in fish livers from 2- to 8-year-old specimens collected from western national park lakes in GLAC,
OLYM, MORA, ROMO, and SEKI, and 18- to 28-year-old specimens from lakes in NOAT, GAAR, and
DENA. Because of the small sample size, no fish from McLeod Lake (DENA) were analyzed for trace
metals.
4-56
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Concentrations of Cu were highest in the livers offish from Burial Lake, Matcharak Lake, and
Oldman Lake, compared with fish livers from the other lakes studied. Copper concentrations in
fish livers are similar in the other national park lakes, with the lowest concentration observed in
PJ Lake (OLYM).
Concentrations of Zn in fish liver were highest in Matcharak Lake (GAAR), which represents
older fish specimens that might exhibit bioaccumulation. The second highest concentration
occurred in Golden Lake (MORA). Zinc concentrations were similar in magnitude in all other
national park lakes—between 100 (j,g/g (dry basis) and 150 (j,g/g (dry basis).
All Ni and V concentrations in fish liver were below 10 (j,g/g (dry basis), with most below about
1 (j,g/g (dry basis). The detection limit for all metals was about 0.6 (j,g/g (dry basis).
4.3.5 Mercury, Trace Metals, and Spheroidal Carbonaceous Particles in Sediments
4.3.5.1 Mercury, Trace Metal, and SCP Focus-Corrected Fluxes
Temporal patterns of mercury, trace metals, and spheroidal carbonaceous particles (SCPs) were
determined from the dated sediment profiles at the core parks. These have been corrected by the
focusing factor as described in Chapter 3. Figures 4-37 to 4-43 show the focus-corrected fluxes
for a selected set of metals and SCPs for both lakes in each park. In addition to atmospheric
deposition, watershed processes influence the delivery of mercury to the lake sediments. Some of
the more important factors are the watershed-to-lake area ratio, total organic carbon (TOC) in the
lake water, and the wetland area associated with the lake (Wiener et al., 2006). In nearly all
lakes, Hg fluxes are larger now, compared to pre-industrial values. The one exception is Hoh
Lake, where the Hg flux showed a large increase between 1910 and 1930, and then decreased to
pre-industrial values.
Figures 4-37 to 4-43 also show the focus-corrected fluxes for Ni, Cu, Pb, V, Zn, and Cd in
(j,g/m2/yr. These metals display a complex and closely matched pattern with some metals
generally rising and falling together in the same lake. We know that many of these variations are
related to watershed processes such as erosion, avalanches, and landslides, which is discussed in
more detail later in this section. In most lakes, there were generally increasing fluxes of Pb and
Cd toward the surface, which we believe to be associated with greater anthropogenic sources.
But at some lakes, a different trend appeared, with metals increasing and decreasing together in a
complex pattern. In most lakes in parks in the conterminous 48 states, the order of flux was
approximately Zn > V > Pb > Cu > Ni > Cd > Hg. In a few sediment cores, V was greater than
Zn. In the Alaska parks, Cu and Ni were much higher than the Pb flux. The highest flux for most
metals was observed at PJ Lake (OLYM). PJ Lake shows a very complex pattern, with multiple
peaks in the flux of all metals between 1920 and 1970. The Pb fluxes were highest in Snyder
Lake (GLAC), Mills Lake (ROMO), and Emerald Lake (SEKI). These same lakes were also high
in SCPs, suggesting a strong influence from regional combustion sources. Although the spatial
pattern for Cd is similar to that of the other metals, the magnitude of the flux is lower than most
other metals by a factor of 100 or more.
SCPs result from high temperature combustion of fossil fuels (see Chapter 3). Because SCPs
range from 5 to 50 (am in diameter, transport of these large atmospheric particles is limited by
their atmospheric lifetime (a few days to one week). Thus SCPs provide a measure of exposure
to fossil fuel combustion within a range of a few hundred to approximately 1,000-2,000 km.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 4-57
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Gates of the Arctic National Park and Preserve
Noatak National Preserve
Burial Lake
2000 -
1980 -
1960 -
! 194°"
>• 1920 -
1900 -
1380 -
1860 -
0
ug/m /yr
\_L
ug/m /yr
ug/m /yr
Hg
Cd(x10)
Matcharak Lake
2000 -
1980 -
1960 -
i- 1940 -
(C
>• 1920 -
1900 -
1880 -
1860 -
0
/yr
d .
e .
/yr
|jg/m /yr
Figure 4-37. Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg (p.g/m /yr) in
Sediment Cores from Burial Lake (NOAT) and Lake Matcharak (GAAR). Cd flux has been reduced by
a factor of 10. No SCPs were detected in the sediment cores from these lakes.
4-58
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Denali National Park and Preserve
Wonder Lake
2000 -
1980 -
1960 -
1940 -
1920 -
1900 -
1880 -
1860 -
0
pg/m/yr
a
v 0
v o
v o
7 0
V
ug/m /yr
12345
|jg/m2/yr
Hg
Cd (x10)
McLeod Lake
9
2000 -
1980 -
1960 -
1940
1920 -
1900 -
1880-
1860-
V
V
TT
T
H
;
M9/m2/yr
M9/m2/yr
2345
Figure 4-38. Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg (ng/m /yr) in
Sediment Cores from Wonder and McLeod Lakes (DENA). Cd flux has been reduced by a factor of 10.
No SCPs were detected in the sediment cores from these lakes.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
4-59
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Glacier National Park
Snyder Lake
SCP (no/mV x103)
0 100 200 300
2000
1980
1960
1920
1900
1380
1360
\iglm /yr
Oldman Lake
2000 -
1980 -
1960 -
is 194°'
* 1920 -
1900 -
1880 -
1860 -
0
0 5 10 15 20
2
ug/m /yr
-*— Hg
—— Cd (x10)
••*•• SCP*(x10a)
SCP (no/m2/yr x1(f)
0 100 200 300
|ig/ma/yr
jig/mz/yr
10
|ig/m2/yr
15 20
Figure 4-39. Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg (jig/m /yr) and SCP
(number/m2/yr, reduced by a factor of 1,000 and shown in the top axis) in Sediment Cores from
Snyder and Oldman Lakes (GLAC). Cd flux has been reduced by a factor of 10. SCP flux for Snyder
Lake is from the secondary core from this lake.
4-60
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Olympic National Park
PJ Lake
SCP (no/m2/yr x103)
0 200 400 600
2000 -
1980 -
1960 -
1940 -
1920 -
1900 -
1880 -
1860 -
|jg/m lyr
|jg/m lyr
10 20 30 40 50 60
-2/yr
-^- Hg
-*— Cd(x10)
•••*•• SCP*{x103)
Hoh Lake
SCP(no/m2/yrx103)
200 400 600
0 SO , 100
yr
Mg/m yr
0 10 20 30 40 50 60
jjg/m /yr
Figure 4-40. Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg (p,g/m2/yr) and SCP
(no/m2/yr, reduced by a factor of 1,000 and shown in the top axis) in Sediment Cores from PJ and
Hoh Lakes (OLYM). Cd flux has been reduced by a factor of 10. Inset boxes for Hoh Lake have
expanded flux scale. SCP flux is from the secondary cores from these lakes.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Mt. Rainier National Park
Golden Lake
2000 -
1980 -
1960 -
te 194° "
> 1920 -
1900 -
1880 -
1860 -
\
LP19
2000 -
1980 -
1960 -
[3 194° '
i
>" 1920 -
1900 -
1880
1860 -
0
Mg/m2yr
i r
SCP (no/m2/yr x103)
0 50 100 150
a .
I I
.00°
\iglm2 lyr
0 5 10 15 20 25
Mg/m2/yr
—^Hg
— •— Cd
•••»•• SCP*
(x103)
SCP
-------
CHAPTER 4. CONTAMINANT DISTRIBUTION
-
B
Rocky Mountain National Park
Mills Lake
SCP(no/mVx103)
10Q
2QQ 3QQ
2000 -
1980 -
1960 -
to 194° "
> 1920 -
1900 -
1880 -
1860 -
. ••"
0 10 20 30 40
|jg/m2,'yr
|jg/m2/yr
Hg
Cd (x10)
SCP(x103)
Lone Pine Lake
SCP (no/rrrV x 103)
100 200
soo
2000 -
1980 -
1960 -
1940 -
1920 -
1900 -
1880 -
1860 -
7
V
V
V
0
*
0,00
°° 0 10 20 30 40
|jg/mV
\iglm !yr
Figure 4-42. Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg (jo,g/m2/yr) and SCP
(number/m2/yr, reduced by a factor of 1,000 and shown in the top axis) in Sediment Cores from
Mills and Lone Pine Lakes (ROMO). Cd flux has been reduced by a factor of 10.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Sequoia and Kings Canyon National Parks
Pear Lake
2000
1980
1960
> 1920
1900
1380
1860
|jg/m2/yr
SCP (no/m2/yr x103)
50 100 150 200
Mg/m2/yr
10
Mg/m lyr
15
-•— Hg
-»— Cd (x10)
•-•• SCP(x103)
Emerald Lake
2000 -
1980 -
1960 -
1940 -
1920 -
1900 -
1880 -
1860 -
0
SCP(no/m2/yrx103)
0 50 100 150 200
e j
10 15
|jg/m /yr
|jg/m /yr
Figure 4-43. Focusing Factor-Corrected Flux of Ni, Cu, Pb, V, Zn, Cd, and Hg (p.g/m lyr) and SCP
(number/m2/yr, reduced by a factor of 1,000 and shown in the top axis) in Sediment Cores from
Pear and Emerald Lakes (SEKI). Cd flux has been reduced by a factor of 10.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Table 4-6 shows the accumulated SCP totals for each lake sediment core, in units of particles per
m2 x 105. In terms of total deposition over the industrial period, the data show a general decline
from south to north. Highest values occur for Emerald and Pear lakes in SEKI, while the four
sites in Alaska are below the limits of detection. The results from sites in Alaska in themselves
suggest very low concentrations of contamination from high-temperature combustion—
equivalent to, or below, concentrations observed in sites in the European Arctic, the Falkland
Islands, and even sites on the South Orkney Islands to the north of the Antarctic Peninsula (Rose,
pers. comm., unpublished data). In GLAC, SCPs are higher at Snyder Lake than at Oldman
Lake, which might be because of the proximity of regional sources (see Section 4.2.6). Similarly,
PJ Lake (OLYM) shows considerably more SCP contamination than Hoh Lake, which might
reflect greater local contamination. In terms of overall contamination, the SCP totals for SEKI
are the highest among all WACAP parks and comparable to those observed in similarly affected
European mountain lakes (in southern Spain, southern Norway, and the northwest UK (Rose,
pers. comm. unpublished data).
Figures 4-37 to 4-43 also show the SCP flux profiles in the WACAP lake sediments (SCPs were
not detected in the Alaskan lakes). In some cases, the SCPs were not analyzed on the same cores
as the other compounds, but instead on parallel cores taken at the same time (in close proximity
in the same lake). Although assumptions are made in transposing dates in this way, at present
this is the best chronology available for SCPs in the sediment cores. It could explain some of the
offsets seen in the SCP profiles, compared to the metals. The SCP dates can be updated when a
better correlation between cores becomes available (e.g., with metal flux).
Table 4-6. Total Integrated SCPs in WACAP Lake Sediment Cores.
Total Number of SCPs in the
Park Lake Core (x 105 m'2)1
NOAT
GAAR
DENA
DENA
GLAC
GLAC
OLYM
OLYM
MORA
MORA
ROMO
ROMO
SEKI
SEKI
Burial
Matcharak
McLeod
Wonder
Oldman
Snyder
Hoh
PJ
Golden
LP19
Lone Pine
Mills
Emerald
Pear
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CHAPTER 4. CONTAMINANT DISTRIBUTION
4.3.5.2 Mercury and Trace Metal Enrichment Factors
The sediment profiles clearly indicate that watershed processes complicate the interpretation.
Therefore, to better isolate and evaluate the human (anthropogenic) influence from atmospheric
deposition over time, we normalized the sediment metal fluxes to titanium (Ti), a conservative
element measured with high precision in the cores (Cowgill and Hutchinson, 1966; Engstrom,
1984). This procedure corrects for watershed disturbances and processes, such as catchment
instability (e.g., avalanches, rapid snow melt) and erosion. The resulting enrichment is reported
as the percent enrichment (PE) by subtracting the pre-industrial background concentrations from
more recent metal concentrations as a modification of Kemp et al. (1976), as follows:
(Mx/Tix) - (Mb/Tib)
Percent Sediment Enrichment = x 100 [4-1]
(Mb/Tib)
where:
Mx = metal concentration (ng/g) at interval depth x
Tix = titanium concentration (ng/g) at interval depth x
Mb = metal concentration (ng/g) at interval closest to year 1870
Tib = titanium concentration (ng/g) at interval closest to year 1870
Figures 4-44 to 4-50 show the PE in the sediment cores for V, Cu, Zn, Pb, Cd, Ni, and Hg for
each lake in the core WACAP parks. A PE value above zero indicates that that element is
enriched in the sediment, relative to titanium and compared with the pre-industrial background
(1870). For an element with a recent anthropogenic increase, we would expect its enrichment to
go from zero (pre-industrial) to positive values after industrialization. Several patterns are
apparent in the enrichment plots, compared to the un-normalized fluxes (Figures 4-37 - 4-43).
All lakes in the conterminous 48 states show significant enrichment in Pb, Cd, and Hg. For all
other metals, enrichment is either zero or fairly small. In the Alaska parks, there is recent
enrichment in Hg at all lakes, but no other consistent patterns.
Atmospheric Pb is found primarily on fine particulate matter (less than 10 microns in diameter).
Thus, deposition of Pb is related largely to sources within about 1,000-2,000 km. Pb deposition
was heavily influenced by the introduction of leaded gasoline in the 1920s. Pb in gasoline was
drastically reduced in the United States starting in the 1970s (Thomas, 1995). Other sources of
lead to lake sediments shown to be important include lead mining, smelting, logging, and other
industrial activities (see Table 3-4 in Chapter 3).
In the four Alaska lakes (Figures 4-44 and 4-45), only Wonder Lake shows a modest increase in
Pb enrichment toward the surface, beginning in about 1920. In parks in the conterminous United
States, Pb shows significant enrichment in all sediment profiles, and most of these lakes show a
peak between 1960 and 1980, with a decrease afterwards. The PE values are by far the largest at
both lakes in MORA. At LP19, Pb PE reached 400% and at Golden Lake, it reached 800%. At
Mills Lake in ROMO, the Pb PE sharply increased in the early 1900s, possibly because of its
proximity to historic lead mines and/or smelting operations. It is worth observing that the Omaha
and Grant lead smelter was built in Denver in 1892 and the stack was the tallest structure in the
world for some time thereafter.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Gates of the Arctic National Park and Preserve
Noatak National Preserve
Burial Lake
-50 -25 0 25 50 75
% Enrichment
-50 -25 0 25 50 75 100
% Enrichment
Matcharak Lake
2000 -
1975 -
-50 -25 0 25 50 75
% Enrichment
-50 -25 0 25 50 75 100
% Enrichment
Figure 4-44. Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from Burial
Lake (NOAT) and Lake Matcharak (GAAR). Sediment Ti values were used to normalize to a
conservative crustal element to reduce the effects of watershed processes on the contaminant profiles.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Denali National Park and Preserve
Wonder Lake
2000 -
-25 0 25 50 75
% Enrichment
-25 0 25 50 75 100
% Enrichment
McLeod Lake
2000 -
-25 0 25 50 75
% Enrichment
-25 0 25 50 75 100
% Enrichment
Figure 4-45. Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from Wonder
and McLeod Lakes (DENA). See Figure 4-44 for more information.
4-68
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Glacier National Park
Snyder Lake
2000 -
0 50 100 150 200 250 0 50 100 150 200 250
% Enrichment % Enrichment
Oldman Lake
2000 -
1975 -
1950 -
1925 -
1900
1875 -|
1850
I
0 50 100 150 200 250 0 50 100 150 200 250
% Enrichment % Enrichment
Figure 4-46. Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from Snyder
and Oldman Lakes (GLAC). See Figure 4-44 for more information.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Olympic National Park
0 50 100 150 200
% Enrichment
0 50 100 150 200
% Enrichment
0 50 100 150 200 0
% Enrichment
50 100 150 200
% Enrichment
Figure 4-47. Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from PJ and
Hoh Lakes (OLYM). See Figure 4-44 for more information.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Mt. Rainier National Park
Golden Lake
2000 -
-50 0 50 400 800
% Enrichment
0 100 200 300 400
% Enrichment
LP19
2000 -|
1975
1950
1925
1900 -
1875 -
1850
100 200 300 400
% Enrichment
—r~
0
100 200 300 400
% Enrichment
Figure 4-48. Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from Golden
Lake and LP19 (MORA). See Figure 4-44 for more information.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Rocky Mountain National Park
Mills Lake
2000 -
1975 -
1950 -
n
«> 1925 -
1900 -
1875 -
1850
0 100 200 300 4000
% Enrichment
100 200 300 400
% Enrichment
Lone Pine Lake
2000 -"
1975 -
1950-
9
,» 1925 -
1900 -
1875 -
1850
100 200 300
% Enrichment
4000 100 200 300 400
% Enrichment
Figure 4-49. Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from Mills and
Lone Pine Lakes (ROMO). See Figure 4-44 for more information.
4-72
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Sequoia and Kings Canyon National Parks
0 50 100 150 200 250 0 50 100 150 200 250
% Enrichment % Enrichment
Emerald Lake
2000 -
0 50 100 150 200 250
% Enrichment
0 50 100 150 200 250
% Enrichment
Figure 4-50. Percent Enrichment of V, Cu, Zn, Pb, Cd, Ni, and Hg in Sediment Cores from Pear and
Emerald Lakes (SEKI). See Figure 4-44 for more information.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Like Pb, Cd is found on fine atmospheric particulate matter. The element is derived from many
different anthropogenic and natural sources (see Table 3-4). Cd enrichment is a result of
increasing anthropogenic sources of the metal. Significant Cd enrichment was not seen
uniformly, but was seen at Snyder and Oldman Lakes (GLAC; Figure 4-46), PJ and Hoh lakes
(OLYM; Figure 4-47), Mills and Lone Pine lakes (ROMO; Figure 4-49), and Pear Lake (SEKI;
Figure 4-50). The largest PE increase for Cd was seen at Mills Lake (Figure 4-49), with a
temporal pattern similar to that of Pb. Thus, sources associated with Pb mining and smelting in
the area could be contributing both elements.
For mercury, all parks show an increase in PE starting around the late nineteenth or early
twentieth century, which is consistent with other sediment and ice core data and is attributed to
the increasing emissions of mercury from human sources. At most parks, the PE values reached
50-100% in the most recent sections of the sediment cores. At Hoh Lake (OLYM; Figure 4-47),
Golden Lake (MORA; Figure 4-48), and LP19 (MORA; Figure 4-48)), PE was much higher in
the last few (top) sediment layers. Some caution is needed, however, in interpreting these results,
because for these lakes, the PE value was much lower and then rose significantly in the top one
or two sediment sections. At Hoh Lake, although the PE value increased, the Hg flux values
were low and generally decreasing.
For two of the Alaska lakes, Matcharak and Wonder (Figures 4-44 and 4-45), we see an increase
in Hg PE beginning in the early 1900s that continues to the surface, with no leveling off or
decline. Both lakes currently have Hg PE values of 85-100%. Burial and McLeod lakes (Figures
4-44 and 4-45) show a smaller increase in Hg PE, beginning in about 1970 for McLeod Lake and
1935 for Burial Lake. These two lakes had the lowest Hg PE (-35-40%) for recent sediments
among all WACAP lakes. Because no SCPs were observed in any of the Alaska lake sediments,
we suspect that these Hg increases result entirely from an increase in anthropogenic global
background of Hg, rather than regional sources.
Oldman Lake and Snyder Lake (GLAC; Figure 4-46) show both differences and similarities in
their Hg PE profiles. Hg in Snyder Lake began to rise in the late 1800s, along with Pb and Cd,
whereas the Oldman Lake profile shows Hg PE beginning to increase about 1930, well after the
increase in Pb and Cd. For both lakes, the Hg PE values increase steadily up to the surface,
where values are fairly low, about 50%, similar to the Alaska lakes with the lowest values. At
Snyder Lake, the SCP flux began earlier than it did at Oldman Lake, supporting the notion of a
regional high temperature combustion source for the earlier rise in Hg PE. SCPs were higher at
Snyder Lake, similar to the results for PAHs (see Section 4.2.6.2).
Both OLYM lakes (Figure 4-47) show similarity among Hg, Pb, Cd, Cu, and Zn PE profiles,
suggesting that they might share a common source for all of these elements. The Hg focus-
corrected fluxes for Hoh Lake were some of the lowest among all WACAP lakes, yet the PE
values increased to very high values. For PJ lake, all metals had much higher fluxes (including
Hg), yet the Hg enrichment values were not as high as those at Hoh Lake.
In the MORA lakes (Figure 4-48), the Hg PE profile for Golden Lake began a very slow increase
in the late 1800s. At LP19, the increase began at about the same time, but the magnitude is much
larger. At the surface, the PE values for LP19 were over 100% for most of the 1900s, and over
200% in the most recent sediment layers. As already mentioned, the top one or two sediment
slices show significant enrichment in Hg, whereas SCPs are declining. This enrichment could be
related to an increase in Hg deposition from global/Asian sources and decreasing SCPs as a
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CHAPTER 4. CONTAMINANT DISTRIBUTION
result of controls on particulate emissions associated with regional high temperature combustion
sources.
In the ROMO lakes (Figure 4-49), recent Hg fluxes were some of the highest among all WACAP
parks (20-40 ug/m2/yr), and the PE profiles are broadly similar to one another. Hg PE values for
Lone Pine Lake show an increase starting around 1900, whereas values for Mills Lake began to
increase somewhat later. The PE value at both sites reached maximum values of 100-150% in the
late twentieth century and have declined slightly since. At Mills Lake, the SCP profile is broadly
similar to the Hg profile, but at Lone Pine Lake, the SCP profile is rather different. This early
increase in Hg is mirrored by Cd and Pb in both Mills and Lone Pine lakes, suggesting a possible
common source. In both lakes, the SCPs have declined considerably in the recent decades. These
patterns are broadly consistent with a decrease in regional emissions from high temperature
fossil fuel combustion, metal smelting, or other industrial sources. These patterns are also
influenced by an increase in global background Hg concentrations, not associated with SCPs,
that have supplanted the regional sources. An increase in global sources (e.g., Wu et al., 2006)
could be replacing Hg deposition that was previously from regional sources.
In SEKI (Figure 4-50), both lakes show an increase in Hg PE in the early 1900s, reaching values
of 100-150%. Hg PE values in Emerald Lake reached this value very quickly between about
1940 and 1955. The concentrations have declined only slightly since then. At Pear Lake, the
values are more variable, peaking around 1975, and have dropped considerably since then. At
both lakes, the Cd and Pb enrichment pattern is similar to the Hg pattern. In Pear Lake, this
pattern is similar to the SCP profile, suggesting a role for regional fossil fuel sources. But in
Emerald Lake, the SCPs have a different pattern, suggesting that the sources are more complex.
The fact that SCPs and Hg in both lakes now appear to be moving in different directions suggests
that other Hg sources (i.e., increasing global background, transpacific, local/regional sources not
associated with SCP) are now affecting these sites.
4.3.6 Source Attribution for Mercury, Trace Metals, and SCPs
Most metals and SCPs reside on, and are transported with, other fine particulate matter. As
mentioned previously, detectable SCPs are in the range of 5-50 um in diameter. Metals are found
primarily on particles less than 10 um diameter. These particles are produced from a variety of
sources, including fossil fuel combustion, metal smelting, and other industrial processes. SCPs
are produced only from high temperature combustion. Transport of the fine and larger particulate
matter is generally limited to distances of no more than about 1,000-2,000 km. Starting in the
late 1970s, the Clean Air Act mandated controls on many industrial sources in the United States.
These controls initially applied only to new sources, but over time, nearly all large sources have
installed, or are in the process of installing, scrubbers or other technologies to capture most
particulate matter. Over the same time frame, Pb in gasoline was also phased out in the United
States. Thus for SCPs, Pb, and Cd, we see a clear decline in fluxes to the sediments for nearly all
WACAP sites in the conterminous 48 states. In Alaska, the SCPs were non-detectable and Pb
and Cd were generally much lower than at sites in the conterminous 48 states; thus we do not
observe the same decline in the Alaska sediment cores.
For Hg, source attribution is more complex. There are both natural and industrial sources and,
once deposited, Hg can be re-emitted to the atmosphere. Mercury can be transported to the
WACAP parks, both on fine particulate matter as well as via several gaseous compounds. While
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CHAPTER 4. CONTAMINANT DISTRIBUTION
particulate mercury and gaseous Hg(II) compounds have short atmospheric lifetimes, gaseous
elemental mercury [Hg(0)] has a lifetime up to one year or more, which results in a fairly
uniform global reservoir of elemental mercury in the atmosphere. Gaseous elemental mercury is
slowly oxidized to Hg(II), which is then quickly removed via wet or dry deposition. The
oxidation is believed to be driven by UV light, which results in a latitudinal gradient in the
depositional flux (Selin et al., 2007). Thus, in regions with significant industrial sources of
particulate Hg or gaseous Hg(II) compounds, such as from coal combustion or waste
incineration, there can be a significant contribution to regional deposition. The complexity of the
Hg cycle suggests that the relative contribution from global vs. regional sources for any location
is complex, and depends on numerous factors. In addition, there are large uncertainties in the
sources and overall cycling of Hg in the atmosphere (Lindberg et al., 2007).
Mercury in the WACAP parks comes from both regional and global sources. As a result of the
Clean Air Act, scrubbers and other control technologies were installed for removal of particulate
matter, sulfur dioxide (802), and nitrous oxides (NOX) beginning in the 1970s. As an unintended
benefit, these scrubbers also remove some of the mercury, mainly particle bound and Hg(II)
compounds. These forms of Hg are also the forms that are most important for regional deposi-
tion. Gaseous elemental Hg is much more difficult to remove from the waste stream. According
to the USEPA, between 1990 and 1999, US industrial emissions of Hg dropped by 44%, from
220 tons to 115 tons per year (see http://www.epa.gov/mercury/control_emissions/emissions.htm).
Using both global (Selin et al., 2007) and regional models (Seigneur et al., 2004), several groups
have simulated Hg deposition patterns. Although there are significant uncertainties, these two
models get broadly similar results: US industrial sources dominate Hg deposition in the eastern
United States, whereas natural and global sources dominate deposition in the western United
States. This largely reflects the greater emissions from coal-fired plants in the eastern United
States. The WACAP results are broadly consistent with this current understanding, but they also
demonstrate the complex biogeochemical pathways of Hg that result in our observations in
sediments, vegetation, snow, and fish.
For the four Alaska WACAP lakes, the average Hg flux to the sediments since 1950 was 4.8
ug/m2/yr, whereas the mean highest flux determined for WACAP lakes in the conterminous 48
states during the same period of accumulation was approximately 21 ug/m2/yr. Thus, in the last
50 years, WACAP lakes in the conterminous 48 states accumulated approximately four times
more Hg in their sediments. This large difference in Hg flux probably results from a combination
of factors, including greater regional anthropogenic sources in the conterminous 48 states and
enhanced oxidation of atmospheric Hg(0) at lower latitudes (Selin et al., 2007).
The overall impact is that the aquatic environment in the WACAP lakes in Alaska appears to
receive 80% less Hg from atmospheric sources than WACAP lakes in the conterminous 48
states. This finding is also confirmed by the snowpack Hg flux (see Section 4.3.1). Nonetheless,
fish from the Alaska lakes had the highest Hg concentrations among fish from all WACAP sites,
reflecting the poor relationship between atmospheric deposition and fish bioaccumulation in
aquatic ecosystems, which probably reflects mainly the complexity of food web transfer of
mercury and methylation, both of which play key roles in bioaccumulation.
Winter deposition of Hg to the snowpack in the Alaska, Washington, and California parks is
probably dominated by the global pool of atmospheric Hg, because of relatively low Hg(0)
oxidation rates and subsequent deposition during the winter months (Selin et al., 2007).
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CHAPTER 4. CONTAMINANT DISTRIBUTION
However, the high SCP flux in SEKI lakes suggests Hg from regional high temperature
combustion sources is also a likely source. For MORA, some Hg deposition from the coal-fired
power plant in Centralia Washington is also likely. At ROMO, local and regional upwind
emissions sources, principally coal combustion, might also contribute. As seen in the sediment
patterns (Figures 4-41 and 4-48), both lakes in ROMO are undergoing recent declines in Hg flux
to the sediments, probably because of reductions in regional emissions. The Hg flux to the
snowpack in OLYM was fairly high (see Figure 4-33), but this probably reflects a very high
snowpack amount, as well as the fact that the average is calculated from only a few samples
collected in a single year.
In summary, the WACAP data show that regional sources of Hg have declined, consistent with
reductions in US emissions of Hg. The WACAP data also suggest that global sources are now
contributing an ever increasing share of the total Hg deposition. Because of the potential for
continuing increases in global mercury emissions (Wu et al., 2006), the NPS should continue to
monitor mercury in some forms within the western parks.
4.4 Nutrient Nitrogen and Sulfur
4.4.1 Spatial Distributions of Nitrogen and Sulfur in Lichens
Mean dry weight (dw) concentrations of nitrogen and sulfur in lichens in the core and secondary
WACAP parks are shown in Figure 4-35 (see Section 4.3.3). Lichen data are reported in
Appendices 4A. 12 and 4A. 13, as well as in the WACAP database. Lichen nitrogen and sulfur
concentrations indicate that deposition of atmospheric pollutants containing nitrogen and sulfur
is enhanced in some WACAP parks. Mean nitrogen and sulfur concentrations were well above
the upper limit for the background range for public lands in the western United States at SEKI
and GLAC, and nitrogen concentrations were also above these background ranges at BAND and
BIBE (see Section 4.3.3 and Appendix 4A.14 for a description of how the background ranges
were calculated). WACAP field lichenologists remarked in their notes that nitrophytic (nitrogen-
loving) lichen species were abundant at these latter two parks. Because ammonium sulfate is a
dominant fine particulate at the BIBE IMPROVE site, it is likely that sulfur deposition is also
elevated in BIBE relative to other remote parks and forests in the western United States. Analysis
of lichen samples collected during WACAP and currently archived at the University of
Minnesota Research Analytical Laboratory could provide corroborative evidence.
4.4.2 Source Attribution for Nitrogen
Lichens generally accumulate nitrogen from atmospheric sources in the following order of
preference: gaseous ammonia, particulate ammonium, particulate nitrates, gaseous nitric acid,
and gaseous nitrogen oxides. Therefore, lichen nitrogen is highly responsive to agricultural
sources, especially ammonia, as well as NOX emissions from fossil fuel combustion. High N
deposition is associated with adverse effects to vegetation. NOx-nitrogen emissions come mainly
from fossil fuel combustion, although there is a small natural contribution from lightning and
soils. NHs-nitrogen sources are associated with agricultural applications (fertilizers), animal
wastes, and industrial sources. Natural sources are much smaller. Thus nitrogen sources are fairly
ubiquitous in the western United States. SEKI probably gets the greatest contributions from both
NH3 and NOX sources, because of its proximity to large urban areas, agriculture, and animal
husbandry.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
4.5 Atmospheric Transport
(Refer to Section 3.4.1 in Chapter 3 for a discussion of the interpretation of atmospheric
trajectories and the methods used.) Figures 4-51 to 4-57 show the air mass back-trajectory cluster
plots for all sites. The back-trajectories for each park were generated by the NOAA-Hysplit
trajectory model for the period 1998-2005. Clusters were generated with 1-, 5-, and 10-day back
trajectories, which represent the airsheds of the WACAP parks at different scales of atmospheric
motion. These differences represent local, regional, and long-range atmospheric transport and the
climatology of each cluster, as shown by two parameters: precipitation and seasonal distribution
of transport patterns. Although the WACAP parks cover a wide geographic range, many of them
share similar climatological drivers (and similar patterns within the clusters). OLYM, MORA,
SEKI, and GLAC all have winter precipitation maxima and summer minima that are driven by
the Aleutian low pressure and Pacific high pressure systems, respectively.
The annual average precipitation at MORA was about 275 cm. For the 1-day clusters, -50% of
this amount comes from winter-dominated clusters E and F, whereas these same clusters contain
only -17% of the total trajectories (see Figure 4-55). Conversely, summer-dominated clusters A
and C are responsible for -17% of the precipitation, with -45% of the trajectories. Although the
1-day clusters capture the climate of the Pacific Coast, the seasonality and precipitation graphs
for the 5- and 10-day clusters show that the percentage of trajectories per cluster and the
percentage of precipitation per cluster are similar. These longer clusters do a poorer job of
describing regional flow patterns at MORA, a trend that was also seen at OLYM, GLAC, and
SEKI. Precipitation patterns at these sites occur on a scale of a few hundred kilometers. The
standard deviations about the cluster means are very high in this region for the 5- and 10-day
clusters, suggesting that longer clusters do not accurately describe regional phenomena. This
result implies that the longer transport patterns do not accurately reflect the transport of regional
emissions to the parks. Instead, the 1-day clusters do a better job of describing the nearby
transport pathways.
As demonstrated in Chapter 3 (SOCs), 50-100% of historic- and current-use pesticide concentra-
tions in snow are believed to result from regional transport of less than 300 km. The 1-day
clusters represent trajectories with lengths ranging from -250 km for the shortest clusters and
-1,200 km for the longest clusters, indicating that the 1-day clusters are most representative of
regional transport to the WACAP parks. The remainder of the transport occurs on scales ranging
up to -7,500 km within 5 days, and -17,000 km within 10 days. For contaminants with long
enough atmospheric lifetimes, such a distance implies that trans-Pacific sources are possible.
ROMO has a precipitation regime unique amongst the WACAP sites. It receives nearly equal
amounts of precipitation in each season. The percentage of trajectories in each cluster is nearly
the same as the percentage of precipitation from each cluster, regardless of season (see Figure
4-56). Winter precipitation comes from the Pacific, and summer precipitation from the Gulf of
Mexico. The cluster plots for ROMO reflect this pattern, especially summer-dominated cluster A
in the 10-day clusters. In addition to bringing precipitation from the Gulf of Mexico, cluster A
could be responsible for transport of SOCs from the more heavily contaminated southeastern
United States. Higher SOC concentrations are observed in multiple media on the east side of the
Continental Divide at ROMO, with different atmospheric sources the likely culprit (see Sections
4.2.1 and 4.2.6). In addition, 1-day cluster A suggests significant local/regional influence during
summer.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
A B C D E F
Figure 4-51.1-, 5-, and 10-Day Cluster Plots for NOAT and GAAR. Each cluster represents the
average transport pathway for a group of individual trajectories. Clusters are sorted shortest to longest,
A-F. Bars represent the percent of trajectories by season in each cluster out of 2,922 total (1998-2005).
Light blue = winter; light green = spring; dark green = summer; orange = autumn. The dark blue dot is the
percent of total precipitation for which each cluster is responsible.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
A B C D E F
Figure 4-52.1-, 5-, and 10-Day Cluster Plots for DENA. Each cluster represents the average transport
pathway for a group of individual trajectories. Clusters are sorted shortest to longest, A-F. Bars represent
the percent of trajectories in each cluster out of 2,922 total (1998-2005). Light blue = winter; light green =
spring; dark green = summer; orange = autumn. The dark blue dot is the percent of total precipitation for
which each cluster is responsible.
4-80
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CHAPTER 4. CONTAMINANT DISTRIBUTION
B
Figure 4-53.1-, 5-, and 10-Day Cluster Plots for GLAC. Each cluster represents the average transport
pathway for a group of individual trajectories. Clusters are sorted shortest to longest, A-F. Bars represent
the percent of trajectories in each cluster out of 2,922 total (1998-2005). Light blue = winter; light green =
spring; dark green = summer; orange = autumn. The dark blue dot is the percent of total precipitation for
which each cluster is responsible.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Figure 4-54.1-, 5-, and 10-Day Cluster Plots for OLYM. Each cluster represents the average transport
pathway for a group of individual trajectories. Clusters are sorted shortest to longest, A-F. Bars represent
the percent of trajectories in each cluster out of 2,922 total (1998-2005). Light blue = winter; light green =
spring; dark green = summer; orange = autumn. The dark blue dot is the percent of total precipitation for
which each cluster is responsible.
4-82
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CHAPTER 4. CONTAMINANT DISTRIBUTION
A B C D E F
Figure 4-55.1-, 5-, and 10-Day Cluster Plots for MORA. Each cluster represents the average transport
pathway for a group of individual trajectories. Clusters are sorted shortest to longest, A-F. Bars represent
the percent of trajectories in each cluster out of 2,922 total (1998-2005). Light blue = winter; light green =
spring; dark green = summer; orange = autumn. The dark blue dot is the percent of total precipitation for
which each cluster is responsible.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
A B C D E F
Figure 4-56.1-, 5-, and 10-Day Cluster Plots for ROMO. Each cluster represents the average transport
pathway for a group of individual trajectories. Clusters are sorted shortest to longest, A-F. Bars represent
the percent of trajectories in each cluster out of 2,922 total (1998-2005). Light blue = winter; light green =
spring; dark green = summer; orange = autumn. The dark blue dot is the percent of total precipitation for
which each cluster is responsible.
4-84
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CHAPTER 4. CONTAMINANT DISTRIBUTION
Figure 4-57.1-, 5-, and 10-Day Cluster Plots for SEKI. Each cluster represents the average transport
pathway for a group of individual trajectories. Clusters are sorted shortest to longest, A-F. Bars represent
the percent of trajectories in each cluster out of 2,922 total (1998-2005). Light blue = winter; light green =
spring; dark green = summer; orange = autumn. The dark blue dot is the percent of total precipitation for
which each cluster is responsible.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
SOCs measured in Alaska are attributed to background sources or long-range transport. Figure
4-52 shows the clusters for DENA. Within 5 days, likely sources include Siberia and coastal
British Columbia. An important aspect of the Alaska clusters is that they are the shortest among
clusters for all the sites. The WACAP sites at DENA and NO AT and GAAR are at low
elevations, so these trajectories were calculated at ground level. The result was slower moving,
less distinguishable air masses.
4.8
WACAP conducted extensive sampling in 8 core and 12 secondary parks in the western United
States from 2003 to 2006. In the core parks, samples of lake water, snow, air, vegetation (lichen
and conifer needles), fish, and lake sediments were collected from two watersheds in each park,
except the Arctic parks NO AT and GAAR, where one watershed was sampled in each park. In
the secondary parks, only lichen, conifer, and air samples were collected in 4-6 sites along an
elevational gradient. Samples were analyzed for SOCs (current-use and historic-use pesticides,
industrial compounds, and PAHs), mercury, and other metals. Lichen samples were also
analyzed for nitrogen content. The data are presented and analyzed in this chapter. From these
observations, we have drawn a number of conclusions, as follows:
1. Total SOC concentrations in snow were highest in interior (GLAC and ROMO) and in
California (SEKI) parks. The highest SOC concentrations in vegetation were measured at
SEKI, GLAC, YOSE, and GRSA.
2. At parks with the highest SOC concentrations in vegetation, the total SOC concentration
was dominated by current-use pesticide residues, notably endosulfans and dacthal.
3. Lichen concentrations of PCBs and the pesticides chlorpyrifos, dacthal, endosulfans, HCB,
a-HCH, g-HCH, chlordanes, and DDTs increased with elevation at most of the WACAP
parks for which there were sufficient data, suggesting that these compounds are undergoing
cold fractionation. Concentrations of PAHs decreased with elevation at most parks.
4. Similar SOC concentrations were detected in lichens and conifer needles, but lichens had
concentrations 2-9 times greater, except for a-HCH and g-HCH, for which concentrations
were similar. Despite lipid normalization, different species of lichens and conifers
accumulated different concentrations of SOCs; sampling a single species aids site-to-site
comparisons.
5. The magnitude of SOC concentrations in the seasonal snowpack varied substantially year to
year, but the percent contribution of each SOC was fairly consistent from year to year for a
given park.
6. Concentrations of flame retardants (PBDEs) and historic-use pesticides in WACAP fish
were approximately 3 times higher and 2-9 times lower, respectively, than concentrations in
fish from similar alpine environments in Europe. Concentrations of dieldrin in fish at some
parks (notably ROMO and SEKI) were significantly elevated compared with concentrations
in fish from similar Canadian studies.
7. At parks in the conterminous 48 states, good correlations were observed between concentra-
tions of CUPs in snow and vegetation versus percent cropland within 150 km and, in the
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CHAPTER 4. CONTAMINANT DISTRIBUTION
case of vegetation, ammonium nitrate concentrations in ambient fine particulates measured
by IMPROVE. Based on this relationship, we conclude that most of the CUP deposition
comes from regional agricultural sources.
8. For some compounds (e.g., dacthal), pesticide application data are missing for significant
regions of the western United States, limiting the ability to accurately identify sources.
9. PAH concentrations in snow, sediment, and vegetation in the Snyder Lake watershed at
GLAC were higher than concentrations in the Oldman Lake watershed and in other
WACAP parks. Several lines of evidence point to the aluminum smelter in Columbia Falls,
Montana, as the most likely source.
10. The evidence suggests that SOC and Hg deposition to Mills Lake, on the east side of the
Continental Divide in ROMO, is higher than deposition to Lone Pine Lake, on the west side.
This finding might be because the Continental Divide serves as a topographic barrier for
transport of SOCs and Hg from populated areas on the east side of ROMO to the west side.
SOC concentrations in air, conifer needles, and fish show no clear evidence of an east side
enhancement.
11. Snow, lake sediment, and vegetation data indicate that Hg flux to parks in the conterminous
48 states is greater than flux to the parks in Alaska. Despite this observation, fish in the
Alaska parks had the highest concentrations of Hg. This finding could be a result of several
factors, including fish age, Hg methylation rate, watershed biogeochemical characteristics,
and foodweb efficiency as it relates to bioaccumulation.
12. Temporal records from sediment cores indicate that in nearly all parks, Hg deposition
increased in the twentieth century because of anthropogenic sources. At some parks, Hg
deposition fluxes have declined somewhat since the 1970-1980 time period, although at
other parks, the Hg flux appears still to be increasing. This finding reflects a complex array
of decreasing regional sources combined with increasing global contributions, complicated
by the effects of watershed processes on the sedimentation record.
13. Pb, Cd, and SCPs indicate regional fossil fuel combustion sources. SCPs in lake sediments
clearly show increasing contributions from industrial sources in the conterminous 48 states
during the late twentieth century. In recent decades, Pb, Cd, and SCPs have declined
substantially, reflecting source reductions resulting from the Clean Air Act and regulations
on lead in gasoline. Lead concentrations in lichens at SEKI and MORA have decreased 5- to
6-fold since the 1980s.
14. In the Alaska lake sediments, SCPs were non-detectable and Pb and Cd showed little signs
of a twentieth century increase. Only the Hg flux showed a consistent increase in the Alaska
lake sediments, reflecting a primary contribution from global sources.
15. Nitrogen concentrations in lichens from SEKI, GLAC, BAND, and BIBE were elevated,
indicating enhanced nitrogen deposition in these parks. Lichen sulfur concentrations
indicated enhanced S deposition at SEKI and GLAC.
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CHAPTER 4. CONTAMINANT DISTRIBUTION
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CHAPTER 5
Biological and Ecological Effects
5.1 Introduction
In this chapter, we present our assessment of the biological and ecological effects of anthropo-
genic semi-volatile organic compounds (SOCs), metals, and fixed nitrogen from atmospheric
sources in national parks, preserves, monuments, and wilderness areas of the western United
States. We begin with an assessment of the accumulation, magnification, and biological effects
of contaminants in WACAP matrices (Section 5.2). We provide evidence for the bioaccumula-
tion of SOCs and mercury (Hg) in fish and biomagnification of SOCs from the atmosphere to
vegetation, and from lake water (and snow) to fish. For fish, abnormal endocrine responses in
male fish, such as elevated estrogen-responsive protein, and the appearance of eggs in testes,
suggest exposure to contaminants having estrogenic effects. Immune system reactions to stress,
e.g., the density of macrophage aggregates (accumulations of immune cells), were also related to
contaminant concentrations. These sub-lethal effects on fish could be related to contaminants,
but other stressors cannot be ruled out with the current dataset.
Later in the chapter (Section 5.4), we discuss the potential ecological effects of current
contaminant concentrations on the food web, including some piscivorous birds and mammals.
We compare current contaminant concentrations in fish to EPA contaminant health thresholds to
evaluate the potential human health effects of fish consumption for sensitive populations or for
subsistence and recreational fishing in the parks.
Parks with enhanced nitrogen deposition are identified and the predictive values of various
nitrogen indicators on SOC concentrations in vegetation are explored. Overall, the extent and
effects of contamination and perturbation are demonstrated, focusing on the watershed level. The
extremely diverse ecosystems at each national park are taken into account through identification
of park-specific contaminants associated with measured or potential adverse effects on biological
and ecological resources or visitor health.
5.2 Bioaccumulation and Biomagnification
5.2.1 Processes of Bioaccumulation and Biomagnification
The chief concerns regarding anthropogenic contamination of aquatic ecosystems are the
processes of bioaccumulation and biomagnification of pollutants and metals. Bioaccumulation is
the overall increase in contaminants in biota, compared to that of water, through time (Gobas and
Morrison, 2000). This phenomenon depends on the rates of contaminant accumulation in the
biota and the rates of metabolism of contaminants and eventual excretion, or other loss, from the
biota. Chemicals not readily excreted from biota are most subject to bioaccumulation. They are
often stored in body fat (lipophilic), and are often bio-active (mimic or stop the natural chemicals
that control body systems). This is not to say that only lipophilic compounds bioaccumulate.
Many metals bioaccumulate, but they are not necessarily lipophilic. Biomagnification is the
overall increase in the contaminant concentrations beyond what is stored in food (Gobas and
Morrison, 2000). For example, we can observe fairly low concentrations of contaminants in
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 5-1
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
aquatic vegetation or phytoplankton, the organisms at the lowest trophic levels. The process of
biomagnification is the increase in contaminant concentrations that occur in biota that ingest the
vegetation, such as snails (Gastropoda). Subsequently, contaminants increase again in the fish
that eat the snails, and in the birds or mammals (including humans) that eat the fish. Therefore,
fairly low input of environmental contaminants to ecosystems can lead to high concentrations in
the biota at the upper trophic levels. For clarification, bioconcentration is the uptake of
chemicals from the water, and bioavailability refers only to the truly dissolved, bio-reactive
fraction of any given chemical (Gobas and Morrison, 2000).
The relative solubility of chemicals in water or octanol (octanol-water partition) dictates, in part,
how readily chemicals bioaccumulate. In general, octanol-water coefficients (KQW) ranging from
4 to 7 have the greatest chance for bioaccumulation (Thomann, 1989). However, bioaccumu-
lation also depends on biological and environmental factors that regulate the uptake and excre-
tion of contaminants in biota (Mackay and Fraser, 2000). For example, toxico-kinetics of indi-
vidual contaminants and chemical mixtures influence the bioavailability of contaminants to fish
(van der Oost et al., 2003). The diet of the fish (food intake) is the main uptake mechanism for
bioaccumulative compounds, whereas the metabolism of, and the individual organ sensitivities
to, pollutants dictates how or if the contaminants are excreted from the biota. In addition,
bioconcentration of contaminants from the water itself is another route of exposure for fishes
(Ban-on, 1990).
5.2.2 Effects of Bioaccumulation and Biomagnification
5.2.2.1 Large Ecosystem Effects of Bioaccumulation
Bioaccumulation can negatively affect the physiological, endocrine, and immune systems of
individual organisms exposed to contaminants (van der Oost et al., 2003). However, attributing
changes in body systems to chemical concentrations should be done within a multiple stressor
framework (Schreck, 2000a, b). That is, contaminants alone might negatively affect biota, but
one should consider that additional stressors could be contributing to changes in the body
systems.
Larger ecosystem effects occur through biomagnification of chemicals in the food web. That is,
piscivorous birds and mammals usually have higher concentrations than fish, and the very top
predators (e.g., polar bears), have the highest chemical concentrations (Mackay and Fraser,
2000). The significance of this dynamic is, first, that by the time population and ecosystem
changes can be observed, the early biomarker signals either were not observed and measured or
were not acknowledged and, second, that environmental contamination has been occurring for a
long time (Figure 5-1) (van der Oost et al. 2003).
An example of large ecosystem effects is the eggshell thinning and population reductions that
occurred among many bird species during the 1950s as a result of extensive use of the organo-
chlorine insecticide dichlorodiphenyltrichloroethane (DDT). Until population and ecosystem
changes were observed, the negative effects of DDT were either unknown or were not observed.
Recovery of some bird populations, such as bald eagles, was largely successful, but required 40
years of extensive financial investment and the commitment of personnel from resource
management agencies.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
"early" bioinarker
signals
"laler*" effects
molecular
sub-cellular
cellular
(issue
systemic
organism
population
community
ecosystem
Figure 5-1. Diagram of Increasing Effects of Contaminants, from Individual to Ecosystem Level.
Re-drawn from van der Oost et al. (2003).
Long-term concerns about bioaccumulation encompass both organism and ecosystem. Mercury,
for example, is a highly persistent natural element and a global pollutant that readily bioaccumu-
lates, biomagnifies, and affects nearly every body system in fish and other vertebrates, including
humans. Because Hg is highly persistent, historic emissions (e.g., since the US industrial
revolution) of Hg can potentially still be incorporated into the food web by the process of
methylation. Furthermore, current emissions of Hg from anthropogenic sources, such as coal-
fired power plants in the developed and developing worlds (e.g., the Asian industrial revolution),
together with the documented long-range atmospheric transport of contaminants (Jaffe et al.,
1999; Wania, 2003; Daly and Wania, 2005), indicate that Hg is likely to have a significant
impact on aquatic and other ecosystems in the future.
Contaminants with short half-lives and non-bioaccumulative degradation products pose a lesser
concern. Although the bioactivity of those compounds could still be of concern for individual
organisms, they do not bioaccumulate and are unlikely to affect the entire food web.
The biota at most risk for adverse effects of bioaccumulation are long-lived, high trophic-level
organisms. In the aquatic environments, adverse effects of bioaccumulation are usually observed
in piscivorous fish, although insectivorous fish can also bioaccumulate contaminants. In
terrestrial environments, birds and mammals that eat fish are susceptible to adverse bioaccumu-
lation effects.
5.2.2.2 Evidence of Bioaccumulation in Fish
Of the many environmental pollutants known to bioaccumulate, Hg is significant because of the
numerous and pronounced deleterious effects on fish, wildlife, and humans (Sweet and Zelikoff,
2001). Incorporation of Hg into the food web is highly complex; biota in lakes with similar
limnological characteristics can exhibit very different Hg concentrations. For efficient
incorporation of Hg into the food web, and for bioaccumulation and biomagnification to occur,
Hg must be converted to an organic form by methylation. However, inorganic Hg can be
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-3
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
absorbed across the gut offish (Hoyle and Handy, 2005). Methyl-Hg is the predominant form
found in fish tissues, ranging from 95% to 100% of the entire body burden (Bloom, 1992).
Methylation is accomplished in the sediment and wetlands by micro-organisms (Ullrich et al.,
2001) and is dependent on lake water pH, total organic carbon, dissolved sulfate (Wiener et al.,
2006), temperature, and alkalinity (Ullrich et al., 2001). Mercury exists in three valence states; 0,
1, and 2; Hg + is the species that is methylated. Methylation of Hg requires a CH3 donor;
methylcobalamin is the most likely donor because it is found under anoxic conditions in micro-
organisms and is the only natural compound capable of donating the carbanion (CHs ). Data
suggest that in bacteria, methylation occurs intra- and extracellularly and that sulfate-reducing
bacteria are the principal micro-organisms responsible for carrying out the methylation of
inorganic Hg (Ullrich et al., 2001). The actual bio-transformation of Hg + to methyl-Hg by
bacteria is poorly understood. Finally, there is also evidence of abiotic methylation of Hg.
Our observations of Hg accumulation in WACAP fish reflect the highly complex nature of Hg
cycling in the environment. In the fish that we captured, Hg was age-dependent in all species up
to approximately 15 years of age (Figure 5-2; Appendix 5A). In fish older than 15 years of age,
the relationship disappears. In these older fish, Hg concentrations could be related to the trophic
status of the fish, as has been demonstrated in other studies (Kidd et al., 1995; Evans et al.,
2005). Another possibility is that metabolism and excretion of the Hg could occur, as has been
modeled by Trudel and Rasmussen (1997). A third explanation is that Hg might increase
steadily, until it eventually reaches toxic levels, and, in conjunction with the likely numerous
other stressors, leads to senescence; as a result, only fish with fairly low concentrations of Hg
reach old age. In our studies, only lake trout (Salvelinus namaycush) reached ages > 20 years,
and lake trout were collected only in Alaska. Therefore, it is not known if the observed break-
down in the relationship between age and Hg is unique to lake trout or is related to the food webs
of which they are a part.
DUU '
~
5
,- 400 -
at
'at 300 -
X
|
•a 200-
0)
o
i
^3 100 "
£
Q
>_
n -
• Brook trout
O Lake trout
A Cutthroat trout
a Rainbow trout
. . -o
• •• e °
• o ® °
Jhi!'-' OB » ° « »
•I'll* °° ° °
• "t B n O
• ™llr|ugQ O
a Aggga O
5
10
20
Age (years)
25
30
35
Figure 5-2. Relationship between Fish Age and Total Whole Body Hg in Trout from All Lakes. In
fish< 15 years of age, F^M= 127.36, R2 = 0.47, P= 1.61 x 10"21.
5-4
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Similarly, age was an important factor for SOC concentrations in fish, but not as important as the
lipid levels of the fish. For most SOCs, percent lipid in the fish was the most reliable single
predictor of SOC concentrations (average lake R2= 0.29), whereas fish age was the second most
reliable predictor (average lake R = 0.22) (Ackerman, 2007). Like Hg, some correlations for
SOCs broke down among the oldest fish.
An important objective of WACAP is to provide the NFS with data on organic and trace element
contamination in the western national parks in various environmental compartments. With these
data, it is possible to make comparisons of contaminant concentrations in fish from these western
parks to contaminant concentrations from studies published in the peer-reviewed literature.
Through these comparisons, it is possible to determine the relative level of contamination in
similar ecosystems around the world. In addition, data from the literature on highly affected
ecosystems provide another perspective on the relative contamination of historically protected
ecosystems in the national parks.
Data on organochlorine concentrations from selected publications from the United States,
Canada, Africa, Asia, and Europe are compared to WACAP fish concentrations in Table 5-1. As
expected, greatly affected areas such as the Ohio River (tributary of the Mississippi River, USA)
and the Columbia River (Pacific Northwest, USA) have fish with organochlorine concentrations
10- to 10,000-fold higher than we report in fish from the national parks. It was surprising,
however, that dieldrin concentrations were markedly higher in fish from SEKI, ROMO, and
GLAC than in farmed Atlantic Salmon (Salmo solar) and wild Pacific salmon (Oncorhynchus
spp.). The lake trout from GAAR and NO AT and brook trout (S.fontinalis) from OLYM
represented the WACAP fish with lowest overall concentrations of SOCs; concentrations in
these fish were generally lower than those reported in these selected studies.
All WACAP fish had lower polychlorinated byphenyl (PCB), hexachlorocyclohexane (HCH),
and hexachlorobenzene (HCB) concentrations than the concentrations reported in the literature.
However, we acknowledge that cumulative PCB concentrations might have been underestimated
in this study because we targeted a limited number of congeners. A surprising finding was that
concentrations of DDTs in fish from SEKI and ROMO were higher than those in many fish
(including a piscivorous species) from Lake Malawi in East Africa, despite the current use of
DDT in Africa for mosquito control.
Mercury concentrations by lake are presented graphically in Section 5.4.1.1; comparisons offish
in this study to concentrations in the peer-reviewed literature are presented in Table 5-2. In
general, mercury concentrations in the trout from the parks in this study were lower than those
reported for lakes in the Midwest and Northeast United States, including Lake Michigan and
Lake Superior. In addition, mercury concentrations were lower in fish at all WACAP parks than
in 1-year-old insectivorous yellow perch (Percaflavescens) at Voyageurs National Park in
Minnesota. The same was true for lake trout and northern pike (Esox lucius) in northern lakes
(50° N latitude and above) in Canada. But the opposite was the case for Arctic char (Salvelinus
alpinus), grayling (Thymallus arcticus), and brook trout from northern lakes in Canada. Also,
mercury concentrations were higher in WACAP lake fish than in brown trout (Salmo trutta) from
similar mountain and sub-Arctic ecosystems in Europe. Juvenile sturgeon (Acipenser
transmontanus) in the Columbia River, USA, had lower mercury concentrations than fish from
the Arctic, Denali, Olympic, and Mount Rainier National Parks, although a 41-year-old adult
female sturgeon had mercury concentrations well above all fish in this study.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-1. Comparison of Concentrations of Selected Organochlorines (OCs) in Fish from the Literature to Fish from WACAP Parks.
Species
Trophic Level
Salvelinus
fontinalis
Insectivorous
Esox lucius
Piscivorous
Salvelinus
namaycush
Piscivorous
Salvelinus
alpinus
Piscivorous
Ctenopharynx
pictus
Planktivorous
Dimidiochromis
kiwinge
Piscivorous
Engraulicyprus
sardella
Planktivorous
Location
Kaweah
River,
California
Unspecified
lakes,
southern
Sweden
Peter Lake,
Northwest
Territories,
Canada
Peter Lake,
Northwest
Territories,
Canada
Lake Malawi
Africa
Lake Malawi
Africa
Lake Malawi
Africa
Tissue
Whole
fish
Muscle
Muscle
Muscle
Whole
fish
Whole
fish
Whole
fish
Selected Organochlorines (range or mean ± error
measure) Units [wet weight (ww) or lipid normalized (lip)]
Measured Congeners or Metabolites
DDTs PCBs Dieldrin Chlordane HCB
(D)* (P)* (N)* (C)* HCH (H)* (B)*
40-66 5-8
ng/g ww ng/g ww
p,p'-DDE Sum 98
congener
0.13-64 0.32-64
ng/gww ng/g ww
p,p'-DDE Sum 24
congener
57.9± 11.3± 46.1 ±112 2.16 ±4.3
148 SD 23.7 SD SD ng/g ww SD ng/g
ng/g ww ng/g ww Sum ww Sum
Sum PCB153
4.62 ± 1.3 ±0.63 3.45 ±1.65 1.68 ±0.8
1 .71 SD SD ng/g SD ng/g ww SD ng/g
ng/g ww ww Sum ww Sum
Sum PCB153
2 ±0.9
SD ng/g
ww Sum
P,P'-
2.9 ±1.3
SD ng/g
ww Sum
P,P'-
5.2 ±3.3
SD ng/g
ww Sum
P,P'-
(| OC > in WACAP, J, OC < in WACAP, = OC similar
to WACAP); Concentrations reduced 30% for
comparisons to muscle
GAAR&
NOAT
1
i
1
i
1
4/
1
DENA
1
1
i
I
1
1
•*•
1
GLAC
1
1
1
ID
IPCH
t
t
i
t
OLYM
1
i
1
i
1
4-
I
MORA
1
I
i
I
t
t
!
1
ROMO
1
1
I
|D
J,PCH
t
t
1
t
SEKI
1
I
i
To
IPCH
t
t
i
t
Ref.
Datta et al.,
1998
Larsson et
al., 1992
Kidd et al.,
1998
Kidd et al.,
1998
Kidd et al.,
2001
Kidd et al.,
2001
Kidd et al.,
2001
5-6
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTERS. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-1. Comparison of Concentrations of Selected Organochlorines (OCs) in Fish from the Literature to Fish from WACAP Parks
(continued).
Selected Organochlorines (range or mean ± error
measure) Units [wet weight (ww) or lipid normalized (lip)]
Species
Trophic Level
Labeotropheus
fuelleborni
Herb-
Insectivorous
Opsahdium
microlepis
Piscivorous
Synodontis
njassae
Insectivorous
Salmo salar
Plankti-
Piscivorous
Salmo salar
Plankti-
Piscivorous
Salmo salar
Plankti-
Piscivorous
Oncorhynchus
nerka
Plankti-
Piscivorous
Location
Lake Malawi,
Aftrica
Lake Malawi,
Africa
Lake Malawi,
Africa
Scotland fish
farm
Norway fish
farm
Chile fish
farm
Southeast
Alaska
DDTs
Tissue (D)*
Whole 1.2±
fish 0.45 SD
ng/g ww
Sum p,p'-
Whole 34 ±16
fish ng/g ww
Sum p,p'-
Whole 58 ±39
fish SD ng/g
ww Sum
P,P'-
Muscle
Muscle
Muscle
Muscle
Measured Congeners or Metabolites
PCBs
(P)*
-300
ng/g lip
Sum
-250
ng/g ww
Sum
- 150
ng/g lip
Sum
- 100
ng/g lip
Sum
Dieldrin Chlordane HCB
(N)* (C)* HCH (H)* (B)*
-30
ng/g lip
-25
ng/g lip
-10
ng/g lip
-10
ng/g lip
(|OC>
in WACAP, 4
to WACAP);
OC < in
WACAP
, = OC similar
Concentrations reduced 30% for
comparisons to
GAAR&
NOAT
1
i
i
i
i
|p=N
|p=N
DENA
1
I
1
i
I,
m m
|P=N
GLAC
t
1
1
1
1
|p|N
|p|N
OLYM
1
i
i
1
i
I
I
muscle
MORA
t
1
1
i
I
1
1
ROMO SEKI
t t
1 1
1 1
|p |ptN
|N
rP 1 PTN
^
IN
|PtN |P|N
iP|N |P|N
Ref.
Kidd et al.,
2001
Kidd et al.,
2001
Kidd et al.,
2001
Hamilton et
al., 2005
Hamilton et
al., 2005
Hamilton et
al., 2005
Hamilton et
al., 2005
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-7
-------
CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-1 . Comparison of Concentrations of Selected Organochlorines (OCs) in Fish from the Literature to Fish from WACAP Parks
(continued).
Selected Organochlorines (range or mean ± error
measure) Units [wet weight (ww) or lipid normalized (lip)]
Measured Congeners or Metabolites
Species
Trophic Level
Oncorhynchus
tschawytscha
Plankti-
Piscivorous
Salmo trutta
Insectivorous
Salmo trutta
Insectivorous
Salmo trutta
Insectivorous
Polyodon
spathula
Piscivorous
Location
Oregon
Ovre
Neadalsvatn
Norway
Ve'ke'
Hincovo
Slovakia
Redon
France /
Spain
Ohio River
USA
DDTs
Tissue (D)*
Muscle
Muscle 0.74 ±
0.31 SD
ng/g ww
Sum
Muscle 36 ±13
SD ng/g
ww
Sum
Muscle 19 ± 13
SD ng/g
ww Sum
Muscle
PCBs Dieldrin Chlordane
(P)* (N)*
-100 ~ 5 ng/g
ng/g lip lip
Sum
1.5 ±0.57
SD ng/g
ww
Sum
17 ±3.5
SD ng/g
ww Sum
8.2 ±4.8
SD ng/g
ww Sum
50 - 3350
ng/g ww
Arochlor
1260
(C)* HCH (H)*
0.28 ±
0.12 SD
ng/g ww
Sum
0.91 ±
0.44 SD
ng/g ww
Sum
1.6 ±0.9
SD ng/g
ww Sum
HCB
(B)*
0.58 ±
0.21
SD
ng/g
ww
0.3 ±
0.11
SD
ng/g
ww
0.6 ±
0.36
SD
ng/g
ww
Sum
(| OC > in WACAP, J, OC < in WACAP, = OC similar
to WACAP); Concentrations reduced 30% for
comparisons to muscle
GAAR&
NOAT
|P|N
tP
IDHB
!DPH
|B
4*
i
DENA GLAC OLYM MORA
|P|N |P|N I |P|N
|D |PH | |D
|PHB |DB |HB
J,DPH J,DPH 4 |
IB |B
|B
4 4 4 1
T Hi
ROMO
|P|N
f.
i
4
>r
SEKI
|P|N
ft
i
4
Ref.
Hamilton et
al., 2005
Vives et al.,
2004
Vives et al.,
2004
Vives et al.,
2004
Gundersen
and
Pearson,
1992
5-8
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTERS. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-1. Comparison of Concentrations
(continued).
of Selected Organochlorines (OCs) in Fish from the
Selected Organochlorines (range or mean ± error
measure) Units [wet weight (ww) or lipid normalized (lip)]
Measured Congeners or Metabolites
Species
Trophic Level
Gymnocypris
waddellii
Salvelinus
fontinalis
Salvelinus
confluentus
Oncorhynchus
mykiss
Pisci-
Insectivorous
Salvelinus
namaycush
Piscivorous
Salvelinus
namaycush
Piscivorous
Acipenser
transmontanus
Piscivorous
Location
Yamdro
Lake Tibet
Mountain
Lakes in
Banff,
Jasper, or
Yoho
National
Parks
Kusawa
Lake
Northwest
Territories
Canada
Quiet Lake
Northwest
Territories
Canada
Columbia
River
Oregon
DDTs
Tissue (D)*
Muscle ~ 2.5
ng/g ww
Sum
Muscle 4.5 ± 5.4
SD ng/g
ww p,p'-
DDE
Muscle 26.66 ±
4.15 SD
ng/g ww
Sum
Muscle 0.53±
0.09 SD
ng/g ww
Sum
Liver 18,700±
7300
ng/g SE
lip Sum
PCBs
(P)*
7.7 ± 10.4
SD ng/g
ww Sum
127
congen
32.45 ±
3.66 SD
ng/g ww
Sum
3.51 ±
0.62 SD
ng/g ww
Sum
Dieldrin
(N)*
134 ±
.45 SE
ng/g lip
Sum
Chlordane HCB
(C)* HCH (H)* (B)*
~ 1 ng/g ~ 1
ww Sum ng/g
ww
Sum
61 ± 52 SD
ng/g ww
gama-chlor-
dane
3.01 ± 0.48 0.62 ±
SD ng/g ww 0.08 SD
Sum ng/g ww
Sum
0.62 ±0.12 0.08 ±
SD ng/g ww 0.02 SD
Sum ng/g ww
Sum
(|OC>
Literature to Fish from WACAP
in WACAP, I
OC < in
Parks
WACAP, = OC similar
to WACAP); Concentrations reduced 30% for
comparisons to
GAAR&
NOAT
1
1
1
|DC
|PH
1
DENA GLAC
1 to
|HB
i to
|PC
1 i
toe toe
|PH |PH
1 1
OLYM
1
i
I
=D
IPCH
i
muscle
MORA ROMO
ID |D
|HB |HB
i :D
IPC
i i
|D |D
|PCH |PCH
1 1
SEKI
ID
|HB
to
|PC
i
|DC
|PH
i
Ref.
Yang et al.,
2007
Demers et
al., 2007
Ryan et al.,
2005
Ryan et al.,
2005
Feist et al.,
2005
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-9
-------
CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-1. Comparison of Concentrations of Selected Organochlorines (OCs) in Fish from the Literature to Fish from WACAP Parks
(continued).
Selected Organochlorines (range or mean ± error (| OC > in WACAP, | OC < in WACAP, = OC similar
measure) Units [wet weight (ww) or lipid normalized (lip)] to WACAP); Concentrations reduced 30% for
Measured Congeners or Metabolites comparisons to muscle
Ref.
Species
Trophic Level
Salvelinus
namaycush
Piscivorous
Salvelinus
namaycush
Piscivorous
Salvelinus
namaycush
Piscivorous
Salvelinus
namaycush
Piscivorous
Sander vitreus
Piscivorous
Location
Lake
Superior,
USA, 1990
Lake Huron,
USA, 1992
Lake
Michigan,
USA, 1992
Lake
Ontario,
USA, 1992
Lake Erie,
USA, 1992
DDTs PCBs Dieldrin
Tissue (D)* (P)* (N)*
Whole- 180 ±50 450 ±140 40 ±4
fish ng/g 95% ng/g 95% ng/g
Cl Cl 95% Cl
Arochlor
1254
Whole- 520 ± 20 1570 ± 90 60 ± 3
fish ng/g 95% ng/g 95% ng/g
Cl Cl 95% Cl
Arochlor
1254
Whole- 3,490 ± 1,160± 190±20
fish 450 ng/g 180 ng/g ng/g
95% Cl 95% Cl 95% Cl
Arochlor
1254
Whole- 2,650 ± 840 ± 120 80 ± 0.9
fish 300 ng/g ng/g 95% ng/g
95% Cl Cl 95% Cl
Arochlor
1254
Whole- 2,200 ± 120 ±10 30 ± 0.2
fish 310 ng/g ng/g 95% ng/g
95% Cl Cl 95% Cl
Arochlor
1254
Chlordane
(C)* HCH (H)*
100 ±20
95% Cl
ng/g
250 ±10
ng/g 95%
Cl
450 ± 30
ng/g 95%
Cl
170 ±90
ng/g 95%
Cl
50 ± 0.2
ng/g 95%
Cl
HCB GAAR &
(B)* NOAT
1
1
1
i
1
DENA
1
i
1
i
I
GLAC
I
I
I
I
1
OLYM
1
i
1
I
i
MORA
1
I
I
1
I
ROMO
1
i
1
i
1
SEKI
I
I
I
I
1
De-Vault et
al., 1996
De-Vault et
al., 1996
De-Vault et
al., 1996
De-Vault et
al., 1996
De-Vault et
al., 1996
*ln the column headings, letters in () following compound names are codes used in the right side of the table to indicate the compounds.
5-10
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTERS. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-2. Comparison of Concentrations of Total Mercury in Fish from the Literature to Fish from WACAP Parks.
Fish
Trophic
Level
Sander vitreus
Piscivorous
Salmo trutta
Insectivorous
Salvelinus alpinusi
Piscivorous
Salmo trutta
Insectivorous
Salmo trutta
Insectivorous
Salmo trutta
Insectivorous
Salmo trutta
Insectivorous
Salvelinus alpinus
Insectivorous
Location
Lakes in North-
central
Wisconsin
Jorisee,
Switzerland
Arresjoen,
Svalbard
Archipelago
Estany Redo,
Spain
Stavsvatn,
Norway
Gossen-
kollesee, Czech
Republic
Ovre
Neadalsvatn,
Norway
Etang d' Aube,
France
Tissue
Muscle
Muscle
Muscle
Muscle
Muscle
Muscle
Muscle
Muscle
Mercury Species (range or
mean ± error measure)
Units [wet weight (ww) or dry
weight (dw)]
Total-Hg
120- 1740 ng/g ww
37 (18-56) ng/g ww
1 79 (39 -441) ng/g ww
68 (15-158) ng/g ww
44 (21-79) ng/g ww
25 (16-39) ng/g ww
21 (14-31) ng/g ww
55 (37-78) ng/g ww
(| Hg > in WACAP, j Hg < in WACAP, = Hg similar to
WACAP)
Concentrations increased for comparisons to muscle
following Peterson et al. 2007
GAAR&
NOAT
1
T
T
T
T
T
T
T
DENA
1
T
=
T
T
T
T
T
GLAC
1
t
1
1
t
t
t
-
OLYM
1
T
T
T
T
T
T
T
MORA
1
T
=
T
T
T
T
T
ROMO
1
T
1
T
T
T
T
T
SEKI
1
t
1
t
t
t
t
t
Ref.
Wiener et
al., 1990
Rognerud
etal.,
2002
Rognerud
etal.,
2002
Rognerud
etal.,
2002
Rognerud
etal.,
2002
Rognerud
etal.,
2002
Rognerud
etal.,
2002
Rognerud
etal.,
2002
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-11
-------
CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-2. Comparison of Concentrations of Total Mercury in Fish from the Literature to Fish from WACAP Parks (continued).
Fish
Trophic
Level
Salvelinus alpinus
Insecti- Piscivorous
Thy ma II us arcticus
Insectivorous
Salvelinus fontinalis
Insectivorous
Lota lota
Piscivorous
Salvelinus
namaycush
Piscivorous
Esox lucius
Piscivorous
Perca flavescens
Planktivorous
Location Tissue
Lakes in Muscle
Northern
Canada (1971-
2002)
Lakes in Muscle
Northern
Canada (1971-
2002)
Lakes in Muscle
Northern
Canada (1971-
2002)
Lakes in Muscle
Northern
Canada (1971-
2002)
Lakes in Muscle
Northern
Canada (1971-
2002)
Lakes in Muscle
Northern
Canada (1971-
2002)
Voyageurs Whole fish
National Park,
USA
Mercury Species (range or
mean ± error measure)
Units [wet weight (ww) or dry
weight (dw)]
Total-Hg
115 ±237 SD ng/g ww
53 ± 45 SD ng/g ww
106± 50 SD ng/g ww
21 0± 135 SD ng/g ww
384 ± 351 SD ng/g ww
378 ± 298 SD ng/g ww
1 82-942 ng/g dw
(| Hg > in WACAP, j Hg < in WACAP, = Hg similar to
WACAP)
Concentrations increased for comparisons to muscle
following Peterson et al. 2007
GAAR&
NOAT
T
T
T
T
I
1
i
DENA
T
T
T
1
1
1
1
GLAC
1
=
1
1
1
1
1
OLYM
T
T
T
I
i
1
I
MORA
T
T
T
i
1
1
I
ROMO
i
T
1
I
1
1
1
SEKI
T
T
T
1
I
i
i
Ref.
Rognerud
etal.,
2002
Lockhart
etal.,
2005
Lockhart
etal.,
2005
Lockhart
etal.,
2005
Lockhart
etal.,
2005
Lockhart
etal.,
2005
Wiener et
al., 2006
5-12
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTERS. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-2. Comparison of Concentrations of Total Mercury in Fish from the Literature to Fish from WACAP Parks (continued).
Fish
Trophic
Level
Perca flavescens
Planktivorous
Salvelinus
fontinalis
Insectivorous
Juvenile
Acipenser
transmontanus
Piscivorous
Adult Acipenser
transmontanus
Piscivorous
Salvelinus
namaycush
Piscivorous
Sander vitreus
Piscivorous
Esox lucius
Piscivorous
Location
Northeast USA
Northeast USA
Columbia River,
Oregon
Columbia River,
Oregon
Lake Michigan,
USA
St. Louis River
Estuary, Lake
Superior, USA
(1979-1987)
St. Louis River
Estuary, Lake
Superior, USA
(1979-1984)
Mercury Species (range or
mean ± error measure)
Units [wet weight (ww) or dry
weight (dw)]
Tissue Total-Hg
Whole fish 230 (O.50-31 80) ng/g ww
Whole fish 310 (0.05-2070) ng/g ww
Muscle 1 70.54 ± 12.67 SE ng/g ww
Muscle 1,094 ng/g ww, A/= 1, Age = 41 y
Whole fish 220 ± 80 SE ng/g (normalization
not specified)
Not specified ~ 250-1500 ng/g (normalization
not specified)
Not specified ~ 250-600 ng/g (normalization
not specified)
(| Hg > in WACAP, j Hg < in WACAP, = Hg similar to
WACAP)
Concentrations increased for comparisons to muscle
following Peterson et al. 2007
GAAR&
NOAT
J.
J.
T
i
1
I
I
DENA
1
1
t
I
1
1
I
GLAC
J
1
1
1
1
i
I
OLYM
1
1
T
i
i
i
1
MORA
1
1
T
1
1
1
1
ROMO
1
1
1
1
1
1
1
SEKI
1
I
=
1
i
1
1
Ref.
Evers et al.,
2007
Evers et al.,
2007
Webb et al.,
2006
Webb et al.,
2006
Mason and
Sullivan,
1997
Glass et al.,
1990
Glass et al.,
1990
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-13
-------
CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Comparison of organochlorines and mercury in WACAP fish (Tables 5-1 and 5-2) with those
reported in the literature indicate that contamination of WACAP watersheds by PCBs, hexa-
chlorobenzene, hexachlorocyclohexanes, DDTs, and chlordanes is comparable to or lower than
in similar mountain areas in Europe, Canada, and Asia, while dieldrin and PBDE contamination
is higher than in similar mountain areas and in Pacific Ocean salmon (Ackerman 2007). In
general, organochlorine concentrations are lower in WACAP fish than in other fish reported in
the literature, but similar to or higher than values in the literature for mercury. Implications of
this finding are unknown and some geographic areas reported in the literature have dramatically
higher Hg than that observed for the national parks. The potential risk to consumers from fish in
the WACAP parks is presented in Sections 5.4.1 and 5.4.2.
5.2.3 Evidence of Bioaccumulation in Vegetation
Bioaccumulation of SOCs in vegetation over time was observed in a subset of WACAP samples
tested for this effect. First- and second-year lodgepole pine (Pinus contortd) and white fir (Abies
concolor) needles from Emerald Lake basin in SEKI were analyzed for pesticides and PCB
concentrations. Each pair of samples consisted of one set of branchlets that had been divided,
with the terminal bud scars as year markers, into first- and second-year needles before analysis.
Concentrations of the current-use pesticides endosulfan (sum of endosulfan 1, endosulfan 2, and
the degradation product endosulfan sulfate) and dacthal were 2-3 times higher in second-year
compared with first-year lodgepole pine needles (Table 5-3).
Although concentration values for second-year needles were all larger than those for first-year
needles (except trifiuralin in white fir) (Table 5-3), the small sample number and high variability
among field replicates yielded insufficient statistical power to provide evidence of significant
differences for the other SOCs. However, when all pairs of measurements for chlorpyrifos,
endosulfans, dacthal, HCB, a-HCH, g-HCH, chlordanes, and PCBs were considered together
(Table 5-4), there was good evidence that second-year needles had, on average, concentrations of
these SOCs about 3 times higher than those in first-year needles and that SOC concentrations are
strongly correlated with needle age.
Table 5-3. SOC Concentrations (ng/g lipid) in One- and Two-Year-Old Needles of White
Fir (Abies concolor) and Lodgepole Pine (Pinus contorta) from the Emerald Lake Basin
of Sequoia National Park. Nearly every value was larger in year 2 than year 1, but
significant differences were demonstrated only for dacthal and endosulfans in pine (t-test, p <
t < 0.05, equal variances). Statistical power was low because of high variability among field
replicates and small sample sizes.
Abies concolor
Group
CUPs
SOC
Chlorpyrifos
Dacthal
Endosulfans
Triallate
Trifiuralin
Yr
1
2
1
2
1
2
1
2
1
N
2
2
3
2
3
2
3
2
2
Mean
0.0
19.7
1555
2007
2448
7573
0.0
0.0
10.5
s.d.
0.0
26.1
1066
1249
963
2419
0.0
0.0
14.9
s.e.
0.0
18.5
616
883
556
1711
0.0
0.0
10.5
N
1
2
1
2
1
2
1
2
1
Pinus contorta
Mean
11.6
20.5
530
1474
510
1325
0.0
0.0
s.d.
17.7
1102
176
177.2
s.e.
12.5
780
124
125.3
5-14
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
-------
CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-3. SOC Concentrations (ng/g lipid) in One- and Two-Year-Old Needles of White Fir (Abies
concolor) and Lodgepole Pine (Pinus contorta) from the Emerald Lake Basin of Sequoia National
Park (continued).
Abies concolor
Group
HUPs
SOC
Chlordanes
HCB
a-HCH
Yr
2
1
2
1
2
1
2
1
N
1
3
2
2
2
2
2
2
2 1
PCBs
g-HCH
138, 153, 183, 187
1
2
1
3
2
3
Mean
0.0
95.2
145.5
64.7
188.5
0.0
122.7
0.0
0.0
72.3
127.3
72.3
s.d.
86.5
194.7
21.0
27.5
0.0
173.5
0.0
84.3
41.6
84.3
s.e.
49.9
137.7
14.8
19.4
0.0
122.7
0.0
48.7
29.4
48.7
N
2
1
2
1
2
1
2
1
2
1
2
1
Pinus contorta
Mean
19.6
32.4
38.7
80.9
148.1
101.3
210.2
0.0
79.2
4.9
90.3
4.9
s.d.
26.5
50.0
40.5
97.3
112.0
123.6
s.
18
35
28
68
79
e
.7
.3
.7
.8
.2
87.4
Table 5-4. Paired T-Test Results Comparing SOC Concentrations (ng SOC/g conifer
needle lipid) in One- and Two-Year-Old Needles of White Fir (Abies concolor) and
Lodgepole Pine (Pinus contorta)*. When all pairs of measurements were considered, the
average SOC concentration was more than 3 times higher in second year than first year
needles for both species (Prob > t < 0.05) and SOC concentrations were strongly correlated
with needle age (R2 > 0.93).
Statistic
Year 2
Yearl
Mean Difference
Increase (fold)
Std Error
Upper 95%
Lower 95%
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
Abies concolor
1186
360
826
+3.3
473.9
1830
-178
17
0.953
1.744
16
0.1004
0.0502
0.9498
Pinus contorta
457
127
330
+3.6
167
709
-48
10
0.933
1.973
9
0.08
0.04
0.96
*Year 2 and Year 1 = Mean concentrations of the individual SOCs: chlorpyrifos, endosulfans, dacthal, HCB, a-HCH,
g-HCH, chlordanes, and PCBs in field replicates of Year 2 and Year 1 conifer needles. Mean difference = average
difference in individual SOC concentrations between first and second year needles. Increase (fold) = multiplicative
increase in the mean difference between year one and year two. Std Error = standard error of the mean; Upper95%
and Lower 95%= 95% confidence intervals around the mean; N = number of measurements; Correlation = correla-
tion between concentrations of individual contaminants and year; t-ratio = t-test statistic; DF = degrees of freedom;
Prob > |t| = probability of incorrectly rejecting the null hypothesis that there is no difference in SOC concentrations
due to year (two-tailed test); Prob > t = probability of incorrectly rejecting the null hypothesis that year 2 concentra-
tions are not greater than year 1 concentrations (one-tailed test); Prob < t = probability of incorrectly rejecting the
null hypothesis that year 1 concentrations are not greater than year 2 concentrations (one-tailed test).
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-15
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
How many years of needles can be found on coniferous trees? Conifer needle retention varies
across species and site conditions; maximum needle longevity is optimized by good site and
micro-site conditions, including adequate nutrients, light, water, and heat, and low stress from
pests, disease, and air pollution (Reich et al., 1994). Needles of most conifers reach at least 3-7
years in age (Li et al., 2006), and in the genus Pinus, often reach 15 years, or in the extreme case
of the bristle cone pine (Pinus longaevd) even up to 45 years (Ewers and Schmid, 1981). Needle
longevity generally increases with latitude and elevation, increasing the time that nutrients are
resident in trees in less favorable environments and compensating for shorter growing periods in
cold temperature (Li et al., 2006; Reich et al., 1996). A relevant example is black spruce (Picea
mariand), collected for WACAP in DENA, central Alaska. Needle retention of this species
varies from 5 to 7 years in its southerly boreal forest range in Quebec, to 13 years in central
Alaska, and up to 30 years under subarctic conditions (Lamhamedi and Bernier, 1994).
Whether or not SOC concentrations in conifer needles continue to increase with needle age was
not addressed by WACAP. Plant uptake of SOCs occurs primarily from the atmosphere via one
of three processes: equilibrium partitioning between the vegetation and the gas phase, kinetically
limited gaseous deposition, and wet plus dry particle-bound deposition. Each of these processes
depends on different atmospheric concentrations, plant properties, and environmental variables
(McLachlan, 1999).
It has been suggested that once the SOCs have been deposited, a two-compartment model for
their storage in plant leaves applies (Tolls and McLachlan, 1994; Hauk et al., 1994), accepted
also by Simonich and Kites (1995) and Collins et al. (2006). The two-compartment model
consists of a fairly small surface compartment with rapid uptake and clearance kinetics (hours),
and a larger reservoir compartment with slow chemical migration (months to years). At least
some compounds are known to require many months to reach needle concentrations that are in
equilibrium with the atmosphere. For example, Douglas-fir (Pseudtsuga menziesii) exposed to
toluene, ethylbenzene, and xlenes for several years usually equilibrated within 5-6 months
(Keymeulen et al., 1993). The composition of the larger reservoir compartment is believed to
influence the retention of organic chemicals, but the physiological relationship between the two
compartments has not been elucidated. Forces working against eventual equilibration include
surface degradation of SOCs, such as the photo-oxidation of endosulfan I and II to endosulfan
sulfate (Simonich and Kites, 1995) and seasonal variation in atmospheric concentrations of
pollutants (Simonich and Kites, 1994). An interesting follow-up to WACAP would be to
determine whether SOC concentrations equilibrate or continue to increase in conifer needles
after year 2, especially at high elevations and northerly latitudes where a large percentage of
needles are in older age classes.
Other ecologically relevant questions include how much SOC contamination of soils occurs
during precipitation (i.e., directly from precipitation but also from the SOC concentration in
particulates collected on the surface of needles between precipitation events that wash through
the canopy during rain or snowfall) and how needle SOC concentrations change as needles
senesce and finally drop to the forest floor. Both pathways are considered to be important
sources of soil SOC contamination (Horstmann and McLachlan, 1998; Weiss, 2000; Nizzetto et
al., 2006), although quantitative data for the western United States are scarce. Another
ecologically important question is whether SOC contributions from litterfall and canopy
leachates are sufficiently high, cumulative or long-lasting to adversely affect populations of
5-16 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
arthropod, fungal, or microbial decomposers, or plant life, either as individual contaminants or
synergistically. The answers to these questions require more research.
Although concentrations of individual SOCs are fairly small, ranging from ng (pesticides and
PCBs) to (j,g (PAHs) per gram of needles, the total quantity of contaminants absorbed by conifer
needles per hectare (ha) can be surprisingly high, especially considering that the exposure is
passive. Table 5-5 shows calculation of annual needle production per ha across a wide range of
site conditions in coniferous forests from New Mexico to British Columbia, using total above
ground annual biomass production and stem: leaf ratios compiled by Hessl et al. (in press) and
available on-line at http://ocid.nacse.org/research/ecophys/index.html.
Table 5-5. Estimates of Total SOC Concentrations (mg/ha) in Second-Year Needles from Western
North American Coniferous Forests. Annual needle biomass production for each of 31 sites1, representing
xeric-continental to coastal-rain forests, was multiplied by average low and high needle SOC concentrations
observed at WACAP parks to estimate per ha SOC concentrations. Needle biomass was more important than
needle concentration; maximum estimated total pesticide and PAH concentrations were < 1 and <7 g/ha,
respectively at all sites.
Measure
Site Data
Mean
StDev
St Err Median
Min
Max
N = 31 Elevation (m)
Mean tree age (yrs)
Carbon Allocation New Stem
C: New Leaf C
Net Above Ground Productivity
(kg/m2/yr)
Annual Needle Production
(kg/m2yr)
Annual Needle Production
(g/ha/yr)
886
103
1.87
1.55
0.634
771
91
0.89
2.48
1.156
267
32
500
72
0.31 1.69
0.86 0.84
0.400 0.286
200
22
0.20
0.12
0.100
2720
450
3.32
10.50
5.000
6,344,810 11,556,610 4,004,090 2,861,110 1,000,000 50,000,000
SOC
concentration
(mg/ha) in
second year
needles*
Parameter
Trifluralin**
Triallate**
Chlorpyrifos
Level
high
high
low
C
1.29
8.93
1.45
Mean
0.54
3.72
0.61
StDev
0.98
6.78
1.1
StErr
0.34
2.35
0.38
Median
0.24
1.68
0.27
Min
0.08
0.59
0.1
Max
4.24
29.34
4.77
Dacthal
Endosulfans
HCB
a-HCH
g-HCH
Chlordanes
high
low
high
low
high
low
high
low
high
low
high
high
low
8.93
1.45
7.45
8.76
65.9
24.5
138
12.1
12.6
9.78
14.7
9.16
1.31
3.72
0.61
3.11
3.65
27.47
10.21
57.7
5.05
5.23
4.08
6.11
a-HCH
6.78
1.1
5.66
6.65
50.04
18.59
105.09
9.19
9.53
7.42
11.13
6.95
1
1.96
2.3
17.34
6.44
36.41
3.19
3.3
2.57
3.86
2.41
0.35
1.4
1.65
12.39
4.6
26.02
2.28
2.36
1.84
2.76
1.72
0.25
0.49
0.58
4.33
1.61
9.09
0.8
0.82
0.64
0.96
0.6
0.09
24.47
28.78
216.5
80.43
454.69
39.78
41.24
32.11
48.16
30.08
4.32
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-17
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-5. Estimates of Total SOC Concentrations (mg/ha) in Second-Year Needles from Western
North American Coniferous Forests (continued).
Parameter
Dieldrin**
DDTs
CUPs
HUPs
Total
Pesticides
PCBs
PAHs
Total SOCs
Level
high
high
low
high
low
high
low
high
low
high
low
high
low
high
low
high
c
13.8
4.70
4.51
9.49
1.13
1.68
1073
20044
Mean St Dev
g-HCH 10.49
1.96 3.56
1.88 3.43
3.95 7.2
18.7
92.5
17.3
26.8
36
119.4
0.47
0.7
447
8360
483
8480
StErr
3.63
1.24
1.19
2.5
0.3
0.44
282
5270
Median
2.6
0.88
0.85
1.78
8.4
41.7
7.8
12.1
16.3
53.8
0.21
0.32
202
3770
219
3824
Min
0.91
0.31
0.3
0.62
3
14.6
2.7
4.2
5.7
18.8
0.07
0.11
70
1320
76
1339
Max
45.37
15.42
14.82
31.17
147.6
729.2
136.5
211.4
284.1
940.7
3.7
5.52
3525
65840
3813
66786
1 Data from the Western Forests Ecophysiology Database (http://ocid.nacse.org/research/ecophys/index.html)
2 SOC (mg/ha) in 2nd year needles: = C * .0657 * annual needle production * 1 x 10~6, where C = mean conifer needle
SOC concentration, ng/g lipid, among the WACAP parks in the lowest or highest group, 0.657 is the mean percentage of
lipid in dry needles (converts ng/g lipid to ng/g dw), and Annual Needle Production is the dry weight (dw) of needles
produced in one year in g/ha; dividing by 1 million converts SOC units from ng to mg. Values for the constant, C, were
calculated from Chapter 4, Table 4-2 and are the mean conifer needle SOC concentrations in parks belonging to lowest
and highest groups (i.e., least vs. most contaminated) assigned by the Tukey-Kramer test; parks that were in both highest
and lowest groups were included in calculations for both low and high C.
3High estimates only are provided for triallate, trifluralin, g-HCH, and dieldrin as these SOCs were detected in only a few
parks or concentrations did not differ between parks. Parks for which all samples were below detection limits for an
individual SOC were not included in the analysis.
Means for parks in the lowest and highest needle SOC concentrations groups assigned by the
Tukey-Kramer park means comparison tests (see Table 4-2 in Chapter 4) were calculated. The
total accumulation of SOCs in second-year conifer needles per ha of forest were estimated by
multiplying needle SOC concentration (ng/g dw) by the dry weight of needles produced each
year (kg/ha) (see Table 5-5). So, in a park exposed to comparatively high concentrations of
current use agricultural chemicals, such as SEKI, an estimate for endosulfan concentration in
second-year needles might range from 80 to 450 mg/ha of forest, depending upon the
productivity of the site, whereas the range for a more remote park might be 1.6 to 9.1 mg/ha.
Because this value considers only second-year needles, and 3-7 years of needles are typically
present, the total amount in live needles/ha at any point in time would be larger. This quantity
obviously could be much lower, as (1) vegetation density varies across the landscape and with
elevation from forest to woodland to krummholtz to bare rock and (2) vegetative productivity
declines to zero. Productivity (i.e., needle biomass) varies much more across sites than needle
SOC concentrations, which were generally less than 8-fold different among the WACAP sites
and parks (i.e., from Alaska to Texas). Forest productivity is therefore a more important variable
5-18
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
in predicting the total amount of SOCs removed from the atmosphere and potentially contributed
to soils by vegetation.
Estimates of total quantities of historic use compounds accumulated by second-year needles per
hectare tended to be lower than estimates of current use pesticides, but within the same order of
magnitude (Table 5-5). For example, in the cleanest, most remote sites, a low productivity forest
would accumulate about 3.0 and 2.7 mg/ha of current and historic use pesticides, respectively. In
contrast, a high productivity forest close to important regional sources of pesticides could
accumulate 729 and 211 mg/ha of current and historic use pesticides, respectively. PCBs were
about 10-fold lower. Summed PAHs were 10 to 1,000 fold higher, or 70 to 65,840 mg/ha for low
productivity remote forests compared with high productivity forests close to sources.
How well are forests scrubbing the air of pesticides relative to the amounts of pesticides applied?
There are 125 million hectares of coniferous forest in the western United States and Alaska (US
Forest Service, 1997; also see Chapter 1 ecoregion maps), of which 101 million hectares are
publicly owned. If the second-year needles on each hectare scrubbed on average 3.65 to 27.5 mg
of dacthal or 10.2 to 57.7 mg of endosulfans annually (from Table 5-5), multiplying by 125
million hectares, the total amount scrubbed per year could be between 456 and 3,430 kg of
dacthal and 1,280 and 7,210 kg of endosulfans, or at most ~2 % of the reported total national
commercial application in 2002 of dacthal and endosulfans, 198,000 and 284,000 kg,
respectively (Figure 4-13 in Chapter 4).
Table 5-6. Endosulfans: Per Hectare Comparison of Estimated Annual Endosulfan Accumula-
tion in Second-Year Conifer Needles and Typical 2002 Endosulfan Application Rates of This
Pesticide in the Western United States. A hectare of forest can accumulate endosulfans at levels as
high as medium regional application rates; total accumulation increases with forest productivity and
proximity to sources. Background colors for conifer needle endosulfan accumulation are matched to
regional application rates in the lower part of the table.
Endosulfans Accumulated in Second-Year Conifer Needles (g/ha)*
Average Productivity
Forest location Low Productivity Forest Forest High Productivity Forest
Remote 0.0016 0.010 0.080
Near Source 0.0091 0.058 0.45
2002 US Endosulfan
Application Rates** (g '
Not applied
Lowest
Low
Medium
Highest
Highest
0
0.0085 to 0.049
0.05 to 0.1 5
0.16 to 0.55
0.55 to 2. 19
>2.20
•
* Estimates from Table 5-1 rows (remote = low SOC concentrations, near source = high SOC concentrations) and
columns (low productivity forest = minimum, average productivity forest = average, high productivity forest =
maximum). Values in Table 4.1 were multiplied by 1000 to convert from mg/ha to g/ha.
**Average annual use of active ingredient in grams per hectare of agricultural land in county (converted from
Ibs/square mile) from Figure 4-14 in Chapter 4.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 5-19
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Because most of the national application occurs in the eastern half of the United States, and
application in the western United States is uneven in distribution and intensity (see US map in
Figure 4-13 in Chapter 4), perhaps a better way of approaching the question is to ask how well
forests scrub the air under differing forest productivity and regional application rates. Table 5-6
shows endosulfans as an example.
A hectare of high productivity forest close to sources can absorb endosulfans in amounts that are
equivalent to a medium application rate, or < 25% of maximum per hectare application rates. At
the other extreme, a hectare of remote, low productivity forest can be expected to absorb a very
small total amount of endosulfans relative to a high productivity site near sources, which would
still be about 10-20% of the amount applied per hectare in a very low use area but < 0.1% of the
amounts applied per hectare in highest use areas. It seems reasonable to conclude that in addition
to climatic factors discussed in Chapter 4 (notably temperature and precipitation), the capacity of
a forest to scrub endosulfans from the air is a function of forest productivity (amount of leaf
area—affected also by species-specific cuticular properties), its proximity to areas where endo-
sulfans are applied, and the application rates in those areas. Pesticides other than endosulfans can
be scrubbed by needles to a greater or lesser extent, depending on their physico-chemical
properties, such as octanol-air partitioning coefficient (K<,a) and air-water portioning coefficient
(Kaw)(Su et al, 2007).
The importance of vegetation in scrubbing the atmosphere of organic contaminants has been
discussed by other authors. For example Simonich and Kites (1994) estimated that as much as
24-72% of PAHs emitted in the atmosphere in the northeastern United States are removed by
vegetation. More recently, Su et al. (2007) found extraordinarily high deposition velocities of
PBDEs, PCBs, and PAHs in boreal and deciduous temperate forests in Canada and Germany.
Model calculations suggest that the forest filter effect is most pronounced for SOCs with a log
Koa between 7 and 11 and a log Kaw > -6 (Wania and McLachlan, 2001). For such chemicals,
uptake in forests can notably decrease air concentrations and markedly decrease the long-range
transport of some SOCs, for example, to the Arctic (Su and Wania, 2005).
5.2.4 Evidence of Biomagnification
How do SOC concentrations in fish and vegetation compare with those in the media that transport
contaminants to ecosystems (i.e., snow, lake water, and air)? The differences can be dramatic, and
illustrative of the ability of biological organisms to accumulate molecules from the environment
against astoundingly large concentration gradients. Figure 5-3 compares patterns and magnitudes of
SOC concentrations in snow, lake water, sediments, lichens, conifer needles, and fish from Emerald
Lake (SEKI). SOC compounds are listed in order of increasing K<,w, or decreasing polarity and
solubility in water. Concentrations of all the media are displayed in picograms (i.e., trillionths of a
gram) per gram wet weight, so they can be compared on the same (log) scale. SOC concentrations in
XAD resin are not discussed; they were used to indicate relative differences between sites and were
not converted to ambient air concentrations. The most striking observation in Figure 5-3 is that
concentrations in biota and sediments are very much higher, by 3 to 7 orders of magnitude, than
those in snow and, especially, lake water.
Another observation is that patterns of accumulation differed among media and among terrestrial
and aquatic environments within the same watershed—evidence of different exposures, accumula-
tion mechanisms, revolatilization rates, and, in the case of biota, the ability to metabolize and
actively degrade and eliminate SOCs. For example, compared to sediments, fish had higher
5-20 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Lake water
Snow
Sediment
Lichen
Conifer
Needles
Fish
en
c
I
PCB187
PCB183
PBDE183
PCB118
PCB153
p.p'DDE
PBDE 99
PCB138
Retene
cis-Nonachlor
trans- Chi ordane
trans-Nonach lor
PBDE 100
Benzo(a) anth racene
cis-Chi ordane
Chrysene + Triphenylene
Hexachlorobenzene
Dieldrin
Pyrene
Chlorpyrifbs
Endosulfan II
Endosulfan I
Phenanthrene
Dacthal
a-HCH
g-HCH
Endosulfan sulfate
0.0001 0.001 0.01 0.1 1 10 100 1000 10000
SOCs (pg/g ww)
SOC Groups
^H Endosulfans
^H HCHs
^^ Dacthal
^— PAHs
^^m Chlorpyrifbs
^^m Dieldrin
H exach lorobenzen e
Chlordanes
PBDEs
PCBs
DDTs
Figure 5-3. Mean SOC Concentrations (pg/g ww) in Lake Water, Snow, Sediments, Lichens,
Conifer Needles, and Fish from Emerald Lake (SEKI). SOCs are ordered by increasing Kow, or
decreasing polarity and solubility in water, color-coded by group. SOC concentrations were 3 to 7 orders
of magnitude higher in sediments and biota relative to snow and water. SOC concentrations in water,
snow, and vegetation, but not sediments and fish, generally decreased with decreasing polarity.
Compared to vegetation, fish were better accumulators of PCBs and dieldrin and poorer accumulators of
PAHs, endosulfans, HCHs, dacthal, and chlorpyrifos. If no data are shown, all samples were below
detection limits; PBDEs were measured in sediments and fish only. SOC concentrations (pg/g ww) are on
Iog10 scale.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-21
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
concentrations of PCBs and most pesticides. Compared to vegetation, fish had higher concentrations
of PCBs, dieldrin, and DDTs, but lower concentrations of other pesticides and PAHs.
Fish and sediments concentrated compounds across the K<,w spectrum tested, which makes sense
because of the complex organic and mineral chemistry of sediments and the presence of both
lipids and water in fish and vegetation. In contrast, SOC concentrations in lake water, snow, and
vegetation tended to decrease along the K<,w gradient, i.e., with decreasing solubility of SOCs in
water. Although this is not surprising for snow and lake water, conifer needles and lichens had
total lipid concentrations that were similar to those in fish. A possible explanation is that fish are
able to accumulate lipophilic substances in fatty internal tissues and organs, whereas lipophilic
SOCs absorbed by the waxy cuticles of conifer needles and by the lipid components of lichens
might be partially revolatilized into the atmosphere during warm weather. Indeed, age and lipid
concentrations were the best predictors of SOC concentrations in WACAP fish (subsection
5.2.2.2), whereas at least some research indicates that SOC concentrations in vegetation can
equilibrate with the atmosphere over time (Keymeulen et al., 1993).
One of the air sampling objectives was to determine whether SOC concentrations in XAD resin
can be used to predict concentrations in vegetation. All three media (i.e., air, lichens, and conifer
needles) were sampled at four WACAP sites in core parks: Wonder Lake (DENA), Snyder and
Oldman lakes (GLAC), and Lone Pine Lake (ROMO). Table 5-7 shows Spearman Rho correla-
tions between concentrations of SOCs in XAD resin, conifer needles, and lichens at these sites.
As a result of differential absorption abilities of SOCs across media, SOCs in vegetation could
not be predicted from concentrations in XAD resin (R2 < 0.08); however, concentrations in
conifers and lichens were correlated (R2= 0.63). Comparison of the patterns of SOC accumula-
tion across the three matrices (Figure 5-4) shows that the XAD resin appears to absorb com-
pounds preferentially according to the K<,w, peaking at endosulfan I and then decreasing.
Although not useful for predicting SOC concentrations in vegetation, PASDs are still a valuable
and simple tool for comparing relative atmospheric concentrations across sites.
Table 5-7. Correlations (R2 coefficients) between Total Pesticide Concentrations
in XAD Resin (pg/g dry XAD), Conifer Needles (ng/g lipid), and Lichens (ng/g
lipid) from Wonder, Snyder, Oldman, and Lone Pine Lake Watersheds.
XAD
Lichens
Conifer
XAD
1.000
0.053
0.073
Lichens
0.053
1.000
0.630
Conifer Needles
0.073
0.630
1.000
5.3 Biological Effects
5.3.1 Effects of Contaminants and the Utility of Biomarkers
Many pollutants (e.g., Hg and especially dioxin) have long been known to be extremely toxic to
biota, even at low concentrations. Toxicity occurs as both an acute response and a chronic or
delayed response. The acute response, not necessarily applicable to WACAP because most
contaminant concentrations are fairly low (0-10 ppb), is similar to that of a drug-overdose.
Concentrations are elevated to the point of large-scale physiological shutdown. The chronic or
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
delayed response is more applicable to WACAP, and more interesting biologically, from our
perspective. For example, at low concentrations when the chemicals are not acutely toxic,
endocrine and immune changes can occur that are measurable in the laboratory with biomarkers.
Efforts can then be made to correlate those changes to contaminant concentrations. WACAP
does not attempt to establish cause and effect, but looks for patterns that warrant future
investigation.
10fc
c
.2
o.i
0.01-J—
0.001
I
3
U>
O
T3
I
o
I
CD
I
O
I
I
-
ID
s
o
to
Q
]XAD pg/g dry
(Lichens ng/g lipid
[Conifer ng/g lipid
o
13
LLJ
c
,2
111
13
1C
C
o
1
£
Figure 5-4. Pesticide Concentrations in Dry XAD Resin (used to sample air), Conifer Needles, and
Lichens from Oldman Lake (GLAC). Compounds are ordered by increasing Kow or decreasing polarity.
Concentrations in vegetation decreased gradually with increasing Kow; concentrations in XAD resin
increased to Endosulfan I, then decreased. Differing affinities for SOCs might explain the poor
correlations observed between SOC concentrations in XAD resin vs. vegetation (Table 5-7).
Fish are bioindicators of contaminant exposure because they are often top predators of aquatic
ecosystems and accumulate organic and metal contaminants, usually via the diet (Thomann,
1989). Salmonids (Oncorhynchus spp. and Salvelinus spp. in this study), are often the keystone
aquatic predators, yet they are prey to birds and mammals (Mackay and Fraser, 2000), where
more significant effects of bioaccumulation (because of higher contaminant concentrations) can
be observed. Measurement of contaminants in fish thus indicates impact to the part of the food
web. Piscivorous animals are likely to have higher contaminant concentrations than the fish and
the organisms forming the base of the food web are likely to have lower concentrations.
The potential effects of contaminants on the fish themselves can be determined by identifying
changes in fish biomarkers. By extension, negative effects on fish can warn of harm to the
ecosystem; therefore, changes in biomarkers are considered an early signal of negative effects
(see Figure 5-1) (van der Oost et al, 2003). Fish biomarkers are tools that can be used to
determine the relationship between contaminants and impaired health in individual fish (NRC,
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
1987). Biomarkers fall into three classifications: (1) markers of exposure, (2) markers of effect
and, (3) markers of susceptibility (NRC, 1987). The biomarkers used in WACAP are markers of
effect because they precede and can predict impaired health (NRC, 1987). The utility of
biomarkers hinges on validating them by correlating exposure of chemicals to the change in the
biomarker (NRC, 1987). Impaired health, for our purposes, refers to any relationships identified
between contaminants and the biomarkers, as changes in biomarkers are considered to be an
abnormal response. The difficulty lies in determining what is normal for the animal. Repeated
sampling of the same water bodies over long periods of time in an effort to monitor changes in
biomarkers was not possible. However, we did obtain samples for reproductive biomarkers for
two different years from several of the WACAP sites, which in most cases replicated our initial
results. The sampling strategy used by WACAP was unprecedented in geographic scale and
ecological variability. As such, this strategy prohibited repeated sampling of the same locations
for contaminant concentrations because of the extremely remote nature of the field sites and the
time needed to complete the work.
Laboratory studies have documented the deleterious action of environmental pollutants on biota;
however, the ecological significance or the significance to overall population health is still
largely unknown and is recognized as a limitation on the use of biomarkers (Mills and
Chichester, 2005). Results from laboratory studies are often limited by the fact that
environmentally irrelevant concentrations of chemicals are often needed in laboratory studies to
replicate observations obtained from the field. Recent work, however, has demonstrated that a
mixture of environmental estrogens induced an estrogenic response, even though the individual
contaminants were below the concentrations needed to induce a response on their own
(Rajapakse et al., 2001, 2002). In addition, the "weak" xenoestrogens, dieldrin, endosulfan, and
o,p'-DDT, induced estrogenic responses from 10"9to 10"12 molar concentrations in vitro
(Wozniak et al., 2005). In a recent study of trout in mountain lakes of Europe, Garcia-Reyero et
al. (2007) found that HCB, PCBs, and DDTs were correlated with estrogenic activity in muscle
extracts of the salmonids they examined.
We recognize that extensive laboratory studies testing the same chemicals and mixtures of
chemicals identified in the field samples for estrogenic and reproductive effect would be
desirable. However these experiments were beyond the scope of WACAP. Instead, we followed
the approach used in the USGS Biomonitoring of Environmental Status and Trends Program
(Schmitt and Detloff, 2000) and earlier argued by Ham et al. (1997) to use multiple biomarkers
for ecotoxicology studies. With these guidelines, researchers can approach the topic of immune
or endocrine disruption from a weight of evidence standpoint, by using multiple biomarkers and
co-existent contaminant concentrations. To that end, we used macrophage aggregates, plasma
vitellogenin (Vtg), 11-ketotestosterone, testosterone, estradiol, and gonad, kidney, liver, spleen,
and gill histopathology to look for signs of abnormal changes in fish resulting potentially from
contaminant concentrations. Appendix 5A provides a summary of all fish health measurements
and biomarker values. In this chapter, we focus on results from macrophage aggregate analysis,
plasma Vtg in male fish, and gonad histopathology, and draw upon the remaining biomarkers for
supporting evidence when appropriate.
The rationale for choosing the biomarkers we did was guided by the following objectives.
1. Utilize accepted biomarkers that are sufficiently supported by the scientific literature.
5-24 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
2. Use biomarkers that can be attributed to a specific suite of contaminants for which the
mechanism of action is established.
3. Use biomarkers that are cost effective, and suitable to current laboratory equipment.
4. Use biomarkers that do not affect other project objectives. For example, because biomarker
and SOC analyses were performed on the same fish, it was desirable to remove as little tissue
as possible from the animal so as to not dilute the contaminant signatures.
5. Identify numerous endpoints that can be measured in one tissue (e.g., sex steroids and Vtg
can be quantified in the blood).
Satisfying these objectives is difficult and we acknowledge that all of our biomarkers do not
satisfy every objective. The most important of these objectives were 1 and 4. In terms of number
1, we argue that efforts to develop biomarkers while simultaneously identifying relationships
between those biomarkers and contaminants would have been difficult to achieve. Secondly, our
foremost project objective was to provide fish intact enough to accurately assay contaminant
concentrations. So biomarkers that destroy most of a tissue (e.g., the liver for mixed function
oxidase analyses) would violate objective number 4. With these objectives in mind, we provide
data on the biomarkers listed (see Appendix 5A for all of our measurements). We report in detail
in the following sections on the biomarkers for which we found significant and plausible
relationships from which some level of inference could be drawn. In addition, we report on
biomarkers deemed to be of interest to the scientific community, the NFS, and the public.
5.3.2 Overview of General Fish Health
An important aspect of WACAP was that SOC and Hg concentrations would be determined in
whole fish, as opposed to individual organs (e.g., liver), and that coincident changes in health
could be determined in those same fish. With that in mind, a health-based necropsy procedure,
similar to that used by Adams et al. (1993) was performed on every fish in the field to identify
abnormalities that might or might not be related to contaminant concentrations. As discussed in
Chapter 3, numerous tissues also were removed from the fish, successfully preserved in the field,
and shipped to the laboratory for further microscopic and analytical procedures to assess the
health of the fish. In general, our necropsy procedures did not reveal any gross abnormalities in
the fish captured during these studies. However, numerous lake trout from GAAR were infected
with nematode worms, later determined to be Raphidascaris spp. (Figure 5-5).
The definitive host is the northern pike (Esox lucius), one of which was captured as by-catch, so
the presence of these parasites was not entirely unexpected. External copepod parasites were
found on lake trout from Burial Lake, NO AT, and tapeworms and other unidentified nematodes
were also found in Matcharak Lake fish (Figure 5-6). These parasitic infections probably did not
result from contaminant concentrations. For complete descriptions of the pathologies identified
in the lake trout from the Arctic and the other trout studied in WACAP, see Appendix 5A.
5.3.3 Biomarkers
5.3.3.1 Macrophage Aggregates (MAs)
Macrophage aggregates (MAs) are focal accumulations of pigmented macrophages occurring
primarily in hematopoietic and hepatic tissues of fishes and other poikilothermic animals, and are
thought to be the primitive analogs to mammalian lymph nodes (WoIke, 1992; Agius and
Roberts, 2003). They can also occur in other organs, such as the gonads of fishes captured from
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 5-25
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Figure 5-5. Incidental Pathology Affecting Multiple Organs from Multiple Lake Trout at Matcharak
Lake (GAAR). Nodules on the liver [arrows in (a)] are encysted larval nematodes shown histologically
[arrows in (b)j. Dissection of the nodules revealed numerous Raphidascaris spp. (Nematoda) wet-mount
preparation shown in phase-contrast, bar= 1 mm (c, d). Hematoxylin and Eosin.
5-26
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Figure 5-6. External (white arrows) Copepod Parasites and Internal (black arrow, circle) Parasites
(tapeworms and roundworms) in Lake Trout from Burial Lake (NOAT) and Matcharak Lake
(GAAR), Respectively. Tapeworm on the ruler is representative of the tapeworms denoted by the black
arrow.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-27
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
degraded environments (Blazer, 2002; Patino et al., 2003). Macrophage aggregates are thought to
be storage centers for cellular debris (WoIke, 1992; Agius and Roberts, 2003); therefore,
toxicants that induce tissue damage are likely to be associated with MAs. In terms of physical
appearance, melanin, hemosiderin, and ceroid/lipofuscin are the pigments contained within the
MAs, ranging in color from golden to brown to black in slides stained with hematoxylin and
eosin (WoIke, 1992; Agius and Roberts, 2003) (Figure 5-7). Increases in pigment content are
suggestive of catabolic, infectious, toxic, or otherwise stressful events or exposures (WoIke,
1992; Agius and Roberts, 2003). In a polluted river in Germany, Meinelt et al. (1997) found
positive correlations between liver, kidney, and spleen MAs and mercury in individual pike
(Esox lucius). Handy and Penrice (1993) induced MAs in the rainbow trout kidney by chronic
per os exposure of 10 mg/kg HgCl" over 42 days. Although this is a high dose, it establishes the
proof of concept that mercuric compounds can induce the formation of MAs in salmonid fishes.
This broad application of MAs for assessment offish and environmental health has been well
documented in many fishes.
5.3.3.1.1 Data Analysis
Our objectives were to use MAs as potential indicators of age-dependent contaminants, such as
Hg, where N = 10-25 per lake, but all contaminants were tested for association with MAs. We
also intended to determine if potential among-lake differences in MAs could be associated with
differences in contaminants in those lakes. Among-lake comparisons were made by ANOVA or
Kruskal Wallis, followed by a Bonferroni post hoc at 95% confidence. Levene's test was used to
determine if there was equal variance in MAs between the lakes. Distributions of MAs from the
spleen and kidney of WACAP fish for which there was corresponding SOC and Hg data (N =
8-10 fish per lake) are shown, by lake, in Figure 5-8. Fewer data are available for these
comparisons because SOCs were determined on a subset of the fish where Hg and biological
data were also available. Relationships between contaminants and MAs were made with simple
linear or log regression. Arcsine square-root transformations were used on the MA data and
log 10 transformations on the Hg data. Before we grouped and analyzed all brook trout for
contaminant and MA relationships, we determined if potential co-variates (age, sex, maturation
state, and condition factor) were different among lakes. Percent area occupied by MAs in the
kidney, spleen, and liver was quantified following the method of Schwindt et al. (2006). Liver
MAs are reported in Appendix 5A.
5.3.3.1.2 Results and Discussion
The following comparisons were made on trout where Hg, SOC, and biological data were
available. In the brook trout (TV = 9-10 fish per lake), no significant differences in kidney or
spleen MAs among lakes were found, although a significant main effect suggested that overall
there were differences among lakes in mean arcsine square root transformed spleen MAs
(ANOVA F6;62 = 2.84, P = 0.02), as well as kidney MAs (ANOVA F6j62 = 2.38, P = 0.04). The
Oncorhynchus spp. (TV = 8-10 fish per lake) were analyzed together because different species or
subspecies were captured at each lake. Any differences could be confounded by the potential
differences between species, but at least the genus is the same. Both spleen (^2,25 = 4.75, P =
0.02) and kidney (F2>25 = 6.84, P = 0.004) MAs were higher in Snyder Lake fish than in Oldman
Lake fish, and fish in both GLAC lakes were not different from rainbow trout (Oncorhynchus
mykiss) at Mills Lake, ROMO. In the lake trout (TV = 8-10 fish per lake), arcsine square-root
transformed spleen MAs were significantly elevated in Wonder Lake fish compared to fish in
Matcharak and Burial lakes (F2j25 = 34.81, P = <0.0001) and higher in Matcharak Lake fish than
5-28 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
•-?!%«<% - £3t- /:•
Figure 5-7. Representative Hematoxylin-Eosin Stained Brook Trout Organs Showing the Relative
Difference between Fish with Very Few or No Macrophage Aggregates (MAs) and Extensive
Accumulations of MAs (a-f) and Outlined High Magnification Hepatic MAs (g-i). Bars = 50 jjm; (a)
Kidney with a few MAs; (b) Kidney with extensive MAs; (c) Spleen with a few MAs; (d) Spleen with
extensive MAs; (e) Liver with no MAs; (f) Liver with extensive MAs; (g) High magnification of liver MAs
corresponding to MAs (arrows) in (f); (h) 2X magnification of the MA corresponding to arrow 1 in G; (i) 2x
magnification of the MA corresponding to arrow 2 in (g). The outlined areas in (g) through (i) are the
computer output of delineated MAs based on pigment selection by the computer program in the liver.
Modified from Schwindt et al. (2006).
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-29
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
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Figure 5-8. Mean Percent, %MAs + 95% Confidence Intervals, for Fish with Corresponding Hg and
SOC Data: (a) Spleen, (b) Kidney. Gray bars = brook trout, White bars = rainbow trout, White hatched
bars = cutthroat trout, and gray hatched bars = lake trout. N = 8-10.
5-30
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
in Burial Lake fish. The significance of these among-lake differences remains to be determined;
no contaminant patterns emerged among lakes that could explain differences in the MAs.
In the WACAP lakes with brook trout, mean fish age and condition factor were not significantly
different among lakes (ANOVA p > 0.05). Comparison of median (because of unequal variance)
arcsine square-root transformed spleen MAs in brook trout for which there were biological and
Hg data (n = 10-25 fish per lake) yielded a significant main effect (Kruskal Wallis Test Statistic
= 14.31, p = 0.03), but differences between individual lakes were not detected (Bonferroni).
Average Hg (logio transformed to normalize data) was significantly elevated at LP19 (MORA)
and Hoh Lake (OLYM), compared to Lone Pine Lake (ROMO) and Golden Lake (MORA)
(ANOVA Fe;93 = 4.33, p = 0.0007). The results from these analyses suggest that the observed
among-lake differences in Hg were not consistently related to differences in MAs. Furthermore,
sex did not appear to affect the regressions because there was no difference in MAs between the
sexes (Tsgfemaie, 59maie = 1-12, p = 0.26). Sexual maturation was not different between sites
(ANOVA p > 0.05, Figure 5-9). Finally, the slopes of the among-lake regression lines were not
different for MAs versus age (F^e = 0.81, p = 0.56) or MAs versus Hg (Fi,6 = 1.43, p = 0.21).
This suggests that MAs respond to age and Hg equally among lakes. Based on these results, we
did not identify any confounding factors affecting MAs, Hg, or age among lakes. Therefore, we
grouped the brook trout data for the following regression analysis.
The following results are based on all brook trout where Hg and biological data are available.
Knowing that MAs are correlated with age in these fish (Schwindt et al, 2006), we identified a
suite of contaminants thought to be associated with MAs based on their suspected age-
dependence. Total whole body Hg was chosen because it was also associated with age, although
all contaminants analyzed in WACAP were screened for potential associations to MAs. In our
results, Hg was positively associated with spleen MAs and age, with the strongest relationships
observed in brook trout (Fi;98 = 82.82, p < 0.0001, R2 = 0.45) (Figure 5-10a). In the brook trout,
positive relationships were also found between MAs and the £PCBs (Fl,68 = 26.04, p < 0.0001,
R2 = 0.28) and the £PBDEs (FM8 = 17.14, p = 0.0001, R2 = 0.20). As mentioned previously,
MAs have been associated with pathogenic infections of microbes in fish. However, in our
analysis of MAs in fish from WACAP, MAs were not influenced by the presence of parasites, or
evidence of other infectious agents (e.g., bacterial kidney disease-like granulomas). That is, MAs
were not concentrated around the parasites or granulomas, nor were they more abundant in fish
with parasite infestations.
To further delineate the relationship between MAs and Hg, we searched for age-independent
associations between MAs and Hg. To do this, we divided our datasets into age classes prior to
regression analysis for every species offish where data were available. The best relationships we
found were in the 4- to 6-year-old brook trout with significant age-independent increases in MAs
and Hg (FM6 = 26.27, p < 0.0001, R2 = 0.36) (Figure 5-1 Ob). In the 1 to 3- and 7- to 13-year-old
trout, only weak relationships, if any, were identified (see Appendix 5C). Dividing the 7- to 13-
year-old classes further did not yield any significant relationships (data not shown). Appendices
5B and 5C contain tables of regression statistics for all permutations performed on the data to
search for relationships between Hg, MAs, and age.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 5-31
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
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Figure 5-9. Average Stage of Gonad Maturation ± 95% Confidence Intervals in Trout for which
Corresponding SOC and Hg Data are also Available. N = 2-11; M = male; F = female; (a) lake trout,
fall spawn, (b) brook trout, fall spawn, (c) cutthroat and rainbow trout, spring spawn.
5-32
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
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by Age Group:
• ••• 1-3 years
^^ 4-6 years
7-13 years
50 100 150 200 250
Whole-body Total Mercury (ng/g ww)
300
Figure 5-10. Co-Linearity between (a) Splenic MAs, Hg, and Age in Brook Trout, and (b) Log-Linear
Relationships between Hg and Brook Trout (Salvelinus fontinalis) Splenic MAs. See Appendix 5C
for regression statistics. Some data are overlapping and x-axis data in (a) are offset slightly.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-33
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Incorporation of Hg varies between life history stages of salmonid fishes (Mathers and Johansen,
1985). Mercury accumulates rapidly during periods of fast growth and increased food consump-
tion (MacCrimmon et al., 1983). Mercury has been associated with MAs (Handy and Penrice,
1993); during periods of fast growth, MAs and Hg increase independently of age, as was
observed in our results for 4- to 6-year-old brook trout. Nevertheless, the well-established fact
that MAs increase with age has not been explained (for review see Agius and Roberts, 2003).
Macrophage aggregates are more likely to result from the bioaccumulation of toxicants, and
long-term multiple hits by acute stressors, than merely a side effect of aging and senescence
alone. This phenomenon is comparable to the "liver spots" that form on fair-skinned humans.
Liver spots (senile lentigines) develop in exposed regions of the skin of older persons because of
long-term cumulative effects of UV radiation, not simply because of age itself (Porta, 2002).
Lake trout (from DENA, GAAR, and NO AT) represented a much smaller data set than brook
trout, and their environment is considerably different from the lakes of the other trout species.
The lake trout were captured from much larger lakes that contained more fish species than lakes
in lower 48 states (see Table 5-2 for species). In addition, the lake trout were relatively older,
with several individuals > 15 years. Significant relationships between Hg and MAs were found
only in lake trout < 20 years old (Appendix 5C). Mercury concentrations decreased in lake trout
> 20 years from the Arctic (GAAR and NO AT), yet, as with other fishes, MAs increased with
age throughout the entire age structure (Appendix 5C).
Some data suggest a decline in MAs following exposure to contaminants (Payne and Fancey,
1989; Bucke et al., 1992). However, those results might have been confounded by movement of
fish in and out of contaminated areas such as polluted bays or estuaries. Lakes in our study, aside
from those in Alaska, were relatively small (< 20 km surface area), and thus fish cannot migrate
in or out of potentially contaminated areas. Therefore, changes in MAs in relation to contamin-
ants are not confounded by migration and are more likely related to individual responses to the
contaminants and/or other stressors. Several explanations, none conclusive, might account for the
breakdown of the relationship between of MAs and Hg in older lake trout.
First, eventual sequestration of Hg in the muscle tissue might render Hg less "bioavailable" to
the kidney, liver, or spleen, thus MAs decline subsequently. This observation holds, to a certain
extent, for the fairly old brook trout as well. In 7- to 13-year-old brook trout, Hg was rather high,
but no changes in MAs were observed (Figure 5-10b). Second, Hg is lethal to rainbow trout at
exposures of 10-20 ug/g and toxic to fish at 1-5 ug/g (Niimi and Kissoon, 1994). It is con-
ceivable that lake trout with higher Hg concentrations died disproportionately, and that the older
fish are represented by those that have experienced less exposure to Hg. Third, ecology changes
in food consumption might account for the rapid, non-age-associated changes in contaminant
concentrations. To our knowledge, there are no studies that describe the fate of MAs after the
stressor has been removed. Therefore, a fourth possibility is that MAs persisted long after the
cause had ceased or declined in the old lake trout. Finally, these older fish might be subjected to
a different MA-inducing stressor that younger fish that we are not aware of. We observed some
interesting relationships when the lakes with lake trout were studied individually. In the lake
trout from Wonder Lake, DENA, no relationship was found between MAs and age, thus the
entire data set was used to determine positive correlations between MAs and Hg (Appendix 5C).
In this case, MAs were significantly correlated with Hg.
5-34 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Rainbow trout were collected only from Mills Lake, ROMO. The age distribution from this
population was restricted; most of the fish were between 4 and 6 years of age, thus it was not
possible to test other age classes. Nevertheless, the entire dataset was used, and MAs were
significantly related to age, as demonstrated in an earlier publication (Schwindt et al., 2006), as
well as to Hg (Appendices 5B and 5C) in this lake. Similarly, in the cutthroat trout (O. clarki
lewisi and O. clarki bouvieri) from GLAC, it was not possible to test numerous age classes and
MAs were related to both age and Hg (Appendices 5B and 5C).
Mercury is toxic to fish at relatively low concentrations (Niimi and Kissoon, 1994). Exposure of
fishes to Hg affects numerous physiological endpoints, including histopathological indicators
such as MAs. For example, Handy and Penrice (1993) illustrated numerous histological changes
in addition to increased kidney MAs following Hg exposure in the laboratory. Corresponding Hg
concentrations in the kidney ranged from approximately 2 to 9 (j,g/g (Handy and Penrice, 1993),
which is within the realm of whole body concentrations found in WACAP. Mercury concentra-
tions in individual organs and muscle tissue in the trout from this study would undoubtedly be
much higher, as analysis of whole fish dilutes the Hg sequestered in target organs and skeletal
muscle. In blue gourami (Trichogaster trichopterus) fed 9 ppb of methyl-Hg and exposed to viral
and bacterial pathogens, the hemosiderin bodies, associated with MAs, were infiltrated by the
white pulp of the spleen and decreased antibody production was also observed (Roales and
Perlmutter, 1980). The reduction in hemosiderin was attributed to the infectious agents, not Hg,
and the authors concluded that Hg induced immuno-suppression as indicated by reduced
antibody production (Roales and Perlmutter, 1980). In the flounder (Platichthys flesus), Pulsford
et al. (1992) observed localization of metals in splenic MAs, but no associations were made
between the metals and changes in MAs.
In summary, we demonstrate the association between MAs and Hg for brook trout, given the
large geographic area sampled. This suggests that spleen MAs might be ideal sentinels for
assessing the incorporation of Hg, or other largely age-dependent pollutants, into the food web.
We do not generalize beyond brook trout, however, because of the small sample sizes and the
limited geographic areas from which we collected the lake, rainbow, and cutthroat trout. Within
lakes (other than those in the Arctic), Hg and age both explained significant amounts of variation
in MAs. At present, we cannot explain why MAs were higher in lake trout from Wonder Lake,
DENA, compared to the fish from the Arctic lakes (GAAR and NO AT). Nor can we explain why
MAs were higher in fish from Snyder Lake, GLAC. We recommend continued Hg monitoring in
whole fish. Sampling offish for MAs could easily be conducted along with Hg sampling.
5.3.3.2 Vitellogenin in the Blood of Male Fish
Vitellogenin is an egg-yolk precursor protein synthesized in the liver of oviparous animals in
response to estrogen, or xenoestrogen (Arukwe and Goksoyr, 2003). Male and immature female
oviparous animals have the capacity to produce Vtg, but endogenous estrogens are rarely present
in sufficient concentrations to induce appreciable amounts of plasma Vtg (Sumpter and Job ling,
1995). Thus, when Vtg is observed in the plasma of male or immature female fish, it suggests
exposure and response to a xenoestrogen (Purdom et al., 1994; Job ling et al., 1996, 1998). These
features have led to Vtg becoming a biomarker for environmental estrogen exposure and used as
such in many studies (e.g., Purdom et al., 1994; Jobling et al., 1996, 1998; Harries et al., 1997;
Christiansen et al., 1998; Gronen et al., 1999; Mills et al., 2001, 2003; Zaroogian et al., 2001;
Palace et al., 2002; Sepulveda et al., 2002; Kavanagh et al., 2004; Feist et al., 2005).
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 5-35
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Metabolism of DDT. It is apparent from these data that
certain metabolites of contaminants are present in higher
concentrations than the applied or active ingredient. For
example, p,p'-DDE, one of the DDT metabolites, is
present in higher concentrations in fish than DDT itself.
The p,p'- and o,p'- isomers of DDT are metabolized in fish
(and in the soil and lake sediment) to ODD and DDE. The
most persistent of these is DDE and ODD is found only in
very minute concentrations because is it rapidly
metabolized.
In fish (and other vertebrates) DDT metabolism occurs in
the liver, where many foreign and endogenous
compounds are metabolized, as well as the blood. The
liver contains the cytochrome P450 (CYP) enzyme family
that is responsible for the oxidative metabolism of many
substances, including DDT. There are many CYP enzyme
sub-families and they reside in microsomes, which are
vesicles that break off of cellular organelles. The CYP
enzymes degrade DDT by dechlorination and
dehydrogenation and eventually DDE (the most persistent
metabolite) is broken down to the water-soluble DDA and
is excreted from the animal. There is also evidence for
non-enzymatic degradations.
DDT
Proton
donor
Liver
ODD
Environmental estrogens (xenoestro-
gens) are artificially produced chemicals
that mimic the estrogens produced in
animals. The proposed mechanism of
action is similar to that of the native
l?p-estradiol (E2). That is, xenoestro-
gens bind to hormone receptors and
either block or initiate the action that
would otherwise be controlled by
endogenous estrogens. The relative
"strength" of xenoestrogens, measured
by the concentration needed to displace
endogenously produced E2 from
receptors, varies considerably. The birth
control hormone, 17a-ethynylestradiol,
is about 10 times stronger than E2, and
most other chemicals (e.g., DDT,
bisphenol A, alkyphenol ethoxylates)
are weakly estrogenic, requiring 103to
106 times the E2 to occupy the receptor
(Zava et al, 1997; Kloas et al, 2000).
Vitellogenin is the most abundant
downstream product of estrogen
receptor activation and is specific to E2
and estrogen-like chemicals. The
(in)activation of hormone receptors by
anthropogenic chemicals is called endo-
crine disruption, the consequences of
which are currently being debated in the
scientific community. Recent evidence
suggests that exposure to the birth-
control hormone 17a-ethynylestradiol
led to a population crash of the fathead
minnow (Pimephales promelas) (Kidd
et al., 2007).
Endocrine disruption is not limited to
the actions of xenoestrogens; other
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^~ hormonal pathways can be disrupted by
different chemicals. For example, the brominated flame retardants affect thyroid hormone
pathways (Legler and Brouwer, 2003; Jahnke et al., 2004). Polyaromatic hydrocarbons and other
aryl-hydrocarbon receptor agonists (such as dioxin) are anti-androgenic (Safe, 1994). The
banned organochlorine pesticide DDT (and its metabolites) is a well-known endocrine disrupter
(Colburn et al., 1993) that binds to estrogen receptors and initiates cellular changes, such as the
synthesis of proteins, such as Vtg. The degradation products of the current-use pesticides
endosulfan and methoxychlor are also weakly estrogenic and have been detected in the fish in
this study. Although not target analytes in WACAP, detergent additives, plasticizers, birth
Blood
ci Proton Heme
^Dehydrochlorinase
(Insects)
Dehydrogenase
DDE
Proposed Mechanisms of DDT Metabolism. Redrawn
from Kitamura et al. (2002).
5-36
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
control hormones, food additives, phytoestrogens (Kuiper et al., 1998; Bennetau-Pelissero et al,
2004; Fox, 2004), and some personal care products are also weakly estrogenic.
5.3.3.2.1 Data Analysis
Our objectives for the use of blood plasma Vtg in WACAP were to screen male fish for potential
response to estrogen-like compounds and relate the Vtg to the contaminant concentrations. We
assumed that male fish do not normally produce Vtg because circulating levels of endogenous
estrogens are very low (10s to 100s ppt) and are not thought to induce Vtg. For comparison, 10
to 100 times as much E2 is needed in females to significantly increase Vtg levels. Despite this, all
male fish in WACAP were analyzed for E2 to be sure that endogenous estrogens were not
influencing the Vtg levels. Indeed, in our results, male plasma E2 fell within the above range
(Appendix 5A), and was not different than the E2 in male fish with low, or non-detectable Vtg,
indicating that fairly high Vtg levels in some male trout were not caused by endogenous estro-
gens. Based on the ranges of concentrations in the literature from polluted and reference sites and
laboratory studies, we consider Vtg above 1 ppm to be abnormal in male fish. To explore
potential relationships between Vtg and suspected or known estrogenic contaminants identified
within those same fish, we performed regression analysis on fish for which contaminant concen-
trations were available and >1 fish exhibited elevated Vtg. Regression analysis could not be
performed on the fish from GLAC because only 1 fish from each lake displayed fairly high
levels of Vtg and, therefore, one data point would anchor the regression line. Vitellogenin
concentrations were determined following the method of Schwindt et al. (2007).
5.3.3.2.2 Results and Discussion
The National Park Service is concerned about contaminant effects (e.g., changes in biomarkers)
on populations as well as on individuals. Therefore, we plotted the Vtg concentrations in male
fish as a scatterplot and as a mean, so that individual concentrations could be easily visualized
with respect to the average (Figure 5-11). Of note were numerous sites from parks in the Rocky
Mountains that had appreciably higher levels of Vtg than the other sites, which were at least an
order of magnitude lower if not non-detectable (Figure 5-11). Results from our samples obtained
in 2003 from ROMO prompted a subsequent study to sample additional waters during the
summers of 2005 and 2006, as well as to repeat the work performed in 2003. Additional waters,
Sprague Lake in 2005 and Spirit Lake in 2006 (both in ROMO), with male fish displaying fairly
high Vtg were discovered, and in 2006, we repeated the findings obtained at Lone Pine Lake in
2003. Spirit Lake is in the same drainage, but upstream from Lone Pine Lake, and does not have
campsites, reducing the influence of localized use. Also, two male fish from GLAC and one male
fish from Golden Lake, MORA, displayed elevated levels of Vtg (Figure 5-11). In lakes sampled
in 2005, Vtg gene expression in the liver was also evaluated. No Vtg gene expression was
evident (Biales, pers. comm.); therefore, it appears that the cause of the slightly elevated Vtg we
found was probably exposure to something not active around the day of fish collection. Although
the sample sizes were very small, significant correlations between known or suspected estrogenic
contaminants and Vtg in both lakes sampled at ROMO in 2003 were found (Figure 5-12).
Although statistical analysis could not be performed, the fish from Oldman Lake, GLAC, was
the only fish in WACAP to have detectable concentrations of o,p'-DDT, as well as the highest
concentrations of p,p'-DDT, well-known endocrine disrupters. These results suggest that not
only are Vtg levels in certain trout elevated relative to within-lake counterparts, they are also
related to concomitant increases in estrogen-like contaminants.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 5-37
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
20
io ^
6
4
1 ol
J! 0.4
£ 0.2
0.1 ^
0.06
0.04
0.02
0.01 ]
0.006
o
o
o
o
o
8
o
O
o
o
AK
Parks
Western
Parks
Rocky Mountain Parks
< iJbo i°> \J nTN £ K
K
Site
Figure 5-11. Mean Vitellogenin + 95% Confidence Intervals and Concentrations from Individual
Male Trout from Lakes or Streams in National Parks in the Western United States and Other Sites.
Bars are shaded to denote region. N = 3 to 75, depending on location, and is listed for each field site after
the site name. Black data points are intersex males. Vtg concentrations determined following Schwindt et
al. (2007). Data are plotted on a Iog10 scale and there is some data overlap.
5.3.3.3 Intersex in Male Fish
Intersex, the presence of both male and female reproductive structures in the same animal, is a
commonly used biomarker of estrogen-like chemical exposure in gonochoristic fishes, such as
salmonids. Many fishes are natural hermaphrodites. The process is essential to the reproductive
life histories of numerous families offish, and it has been argued that there is a "baseline" level
of intersex, even in gonochoristic fishes (Devlin and Nagahama, 2002; Sumpter and Johnson,
2005). Regardless, the underlying assumption that trout, by genetic determination, are phenol-
typically male or female implies that any deviation (i.e., intersex) from that is abnormal (Bortone
and Davis, 1994). Although this might be true, the difficulty lies in attributing the abnormality to
some random genetically or environmentally induced baseline level or to the effect of estrogen-
like compounds, as in the case of reports of feminized male fish in the scientific literature (e.g.,
Jobling et al., 1998; Woodling et al., 2006). Even in the laboratory, consistent induction of
intersex with "weak" estrogens is difficult (Carlson et al., 2000).
5-38
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
(a)
(b)
28
26 ]
24
22
20 -I
18
16 ]
14
12
10 ]
8
6
4
2
0
Mills Lake Male Rainbow Trout
o
•o o-
O.UU
2.75 -
2.50 -
2.25 -
2.00 -
1.75 -
1.50 -
1.25 -
1 riri
I .UU
0.75 -
0.50 -
0.25 -
n nn
Lone Pine Lake Male Brook Trout o
9
r, *
Q>
(C)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Endosulfan Sulfate (ng/g) Wet Weight (d)
0 2 4 6 8 10 12 14 16 18 20 22
cis-Nonachlor (ng/g) Lipid
2.75 -
2.50
2.25 -
2.00 -
1.75 -
1.50 -
1.25 -
0.75 -
0.50 -
0.25
n nn
Lone Pine Lake Male Brook Trout o
*
cP
CD
2.75 -
2.50 -
2.25 -
2.00 -
1.75 -
1.50 -
1.25 -
1 nn
I .UU
0.75 -
0.50 -
0.25
n nn
Lone Pine Lake Male Brook Trout °
_
0 0
100 200 300 400
Sum DDXs (ng/g) Lipid
500
0 20 40 60 80 100 120 140
Sum PCBs (ug/g) lipid
Figure 5-12. Scatterplots Comparing Suspected Endocrine Disruptors and Plasma Vitellogenin
(Vtg), a Commonly Used Indicator of Estrogenic Contaminants in Male Trout. For the bottom two
graphs, "Sum" indicates that individual contaminant concentrations were added together to arrive at the
sum concentration. For DDT, the o,p'- and p,p'- isomers of DDT, ODD, and DDE were summed. For the
PCBs, congeners 74, 101, 118, 153, 138, 187, and 183 were summed. The data point in black is an
intersex male trout. The dashed line is the quantitation limit for the assay. N = 4 for Mills Lake and N = 6
for Lone Pine Lake. For Mills Lake, Vtg v. Endosulfan sulfate F,,2 = 78.38, R2 = 0.97, P= 0.012. For Lone
Pine Lake, Vtg v. cis-Nonachlor F-\ 4 = 77.49, R2 = 0.95, P = 0.0009. Lone Pine Lake Vtg v. £PCBs F-\ 4 =
100.66, R2 = 0.96, P= 0.0006. Lone Pine Lake Vtg v. £DDTs F1>4 = 645.33, R2 = 0.99, P< 0.0001.
Yet, intersex has been used as a biomarker by numerous researchers (Vigano et al., 2001;
Gercken and Sordyl, 2002; Kirby et al., 2004) and is considered the most reliable biomarker of
reproductive abnormalities. This suggests that intersex should be validated, as well as possible,
for study organisms. However, validation of intersex in trout is more difficult than for Vtg. It is a
time-consuming endeavor and, to our knowledge, has not been attempted by other researchers. In
an effort to validate the use of intersex in trout as a biomarker, we obtained trout from the
University of Washington School of Fisheries in Seattle and from the California Academy of
Sciences in San Francisco to determine if intersex could be observed in fish captured before the
large-scale anthropogenic emission of the endocrine disrupting compounds (pre-1940s). The
sites, species, sex ratio, and number intersex for every trout analyzed in these studies are shown
in Figure 5-13 and Table 5-8.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-39
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Figure 5-13. Counties or Boroughs (Alaska) Where Museum (white boxes) and/or WACAP (black
boxes) Fish Samples Were Collected and Gonads Analyzed for Sex and Intersex. The key to the
numbers on the map, and the numbers of male, female, and intersex fish is in Table 5-8.
5-40
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-8. Characteristics of
Map#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
State
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
County
Glacier
Glacier
Glacier
Glacier
Glacier
Glacier
Glacier
Glacier
Glacier
Glacier
Glacier
Garfield
Grand
Grand
Grand
Grand
Larimer
Larimer
Larimer
Larimer
Larimer
Larimer
Larimer
Larimer
Alamosa
Rio Grande
Lake
Lake
Intersex Trout Analyzed from Current and Historic Sampling.
Location
Middle Fork, Flathead River
Coal Creek
Fish Creek
Arrow Lake
Lincoln Creek beaver ponds
Trout Lake
Park Creek
Isabel Lake outlet
Lower Snyder Lake
Lower Snyder Lake
Oldman Lake
Trappers Lake
Lone Pine Lake
Lone Pine Lake
Spirit Lake
Haynach Lake
Mills Lake
Mills Lake
North Fork, Big Thompson R.
Lake Haiyaha
Poudre Lake
Sprague Lake
Sprague Lake
Dream Lake
Conejos River
Rio Grande
Twin Lakes
Arkansas River
Park
GLAC
GLAC
GLAC
GLAC
GLAC
GLAC
GLAC
GLAC
GLAC
GLAC
GLAC
ROMO
ROMO
ROMO
ROMO
ROMO
ROMO
ROMO
ROMO
ROMO
ROMO
ROMO
ROMO
Year
1934
1934
1934
1934
1934
1934
1934
1934
1934
2005
2005
<1871
2003
2006
2006
2006
2003
2006
2005
2005
2005
2005
2006
2006
1889
1889
18891
1889
Species
Oncorhynchus clarki subsp.
O. clarki subsp.
O. clarki subsp.
O. clarki subsp.
O. clarki subsp.
O. clarki subsp.
O. clarki subsp.
O. clarki subsp.
O. clarki subsp.
O. clarki lewisi
O. clarki bouvieri
O. clarki pleuriticus
Salvelinus fontinalis
S. fontinalis
S. fontinalis
O. clarki bouvieri
O. my kiss / clarki
O. mykiss / clarki
S. fontinalis
O. clarki
S. fontinalis
S. fontinalis
S. fontinalis
O. clarki stomias
O. clarki virginalis
O. clarki virginalis
O. clarki stomias
O. clarki stomias
M/F
0/1
2/2
1/2
2/0
3/6
3/2
1/2
1 /1
5/2
9/6
11 /4
0/1
7/8
10/20
9/6
9/6
6/9
16/11
8/7
7/8
7/9
11 /5
4/8
3/12
2/1
2/0
3/0
2/0
Intersex
0
0
0
0
0
0
0
0
0
0
1 Male
0
1 Male
1 Male
1 Male
2 Male
0
0
0
1 Male
0
0
0
1 Male
0
0
2 Male
0
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-8. Characteristics of Intersex Trout Analyzed from Current and Historic Sampling (continued).
Map#
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
State
CA
CA
CA
CA
CA
CA
CA
CA
CA
UT
ID
WA
WA
WA
WA
WA
WY
AK
AK
AK
AK
County
Tulare
Tulare
Tulare
Tulare
Tulare
Tulare
Mariposa
Plumas
Shasta
Utah
Cassia
Pierce
Pierce
Clallum
Clallum
Clallum
Teton
NW Arctic
North Slope
Denali
Denali
Location
Volcano Creek
Golden Trout Creek
Golden Trout Creek
Cottonwood Lakes
Emerald Lake
Pear Lake
near Merced Lake
Gold Lake
Fall River
Provo River
Cottonwood Creek
LP19
Golden Lake
PJ Lake
PJ Lake
Hoh Lake
Pacific Creek
Matcharak Lake
Burial Lake
McLeod Lake
Wonder Lake
Park
SEKI
SEKI
YOSE
MORA
MORA
OLYM
OLYM
OLYM
GAAR
NOAT
DENA
DENA
Year
1891
18931
1904
1912
2003
2003
1921
1899
1898
18891
1894
2005
2005
2003
2005
2005
1891
2004
2004
2004
2004
Species
O. mykiss aguabonita
O. mykiss aguabonita
O. mykiss aguabonita
O. mykiss aguabonita
S. fontinalis
S. fontinalis
S. fontinalis
O. mykiss
O. clarki
O. clarki virginalis
O. clarki lewisi
S. fontinalis
S. fontinalis
S. fontinalis
S. fontinalis
S. fontinalis
O. clarki
S. namaycush
S. namaycush
Lota lota
S. namaycush
M/F
1 /O
7/1
2/0
0/2
11 /5
14/3
1 /1
1 /O
1 /1
1 /1
0/1
9/6
12/3
4/11
5/10
10/5
2/0
7/8
8/7
2/0
8/7
Intersex
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Collection date is inferred from information associated with the specimens.
5-42
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
5.3.3.3.1 Data Analysis
Our objectives were to identify abnormalities in the gonads of male and female fish. In the event
that abnormalities were identified, we searched for possible hypothesis-generating relationships
to aid in interpreting the significance of the abnormalities. To find the intersex fish, evidenced by
ova-testis, or other abnormalities, we scanned the entire gonad section, beginning with 100X total
magnification, by compound light microscopy. When oocytes were visualized in the testis, we
increased the magnification to 400X to confirm and capture digital images. In some instances,
25X or 50X total magnifications were sufficient to observe the maturing ova present in the testis.
To determine the extent of testicular abnormalities, we developed a grading system, similar to
that developed by Jobling et al. (1998), for the degree of abnormality observed in the male
gonads. The testes observed in these studies are characterized as the following: (1) normal testis,
(2) poorly developed testis for the size of the fish (i.e., does not show signs of reproductive
maturity), (3) normally developing testis with primary or perinucleolar oocytes, and (4) poorly
developed or degenerative testis with perinucleolar oocytes and/or vitellogenic oocytes (Figure
5-14). The numbers offish we observed in each category, separated by geographic region and
current or historic sampling, are listed in Table 5-9. The proportion of current and historic sites
with intersex fish in the Rocky Mountains was compared with Fisher's Exact Test and the results
are reported in Table 5-10.
Figure 5-14. Categories of Relative Gonad Abnormality: (a) Normal immature testis, (b) Poorly
developed testis or degenerative testis, for the size of fish, (c) Normally developing testis with
primary or perinucleolar oocytes (inset is the magnified oocyte at the arrow), and (d) Poorly
developed testis with perinucleolar oocytes and/or vitellogenic oocytes (arrows). Hematoxylin and
Eosin; bars = 50 |Jim.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-9. Categorization of Trout Testes by Abnormality, Geographic Region, and
Current or Historic Sampling.
Testis Category
Region
Sample
Total Males
Rocky Mountains
Sierra Nevada
Olympics/Cascades
Denali, Alaska
Arctic Alaska
Current
Historic
Current
Historic
Current
Historic
Current
Historic
Current
Historic
117
30
25
12
40
1
10
0
15
0
107
28
25
11
40
1
10
15
2
0
0
0
0
0
0
0
5
2
0
0
0
0
0
0
3
0
0
0
0
0
0
0
Table 5-10. Comparison of Sites with Intersex Fish from the Rocky Mountains.
Dates
1871-1934
2003-2006
Intersex
1
6
Not Intersex
13
5
Proportion
0.07
0.54
p (Fisher's Exact Test)
0.0213
5.3.3.3.2 Results and Discussion
In the historic samples, we dissected a total of 85 trout, finding 42 males, 28 females, 2 intersex
males (counted as male), and 15 fish specimens for which we could not identify sex because of
the poor quality of the specimen. Of note, we obtained westslope cutthroat trout samples from
the University of Washington captured in 1934 from Lower Snyder Lake, the same lake we
sampled from GLAC in 2005. In both the current and the museum samples, intersex trout from
this lake were not found (Figure 5-13; Table 5-8). In the current samples, 207 total male fish
were sampled and 8 of 117 male fish in the Rocky Mountains were intersex. The within-lakes
frequencies ranged from 9% to 33%, and 50% of these intersex fish also produced elevated
concentrations of Vtg.
We found two historic samples collected in the late 1800s from Twin Lakes, Colorado, in the
Rocky Mountains that were intersex (Figure 5-15). This lake is quite close to ROMO. It is also in
the vicinity of where extensive heavy metal mining took place in the 1800s. This is the earliest
known intersex trout, to our knowledge, and a noteworthy finding in and of itself. We are
unaware of the work showing that metals can lead to intersex among teleosts fishes, but there is
evidence that cadmium has estrogenic properties (Johnson et al., 2003). There is also evidence
that elevated concentrations of some heavy metals such as cadmium can cause testicular injury
(Sangalang and O'Halloran, 1972, 1973). Our observations of intersex male trout in very old
specimens, long before the manufacture of organic contaminants, warrants a more extensive
investigation into the depth and breadth of this phenomenon, albeit it is beyond the scope of
WACAP. Reeder et al. (2005) analyzed the gonads of 814 cricket frogs (Acris crepitans) dating
from 1852 to 2001 and found that the frequency of intersex fluctuated with relative
anthropogenic input to the environment.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Figure 5-15. Intersex Male Greenback Cutthroat Trout from Twin Lakes, Colorado, Captured in the
Late 1800s. Primary oocytes (arrows) are surrounded by normally developing testis, indicating category c
gonad abnormality (see Figure 5-14). Hematoxylin and Eosin; bar = 50 urn.
While the frequency of intersex trout we found across the West and in Alaska is quite low, it
appears concentrated in the Rockies where the sites with intersex fish today is significantly
greater in number than in the past (Table 5-9). The severity of abnormalities observed in the
current samples is also greater (Table 5-9). All three intersex trout from Lone Pine and Spirit
Lakes displayed category 4 gonad abnormalities, and had low androgen and estrogen levels and
elevated levels of Vtg during the time of year when the other trout were nearing sexual maturity.
Based on this information, we argue that these individuals are incapable of reproduction,
whatever the cause. Identifying the cause of these observations is impossible with the current
dataset. If similar numbers of fish from the museums were sampled, then the possibility of
finding similar numbers of category 4 testis abnormalities is definitely possible.
The intersex fish at Lone Pine Lake in 2003 (Figure 5-16a-c) had the second highest concentra-
tions of p,p'-DDT and 10 to 100 times the dieldrin, both documented endocrine disrupters, of the
other fish analyzed in WACAP. This finding implies that contaminants are at least influencing
the reproductive health of that individual. Furthermore, that similar levels of disruption were
found at Lone Pine Lake in 2003 and 2006 indicates that the observed abnormalities are not
transient phenomena. The consequences of 1 in 7 and 1 in 10 males in 2003 and 2006,
respectively, not reproducing, if indeed they cannot reproduce, are unknown. Long-term
monitoring is needed to determine if the population is stable. The intersex fish at Oldman Lake,
GLAC, was category 3 (Figure 5-16d), had elevated Vtg levels, and had the highest concentra-
tions of chlordanes (cis, trans, oxy and nonachlors), the dioxin-like PCB 118, and was the only
fish in the study with detectable concentrations of o,p'-DDT, also a confirmed xenoestogen. This
fish also had normal levels of plasma androgens and, aside from being intersex, had normally
developing gonads.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Figure 5-16. Intersex Male Trout from Lone Pine Lake, ROMO (a-c) and Oldman Lake, GLAC (d).
The insets (a-c, 400x), areas denoted by the corresponding letters and arrows on the low magnification
image (composite, 50x), depict perinucleolar oocytes surrounded by a poorly developed testis. In (d)
primary oocytes (arrows) are surrounded by normally developing testis (composite, 400x). Hematoxylin
and Eosin; bars = 50 urn.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
In summary, the use of intersex as biomarker for endocrine or reproductive disruption is not
perfect; however, our studies signify the importance establishing a baseline to which future
sampling can be compared. Intersex had not been observed, to our knowledge, in trout from
ecologically protected areas. In the scientific literature, increased frequencies or severity of
intersex is highly suggestive, if not indicative, of reproductive dysfunction (Devlin and
Nagahama, 2002), whatever the cause. Our recommendations are to continue monitoring lakes in
the Rocky Mountains, including ROMO and GLAC, for Vtg, sex steroids, and gonad
abnormalities. In addition, we recommend that sampling be expanded to include Grand Teton
and Yellowstone National Parks, as they are roughly mid-way between ROMO and GLAC.
Other parks in the Rocky Mountains could consider initiating similar monitoring programs.
These future efforts, in coordination with expanded sampling of museum specimens, could help
provide resource managers with the information necessary to determine if biomarkers of
reproductive endocrine disruption are changing through time.
5.3.3.4 Interpretation and Integrated Analysis: Emphasizing Park and Regional Differences in
Biomarkers and Contaminants
Our analysis of biomarker responses and their relationship to contaminant concentrations
focused on effects on trout in general. That is, we identified relationships between MAs and Hg
in brook trout from all the parks. However, we have not explained among-lake differences in
MAs, for the lake trout and Oncorhynchus spp. and how they might relate to contaminant
concentrations. The question that has not been addressed is: Are MAs higher in fish with higher
contaminant concentrations? At present we cannot answer this question because no readily
apparent statistical relationships emerged (because of small sample sizes). In other analyses, we
have related contaminants suspected to have estrogenic action to biomarkers that respond to
estrogenic contaminants. One question that arises from these analyses is: Why is the evidence for
endocrine disruption (Vtg and intersex male fish) confined to parks in the Rocky Mountains?
The following paragraphs are intended to address this question as best possible, considering the
small sample sizes and subtle among-park and regional differences.
5.3.3.4.1 Biomarkers Related to Reproductive Disruption
Vitellogenin persists in the blood for about 2 weeks following an acute induction (Schultz et al.,
2001), so the window of detectability is limited for this biomarker. For Emerald and Pear Lakes
(SEKI), where some contaminant concentrations were higher than those of Lone Pine Lake
(ROMO) and Golden Lake (MORA) (Figure 5-17), the absence of Vtg could be related to the
absence of estrogenic contaminants in the blood. Without mobilization of contaminants from fat,
or ingestion of contaminated food, it is unlikely that elevated Vtg would be observed, despite
higher contaminant concentrations in SEKI. So, one reason for not observing elevated Vtg in
SEKI is that the fish were sampled outside the window of sensitivity to the contaminants or the
biomarker. The lakes with Oncorhynchus spp. (Mills Lake, ROMO, and both lakes in GLAC) all
had fish with elevated levels of Vtg. Our analysis of contaminant sums by class (e.g., the
chlordanes) revealed subtle differences in mean concentrations (generally higher at Oldman
Lake, GLAC), but Vtg levels in the rainbow trout (from Mills Lake, ROMO) were generally
higher than in the other species. This could be a result of the species-specific sensitivity to the
chemicals, including the contaminants measured in WACAP, as well as those not measured. An
alternative explanation is that the polyclonal antibody used in the assay was generated against
rainbow trout Vtg (see Schwindt et al., 2007), so these antibodies might recognize more epitopes
on the rainbow trout Vtg than on the Vtg in the other salmonids.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 5-47
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
(a)
(b)
0)
.£
re
PJ
Hoh
Golden
LP19
Lone Pine
Pear
Emerald
(c)
Snyder
Oldman
Mills
{ c brook trout
! c
b
b
b
a.b
a
0 20 40 60 80 100 120
Sum Endosulfans ng/g lipid
f rain bow &
b cutthroat
trout
a
b
PJ
1
Hoh> f •
Golden ft
LP19 (|
Lone Pine
Pear
Emerald
-•
0
b brook trout
b
b
b
b
a,b
a
1234
(d)
Snyder
Oldman
rainbow &
cutthroat
trout
40 80 120 160
Sum Chlordanes ng/g lipid
200
0.5 1 1.5 2 2.5
Sum HCH ng/g lipid
Figure 5-17. Box and Whisker Plots of Select Groups of Organochlorines Analyzed by Brook Trout
(a,b) or Oncorhynchus spp. (c,d). Median sum endosulfans (a) and DDTs (b) are compared by Kruskal
Wallis because of unequal variance. Sum chlordanes (c) and HCHs (d) are compared by ANOVA
followed by a Bonferroni post hoc at 95% confidence. Different letters denote significantly different
medians or means at p < 0.05. Boxes are the middle 50% of the data divided by the median (line); the "+"
symbol represents the mean. Whiskers are the range of data and individual points are outliers. Sum
endosulfans = endosulfan I, II, and sulfate. Sum DDTs = o,p'- and p,p'- isomers of DDT, ODD, and DDE.
Sum chlordanes = cis, trans, oxy-chlordane, and nonachlor. Sum HCH = a-, d-, and g-HCH. Data below
the detection limits are represented as Y2 the EDL.
Alternative explanations for the presence of intersex fish and elevated Vtg include the presence
of estrogens from human birth control, perhaps from humans swimming in the lakes. It is not
known if the numbers of humans on birth control that would urinate in the lakes during a
possible backcountry swim could excrete a quantity of estrogenic substance sufficient to be
endocrine disrupting. Other sources of estrogenic substances could be from horses and wildlife
that frequent the lakes, but several of these lakes in which intersex fish are present are not
accessible to horses or mules. In addition, the unknowns mentioned for human excretions would
apply to wildlife. One other possibility is that some lakes might contain fish that have fed on
artificial baits introduced by anglers that contain endocrine disrupting substances to a sufficient
amount. However, attributing the endocrine disruption to sources other than airborne contamin-
ants appears considerably more speculative. In addition, the elevated Vtg evident in our labora-
tory population of trout is probably attributable to the presence of estrogenic substances known
to be present in artificial fish feeds.
The intersex condition was found only in parks in the Rocky Mountains and the limited number
of intersex fish prevents establishing statistical relationships with contaminants. However,
dieldrin was much higher in the intersex brook trout from Lone Pine Lake, ROMO and o,p'-DDT
was detectable only in the intersex Yellowstone cutthroat trout from Oldman Lake, GLAC. It
5-48
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
could be purely coincidental that these estrogenic contaminants were higher in the intersex fish.
At present there is no robust scientific explanation for the regional confinement of the intersex
condition to the Rocky Mountains; however, our data suggest that the incidence is increasing.
Perhaps if more fish were sampled from the lakes in SEKI, MORA, or OLYM, or if more lakes
were sampled in SEKI or other parks in the Sierra Nevada, intersex fish would be found. The
percentages of intersex fish we found in ROMO (9 to 33%) were fairly high, compared to those
reported in the literature (range from 1% to 0.0002%). Therefore, it could simply be a matter of
sampling enough fish from the other parks to increase the probability of finding intersex fish.
Six of 11 water bodies in ROMO had intersex fish. This is a remarkable occurrence that is
biologically fascinating, and potentially alarming to resource managers and stakeholders. The
established implications in the literature for high frequencies of intersex, is that endocrine or
reproductive disruption is occurring. Whether these observations are caused by contaminants is
impossible to say with the current dataset. We attempted to use a weight of evidence approach to
establish some credible evidence for endocrine disruption (or lack of disruption). Our data show
that some intersex fish also produce Vtg, have underdeveloped testis, and low sex steroids at a
time of year when cohorts are nearing reproductive maturity. We even provide evidence that Vtg
is related to some organochlorine concentrations. One would be hard pressed to find such
compelling evidence for reproductive disruption based on observational data in the literature. To
our knowledge, there is a paucity of studies in which contaminants and reproductive biomarkers
have been measured in the same fish. Regardless, without experimental spiking of lakes in
national parks with contaminants, measuring the contaminants in the fish, and finding
statistically significant changes in the proportion of intersex fish, there is no way to attribute the
intersex condition to contaminants, or anything for that matter. Intersex can be caused by genetic
mutations, parasites, abrupt temperature changes, hormonal abnormalities, and contaminants.
The fact that our observations occurred in two different genera, in three different species, and in
allopatric populations reduces the possibility that endogenous factors (mutations and hormonal
abnormalities) are at fault. Abrupt temperature changes are unlikely in these lakes and parasites
were not found in the intersex fish (see Appendix 5A).
Finding intersex fish in the museum samples, prior to the manufacture of the organochlorines
and other synthetic chemicals was indeed surprising. These results call to attention the possibility
of some level of intersex in trout that is not associated with organic pollutants. The area around
Twin Lakes was subjected to extensive ore mining. Perhaps metal contamination of waters
resulting from natural or mining induced acid rock drainage was at fault. Or, the stocking of fish
might have resulted in hybridization that induced a genetic abnormality (Metcalf et al, 2007).
Clearly, more sampling of the museum specimens is necessary. Focusing on acquiring not only
more fish from each water body, but from more water bodies in the western and northeastern
parts of the USA is needed before definitive conclusions can be drawn.
In summary, there is strong inference that our results regarding intersex and Vtg in some
populations in the Rocky Mountain parks can better be explained by endocrine disruption related
to contaminants than by other potential causes. We also believe that it is the more responsible
explanation from the perspective of conservation biology. The consequences to park resources
would not be negatively affected if further investigation found this contention to be wrong.
However, consequences could be negative if we do not take this contention seriously and it is
indeed true, as we think.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
5.3.3.5 Contaminant (SOCs and Metals) Accumulation in Moose and Potential Biological Effects on
Moose in the Parks
The WACAP Research Plan included analysis of moose tissue samples for SOCs and metals in
an attempt to make an explicit linkage between the Alaskan food web that WACAP was
exploring for contaminants and the humans that use this food web for obtaining subsistence
foods. Moose was chosen because Alaska NPS experts advised WACAP scientists that this
subsistence animal was considered one of the most widely distributed and used terrestrial game
animal in Alaska. Moreover, the range of an individual moose is generally small, compared to
that of caribou, the other subsistence animal considered. The smaller range of the moose would
tie the results of contaminant analysis to a smaller area of the Alaskan landscape. We intended to
obtain moose muscle and liver samples from subsistence hunters in or near the three core
WACAP sites in Alaska over a 3-year period. We allocated a total of 35 SOC and metal samples
for this effort. We hoped to obtain at least three moose samples per year from each park for 3
years. At the time of the peer review, the reviewers recognized this component of WACAP as an
attempt to link the more rigorous WACAP science effort to the life of the subsistence human.
However, the peer review panel suggested that we not try to integrate this effort into the main
WACAP science effort focused on explicit objectives and having a rigorous, controlled sampling
design and schedule. Rather, understanding the interest in making the connection to the human
component of the food web, they suggested we continue this as a minor effort in WACAP.
We worked through NPS personnel at DENA, NO AT, and GAAR toward the goal of obtaining
moose samples from the three core Alaskan parks. We prepared information packets, tissue sub-
sampling materials, and packaging and mailing items for the NPS contacts to give to potential
moose hunters. In total, during the 3-year effort, we obtained tissue samples from only three
animals, all collected in DENA. Two samples were obtained in 2004 and one in 2005. The
moose samples were collected and delivered to the WACAP laboratory, generally following the
simple procedures we outlined in a flyer distributed to subsistence hunters. Samples received at
the WACAP laboratories arrived frozen solid and wrapped, and were accompanied by meta data
regarding the location of the animal when it was harvested.
5.3.3.5.1 SOCs in Moose Tissue
The moose tissue analyses for SOCs revealed that few of the target compounds were detected in
the tissues and, when present, the concentrations of the compounds were generally quite low
(Table 5-11). Many of the compounds were below detection limits for the analysis, or were
flagged for various reasons. As a general rule, when sample concentrations approach the
detections limits for an analytical procedure, the number of flags tends to increase.
Fifteen SOC compounds were detected at least once in either muscle or liver tissue of the three
moose. With three moose sampled, that means that we could have detected the presence of these
15 compounds 45 times for each of the 2 tissue groups, liver and muscle. For liver, 12 out of a
possible 45 samples contained detectable concentrations of SOCs. In muscle, we detected SOCs
in half that number, 6 of a possible 45 samples. One of the animals had a fairly high concentra-
tion of p,p'-DDD in its liver, at 340 ng/g lipid. On the other hand, many of the concentrations of
detected SOCs were very low.
The generally low detection frequencies and the absence of any major patterns among SOC
compound groups, among individual moose, or between moose tissue types suggest that moose
for which we obtained samples were not biomagnifying SOCs to a level of concern at this time.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-11. SOC Concentrations in Moose Meat and
Liver.
Fish Tissue
SOC
Endosulfans
PCB 153
TFLN
HCB
CLPYR
Dieldrin
ACE
FLO
FLA
PYR
pp-DDD
op-DDT
pp-DDT
MXCLR
CHR/TRI
B[b]F
Unit
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
FLAG
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
ng/g lipid
ng/g wet wt
Liver
BDL
DF
DF
X
X
DF
DF
DF
DF
BDL
56
6.9
X
X
X
X
BDL
BDL
340
42
b
BDL
BDL
DF
DF
DF
DF
BDL
BDL
BDL
BDL
Liver
BDL
0.025
0.0062
X
X
0.72
0.18
BDL
BDL
BDL
BDL
BDL
2
0.51
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
3.2
0.8
BDL
BDL
BDL
BDL
Liver
BDL
DF
DF
X
X
DF
DF
BDL
BDL
BDL
44
11
X
X
X
X
1.2
0.32
32
8.4
BDL
BDL
2.4
0.63
BDL
BDL
0.46
0.12
1
0.26
Meat
BDL
DF
DF
BDL
BDL
DF
DF
1.2
0.15
BDL
X
X
X
X
BDL
BDL
BDL
BDL
BDL
BDL
630
76
BDL
BDL
18
2.2
BDL
BDL
BDL
BDL
Meat
BDL
0.48
0.0037
0.62
0.0048
X
X
BDL
BDL
BDL
BDL
BDL
X
X
7.2
0.055
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
Meat
BDL
DF
DF
X
X
DF
DF
BDL
BDL
BDL
BDL
BDL
BDL
BDL
X
X
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
BDL
SOC names: PCB = polychlorinated biphenyl; TLFN = trifluralin; HCB = hexachlorobenze; CLPYR = chlorpyrifos;
ACE = Acenaphthene; FLO = fluorene; FLA = fluoranthane; PYR = pyrene; MXCLR = methoxychlor; CHR/TRI =
Chrysene + Triphene; B[b]F = Benzo(b)fluoranthene
Flags: BDL = below detection limit; DF = detected, but flagged as not meeting QA Objectives; X = no
value reported, lab blank > 33% of sample value; b = value is above the calibration range
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
However, this screening was based on only three animals taken from one national park (DENA).
Notably, moose are herbivores, located quite low on the food web, and would not be expected to
demonstrate biomagnification to the same degree as a predator.
5.3.3.5.2 Metals in Moose tissue
Moose liver and meat metal concentrations for the three animals sampled are provided in Table
5-12 for seven metals: Cadmium (Cd), Copper (Cu), Nickel (Ni), Lead (Pb), Vanadium (V), Zinc
(Zn), and Mercury (Hg).
To consider the three WACAP moose samples from DENA in context with other moose sampled
from North America, we present the mean and standard deviation for the WACAP moose liver
and muscle tissue samples along with literature citations from Canada (Yukon), the Coville River
area of Alaska, and "Other Alaska Killed Moose" (Gamberg et al, 2005; O'Hara et al, 2001) in
Table 5-11.
We compared WACAP moose liver sample values to Cd, Cu, and Zn concentrations from the
previously published studies. For Cd, Cu, and Zn, the mean WACAP moose sample concentra-
tions were always lower. However, these differences were significantly different only at the 0.05
level for Zn in liver from Coville, Alaska. Although these general findings are good news for
subsistence users of moose, these very low Cu concentrations in moose have been determined to
adversely affect Fe adsorption mobilization, transformation, and incorporation into hemoglobin
(Owen, 1982; Suttle, 1991). The WACAP mean moose liver Cu concentrations of 9.4 ± 4.9 ug/g
ww were considered to be in the deficient range (from 5 to < 10 ppm ww) for Cu by Frank et al.
(1994). Two of the three WACAP moose tissue samples analyzed in this study fell within this
deficient range for Cu. This finding might be of interest to DENA wildlife biologists.
For the sparse data available from other studies for metals in moose tissue, the WACAP (DENA)
moose tissue sample values were (1) lower than all values reported for Cd and Cu and (2) similar
in Zn concentrations to the Coville (Alaska) moose but much less than the mean Zn concentra-
tions for other Alaska moose (Table 5-11). However, as with liver samples, in only one instance
was there a significant difference (P < 0.05) and that was for Cd in muscle from the Coville,
Alaska, study.
5.4 Ecological Effects
5.4.1 Mercury
Mercury is a persistent bioaccumulating and biomagnifying non-essential metal. It is present in
three forms in the atmosphere (elemental, reactive gaseous, and particulate), but it must be
methylated to an organic form for efficient incorporation into the food web. Methylation is
accomplished in the sediment or water column of a water body by microbial organisms (Ullrich
et al., 2001). Source apportionment of Hg is difficult, because it does not degrade and is a global
pollutant. Current thinking is that most Hg entering the national parks is via atmospheric deposi-
tion from local, regional, and trans-Pacific sources (Keeler et 1., 2006; Wiener et al., 2006). It is
estimated that up to 75% of the Hg entering the atmosphere is from anthropogenic sources
(Nriagu, 1990) such as combustion; steel, iron, coke, and lime production; smelting; petroleum
refining; and mercury cell chlor-alkali production (Keeler et al., 1995; Landis et al., 2004).
Methyl-mercury affects the brain and nervous system, reproductive system, and immune system
and is not readily excreted from animals. The concentrations of whole-body total Hg in WACAP
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-12. Metal Concentrations in Moose Meat and Liver (ng/g wet weight).
Samples
Hg
Date ng/g
Collected ww
Cd
Mg/g
ww
Cu
Mg/g
ww
Ni Pb
Mg/g Mg/g
ww ww
V Zn
Mg/g Mg/g
ww ww
Liver
WACAP
WACAP
WACAP
WACAP
Mean
Yukon1
Coville R.
Alaska2
Other Alaska
Killed Moose2
9/13/2004 11.88
9/27/2004 32.66
9/23/2005 6.88
Mean 17.14
±s.d. ±13.67
Mean
± s.d.
Mean
±s.d.
Mean
1.7
8.3
1.2
3.8
±4.0
4.94
±3.52
3.13
±2.5
11.9
8.2
14.8
5.2
9.4
±4.9
40.3
±47.7
9.8
±12.7
103.9
0.65 0.023
0.16 0.017
0.009 0.012
0.27 0.017
± 0.33 ± 0.006
0.1
±0.63
-
-
0.004 13
0.006 40
0.048 16
0.019 23
±0.025 +15
34.9
±36.0
66.5
±58.1*
73.3
Meat
WACAP
WACAP
WACAP
WACAP
Mean
Yukon1
Coville R.
Alaska2
Other Alaska
Killed Moose2
9/13/2004 25.82
9/27/2004 4.56
9/23/2005 2.06
Mean 10.81
±s.d. ±13.06
Mean
±s.d.
Mean
± s.d.
Mean
± s.d.
0.010
0.022
0.006
0.013
± 0.008
0.03
±0.03
1.78
±1.69*
-
1.7
0.8
1.3
1.3
±0.4
1.48
±0.53
-
5.53
0.004 0.008
0.019 0.029
0.080 0.011
0.13 0.016
±0.15 ±0.011
0.03
±0.09
-
-
0.003 69
0.001 47
0.030 71
0.011 62
±0.016 ± 14
51.7
±27.9
-
183.3
*Significant differences (P <0.05) between WACAP moose samples and the other studies based on the T-
test
All concentrations based on wet weights.
Metals were analyzed on freeze-dried tissue, and Hg was analyzed on liquid nitrogen ground tissue, not
dried.
Moose tissue collected on same date are from the same animal.
1Gamberg etal., 2005.
2O'Haraetal., 2001.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
fish are shown in Figure 5-18. At numerous sites, mean lake concentrations were above con-
taminant health thresholds for piscivorous biota. The contaminant health threshold for humans is
300 ng/g wet weight (USEPA, 2001), and is based on methyl-Hg in the fillet for a general
population of adults with 70 kg body weight and 0.0175 kg fish intake per day (approximately
the same intake rate used for determining consumption thresholds for recreational fishers). We
converted the threshold value to 185 ng/g whole-body total Hg based on (1) 95-100% of the Hg
in fish being methyl-Hg (Bloom, 1992), and (2) conversion from fillet to whole-body basis by
the formula [log (fillet biopsy Hg) 0.2545 + 1.0623 log (whole-fish Hg)] developed by Peterson
etal. (2007).
Whole Fish Total Mercury
Human 185 ng/g
River Otter 100 ng/g
Mink 70 ng/g
— Kingfisher 30 ng/g
Lake
Species
Lake
Mean
Lake Trout |H
Burbot and Whitefish [ I
Cutthroat Trout HM
Brook Trout ^B
Rainbow Trout r\ XI
Individual
Fish
O
O
Figure 5-18. Fish Whole-Body Lake Mean (bars) and Individual Fish (symbols) Total Mercury and
Contaminant Health Thresholds for Various Biota. The mean ng/g total Hg in fish at NOAT exceeds
the human contaminant health threshold; the ng/g total Hg in some fish at GAAR (Matcharak), OLYM (PJ,
Hoh), MORA (LP19), and SEKI (Pear) exceeds the human contaminant health threshold. The mean ng/g
Hg concentration in fish at all parks exceeds the kingfisher contaminant threshold, and the mean at seven
lakes exceeds all wildlife thresholds—NOAT (Burial), GAAR (Matcharak), DENA (Wonder), OLYM (PJ,
Hoh), MORA (LP19), and SEKI (Pear). The human threshold is 300 ng/g wet weight (USEPA, 2001), and
is based on methyl-Hg in the fillet for a general population of adults with 70 kg body weight and 0.0175 kg
fish intake per day. 95-100% of Hg in fish is methyl-Hg (Bloom, 1992), and 300 ng/g in the fillet is equiva-
lent to 185 ng/g ww whole body methyl-Hg (Peterson et al., 2007). Contaminant health thresholds in
piscivorous animals (wildlife) are based on 100% fish in the diet for whole body total Hg, as determined
by Lazorchak et al. (2003). Data are plotted on a Iog10 scale; the y-axis starts at 10 ng/g.
5-54
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
5.4.2 Selected SOCs with Contaminant Health Thresholds for Piscivorous Biota
Potential risk to piscivorous biota from fish consumption is of concern. Lazorchak et al. (2003)
developed SOC fish contaminant health thresholds for mink (Mustela vison), river otter (Lutra
canadensis), and belted kingfisher (Ceryle alcyori) in the mid-Atlantic States, USA, and we used
these values to identify national parks that contained fish with contaminant concentrations above
the various criteria. Although the criteria were developed for the mid-Atlantic states, mink, otter,
and kingfishers inhabit nearly all the field sites in this study (Table 5-13). As indicated by
Lazorchak et al. (2003), deleterious effects on the wildlife can vary, because effects are
contingent upon numerous other factors. Individual differences in responses to contaminants
depend on sex, reproductive strategy and status, exposure to other stressors, and the overall
health of the animal.
5.4.2.1 PCBs
Polychlorinated biphenyls were used for insulation and cooling of electrical transformers and
capacitors, among many other uses. Being ring-structures, similar to cholesterols and steroid
hormones, PCBs are highly bio-active inducing birth-defects, reproductive failure, liver damage,
and tumors. Like mercury, PCBs are a global pollutant, are environmentally persistent, and they
bioaccumulate and biomagnify in the food web. Because of the toxic nature of PCBs, legislation
in the United States banned production and use in 1979; however, it is estimated that 82 million
kg of PCBs remain in various forms. PCBs arrive at the national parks via regional and long
range atmospheric transport (Eisler, 1986). The concentrations of the sum of PCBs in WACAP
fish are shown in Figure 5-19; concentrations in all fish were below contaminant health
thresholds for piscivorous biota.
5.4.2.2 DDT and Metabolites
DDT is an organochlorine insecticide and is similar to the PCBs in that it is bioactive because of
the ring-structure. Also, as for the PCBs, legislation stopped the large-scale use of DDT in the
United States in 1972. However, DDT is still used in the developing world. It is one of the few
chemicals that effectively reduce numbers of Anopheles gambiae, the mosquitoes that carry
malaria. In fact, the World Health Organization recently advocated that DDT be "painted" on the
inside of dwellings in Africa to help curb the spread of malaria. DDT was used extensively in the
United States from the mid-1940s to the early 1960s as an insecticide, after which Rachel Carson
publicized the negative effects of DDT, and other organochlorines, on birds in the book, Silent
Spring. Henceforth p,p'-DDT, the isomer o,p'-DDT, and the metabolites, o,p'-DDE and p,p'-
DDE, have received extensive testing for various biological effects in the scientific literature.
The effects range from acute toxicity to reproductive or developmental abnormalities, and
endocrine and immune disruption. The concentrations of the sum of DDTs in WACAP fish are
shown in Figure 5-20. The mean DDT concentration at Oldman Lake (GLAC) and the
concentrations in some individual fish at Pear and Emerald Lakes (SEKI) are above contaminant
health thresholds for piscivorous birds.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-13. Species Represented in Each Guild of the Loop Analysis.
Guild
Species in Each Guild
Detritus
Not Specified
Invertebrates
Numerous
Fish
Salvelinus fontinalis
OLYM, MORA, ROMO,
SEKI
Thymallus arcticus
NOAT, GAAR, DENA
Prosopium cylindraceum
NOAT, GAAR, DENA
S. namaycush
NOAT, GAAR, DENA
Oncorhynchus mykiss
ROMO
Coitus spp.
NOAT, DENA, MORA
Esox lucius
GAAR
O. clarki subsp.
GLAC
Gasterosteidae spp.
NOAT, GAAR
Lota lota
DENA
Mammals
Lutra canadensis
NOAT, GLAC, MORA,
ROMO
Sorex palusths
OLYM, MORA
Mustela vison
DENA, GLAC, OLYM,
MORA
Spilogale putorius
OLYM
Ondatra zibethicus
DENA, GLAC
Mustela erminea
MORA
Birds
Ceryle alcyon
DENA, GLAC, OLYM,
MORA, ROMO, SEKI
Gavia spp.
NOAT, GAAR, DENA,
GLAC
Larus spp.
NOAT, GAAR, DENA
Anas spp.
NOAT, DENA, OLYM,
MORA, ROMO
Podicipedidae spp.
GAAR, DENA, MORA,
Sterna paradisaea
NOAT, GAAR, DENA
Scolopacidae spp.
OLYM, ROMO
Mergus spp.
GAAR, OLYM, MORA, ROMO
Pandion haliaetus
DENA, GLAC, OLYM, MORA,
ROMO
Aquatic Birds
Ardea herodias
GLAC, OLYM, MORA, SEKI
Aquila chrysaetos
MORA, SEKI
Cinclus mexicanus
DENA, OLYM, ROMO
Strigidae spp.
OLYM, MORA
Buteo regalis
MORA
Haliaeetus leucocephalus
GLAC, OLYM, MORA, ROMO
Falco spp.
ROMO, SEKI
Notes:
NOAT = Noatak National Preserve
GAAR = Gates of the Arctic National Park and Preserve
DENA = Denali National Park and Preserve
GLAC = Glacier National Park
OLYM = Olympic National Park
MORA = Mount Rainier National Park
ROMO = Rocky Mountain National Park
SEKI = Sequoia and Kings Canyon National Parks
5-56
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Whole Fish Sum PCBs
0)
'ID
1
I
in
m
I
0,001
Lake
Lake
Species Mean
Lake Trout
Burbot and Whitefish 1 1
C utth ro at Tro ut '.*.',
Brook Trout ^^|
Rainbow Trout 1 \ "H
Individual
Fish
0
O
•
•
0
Figure 5-19. Fish Whole-Body Lake Mean (bars) and Individual Fish (symbols) Sum PCB
Concentrations, with Contaminant Health Thresholds for Various Wildlife. N = 10, except N = 6 for
McLeod Lake. Contaminant health thresholds in piscivorous animals are based on 100% fish in the diet
for whole body total PCBs as determined by Lazorchak et al. (2003). Data are plotted on a Iog10 scale and
below detection limit values are reported as Y2 the EDL.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-57
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
1000
Whole Fish Sum DDTs
River Otter 490 ng/g
Mink 360 ng/g
Kingfisher 20 ng/g
Lake
Lake
Species Mean
Lake Trout ••
Burbot and Whitefish I I
Cutthroat Trout an
Brook Trout I I
Rainbow Trout l\ N
Individual
Fish
0
0
0
•
0
Figure 5-20. Fish Whole-Body Lake Mean (bars) and Individual Fish (symbols) Sum DDT
Concentrations (DDT, DDD, and DDE), with Contaminant Health Thresholds for Various Wildlife. N
= 10, except N = 6 for McLeod Lake. Contaminant health thresholds in piscivorous animals are based on
100% fish in the diet for whole body total DDTs as determined by Lazorchak et al. (2003). Data are
plotted on a Iog10 scale. All lake means (bars) were derived from the sums of DDTs in individual fish in
each lake; more than 50% of the DDT forms used in the sums were below detection limits and were
replaced with values equal to Y2 the estimated detection limit. The form of DDT most frequently detected
in the fish was p,p'-DDE.
5.4.2.3 Chlordanes (cis- and trans-chlordane, and cis- and trans-nonachlor)
Chlordane is a broad use pesticide. The technical mixture contained some or all of the additional
chemicals listed in the previous subsection. Chlordane is an organochlorine, like DDT, and
concerns about potential carcinogenic effects led to banning chlordane in 1983. Chlordane in
wildlife is highest in areas where the pesticide has been used to control underground termites.
Highly lipophilic, chlordane accumulates in the fatty tissues of animals such as the gonads, liver,
and brain. Chlordane differs from Hg, PCBs, and DDT in that it does not readily bioaccumulate,
but high concentrations have been found in top marine predators.
5-58
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Chlordane is transported to the national parks in the atmosphere and is widespread in the
environment. It is a suspected carcinogen and endocrine disrupter (Eisler, 1990). Concentrations
of the sum of chlordanes in WACAP fish are shown in Figure 5-21; the concentration sum in one
fish in Oldman Lake (GLAC) was above contaminant health thresholds for piscivorous birds.
The concentrations of chlordane are low enough to protect piscivorous mammals.
Whole Fish Sum Chlordanes
1000 -
51
1
t
o
E
c/i
400 •>
io-r
1 -
0.4
0.1
0.04
0.01
0,004
0.001
8
t
J,
8
o
e
River Otter 1140 ng/g
Mink 830 ng/g
Kingfisher 4.5 ng/g
//£&//////&/
Lake
Lake
Species Mean
Lake Trout :
Burbot and WWtefish I I
Cutthroat Trout RflG
Brook Trout ^Hj
Rainbow Troul I^M
Individual
Fish
0
0
0
•
•
Figure 5-21. Fish Whole-Body Lake Mean (bars) and Individual Fish (symbols) Sum Chlordane
Concentrations, with Contaminant Health Thresholds for Various Wildlife. N= 10, except N = 6 for
McLeod Lake. Contaminant health thresholds in piscivorous animals are based on 100% fish in the diet
as determined by Lazorchak et al. (2003). Data are plotted on a Iog10 scale and below detection limit
values are reported as Y2 the EDL.
5.4.2.4 Dieldrin
Dieldrin is a breakdown product of the organochlorine pesticide aldrin, but once dieldrin itself
proved to be an effective pesticide, it was produced along with aldrin. Dieldrin was produced in
Denver, Colorado, from 1952 to 1973 (Walden Research Division of Abcor, 1975). In the United
States, it was banned for agricultural use in 1974 and for most uses in 1987. Perhaps not coinci-
dentally, some of the highest dieldrin concentrations in fish in this study were found in Rocky
Mountain National Park, less than 160 km from Denver, where it was once manufactured. Like
the PCBs, DDT, and chlordane, the aldrin/dieldrin/endrin family of pesticides are members of
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-59
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
the so called "dirty dozen," a group of persistent bioaccumulative chemicals that have been
largely banned from production and use. The bioaccumulation of dieldrin has been observed in
piscivorous and non-piscivorous birds, and hatchling success is diminished in the former that
have been exposed to dieldrin. The maternal transfer of dieldrin to fish eggs has also been
documented. Acutely toxic, dieldrin is carcinogenic and is a suspected endocrine disrupter, with
potential developmental and reproductive effects (Jorgenson, 2001). The concentrations of
dieldrin in fish are shown in Figure 5-22; concentrations are low enough to protect piscivorous
wildlife.
Whole Fish Dieldrin
Kingfisher 360 ng/g
River Otter 30 ng/g
Mink 20 ng/g
,rf>
Lake
Lake
Species Mean
Lake Trout IB
Burbot and Whiteftsh 1 1
Cutthroat Trout Km
Brook Trout SB
Rainbow Trout ix "N
Individual
Fish
9
O
9
9
9
Figure 5-22. Fish Whole-Body Lake Mean (bars) and Individual Fish (symbols) Sum Dieldrin
Concentrations, with Contaminant Health Thresholds for Various Wildlife. N= 10, except N = 6 for
McLeod Lake. Contaminant health thresholds in piscivorous animals are based on 100% fish in the diet
as determined by Lazorchak et al. (2003). Data are plotted on a Iog10 scale and below detection limit
values are reported as Y2 the EDL. "1" indicates that dieldrin was detected in 50-70% of the samples (PJ
Lake, OLYM); "2" indicates that dieldrin was detected in < 50% of the samples; the mean value on the
graph is 1/2 the EDL (Hoh Lake, OLYM).
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
5.4.3 Human Health Risks from Consumption of SOCs in Fish
A formal human risk assessment for the consumption of SOCs in fish was beyond the scope and
resources of WACAP. Instead, we used USEPA's (2000) "Guidance for Assessing Chemical
Contaminant Data for use in Fish Advisories" to adjust contaminant health thresholds for
WACAP Adjustments for Contaminant Health
Thresholds (Fish Consumption)
Consumption offish offers many health benefits but
can also increase exposure to environmental pollu-
tants. Some pollutants are carcinogenic, teratogenic,
and/or mutagenic; therefore, impaired health
resulting from the consumption of contaminated
foods is of concern. The EPA and other agencies
(e.g., the World Health Organization) develop
benchmarks for the consumption offish in amounts
that should not raise the risk of developing cancer or
other chronic conditions. In this report, we call these
benchmarks contaminant health thresholds. Their
use is intended to provide an estimate of potential
health risk resulting from the consumption offish
from lakes in the national parks we studied. The
contaminant health thresholds do not consider the
beneficial aspects of eating fish.
Different populations of humans consume fish at
different rates; therefore, contaminant health thres-
holds are different for recreational and subsistence
fishing. The values are calculated for 70-kg adults.
For recreational fishing, it is assumed that 2.3
8-ounce fillets are consumed every month for a
lifetime; for subsistence fishing, it is assumed that 19
8-ounce meals of whole fish are consumed every
month. Based on these estimated amounts offish
consumed, the contaminant health thresholds are
concentrations of contaminant exposure that would
raise the risk of cancer above 1:100,000 (0.001%).
We adjusted the recreational fishing contaminant
health threshold values upwards 32% to account for
the estimated amount of chemical lost from fresh,
whole fish during filleting and cooking. By making
these adjustments, we were able to compare the
benchmark, developed for cooked fillets, to our data,
gathered from whole uncooked fish. In all the
graphs, the concentrations were not adjusted; only
the thresholds were adjusted to account for the
difference between whole fish and filleted cooked
fish. In the subsistence fishing scenario, no
adjustment was made—per the EPA (2000)
recommendation. Raw, whole-fish concentrations
were compared with reference doses and cancer
risk threshold values.
recreational and subsistence fishers who eat
fish only from WACAP lakes. The
contaminant health threshold is the point at
which a 70-kg person who consumes 17.5 g
offish per day (recreational fishing) or 142
g/day (subsistence fishing) would increase
the lifetime risk of developing cancer by 1 in
100,000 for carcinogenic contaminants, or
measurably increase the risk of chronic
conditions from toxic contaminants.
Concentrations of SOCs in individual fish
and the average concentration (by WACAP
lake) were then compared with the USEPA
contaminant health thresholds (Figures 5-23
through 5-27). It is assumed that recreational
fishers, but not subsistence fishers, reduce
their contaminant exposure 32% by trim-
ming and cooking their fish. Specifically,
cancer slope factors and reference doses for
the 13 SOCs that were detected in > 50% of
WACAP fish were obtained from the EPA-
ORD Integrated Risk Information System
database (2007) and forward-multiplied to
estimate individual contaminant concen-
trations in fish tissue sufficient to exceed
EPA contaminant health thresholds for
humans eating fish.
A total of 13 SOCs in 136 fish from the 14
WACAP lakes were compared with
calculated contaminant health thresholds
(Figures 5-23 through 5-27). Concentrations
in fish ranged from <0.23 pg/g to 120 ng/g,
and averaged 0.55 ng/g wet weight. Most
contaminant concentrations in fish from the
WACAP lakes fell below contaminant
health thresholds calculated for recreational
and subsistence fishing. However, over half
(77 of 136) of the individual fish from 11 of
the 14 lakes analyzed carried concentrations
exceeding subsistence fishing contaminant health thresholds for dieldrin (Figure 5-23) and/or
p,p'-DDE (Figure 5-25).
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
5-61
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
-
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Average Fish Thresholds
Dieldrin h ' I V Recreationa
a-HCH I I O Recreationa
Subsistence
T
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a-HCH
Dieldrin
a-HCH
Dieldrin
Lake
Figure 5-23. Concentrations of Historic-Use Pesticides for Dieldrin and a-HCH in Individual Fish
(symbols) and Lake Average Fish (bars) Compared to Contaminant Health Thresholds for Cancer
for Fish Consumption for Recreational and Subsistence Fishers (USEPA, 2000). Data are plotted on
a log scale; below detection limit values are reported as Vi the EDL. Some fish from SEKI, ROMO, and
GLAC exceed contaminant health thresholds for dieldrin for recreational fishing. The lake average
concentration offish from SEKI, ROMO, Golden Lake (MORA), Oldman Lake (GLAC), DENA, and NOAT,
and some fish from GAAR and LP19 (MORA), exceed contaminant health thresholds for dieldrin for
subsistence fishing. Exceedances imply that a lifetime consumption can increase the risk of developing
cancer by more than 1 in 100,000. If no label is present at the top of a bar, the component was detected
in at least 70% of the samples. "1" indicates the analyte was detected in 50-70% of the samples; "2"
indicates the analyte was detected in less than 50% of the samples.
5-62
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
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-------
CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
100 -.
10-
£
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I
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0.01 -
0.001
Lake Individual
Average Fish
Thresholds
ppDDE • V
Sum Chlordanes I I O
Mirex r I o
Recreational
Subsistence
Recreational
Subsistence
Recreational
Subsistence
Chlordanes
Mirex
v
T
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VO
o
o
o
o
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a
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§
ppDDE
Chlordanes
ppDDE
Mirex
Lake
Figure 5-25. Concentrations of Historic-Use Pesticides (p,p'-DDE, Chlordanes, mirex) in Individual
Fish (symbols) and Lake Average Fish (bars) Compared to Contaminant Health Thresholds for
Cancer for Fish Consumption for Recreational and Subsistence Fishers (USEPA, 2000). Data are
plotted on a log scale; below detection limit values are reported as 1/2 the EDL. Concentrations of all
compounds were below contaminant health thresholds for recreational fishers, but a lifetime consumption
of fish from Pear and Emerald Lakes in SEKI and Oldman Lake in GLAC could increase cancer risk for
subsistence fishers from p,p'-DDE. If no label is present at the top of a bar, the component was detected
in at least 70% of the samples. "1" indicates the analyte was detected in 50-70% of the samples; "2"
indicates the analyte was detected in less than 50% of the samples.
5-64
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
100000 !
10000 !
Lake
Average
Dacthal I I
Sum Endosulfans 1 1
Methoxychlor ^^|
Individual
Fish
V
o
0
Thresholds
Recreational
Recreational
Recreational
Dacthal
Methoxychlor
Dacthal
Methoxychlor
Endosulfans
Endosulfans
10 i
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Figure 5-26. Concentrations of Current-Use (dacthal, endosulfans) and Historic-Use (methoxy-
chlor) Pesticides in Individual Fish (symbols) and Lake Average Fish (bars) Compared to
Contaminant Health Thresholds for Chronic Disease for Fish Consumption for Recreational and
Subsistence Fishers (USEPA, 2000). Data are plotted on a log scale; below detection limit values are
reported as V2 the EDL. Concentrations of all compounds were below contaminant health thresholds at all
lakes. If no label is present at the top of a bar, the component was detected in at least 70% of the
samples. "1" indicates the analyte was detected in 50-70% of the samples; "2" indicates the analyte was
detected in less than 50% of the samples.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
CLPYR
CLPYR
g-HCH
g-HCH
(cancer threshold)
g-HCH
(cancer threshold)
Lake
Figure 5-27. Concentrations of Current-Use Contaminants PBDEs, g-HCH, and Chlorpyrifos
(CLPYR) in Individual Fish (symbols) and Lake Average Fish (bars) Compared to Contaminant
Health Thresholds for Chronic Disease (and Cancer Thresholds for g-HCH) for Fish Consumption
for Recreational and Subsistence Fishers (USEPA, 2000). Data are plotted on a log scale; below
detection limit values are reported as Vi the EDL. Concentrations of all compounds were below
contaminant health thresholds at all lakes. If no label is present at the top of a bar, the component was
detected in at least 70% of the samples. "1" indicates the analyte was detected in 50-70% of the
samples; "2" indicates the analyte was detected in less than 50% of the samples. * indicates results were
available from only one sample from the site, and no average is presented.
5-66
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
The numbers of fish in each lake that exceeded these thresholds and the recreational fishing
threshold for dieldrin are listed in Table 5-14. Although some individual fish (5) exceeded
recreational fishing thresholds for dieldrin, the average fish contaminant concentration per lake
did not exceed recreational fishing thresholds in any of the 14 lakes, but did exceed the subsis-
tence fishing thresholds for dieldrin or p,p'-DDE in 9 of the 14 lakes. No other contaminant
concentrations measured in fish from the core WACAP parks exceeded human contaminant
health thresholds, and concentrations of the other target contaminants detected in fish were one
to seven orders of magnitude below the adjusted recreational human contaminant health
thresholds. PBDEs and the current-use pesticides dacthal, endosulfan, chlorpyrifos, and
methoxychlor were at least three orders of magnitude below all contaminant health thresholds.
Table 5-14. Number of Fish Exceeding Human Cancer Thresholds
Number of Fish out of 10 Sampled
that Exceeded Threshold
Population
Contaminant
Park
NOAT
GAAR
DENA
DENA
GLAC
GLAC
OLYM
OLYM
MORA
MORA
ROMO
ROMO
SEKI
SEKI
Lake
Burial
Matcharak
Wonder
McLeod*
Snyder
Oldman
PJ
Hoh
Golden
LP19
Mills
LonePine
Pear
Emerald
Cancer Threshold
(ng/g wet weight)
Subsistence
Fishing
p,p'-DDE
0
0
0
0
0
9
0
0
0
0
0
0
3
4
14
Dieldrin
7
3
7
2
0
9
0
0
5
4
8
10
10
9
0.31
Recreational
Fishing
Dieldrin
0
0
0
0
0
1
0
0
0
0
2
1
1
0
3.7
* Number out of 6 total from lake
The EPA's calculated contaminant health thresholds offer a uniform approach to evaluating
human health risks from consumption of contaminated fish, but individual risk is probably higher
or lower. Potential interactions or synergistic effects from the multiple contaminants present in
fish could yield higher risks than those reported here. However, because most of the total risk is
attributed to the contaminant dieldrin, cancer risks from additive interactions are not significantly
different from exceedances of the dieldrin threshold. Also, the likelihood of these risks being
realized is small, because (1) the lakes are remote, with small fish populations (which limit the
people present and the frequency of fishing), (2) the risk scenario assumes lifetime consumption
(which is probably rare), and (3) the acceptable risk values (1:100,000) add a safety factor.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Finally, salmonid consumption is associated with nutritional and health benefits, such as
increased consumption of omega-3 fatty acids and reduced consumption of unhealthy fats. For
some people, these benefits probably outweigh contaminant risk. Health risks from contaminants
in fish can be reduced by removing the skin before cooking and by draining fats during cooking.
5.4.4 Potential Ecological Effects of SOC and Metal Contaminant Loads on Aquatic
Systems in the Parks
Information generated from sampling allows hypotheses to be generated about the health and
reproductive fitness of individual fish. We were also interested in making inferences regarding
potential effects of contaminants on the fish populations and other components of the biotic
communities of the lakes. We wanted to know if the presence of contaminants at levels with
potential biological effects on the fish would affect the demographic structure of the fish
populations. Furthermore, we conducted analyses to allow inferences about potential effects to
bird and mammal populations that are dependent on these specific lakes for at least some part of
their life cycle.
We obtained faunal lists for each of the respective lakes studied. These lists focused on birds and
mammals known to use the lakes (see Table 5-13). For some lakes, the identity of species repre-
senting other taxa was available. For assessment of ecological risk of the vertebrate fauna of the
parks to contaminant exposure, the use of population viability analyses would be desirable.
However, quantitative data is lacking for such an assessment. There is no information on the
abundance, age structure, birth or death rates, or predation rates. It is thus unfeasible for us to
conduct any quantitatively-based risk assessment. Instead, we used a qualitative analysis (loop
analysis) that is useful for allowing inference about systems such as these that are only partially
specified.
We constructed generalized trophic webs for communities of the lakes sampled. Li and Li (1996)
discuss how organisms can be classified into functional groups (guilds) and we followed their
recommendations. Briefly, we assigned the biota into trophic guilds as follows: (1) mammals
that eat fish, (2) birds that eat fish, (3) birds that eat invertebrates, (4) fish that eat invertebrates,
(5) fish that eat fish, (6) predacious invertebrates, (7) herbivorous and detritivorous invertebrates,
and (8) plant material and other detritus. Two different loop analyses were run. One was for
lakes in parks in the conterminous United States that contain mainly one species of fish. Such
systems are characterized by straight chain relationships between guilds. The second was for
systems such as those in Alaska, and perhaps for Emerald Lake in Sequoia, where more than one
fish species was present (Figure 5-28). Of course, piscivorous fish are also prey during early life
stages. In addition, fish of two fish systems can shift between guilds 4 and 5 over time, as
feeding ecology and abundance of various prey species change through time.
Considerable variation is likely, regarding the potential impact to the fish populations, because of
the use of lakes by birds and mammals. That is, depending on whether predators were resident or
transitory, potential impact to the fish population (and to the predators) could vary according to
the relative predatory pressure on the population. In the case of resident keystone predators
(birds and mammals), a top-down effect on fish would probably occur. At these lakes, birds and
perhaps mammals use central place foraging. That is, these top fish predators would be resident
over a substantial period of time and feed almost solely at this site, foraging for young as well as
themselves. For such systems, predator guilds would be considered omnivorous. Although
warm-blooded vertebrate species forage, such as the ouzel (Cinclus mexicanus), a bird
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
LEGEND
Negative effect on the guild with the semi-circle resulting from
an interaction with the guild (circles) originating the line.
Negative "self-loop" resulting from some factor external to
the lake system.
The flow of nutrients and contaminants.
Figure 5-28. Trophic Model for Lakes with Two-Fish Guilds Representing Alaska Systems. Circle
with numbers are guilds and 1= piscivorous mammal; 2 = piscivorous bird; 3 = insectivorous bird; 4 =
insectivorous fish; 5 = piscivorous fish; 6 = invertebrate predator; 7 = invertebrate; and 8 = detritus. One-
fish models would be similar, except that they would contain only one of the fish guilds. The self loops
represent self-regulation, such as in the form of logistic growth, which accounts for intraspecific
competition. The relative size of the circles indicates relative approximate position in the food web with
larger circles indicating higher trophic level.
representative of a trophic guild that forages on aquatic invertebrates, we do not believe that the
competition between members of this guild and fish would result in any population-level impact
on the fish. This contention is based on their small biomass and the fact that the foraging habitat
for this guild is relatively sparse in these lakes. Hence, there would be no top-down effect by this
guild on the fishes, but there could be bioaccumulation and amplification of contaminants up the
food chain from invertebrates into species such as ouzels. In the case of transitory top-order
predators (perhaps only as a brief stopping place during migration), pressure on the fish would
not be likely to affect the fish population. However, there could still be effects of toxicants via
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
trophic transfer on these top-order predators, adding to concentrations accumulated elsewhere.
Given that concentrations of certain endocrine disrupting contaminants found in fish in the
WACAP lakes exceed tolerance thresholds for several species of predaceous birds and mammals
(Figures 5-18 to 5-22), we also ran two other variations of the model. For non-omnivory systems,
there would be no top-down control by the predators on the fish; for systems with omnivory,
there would be population-level effects. In either case, the contaminants could have an effect on
the warm blooded vertebrates foraging in the lakes.
5.4.4.1 Assumptions
We assumed that avian and mammalian predators at a lake function as though they are resident,
foraging for themselves and for their progeny during the time that the aquatic system is free from
ice. Any negative impacts on the fish predators are assumed to result from a reduced prey base as
well as directly from contaminants. This predation pressure would have an impact on the biotic
system of the lakes. We acknowledge that this is a significant assumption because we have no
measurements on the impact of predators on the fish or the impact of contaminants on pisci-
vorous biota. We are aware that mammals and birds, such as kingfishers and ouzels (the duck-
like birds), and birds such as ospreys and eagles, probably forage beyond the confines of a
sampled lake, but for this exercise, we assumed that the other foraging sites would have similar
or greater (see Tables 5-1 and 5-2) concentrations of contaminants. Of course, birds are
migratory and can be exposed to different contaminants when they are not resident at WACAP
lakes, and we do not address the interactive effects of contaminant loading and unloading over
the seasons for these species. Therefore, we used negative self loops in the model (Figure 5-28).
In addition, birds and mammals export contaminants from the lake system.
We also did not consider the following in the analysis. Allochthonous matter, such as leaves and
needles, which provides energy input into a lake system, is assumed to be accessed by higher
trophic levels through the detritus; primary production in the lake is also subject to other inputs
to the system. Thus negative self loops are also used for that guild. We also assume that
allochthonous fish prey (e.g., terrestrial insects) are probably unimportant over the course of a
year, compared with prey produced within the lake itself. We also ignored in the model the fact
that constituents of aquatic organisms that are not removed from the lake will ultimately find
their way into the detritus. Biomagnification of persistent organic pollutants through the food
web, including aquatic ones, has been well documented (Kelly et al., 2007).
5.4.4.2 Loop Analysis
We used loop analysis (constructing signed diagraphs; see Figure 5-28) to analyze the trophic
network of the lake systems. It resulted primarily in inferences about the flow of nutrients from
which hypotheses about numbers of individuals could be made. Flow of contaminants through
the system would be similar to that of nutrients. Hence, hypotheses can be generated about
effects of contaminants on the various trophic guilds.
Loop analysis has been elaborated upon and validated in a series of recent publications (Hulot et
al., 2000; Dambacher et al., 2002, 2003a, 2003b, 2005; Arkoosh et al., 2004; Zavaleta and
Rossignol, 2004; Ramsey and Veltman, 2005). The computer programs are available in
Dambacher et al. (2002), updated 2006, and at http://www.ent.orst.edu/loop/. We initiated the
press (negative demographic effect of the contaminants) at the level of the fish in the system
(Figure 5-28, Table 5-15a). For the two-fish system (Figure 5-28, Table 5-15b), separate presses
were performed on each fish guild, but we interpret the results together as an intact system. The
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-15a. Effects of a Negative Press Perturbation1 on Fish in a
One-Fish Guild Ecosystem.
Guild2
1
2
3
4
Guild2
1
2
3
4
No
A3 in Life Expectancy
4Press 4
No effect
No effect
Increase
Increase
Omn ivory
A in Abundance
Press 4
No effect
No effect
No effect
No effect
Omn ivory
A3 in Life Expectancy
4Press 4
Increase
Increase
Increase
Increase
Table 5-1 5b. Effects of a Negative Press
Two-Fish Guild Ecosystem.
A in Abundance
Press 4
Decrease
Decrease
Decrease
Ambiguous
Perturbation1 on Fish in a
Guild No Omnivory
1
2
3
4
5
Guild2
1
2
3
4
A3 in Life Expectancy
4Press 4
No effect
No effect
Increase
Increase
Increase
Omnivory
A3 in Life Expectancy
4Press 4
Increase
Increase
Increase
Increase
Increase
A in Abundance
Press 4
No effect
No effect
Decrease
Decrease
Ambiguous
A in Abundance
Press 4
Decrease
Decrease
Decrease
Ambiguous
Ambiguous
1 Press perturbation = decreased birth to fish resulting from contaminant
induced fish death.
2See Figure 5-28 for key to guilds.
3A = change.
4The guild receiving a negative press perturbation.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
modeling and interpretation of the results were performed by Drs. Phil Rossignol and Hiram Li
and we acknowledge their contributions (Department of Fisheries and Wildlife and Oregon
Cooperative Fish and Wildlife Research Unit, USGS, respectively, Oregon State University).
5.4.4.3 Loop Analysis Sensitivity
Two assessments were made to determine the sensitivity of the loop analysis model, one for
stability and the other for predictability. The most complex community run by us in the loop
analyses was used to test sensitivity.
5.4.4.3.1 Sensitivity of Stability
Sensitivity of stability is evaluated with simulations of the community matrix over a range of
randomly selected interaction values, as explained on the Loop Analysis site:
http ://www. ent.orst. edu/loop/
The stability test is based on the signed digraph, in which the relationships between species are
(+1, -1, 0). The qualitative stability offers no insight into the stable structure of quantitative
domain of the system. For example, a signed digraph has an overall negative feedback with two
negative loops (-2) and one positive loop (+1). However, if we have the measure of interaction
strengths between species, the values of two negative loops could be -0.2 and -0.3, and the value
of a positive loop could be 0.6. In this case, the strength of overall feedback would be 0.1 and
then we would obtain a positive overall feedback.
In order to know the probability that the system is also stable in a quantitatively specified matrix,
5,000 quantitative matrices are constructed based on the unchanged sign structure of the system.
Non-zero elements of each matrix are quantitatively specified with a pseudorandom number
generator that assigns interaction strength but not a sign from a uniform distribution between
0.01 and 1.0. The stability of each quantitatively specified matrix is then examined in terms of
Hurwitz criteria I and II.
• Hurwitz criterion I: Characteristic polynomial coefficients are all of the same sign.
• Hurwitz criterion II: Hurwitz determinants are all positive
The results of 5,000 simulations indicate that the system is very stable, particularly considering
its fairly high connectivity:
• Pass Hurwitz criteria I and II: 4,305
• Pass Hurwitz criterion I, but not II: 50
• Pass Hurwitz criterion II, but not I: 0
• Not pass Hurwitz criteria I and II: 645
5.4.4.3.2. Sensitivity of Predictability
Sensitivity of predictions can be estimated from the weight of the predictions. These weights
represent the likelihood that the prediction will be in the direction generated by the adjoint
matrix. These weights correspond to simulation results, by the method discussed in Dambacher
et al. (2003b). This matrix represents the probability, based on average proportion of correct
sign, of a correct sign in a prediction (adjoint matrix) from simulations with uniformly
distributed interaction strengths and interdependent trophic relationships:
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
0.
0.
0.
0.
0.
0.
0.
0.
.88
.75
.69
.50
.80
.70
.50
.50
0.
0.
0.
0.
0.
0.
0.
0.
.75
.88
.69
.50
.80
.70
.50
.50
0.66
0.66
0.85
0.50
0.71
0.83
0.50
0.50
0.82
0.82
0.50
0.95
0.75
0.77
0.83
0.83
0.55
0.55
0.73
0.95
0.95
0.55
0.83
0.83
0.57
0.57
0.64
0.50
0.57
0.90
0.87
0.87
0.63
0.63
0.77
0.50
0.66
0.57
0.85
0.85
0.63
0.63
0.77
0.50
0.66
0.57
0.85
0.83
5.4.4.4 Results
We assume that the primary impact of a toxin would be a negative press perturbation (i.e., a
deleterious effect on fish). The mechanism would be reduced reproduction. Impact is assessed as
changes in abundance and life expectancy (inverse of turnover). During our analysis, both
systems tested appear to be stable, indicating that inferences can be drawn on the result of the
press perturbation.
For the one-fish system, we can infer from the predicted results that, with little predation
pressure from higher trophic levels, the abundance offish would decrease. The results about
abundance are ambiguous for single-fish systems where there is top-down control on the fish by
predators, assuming that there would be fewer insectivorous birds, and those remaining would
have a reduced life span. The populations of fish in both types of systems would have an older
age structure, because of a decreased birth rate. Piscivorous mammals and birds are expected to
live longer, but maintain fewer numbers (5-15a) in cases where they forage substantially on the
contaminated fish. Invertebrate-eating birds would be expected to have a decreased abundance
and an increase in the proportion of older individuals in the population.
The loop analysis infers that a press on fish in the two-fish system would lead to a response in
piscivorous and invertebrate-eating birds and mammals similar to that of the one-fish system.
There would probably be an increased life expectancy and reduced abundance of invertebrate-
eating birds and those birds and mammals eating large numbers of contaminated fish. The birth
rate would be expected to decrease in fish, with or without top-down control by predators, and
the abundance of fish would probably decrease in predatory fish in systems without top-down
control. Effects of a press on piscivorous fish in general and on invertebrate-eating fish in lakes
with top-down control by avian or mammalian predators are ambiguous (Table 5-15b), because
these fish, being more or less in the middle of the food chain, would be responsive to the
contaminants directly, but also indirectly through the action of the contaminants on their
predators.
For the analyses for scenarios that were complex and that included considerable interaction
between various guilds (not just in a single straight chain food web), there was considerable
ambiguity in the results. This is a logical result and stems from the fact that predators affect
forage and the forage can be toxic to the predators. Therefore the system does not behave
according to a simple predator-prey relationship. Paradoxical results were often found in these
situations; for example, the presence of a predator could actually result in an increase in
abundance of its prey because the contaminant in the prey would reduce the abundance of
predators. We therefore thought it would be best to be conservative and not interpret these results
(ambiguous cases in the tables). Results related to the age structures of the populations must also
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
be interpreted with caution. The sign diagraph analyses show effects on birth rate. Therefore, in
those cases where a decrease in birth rate was suggested, the interpretation would be that the age
structure favored older individuals. So, there could be the same number, or even fewer, older
individuals in that population, but proportionately there would be many fewer younger
individuals than there were before the press. A population with proportionately older individuals
does not de facto infer a population that is healthier than one with younger individuals.
For the cases in which the model results are ambiguous, one could rely on other information
upon which to make an inference about contaminant effects. For example, in systems where the
top predators use central place foraging, the toxicity tolerance information available (see Figures
5-18 to 5-22) would suggest the hypothesis that the abundance of those species would be
negatively impacted.
5.4.4.5 Conclusion
Birds eating only invertebrates could be subjected to bioaccumulation and magnification of
contaminants, and thus experience toxic effects. The ultimate risk to birds and mammals preying
on fish, and especially species of fish that eat other fish, would be both the negative effects of a
decreased prey base and the potential negative effects of bioaccumulation and magnification of
contaminants.
The model runs allow the following inferences: Fish populations in lake communities with only
one species offish represented, hence with an absence of the omnivory loop, would be expected
to experience a decrease in abundance and a decrease in life expectancy. These decreases could
happen because of the potential negative effects of bioaccumulation and magnification of
contaminants. Effects on abundance offish in systems with more than one species present are
ambiguous, but there would be fewer births and hence an older-aged population. Mammals and
birds preying on these fish would experience double impacts (altered prey base and contam-
inants), leading to fewer births and a reduced abundance in systems without the omnivory loop.
Birds eating only invertebrates could be subjected to bioaccumulation and magnification of
contaminants and hence experience toxic effects.
We believe that piscivorous predators (birds, mammals, and fish) that forage on fish could be
affected by contaminants. This is likely only if the predators forage on fish in the national parks
with concentrations of contaminants that exceed tolerance levels for the birds and mammals. On
a lake-by-lake and park-by-park basis, judgment regarding applicability of our conclusions
would rest on knowledge unavailable to us regarding the period of time the birds and mammals
were actually using those respective lakes.
5.5 Nitrogen Deposition Effects and Relationships
5.5.1 Ecological Effects of Enhanced Nitrogen Deposition in the Western United
States
Nitrogen inputs to the United States from anthropogenic sources doubled between 1961 and
1997, mainly from inorganic N fertilizer use and emissions of nitrogen oxides from fossil fuels
(Howarth et al, 2002; Burns, 2003). Chronic enhanced nitrogen deposition and excess available
nitrogen can lead to a myriad of adverse ecological effects in forests in the western United
States, such as eutrophication of water bodies, nitrate-induced toxic effects on aquatic biota,
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
changes in plant community composition and aquatic communities through removal of N
limitations on biotic activity, disruptions in nutrient cycling, decreased soil capacity for N
retention, and increased emissions from soil of nitrogenous greenhouse gases (Fenn et al., 1998;
Fenn et al., 2004). Current upward trends in population growth and energy use throughout the
western United States suggest a need for continued monitoring of atmospheric deposition N and
its ecological effects.
5.5.2 Evidence of Enhanced Nitrogen Deposition in Some Parks from Lichen N
The previous chapter explains that we found indication of enhanced N deposition to terrestrial
ecosystems based on WACAP lichen N data at SEKI, GLAC, BIBE, and BAND. Lichens from
these parks exceeded thresholds for background sites in the western United States; N in lichens
from other parks was within expected ranges for clean sites. Elevated lichen nitrogen
concentrations are associated with adverse changes to lichen community composition (Geiser
and Neitlich, 2007; Jovan and McCune, 2005) and are an indicator that other N-sensitive
ecosystem components could be affected.
Another measurement of nitrogen availability obtainable at most of the WACAP parks are
ambient concentrations of fine particulate (<2.5 um) ammonium nitrate and ammonium sulfate
sampled by IMPROVE. The IMPROVE network was established by federal land management
agencies to meet federal land manager responsibilities under the Clean Air Act to monitor
visibility in Class I areas. Three days a week, synchronized nationally, 24-hour samples of
particulate matter <2.5 um diameter are collected onto a filter from a height of about 3 m (these
fine particulates are associated with declines in visibility and adverse human health effects). The
chemistry of the particulates is determined following national protocols at the University of
California, Davis. Particulates composed of ammonium sulfate and ammonium nitrate are
reported in ug m"3. These data have value as indicators of nitrogen availability at the site, from
agricultural as well as urban-industrial sources. IMPROVE data and trends analyses can be
obtained from the IMPROVE website at http://www.coha.dri.edu/index.html.
Mean ambient atmospheric ammonium nitrate and ammonium sulfate concentrations (ug/m3)
reported from WACAP IMPROVE sites, 1999-2004, are displayed in Figure 5-29. SEKI has
higher concentrations of both pollutants than all other parks except BIBE, which has the highest
ammonium sulfate concentrations. Among the core parks, GLAC, ROMO, MORA, and OLYM
are not different from each other and DENA is lowest (Tukey-Kramer multiple means
comparisons, a = 0.05). Only nitrate and sulfate concentrations are measured, as these anions are
assumed to be balanced by ammonium. IMPROVE data can be further explored by examining
trends over seasons and years and by comparing peaks during worst days instead of averages.
The point of including the data here is to provide further evidence of enhanced nitrogen
deposition at SEKI and BIBE. There is no IMPROVE monitor at KATM; data from the Tuxedni
National Wildlife Refuge monitors were used in surrogate.
5.5.3 Correlations between Agricultural Chemicals and Measures of Agricultural
Intensity, Atmospheric Pollutants that Contain Nitrogen, and Human
Population
Correlations among mean annual ammonium nitrate concentrations in airborne fine particulates
sampled by park IMPROVE monitors from 1998-2004, the agricultural intensity index, nitrogen
concentrations in WACAP lichens and SOC concentrations in WACAP vegetation were
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
calculated (Table 5-16). Agricultural intensity calculations are described by Hageman et al.
(2006). See also Chapter 3 for more information about agricultural intensity, IMPROVE, and
population density calculations.
Concentrations of the CUPs, chlordanes, dacthal, and endosulfans in lichens and in conifer
needles, DDTs in conifer needles, and PAHs in lichens correlated well (Spearman's Rho 0.62 to
0.85) with both agricultural intensity and concentrations of ammonium nitrate in fine particulates
<2.5 (am diameter sampled by park IMPROVE monitors. Trifluralin in conifer needles was
strongly correlated with IMPROVE ammonium nitrate.
Park
Figure 5-29. Mean Annual Concentrations (ug/m3) of Ammonium Nitrate and Ammonium Sulfate in
Ambient Fine Particulates (< 0.25 Mm) Measured by IMPROVE at WACAP Parks, 1998-2004. Parks
are sorted by increasing ammonium nitrate concentration. Red bars indicate core parks; green bars
indicate secondary parks. Error bars indicate one standard error around the mean. SEKI has higher
concentrations of both pollutants than all other parks except BIBE which has highest ammonium sulfate
concentrations. Among the core parks GLAC, ROMO, MORA, and OLYM are not different from each
other and DENA is lowest (Tukey-Kramer multiple means comparisons, a = 0.05).
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Table 5-16. Rank Correlations among SOC Concentrations in Vegetation (pink), Agricultural
Intensity, Mean 1998-2004 Ammonium Nitrate Concentrations in Fine Particulates Measured by
IMPROVE (green), and Population Density (gray) for the 20 WACAP Parks. Bold-faced variables were
highly significant in both lichen and conifer vegetation.
Conifer Variable
Agintensity
Agintensity
Agintensity
Agintensity
Agintensity
Amm_NO3 |jg/m3
Amm_NO3 jjg/tn3
Amm_NO3 jjg/tn3
Pop. 150-km radius
Pop. 75-ktn radius
Pop. 75-km radius
Pop. 75-km radius
Pop. 75-km radius
Pop. 75-km radius
DDTs
HCB
Endosulfans
a-HCH
PAHs
Endosulfans
PAHs
Endosulfans
PCBs
PAHs
Endosulfans
HCB
DDTs
PAHs
PCBs
Dacthal
PAHs
Chlordanes
PCBs
Endosulfans
By Conifer
Variable
Dacthal
Endosulfans
Amm_NO3 jjg/tn3
PAHs
Chlordanes
PAHs
Endosulfans
Dacthal
Pop. 75-km radius
Amm_NO3 jjg/m3
Endosulfans
PAHs
PCBs
Agintensity
Chlorpyrifos
a-HCH
Chlordanes
Chlordanes
Chlordanes
Dacthal
A-HCH
DDTs
DDTs
Endosulfans
A-HCH
Chlordanes
Chlordanes
Dacthal
Endosulfans
Chlordanes
HCH
Chlorpyrifos
Chlorpyrifos
HCB
Spearman
Rho
0.873
0.777
0.758
0.745
0.646
0.655
0.644
0.623
0.900
0.721
0.698
0.671
0.664
0.629
0.943
0.874
0.864
0.862
0.847
0.843
0.828
0.821
0.821
0.818
0.807
0.790
0.786
0.771
0.753
0.738
0.735
0.700
0.685
0.672
Prob
>|Rho|
<.0001
<.0001
0.0002
0.0003
0.0038
0.0023
0.0029
0.0058
<.0001
0.0005
0.0009
0.0016
0.0051
0.0039
0.0048
<.0001
<.0001
<.0001
<0001
<.0001
<.0001
0.0234
0.0234
<.0001
<0001
<.0001
0.0362
0.0002
0.0008
0.0007
0.0003
0.0037
0.0139
0.0016
Lichen Variable
Agintensity
Agintensity
Agintensity
Agintensity
Agintensity
Amm_NO3 ug/m3
Amm NO3 ug/m3
Amm_NO3 jjg/m3
Amm_NO3 jjg/m3
Pop. 75-km radius
Pop. 150-km radius
Pop. 75-km radius
Pop. 150-km radius
Chlordanes
Dacthal
DDTs
PCBs
Endosulfans
Dacthal
Dacthal
PCBs
a-HCH
Endosulfans
HCB
Endosulfans
PCBs
PCBs
PCBs
PAHs
PAHs
PCBs
PAHs
By Lichen
Variable
Dacthal
Amm_NO3 jjg/m3
DDTs
Endosulfans
Chlordanes
Triflualin
DDTs
Dacthal
Endosulfans
Dieldren
Pop. 75-km radius
Amm_NO3 jjg/m3
Agintensity
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dacthal
Chlordanes
DDTs
Chlordanes
g-HCH
Chlordanes
a-HCH
DDTs
g-HCH
a-HCH
Dacthal
a-HCH
g-HCH
Endosulfans
HCB
Spearman
Rho
0.849
0.787
0.779
0.744
0.566
0.762
0.724
0.700
0.630
1.000
0.914
0.760
0.677
1.000
1.000
1.000
1.000
0.922
0.846
0.818
0.798
0.795
0.784
0.773
0.764
0.721
0.674
0.663
0.653
0.653
0.644
0.638
Prob
>|Rho|
<.0001
<.0001
0.0047
0.0002
0.0116
0.0280
0.0117
0.0006
0.0029
0
<.0001
0.0001
0.0010
0
0
o
0
<.0001
<.0001
0.0021
<0001
<.0001
<.0001
<.0001
0.0062
0.0007
0.0016
0.002
0.0018
0.0025
0.0029
0.0025
Human population size within a 75-km radius of WACAP parks correlated most strongly with
endosulfan, PAH, and PCB concentrations in conifers (Spearman's Rho 0.66 to 0.70) and with
dieldrin concentrations in lichens (Spearman's Rho 1.00). Other radii tested (25, 150, 300) did
not predict SOC concentrations as well. Human population size, agricultural intensity, and
ammonium nitrate concentrations in ambient fine particulate were all strongly correlated with
each other. In the west, the most productive agricultural areas and largest urban areas are often
located in the same geographical and climatic zones.
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Concentrations of endosulfans were strongly correlated with chlordanes, dacthal, DDTs, and
PCBs in vegetation samples, regardless of vegetation type. Chlordanes were strongly correlated
with dacthal and DDTs, and HCB was strongly correlated with a-HCH and PAHs in both
vegetation types. Many other SOCs were strongly correlated with other SOCs in one vegetation
type but not the other. Although correlations do not imply direct or causal relationships between
variables, they can serve as indicators of each other. For example, high IMPROVE Amm_NO3
is a fairly good predictor of high dacthal, endosulfan, trifluralin, DDT, and PAH concentrations
in conifer needles and/or lichens relative to cleanest sites.
5.6 The Influence of Environmental Factors on Fish Hgtot
Observational and experimental studies show that total mercury (Hgtot) in fish is strongly
influenced by watershed and food web characteristics (Wiener et al., 2006), and that the interplay
among these variables is complex and varies in unpredictable ways. Thus we are not able to
predict Hgtot in fish from an unknown lake, even when we have basic information such as basin
characteristics, area of wetlands, TOC in the lake water, and the general structure of the food
web. The best we can currently do is to suggest that the top predatory fish in any system are
likely to have the highest Hgtot There appears to be no strong relationship between Hg
atmospheric deposition and Hg concentration in fish at the site level. When aggregated at a large
scale (i.e., the state level), it has been demonstrated, with important caveats, that wet atmospheric
deposition of mercury can account for about two-thirds of the methyl mercury (MeHg) in
largemouth bass (Micropterus salmoides) (Hammerschmidt et al., 2006). The WACAP data
clearly demonstrate this inability to anticipate Hgtot in fish.
Figure 5-30 is a plot of the average fish Hgtot concentration for each WACAP lake against lake
water total phosphorus (Ptot)- Two of the four lakes with the highest Hgtot concentrations in target
fish are Arctic lakes. The lake with the highest average Hgtot is Burial Lake; this average value
exceeds the USEPA criterion for human consumption. The other WACAP Arctic lake,
Matcharak, is the fourth highest of all. These results are surprising for two reasons. First, the
sedimentary records show that Hgtot flux to the Alaska lakes in the last 50 years was only about
one-fourth of the Hgtotfiux observed in WACAP lake sediments in the lower 48 states (see
Section 4.3.5). Secondly, it is also true that the Arctic lake food webs tend to have fewer levels
and are simpler than those in the lower 48 states; by conventional interpretation, this would lead
to less biomagnification of Hg in fish. LP19 (MORA) and Hoh Lake (OLYM) are second and
third highest, respectively, with respect to fish Hgtot.
All WACAP lakes, except Burial Lake, are considered to be oligotrophic (water column Ptot < 5
ug/L); Burial Lake approaches being mesotrophic with water column Ptot = 9 ug/L (Wetzel,
1983). Since Burial Lake has at least double the concentrations of Ptot observed in any other
WACAP lake, this could be an important factor contributing to the higher total Hg found in fish.
However, several studies have indicated that increased algal primary production can reduce the
uptake of MeHg in fresh waters as a result of dilution in greater planktonic biomass (Pickhardt et
al., 2002). Only the methyl form of mercury can enter the food web and bioaccumulate. Lake
trout (the target fish in both Arctic lakes) have been shown to rely heavily on snails derived from
the benthic food web (Hershey et al., 1999). This was confirmed at the time of WACAP
sampling in that the gross anatomical evaluation showed stomach contents of fish from the
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Arctic lakes comprised mostly snails. Our evidence and the literature suggest that the Arctic food
web in which the lake trout reside is quite short and simple: periphyton, snails, and lake trout.
Average Hg Values in Fish vs. Total P
per Lake
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Figure 5-30. Average Total Mercury Values for Whole Fish Plotted Against Total Phosphorus (TP)
in the Lake Water for All Lakes in the Core Parks. Notably, Burial Lake was mesotrophic with respect
to TP and had the highest TP while also having the highest Hgtot. All other lakes were oligotrophic. The
human contaminant health threshold is 300 ng/g wet weight (USEPA, 2001), and is based on methyl-Hg
in the fillet for a general population of adults with 70 kg body weight and 0.0175 kg fish intake per day.
95-100% of Hg in fish is methyl-Hg (Bloom, 1992), and 300 ng/g in the fillet is equivalent to 185 ng/g ww
whole body methyl-Hg (Peterson et al., 2007). Contaminant health thresholds in piscivorous animals are
based on 100% fish in the diet for whole body Hgtot as determined by Lazorchaket al., (2003).
How might the high Hgtot in Arctic fish be explained? One clue might be that the Arctic lakes had
the highest DOC of all WACAP lakes (Burial = 3.3 mg/L; Matcharak = 4.7 mg/L). The range of
DOC in the lakes in the lower 48 states was 0.65 - 2.25 mg/L (mean 1.3 mg/L). This suggests
that there is a greater connection to sources of DOC, such as wetlands, sediment production
littoral zones, and melting permafrost zones, than in other lakes. These locations are known and
likely sites of mercury methylation (St. Louis et al., 1996) and DOC has been shown to be an
important pathway for conveying methyl mercury from sites of methylation (i.e., anoxic, organic
rich areas) to lakes. The high P content of the Burial Lake would stimulate periphytic algal
growth on the lake sediment surface in the photic zone (depth < ~ 10 m), providing the snail
"grazers" with an abundant food source. It is probable that the combination of elevated P and
DOC with the importance of the snail/ periphyton food web combined to provide a very efficient
mechanism to transport MeHg into fish by using a short but efficient pathway. In other systems,
lacking the snail/periphton linkage in an extensive photic zone, mercury reaching the sediment
surface via "plankton rain" is typically lost to the accumulating sediment sink and little is
conveyed back into the water column.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
Another important function of DOC relating to MeHg in lakes and the bioaccumulation in fish is
that DOC can attenuate light penetration and thereby decrease photodegradation of MeHg. This
dynamic has been shown to be significant in an Arctic lake (Hammerschmidt et al., 2006) and
would be likely to occur in Burial and Matcharak Lakes because of the fairly high DOC found
there.
We determined that LP19 had the highest Hg flux (current vs. pre-industrial) ratio in lake
sediments and that Hoh Lake had the lowest flux ratio of all WACAP lakes (Section 4.3.5), yet
we observe that the mean Hgtot in fish for these two lakes is almost identical. Clearly, there are
factors other than mercury flux to the lakes, as indicated from the sediment record, responsible
for Hgtot concentrations in fish.
One of the best current tools available to scientists to examine the food web structure of an
aquatic system and, therefore, to develop a quantitative understanding of the bioaccumulation of
Hgtot, is the application of stable isotope techniques (Kidd et al., 1995). Application of this
technique to an exploratory observational research project such as WACAP was considered, but
because we had no idea of the structure of the fish contaminant data, and of Hg concentrations in
particular, it was not deemed to be cost effective. Application of this tool would be useful in
deciphering causes behind the high concentrations of Hgtot found in fish from selected WACAP
lakes.
5.7 Summary
In this chapter we present an assessment of bioaccumulation in vegetation, fish, and moose;
biological effects in fish; potential adverse ecological effects to piscivorous wildlife; and human
health risks from atmospheric sources of anthropogenic semi-volatile organic compounds
(SOCs), metals, and fixed nitrogen in national parks, preserves, monuments, and wildernesses of
the western United States. The principal findings are itemized in the following subsections:
5.7.1 Bioaccumulation
• SOC concentrations were orders of magnitude higher in biota (fish, vegetation) and
sediments compared to snow and air. Vegetation tended to accumulate more PAHs, CUPs,
and HCHs, and fish tended to accumulate more PCBs and less volatile chlordanes, DDTs,
and dieldrin.
• SOC burdens in conifer needles approximately tripled between first and second years.
• Western US coniferous forests have the capacity to accumulate annually in 2nd year needles
amounts of pesticides that are comparable on a per ha basis to a significant fraction of
regional pesticide application rates, providing an ecosystem service with un-examined
ecological consequences. Forest productivity (annual needle biomass production), conifer
species, proximity to sources, and application rates at the sources all appear to be important
factors in needle concentrations.
• Fish lipid and age were the most reliable predictors of SOC concentrations.
• Whole-body mercury was age-dependent in all fish up to approximately 15 years of age. In
lake trout older than 15, mercury was not age-dependent.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
• Mean ammonium nitrate concentration in ambient fine particulates < 2.5 (am diameter,
sampled at park IMPROVE monitors, was a fairly good predictor (Spearman's Rho
correlation coefficient > 0.62) of dacthal, endosulfan, chlordane, trifluralin, DDTs, and PAH
concentrations in vegetation.
5.7.2 Adverse Biological Effects Observed in Fish
• Most fish appeared normal during field necropsies. Lake trout from the Arctic had parasite
infestations of varying severity but there was no evidence that this was related to contaminant
concentrations.
• Kidney and spleen macrophage aggregates, a biomarker of tissue damage, varied
considerably but between-site differences were not related to contaminant concentrations.
• Spleen macrophage aggregates were highly correlated with tissue mercury concentrations
and age in brook, rainbow and cutthroat trout.
• Fish with both male and female characteristics (ova-testis) were found in ROMO and GLAC
lakes. The incidence of intersex has significantly increased since the pre-organic pollutant era
(pre-1930s).
• Elevated concentrations of vitellogenin, a female protein involved in egg production, were
found in male fish from MORA, ROMO, and GLAC. At ROMO, vitellogenin appeared to be
related to the concentration of several organochlorines. Small sample sizes limit the
inferences that can be made and suggest that further sampling and analysis of SOC
concentrations and vitellogenin might be warranted.
5.7.3 Potential Adverse Ecological Effects
• Mercury concentrations in fish exceeded contaminant health thresholds for some piscivorous
mammals and birds in most parks (see Section 5.4.1 for caveats). Concentrations of the sum
of the forms of DDT, ODD, and DDE in some fish in GLAC and SEKI exceeded
contaminant health thresholds for piscivorous birds.
• Modeling of contaminant and nutrient transport through hypothetical food webs also suggests
that contaminants may be adversely affecting life expectancy and abundance in piscivorous
wildlife.
• IMPROVE fine particulate monitoring data and lichen nitrogen and sulfur concentrations in
WACAP parks indicate that anthropogenic deposition of atmosphere nitrogen and sulfur-
containing fertilizing and acidic compounds are enhanced in SEKI, GLAC, and BIBE.
Elevated nutrient deposition is associated with adverse effects to sensitive species,
community dynamics, and ecosystem processes.
5.7.4 Health Risks to Humans
• Over half (77 of 136) of the individual fish, from 11 of the 14 WACAP lakes, carried
concentrations exceeding subsistence fishing contaminant health thresholds for dieldrin
and/or p,p'-DDE; thresholds were calculated using USEPA guidelines. Consuming fish
exceeding a contaminant health threshold implies an increased risk (by 1 in 100,000) of
developing cancer during a lifetime of frequent fish consumption.
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CHAPTER 5. BIOLOGICAL AND ECOLOGICAL EFFECTS
• Concentrations of chlorpyrifos, dacthal, endosulfans, methoxychlor, mirex, HCB, a-HCH,
g-HCH, chlordanes, heptachlor epoxide, and PBDEs, the other pesticides, and industrial
compounds detected in >50% offish, were 1-7 orders of magnitude lower than contaminant
health thresholds for subsistence fishing.
• Risks from recreational fishing consumption were lower than risks from subsistence fishing,
but concentrations of dieldrin in five individual fish from SEKI, ROMO, and GLAC
exceeded contaminant health thresholds for recreational fishing.
• The average mercury concentration in fish from Burial Lake (NOAT) and in some individual
fish from PJ and Hoh lakes (OLYM), LP19 (MORA), and Pear Lake (SEKI) exceeded the
USEPA contaminant health thresholds for humans.
• SOC and metal concentrations in the three moose samples, all from DENA, were low and not
of concern with regard to human health effects. The DENA moose were nutritionally
deficient in copper, which might be of interest to DENA wildlife biologists.
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CHAPTER 6
Recommendations and Conclusions
6.1 WACAP Recommendations to NPS
6.1.1 Introduction
These recommendations are related to the original five WACAP objectives and have been
specifically requested by the NPS as a product of this project. They are based on the results of
WACAP (as well as other relevant scientific literature, in some cases).
The WACAP objectives were to determine:
1. If contaminants are present in western national parks
2. Where contaminants are accumulating (geographically and by elevation)
3. Which contaminants pose a potential ecological threat
4. Which indicators appear to be the most useful to address contamination
5. What the sources were for contaminants measured at the national park sites
Recommendations stem from the question posed by NPS, "What did you learn in this project that
could help guide or focus future work within the NPS on contaminants in western U.S. parks?"
6.1.2 Presence of Key Contaminants
• Dieldrin, DDT, chlordane, and
mercury were found to be key
compounds/elements that are
atmospherically deposited within
WACAP parks because they have a
combination of higher concentrations
and greater toxicity in the food web
than other analytes.
Therefore NPS could consider
focusing on dieldrin, DDTs,
chlordane, and mercury in future
work when assessing highest risk to
western park resources is desired.
Since spectral analytical techniques
for the semi-volatile organic
compounds (SOCs) often result in identification of multiple compounds, the benefits of
looking at additional compounds at minimal extra cost should be considered.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSONS
• Mercury concentrations in fish tissue routinely exceeded piscivorous animal thresholds, and
in some parks, human contaminant health thresholds were also exceeded.
Therefore, assessing mercury concentrations and impacts on fish could be considered a
high priority for future analysis in parks. As evidence is emerging concerning the
important role of selenium in binding with methyl mercury in organisms, and possibly
reducing the toxic effects of mercury in the environment, future work on mercury
should consider also measuring and evaluating selenium.
• Deposition of HUPs (historic-use pesticides) was shown to be decreasing fairly consistently,
while deposition of CUPs (current-use pesticides) is increasing in many parks.
Therefore, NFS could consider focusing on current-use pesticides and other current-use
chemicals.
• Sediment records show that deposition of some current-use SOCs and PBDEs is increasing
over time in some areas: McLeod Lake (DENA) shows increasing endosulfans; Matcharak
Lake (GAAR) shows increasing PAHs; Oldman and Snyder lakes (GLAC) show increasing
dacthal and endosulfans; PJ Lake (OLYM) shows increasing dacthal and endosulfans; LP19
and Golden Lake (MORA) show increasing endosulfans; Lone Pine Lake (ROMO) shows
increasing endosulfans and Mills Lake (ROMO) shows increasing endosulfans and PBDEs.
Because increased use and deposition of these SOCs has been documented, the deposi-
tion and potential health effects (on wildlife and humans) of these current-use pesticides
could continue to be assessed over time, particularly in parks near agricultural sources
(such as SEKI, ROMO, and GLAC). The most effective approach for tracking the
changes in deposition in these compounds over time, and the thresholds for their impacts
to human and wildlife health, would need to be determined.
6.1.3 Locations of Contaminant Accumulation
• Contaminants were found in all WACAP lakes; in some cases, the concentrations in fish
were found to exceed important human and wildlife thresholds. It might be perceived that the
two lakes per park that WACAP examined are somehow outliers and that they do not repre-
sent the total population of lakes within parks. From a strictly statistical perspective, these
lakes are not representative of the population of lakes. However, the lakes were selected to
provide "clean," unambiguous signals of atmospherically deposited contaminants and in no
way were they selected to provide the highest or lowest contaminant concentrations.
Researchers conducting future work might choose to consider implementing a robust
statistical sampling design for specific parks that would provide a quantitative estimate
of the contaminant condition of all lakes in the population.
• SOC and nitrogen concentrations in WACAP parks were shown to be closely associated with
proximity to regional sources (agricultural, point, and urban sources).
Therefore NFS could consider monitoring SOC and nitrogen concentrations in areas
closest to these source types, where identification of "hot spots" of contaminants is
desired.
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSIONS
• Of the SOCs detected in air and vegetation
throughout the 20 core and secondary
WACAP parks, endosulfans and dacthal
were the dominant current-use pesticides,
and HCB and a-HCH were the dominant
historic-use pesticides. In general, GLAC
and SEKI, followed by YOSE and GRSA,
had the highest concentrations of SOCs
among the WACAP parks; air and
vegetation concentrations of CUPs were
lowest in Alaska parks; concentrations of
HUPs did not differ among the parks.
These findings imply risk to terrestrial
ecosystems, but accumulation in aquatic
ecosystems is unknown.
Therefore monitoring of SOCs in vegetation and other indicators (snow, fish, sediment)
might be fruitful in additional parks in California and the Rocky Mountains.
• GRSA contains very high concentrations in lichens (compared with most other parks) of
dacthal, endosulfans, HCB, a-HCH, g-HCH, chlordanes, and DDT.
Further investigation of the sources and extent of these contaminants in GRSA
ecosystems might be desirable.
• Industrial and agricultural contaminants were abundant in ROMO ecosystems. Fish gonadal
abnormalities (feminization of males and abnormalities in immature females) were observed
in the park, and some of these fish had high contaminant burdens. The relationship between
the two results is statistically significant, but complicated because of the small sample sizes.
The NFS might wish to conduct future studies regarding causes of fish abnormalities,
including evaluation of potential interactions of multiple contaminants.
• Pacific Coast parks contain a combination of high contaminant concentrations in needles and
dense forest foliage.
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSONS
Therefore, NFS might wish to assess whether this combination could result in high
loading of contaminants to the ecosystem from canopy leachates and forest litter fall in
these parks.
• High concentrations of some contaminants in a single snow sample in DENA (2,500-m
elevation), along with greater precipitation amounts at higher elevations, suggests potential
for greater contaminant loading and higher ecological effects at high elevations in this park.
WACAP suggests further exploration of contaminants at elevational gradients in this
park.
6.1.4 Ecological Threat From Contaminants
• Fish are a key indicator in parks because, as shown in this study and others, bioaccumulation
of contaminants in their tissues puts them at risk for adverse effects, as well as the species
that eat them (birds, wildlife, humans).
Therefore, NFS might wish to give first priority in bioaccumulation studies to assessing
contaminant concentrations in fish, to determine current risk to fish and consumers of
fish, before investigating other food web indicators.
• Fish were chosen as the primary indicator of ecosystem health in WACAP. However, other
studies have shown that each ecosystem food web varies in how it accumulates contaminants.
Therefore, although fish are
recommended as a key indicator for initial
assessment of contaminant impacts, when
fish contamination concentrations conflict
with other measurements, fish
concentrations might not reflect all parts
of the food web. In these cases, assessing
food web structure and ecosystem
processing variables may help determine
whether further bioaccumulation
assessments might be needed in other
indicators (e.g., song birds, insects,
amphibians, mammals).
• Contaminant concentrations were generally correlated with fish age and lipid (in this and
other studies), because fish tend to accumulate more contaminants in tissues as they eat and
grow older.
Therefore, determining the ages and lipid of fish assessed for contaminants in future
studies could help provide comparability among data sets or aid in the understanding of
variability among fish.
• Concentrations of mercury in fish were not always directly related to levels in atmospheric
deposition and flux (e.g., in some Alaska sites, mercury deposition was low, but concentra-
tions in fish were high). Bioaccumulation and biomagnification of mercury is controlled
largely by methylation processes and food web structure.
6-4
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSIONS
Therefore future studies should consider these factors in study designs, when
assessment of areas of highest risk is desired.
• Some lakes in GLAC and ROMO contain reproductively abnormal male fish, evidenced by
the presence of eggs and sperm in the same fish, testicular abnormalities, elevated levels of
female-specific protein in the blood, and elevated SOC concentrations in these fish.
Further research in other lakes in these parks as well as other parks in the Rocky
Mountains (GRTE, GRSA) could be conducted to assess the spatial extent of these
conditions, associated fish SOC concentrations, and the potential impact on fish
populations in these parks.
• There is strong inferential evidence that the intersex condition and female-specific protein
(Vtg) found in some fish in the study can be explained by endocrine disruption related to
contaminants.
There is a chance that further investigation could find this contention to be wrong, but
the "precautionary principle" argues for continuing to explore these potential linkages
between fish condition and contaminants in western parks.
• Other studies have shown that fish accumulated greater amounts of some SOCs in livers than
in fillets.
Because NFS is concerned about effects of bioaccumulation on fish, birds, and wildlife,
analysis of whole fish in parks (rather than fillets alone) is suggested to enable compari-
son of results with fish and wildlife toxicity thresholds. Conversions of concentrations
from whole fish to fillet values can subsequently be made to estimate risk to humans.
• Willow bark was evaluated in this study to assess accumulation of contaminants in
vegetation that could be browsed by animals. It was difficult, however, to identify the willow
species in the field, and willow bark was more difficult to analyze for SOCs in the lab.
Therefore willow bark is not currently recommended as a useful ecosystem indicator in
NFS future studies.
x
Select PBDE concentrations were fairly
high in WACAP fish (higher than in
Pacific Ocean salmon) and increasing in
some sediments. In addition, human and
wildlife contaminant health thresholds
for select PBDEs have not yet been
adopted, but are more accurate than the
current threshold.
Therefore in future studies, NFS might
wish to measure select PDBEs in fish
to determine if there is a temporal
trend in concentrations or risk.
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSONS
6.1.5 Sources of Contaminants
• This study identified several parks in which individual contaminants or suites of
contaminants suggest current local or regional source contributions.
NFS might wish to further investigate recent local source contributions of: (1) PAHs at
GLAC, (2) current-use SOCs at ROMO, GLAC, and SEKI, and (3) PBDEs at ROMO
and MORA.
• Sediment records showed steadily increasing mercury deposition over time at lakes in two
parks (MORA and ROMO). Because these patterns differ from those at other western parks,
the sources of mercury could be local rather than global.
NFS could consider additional work to determine the extent to which local sources
contribute to mercury deposition at these parks.
• Long-range, global (including trans-Pacific) SOC and mercury sources contribute a greater
percentage of the total SOC deposition in OLYM, DENA, and NO AT than do regional North
American sources.
NFS might wish to continue monitoring the deposition of SOCs and mercury in these
parks to better track the relative contribution of global sources over time.
6.1.6 Understanding Contaminant Processes in Ecosystems
This project has helped to elucidate some of the questions that will have to be addressed in the
future for a better understanding of how contaminants move into and through park ecosystems.
Understanding these processes, mechanisms, and ecosystem interactions will be important in
advancing contaminants research in the United States. However, because many of these types of
questions are beyond the scope of the key WACAP objectives, they have not been developed as
specific recommendations to NPS. However, several of the questions that have been identified
by WACAP investigators are included here as potential areas of interest for future research:
• Which ecosystem variables are the best predictors of contaminants risk to lake catchments?
• How important are various deposition processes, including rain, snow, fog, rime ice, and dry
deposition, in delivering contaminants to ecosystems?
• What is the fate (mass balance/budget) of SOCs in deposition? How much is re-volatilized to
the atmospheric from vegetation, soil, or water? How much accumulates in terrestrial or
aquatic ecosystems? How much is transported to downstream environments in stream flow?
• How can SOC concentrations be used to predict other contaminants?
• Can information about the half-lives of contaminants found in parks be used to predict future
trends for increases or decreases in these ecosystems?
• What is the effect of interaction among multiple contaminants in ecosystems?
• What are the ecosystem variables in Alaska parks that result in higher bioaccumulation of
mercury in fish there?
• What will be the synergistic impacts on bioaccumulation of contaminants with global
change, regional land-use change, wildfire, and other regional-scale anthropogenic changes?
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSIONS
• What are the impacts of contaminants in parks on other parts of the food web not studied in
the project (e.g., mink, piscivorous birds, amphibians)?
• What are the speciated components of mercury in the air, and what do the proportions of
reactive gaseous mercury and particulate mercury tell us about regional mercury source
contributions?
• How do the types and locations of vegetation selected for sampling influence contaminant
uptake (e.g., vegetation under snowpack compared with vegetation exposed year round).
• What is the extent of the cold fractionation effect of SOCs in vegetation accumulating
preferentially at high elevations in some parks?
• How should contaminants be tracked over time in parks where they have been identified as
potentially increasing (in sediments, snow, air, vegetation, and fish)?
• What are thresholds of concern for the effects of bioaccumulation of emerging contaminants
(e.g., PBDEs) on fish, wildlife, and humans?
• Over what timeframe do various contaminants degrade in sediments? Do concentrations of
contaminants change over time once they have been deposited?
6.2 Conclusions
The transport, fate, and ecological effects of anthropogenic contaminants from atmospheric
sources were assessed in air, water, snow, sediment, vegetation, and fish in eight core national
parks in the western United States. In addition, air and vegetation were sampled in twelve
secondary national parks, preserves, and national forests in the western United States to further
enhance spatial interpretations of the data. Samples were analyzed for SOCs (CUPs and HUPs,
industrial compounds, and PAH), mercury, and other metals. Relative to the WACAP objectives,
major conclusions are as follows.
Out of more than 100 SOCs tested (excluding
PBDEs in fish and sediments), 70 were found at
detectable concentrations in air, snow, water,
vegetation, sediment, and/or fish. Six contam-
inants of highest concern were identified for the
eight core park ecosystems studied: mercury,
dieldrin, DDT, PCBs, chlordane, and PAH.
These contaminants are of highest concern
because of (1) the high concentrations detected,
(2) the bioaccumulation documented, and/or
(3) their toxic or persistent characteristics in the
environment. Other contaminants identified as
potential concerns are PDBEs, endosulfans,
dacthal, chlorpyrifos, HCB, a-HCH, and g-HCH, because they are (1) in current use, (2) are
present at comparatively high concentrations in vegetation or fish, and/or (3) are increasing over
time in sediment cores.
Contaminants were shown to accumulate geographically based on proximity to individual
sources or source areas. Pesticide concentrations for both HUPs and CUPs were highest in parks
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSONS
and park watersheds closest to agricultural areas. Concentrations of industrial contaminants
(PAHs and mercury) were sometimes elevated near parks where local/regional sources produce
these contaminants. This finding is counter to the original working hypothesis that most of the
contaminants found in western parks would originate from eastern Europe and Asia and travel
across the Pacific to the western United States. There was evidence that this phenomenon does
occur, but contaminant contributions from trans-Pacific sources of SOCs were small compared to
other regional sources closer to the parks. Regarding mercury, in particular, deposition is
composed of a complex mixture of local, regional, and global sources.
Contaminants were found to bioaccumulate in ecosystems (higher concentrations in older vege-
tation than in younger vegetation), and biomagnify at higher levels of the food web (concentra-
tions in fish higher than those in air, snow, or water). Bioaccumulation and biomagnification of
contaminants in ecosystems have been shown in other studies to occur elsewhere, but not at these
regional scales in remote ecosystems in the western United States.
Among the contaminants found in western park ecosystems, mercury and dieldrin are likely to
pose the greatest ecological threat. Mercury is a common component of coal. On a global scale,
approximately two-thirds of the anthropogenic mercury emitted is from the combustion of fossil
fuels. When mercury is deposited in the environment and biologically converted to its toxic form
(methyl mercury), it can bioaccumulate readily in food chains and cause neurological and other
detrimental effects in humans, fish, and other organisms. Although mercury deposition in Alaska
parks was low, in-lake biological processes specific to the lakes in these parks contributed to
concentrations in fish that exceeded contaminant health thresholds for humans and wildlife. The
average mercury concentration in fish from Burial Lake (NOAT) exceeded the human contamin-
ant health threshold. Average mercury concentrations in fish at GAAR and DENA (Wonder
Lake) fell between the human and otter contaminant health thresholds, and mercury concentra-
tions in all fish from the four Alaska lakes, except for one fish from McLeod Lake (DENA),
were above the contaminant health thresholds for kingfisher (see Figure 5-18). Mercury concen-
tration thresholds for risk to birds and wildlife were routinely exceeded at most lakes in most
parks. Dieldrin is an acutely toxic insecticide, categorized as a carcinogen and a known
endocrine-disrupting compound. It was banned from use in the United States in 1987 and in
Canada in 1990. However, concentrations of dieldrin found in fish in some parks in this study
were still high, and in some cases exceeded USEPA contaminant health thresholds for increased
cancer risk to humans, but not the thresholds for risk to birds and wildlife. It is not currently
known why concentrations remain high in parks near agricultural areas two decades after the
product was banned, but dieldrin is known to be persistent in the environment.
The ecological indicators found to be most useful in interpreting contamination in this study
were fish, sediments, conifer needles, and lichens. Fish were important as an indicator of
bioaccumulation of contaminants and potential impacts to food webs. Sediments provided a
historical context, documenting changes in contaminants over time, and retaining clues about
contaminant sources through historical deposition of metals and SCPs (spheroidal carbonaceous
particles). Second-year conifer needles proved to be an effective current measure of several types
of contaminant concentrations over large spatial scales. Because their age is known and their
biomass is large, conifer needles provided an ecologically relevant measure of yearly
contaminant loading in vegetation. Lichens, with a higher capacity for SOC accumulation, and
with occurrence in both forested and arctic-alpine ecosystems, indicate differences in SOC
concentrations along elevational gradients and among sites within parks. However, lichens
6-8 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSIONS
cannot be aged, and therefore, the period of time over which contaminants are accumulated in
lichens is unknown, complicating their interpretation. Consistently sampling a single species and
collecting samples above or below winter snow lines improves the sensitivity of vegetation
indicators.
The sources of contaminants in western national parks vary by region. In Alaska, there are few
local or regional sources of contaminants, and deposition of contaminants is influenced primarily
by transport from other source regions. In general, deposition of CUPs and HUPs is most
strongly influenced by proximity to agricultural and industrial areas. An aluminum smelter near
GLAC contributes to high concentrations of PAHs in snowpack, sediment, and lichens at Snyder
Lake.
The knowledge gained from this project should add considerably to the state of the science about
contaminant transport, flux, and biological and ecological effects in remote ecosystems in the
western United States. However, it also serves to raise many additional questions. Related work,
if conducted in the future, might explore some of these areas, identifying the various temporal
and spatial dimensions of contaminant pathways and defining and documenting the extent and
magnitude of specific ecological effects.
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT 6-9
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CHAPTER 6. RECOMMENDATIONS AND CONCLUSONS
6-10
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R-22 WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT
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-------
Western Airborne Contaminants Assessment Project
Final Report: Volume I
Site Type
All media sampled at
lake sites in core parks
0 Vegetation only sampling sites
(in core parks, in addition to the
lake sites)
,g| Snow only sampling sites
(in core parks, sites outside
of lake sites)
Air sampling sites
EPA Ecoregions-Level 1
ARCTIC CORDILLERA
GREAT PtAINS
^H MARINE WEST COAST FOREST
MEDITERRANEAN CALIFORNIA
NORTH AMERICAN DESERTS
HH NORTHERN FORESTS
B NORTHlftESTERN FORESTED MOUNTAINS
SOUTHERN SEMI-ARID HIGHLANDS
•• TAIGA
HH TEMPERATE SIERRAS
HB TROPICAL DRY FORESTS
HB TUNDRA
500 1.000
2.000
&EPA
United States
Environmental Protection
Agency
PRESORTED STANDARD
POSTAGE & FEES PAID
EPA
PERMIT NO. G-35
Office of Research and Development (81 OR)
Washington. DC 20460
Official Business
Penalty for Private Use
$300
-------
WESTERN AIRBORNE CONTAMINANTS ASSESSMENT PROJECT FINAL REPORT: VOLUME II
The Fate, Transport, and Ecological Impacts of Airborne Contaminants
in Western National Parks (USA)
Appendices
Burial Lake, Noatak National Preserve
Photo: Adam Schwindt
Dixon H. Landers
Staci Simonich
Daniel Jaffe
Linda Geiser
Donald H. Campbell
Adam Schwindt
Carl Schreck
Michael Kent
Will Hafner
Howard E. Taylor
Oregon State
UHl VEftSI TV
osu
Kimberly Hageman
Sascha Usenko
Luke Ackerman
Jill Schrlau
Neil Rose
Tamara Blett
Marilyn Morrison Erway
Technical Editor:
Susan Christie
UNIVERSITY OF
WASHINGTON
EPA/600/R-07/138
January 2008
-------
-------
The Fate, Transport, and Ecological Impacts of Airborne
Contaminants in Western National Parks (USA)
Volume II: Appendices
Dixon H. Landers
USEPA, NHEERL
Western Ecology Division
Corvallis, Oregon
Staci Simonich
Dept. of Env. & Molecular Toxicology &
Dept. of Chemistry, Oregon State Univ.
Corvallis, Oregon
Daniel Jaffe
University of WA-Bothell
Bothell, Washington
Linda Geiser
US Forest Service Pacific NW Region
Air Program
Corvallis, Oregon
Donald H. Campbell
U.S. Geological Survey
Denver, Colorado
Adam Schwindt
Dept. of Microbiology
Oregon State University
Corvallis, Oregon
Carl Schreck
Oregon Cooperative Fish and Wildlife
Research Unit, USGS-BRD
Oregon State Univ. Corvallis, Oregon
Michael Kent
Dept. of Microbiology
Oregon State University
Corvallis, Oregon
Will Hafner*
University of Washington-Bothell
Bothell, Washington
Technical Editor: Susan Christie
* Current Affiliations:
Hafner: Science Applications International Corp., Bothell, Washington
Hageman: Dept. of Chemistry, University of Otago, Dunedin, New Zealand
Usenko: Environmental Science Dept., Baylor University, Waco, Texas
Ackerman: FDA-Center for Food Safety and Applied Nutrition, College Park, Maryland
Howard E. Taylor
U. S. Geological Survey
Boulder, Colorado
Kimberly Hageman*
Dept. of Env. & Molecular Toxicology
Oregon State University
Corvallis, Oregon
Sascha Usenko*
Dept. of Chemistry
Oregon State University
Corvallis, Oregon
Luke Ackerman*
Dept. of Chemistry
Oregon State University
Corvallis, Oregon
Jill Schrlau
Dept. of Chemistry
Oregon State University
Corvallis, Oregon
Neil Rose
University College London
London, United Kingdom
Tamara Blett
NPS-Air Resources Division
Lakewood, Colorado
Marilyn Morrison Erway
Dynamac Corporation
c/o USEPA, NHEERL
Western Ecology Division
Corvallis, Oregon
-------
Volume II - Appendices
Appendix 1A
Appendix 3 A
Appendix 3B
Appendix 4A
Appendix 5A
Appendix 5B
Appendix 5C
Summary of Site Characteristics in Core and Secondary Parks 1 A-l
Summary of Sampling and Analysis Plan by Environmental
Medium 3A-l
Sampling Information, Methods, and Data Quality 3B-1
Detailed Information on Contaminants in Vegetation, Including
Elevation Trends 4A-1
Fish Biological Data 5A-1
Correlations between Hg and Age 5B-1
Correlations between Macrophage Aggregates and Hg 5C-1
This report is the final report for the Western Airborne Contaminants Assessment Project (WACAP), and is
available online at http://www.nature.nps.gov/air/studies/air_toxics/wacap.cfm and
http://www.epa.gov/nheerl/wacap
Proper citation of this document is:
Landers, D.H., S.L. Simonich, D.A. Jaffe, L.H. Geiser, D.H. Campbell, A.R. Schwindt,
C.B. Schreck, M.L. Kent, W.D. Hafner, H.E. Taylor, KJ. Hageman, S. Usenko, L.K.
Ackerman, I.E. Schrlau, N.L. Rose, T.F. Blett, and M.M. Erway. 2008. The Fate,
Transport, and Ecological Impacts of Airborne Contaminants in We stern National Parks
(USA). EPA/600/R-07/138. U.S. Environmental Protection Agency, Office of Research
and Development, NHEERL, Western Ecology Division, Corvallis, Oregon.
DISCLAIMER: Funding for this work was provided by the National Parks Service of the
Department of the Interior, the U.S. Environmental Protection Agency, and the U.S.
Geological Survey. It has been subjected to review by these government entities and
approved for publication. Approval does not signify that the contents reflect the views of
the U.S. Government, nor does mention of trade names or commercial products constitute
endorsement or recommendation.
WESTERN AIRBORNE CONTAMINANT ASSESSMENT PROJECT
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APPENDIX 1A
Summary of Site Characteristics in Core and Secondary Parks
Table 1A-1. Summary of Physical Attributes of the Lake Catchments in Core Parks
Park
NOAT
GAAR
DENA
DENA
GLAC
GLAC
OLYM
OLYM
MORA
MORA
ROMO
ROMO
SEKI
SEKI
Site
Burial
Matcharak
Wonder
McLeod
Snyder
Oldman
PJ
Hoh
Golden
LP19
Mills
Lone Pine
Pear
Emerald
Latitude
(dd)
68.43
67.75
63.48
63.38
48.62
48.5
47.95
47.9
46.89
46.82
40.29
40.22
36.6
36.58
Longitude
(dd)
159.18
156.21
150.88
151.07
113.79
113.46
123.42
123.79
121.9
121.89
105.64
105.73
118.67
118.67
Bathymetry
Source
vaga/j605
vaga/j605
scan
vaga/j605
Ianders/j700
Ianders/j663
Ianders/j663
vaga/j605
scan
scan
Ianders/j597
vaga/j605
scan
scan
Elevation (m)
(from NFS)
427
488
610
609
1600
2026
1433
1384
1372
1372
3030
3024
2904
2800
Lake
Elevation
(m)
(from drg*)
429.8
502.3
605.0
563.9
1597.2
2025.7
1383.8
1379.2
1368.6
1371.6
3029.7
3017.5
2907.8
2810.3
Lake
Surface
Area
(ha)
65.5
300.7
265.6
35.9
2.6
18.2
0.8
7.7
6.6
1.8
6.1
4.9
7.3
2.5
Lake Volume
(m3)
5297945.2
21889008.3
77653853.3
1847704.4
38297.5
1266062.9
19099.3
396198.1
689577.5
99878.6
78251.1
128324.6
578000
160000
Lake
Max
Depth
(m)
24.1
20.4
70
13.5
3.5
17
6.4
14.9
23.9
12.1
9
9.7
27
10
Mean
Depth
(volume/
surface
area)
8.1
7.3
29.2
5.2
1.5
7.0
2.5
5.2
10.4
5.4
1.3
2.6
7.9
6.3
Shoreline
length
(km)
3.1
10.7
9.7
3.2
0.7
1.8
0.3
1.1
1.0
0.5
1.3
0.8
1.3
0.6
Shoreline
Development*
1.07
1.74
1.68
1.53
1.24
1.17
1.08
1.09
1.07
1.11
1.46
1.07
1.31
1.12
Watershed
Area (ha)
264.9
2388.3
3212.4
236.8
303.7
230.3
56.2
43.9
106.1
44.9
1208.9
1830.0
142.0
121.3
Watershed
Elevation -
lowest (m)
(from drg*)
429.8
502.3
605.0
563.9
1597.2
2025.7
1383.8
1379.2
1368.6
1371.6
3029.7
3017.5
2907.8
2810.3
Watershed
Elevation -
highest
(m) (from
drg*)
535.2
1162.2
876.3
731.5
2761.5
2811.8
1904.1
1684.0
1720.6
1652.0
4344.9
3998.4
3453.1
3439.4
Watershed
Area/ Lake
Volume
(m2/m3)
0.50
1.09
0.41
1.28
79.30
1.82
29.41
1.11
1.54
4.49
154.49
142.61
2.46
7.58
Annual
HRT*
Based
onET*
(y)
24.559
7.643
6.836
4.131
0.005
0.235
0.003
0.107
0.067
0.024
0.009
0.012
0.251
0.087
Fish
Species
Lake Trout
Lake Trout
Lake Trout
Burbot,
Round
Whitefish
Wests I ope
Cutthroat
Trout
Wests I ope
Cutthroat
Trout
Brook
Trout
Brook
Trout
Brook
Trout
Brook
Trout
Rainbow,
Cutthroat
Trout
Brook
Trout
Brook
Trout
Brook
Trout
Notes:
*drg (digital raster graphic) lake boundaries used for calculations; except lake volume for Pear and Emerald from Sickman and Melack, 1989
*shoreline length/2*square root of (pi * surface area)
*HRT = Hydraulic Residence Time of lake based on ET = Evapotranspiration in % of precipitation
1A-1
-------
Table 1A-2. Summary of Chemical Attributes of the Lake Catchments in Core Parks
Park
NOAT
GAAR
DENA
DENA
GLAC
GLAC
OLYM
OLYM
MORA
MORA
ROMO
ROMO
SEKI
SEKI
Site
Burial
Matcharak
Wonder
McLeod
Snyder
Oldman
PJ
Hoh
Golden
LP19
Mills
LonePine
Pear
Emerald
WACAP
No
46003
46000
46005
46008
56009
56006
56014
56011
56004
56001
36004
36006
36003
36001
Date
Collected
8/04/2004
8/2/2004
8/14/2004
8/1 0/2004
8/25/2005
8/21/2005
9/14/2005
9/11/2005
8/14/2005
8/10/2005
9/11/2003
9/14/2003
8/26/2003
8/25/2003
PH
Value
7.57
8.31
8.18
7.24
6.42
8.24
8.14
7.52
6.47
6.63
6.61
6.67
6.10
6.22
Specific
Conductance
(uS/cm)
35.08
248.10
190.10
8.41
16.80
159.10
127.40
63.69
10.08
10.72
12.04
14.02
4.02
5.42
ANC
Value
(ueq/L)
272.98
1967.03
1693.60
51.02
162.08
1573.73
1092.95
512.45
69.05
80.14
50.81
91.52
23.99
26.34
Turbidity
(NTU)
0.32
0.35
0.34
0.29
0.64
0.35
0.36
0.39
0.52
0.31
0.55
0.31
0.23
0.26
Total
Suspended
Solids
(mg/L)
1.4
72.2
0.5
-0.6
1.7
-0.8
1.2
0.8
0.2
0.0
0.2
0.2
0.0
0.3
Color
(APHA
Pt-Co
Units)
10
10
15
5
10
2
5
7
4
8
5
5
0
0
DOC
(mg/L)
3.32
4.71
2.10
2.25
0.65
0.70
1.05
0.74
1.88
1.37
1.55
1.74
0.82
0.94
DIG
(mg/L)
3.27
23.18
20.29
0.90
4.41
19.98
12.18
5.71
1.18
1.32
1.01
1.44
1.13
1.04
NH4-N
(mg/L)
0.01
0.01
0.00
0.01
0.01
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.02
0.01
SiO2
(mg/L)
0.28
3.39
2.95
0.17
1.41
1.88
4.24
2.57
5.51
6.61
1.84
2.95
1.43
2.28
Total
N
(mg/L)
0.23
0.28
0.11
0.13
0.10
0.07
0.09
0.06
0.07
0.07
0.38
0.17
0.11
0.17
Total
P
(ug/L)
9.06
1.09
0.50
1.04
2.67
0.55
2.78
1.16
0.60
0.92
3.33
2.70
0.59
1.47
Cl
(mg/L)
0.18
0.87
0.12
0.11
0.06
0.09
0.31
0.67
0.63
0.55
0.18
0.16
0.18
0.18
NO3
(mg
N/L)
0.00
0.00
0.00
0.01
0.02
0.00
0.00
0.02
0.02
0.00
0.23
0.06
0.05
0.07
SO4
(mg/L)
1.46
27.02
14.31
0.26
0.51
3.65
9.03
4.15
0.38
0.38
1.00
1.40
0.33
0.31
Ca
(mg/L)
4.50
37.30
32.00
0.95
1.85
21.17
21.33
10.50
0.64
0.91
1.27
1.74
0.34
0.39
Mg
(mg/L)
1.15
6.37
4.24
0.16
0.74
8.00
1.84
0.70
0.17
0.17
0.15
0.19
0.02
0.04
Na
(mg/L)
0.36
4.14
1.08
0.15
0.36
0.26
1.61
1.15
0.85
0.91
0.51
0.64
0.21
0.38
K
(mg/L)
0.40
0.52
0.71
0.31
0.13
0.21
0.32
0.12
0.14
0.26
0.13
0.14
0.11
0.13
Zn
(mg/L)
0.03
0.03
0.01
0.03
0.01
0.00
0.00
0.00
0.01
0.00
0.04
0.02
0.03
0.05
Se
(ug/L)
-0.38
0.86
-0.04
0.11
-0.24
-0.53
-0.08
0.13
-0.55
-0.43
-2.13
-2.13
-1.97
-1.95
Chl-a
(Mg/L)
0.81
0.96
0.49
0.61
4.73
0.77
1.77
0.83
0.35
0.60
3.02
1.95
0.64
0.62
1A-2
-------
Table 1A-3. Summary Characteristics of Vegetation and Air Sampling Sites in Core and Secondary Parks (for key, see last page)
Site
Name Media
Lat. Long.
Elev.
(m)
Ann.
Temp.
(°C)
nn. Canopy Lichen
Ppt. Cover N %
(cm) (%) Landform dw
Lichens
collected
Conifers
collected
Location
Habitat
Park: Bandelier National Monument, New Mexico (BAND)
Ag Intensity Index: 2.5; IMPROVE AmmNOS: 0.25 |jg/m3; IMPROVE AmmSO4: 1 .00 |jg/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Southern Rockies
BAND1 Vegetation
BAND2 Vegetation
BANDS Vegetation
BAND4 Vegetation
BAND5 fetation
Park: Big Bend National
Ag Intensity Index: 0.5;
North American Biome:
Air
BIBE1 vegetation
Air
BIBE2 vegetation
BIBE3 Alr' . ..
vegetation
Air
BIBE4 vegetation
BIBE5 Vegetation
35.7279 -106.2745
35.7989 -106.2846
35.8241 -106.3611
35.8262 -106.3893
35.8642 -106.4178
1854
2076
2348
2576
2926
10.5
9.9
8.1
6.4
5.4
34
39
47
55
62
43
34
36
72
36
Park, Texas (BIBE)
IMPROVE AmmNOS: 0.26 ug/m3; IMPROVE AmmSO4: 2.82
EPA Ecoregion 3 - North American Deserts: Southern Deserts
29.1870 -102.9718
29.3079 -103.1828
29.2850 -103.2799
29.2534 -103.2979
29.2465 -103.3049
560
1067
1608
1920
2316
Park: Crater Lake National Park, Oregon (CRLA)
Ag Intensity Index: 4.2; IMPROVE AmmNOS: 0.1 1 ug/m3;
North American Biome: EPA Ecoregion 3 - Northwestern
CRLA1 Vegetation
CRLA2 Vegetation
CRLA3 Vegetation
CRLA4 Vegetation
Air
CRLA5 vegetation
42.8364 -122.1459
42.8821 -122.1914
42.9346 -122.1776
42.9194 -122.0289
42.9233 -122.0162
1798
1859
2043
2423
2713
Park: Denali National Park and Preserve, Alaska (DENA)
Ag Intensity Index: 0 IMPROVE AmmNOS: 0.042 ug/m3
North American Biome: EPA Ecoregion 3 - Taiga: Interior
DENA1 Vegetation
Air (2),
fish, lake
DENA2: water,
Wonder snow,
sediments,
vegetation
63.7740 -151.0194
63.4538 -150.8720
221
655
21
18.6
17.5
16.7
16.7
26
34
47
52
52
3
0
23
65
59
Flatland
Flatland
Toe slope
Mid-slope
Upper
slope
ug/m3
Valley
Flatland
Mid-slope
Mid-slope
Upper
slope
1.79
1.58
1.58
1.26
1.56
1.40
1.56
Xanthoparmelia
Usnea
Xanthoparmelia
Usnea
Usnea
Usnea
Usnea
Pinus edulis
Pinus edulis
Pinus
ponderosa
Pinus
ponderosa
Pinus
ponderosa
Pinus
cembroides
Pinus
cembroides
Pinus
cembroides
S terminus of mesa between Lummis and White
Rock Canyon, where Burro Trail descends into
Lummis Canyon.
On the mesa just NW of Juniper Campground
On the lower e slopes of Frijoles Peak ~ 1 .6 km
along trail from Ponderosa group camp.
Lower SW slope of Cerro Grande, accessed from
1 .6 km hike on Apache Spring trail.
On the saddle SW of Cerro Grande Peak at the
Wedge of the meadow.
Rio Grande Village in cottonwood/ grass area
near hot springs (railhead; -60 m from picnic area
parking lot and 60 m from the hot springs road.
76 m at 354° from the water tank along the road
to K-Bar Camp near Panther Junction.
N side of Panther Pass along Chisos Basin Rd.
(~90 m E of road) in Green Gulch Creek drainage
basin.
Pinnacles campground
On the N slope of Emory Peak on saddle below
peak.
One-seed juniper woodland with scattered oak and two-needle pifion on a moderately steep W-facing
slope. Ground is rocky with some large outcrops. Almost all pifion were dead along mesa on hike in (cause
could be drought and/or bark beetles).
One-seed juniper woodland with oaks and two-needle pifion on a mesa. Most of the pines were dead as a
result of past drought and bark beetles. Usnea and Xanthoparmelia are abundant here. Nitrophilous lichens
abundant on some trees.
Pinus ponderosa stand with Oregon white oak and low-growing Ceanothus species in the understory. Fire
came through within the last few decades (fire scars on bark and an open canopy). Ground cover is grassy
with open gravel/mineral soils.
Ponderosa pine/Douglas-fir stand on the edge of a SE-facing drainage. Fire came through recently (burn
scars on bark and little vegetation in understory). Mostly duff and gravelly soil.
On the edge of a Douglas-fir forest with some ponderosa pine, Engelmann spruce, and quaking aspen
bordered by a large open meadow. Nitrophilous lichens were observed.
Cottonwood woodland bordered by dense mesquite shrublands.
Desert shrubland with honey mesquite, yucca, and cacti.
Mexican pifion/oak woodland on a low-grade N-facing slope. The ground is rocky and the cover is mostly
bunch-grass.
Mixed Mexican pifion/drooping juniper/juniper/oak stand; on a bench on the NW mountain slope. Dry, many
large boulders; deciduous and evergreen oaks in understory.
On a steep N-facing slope bordering the ridge top. Vegetation is dominated by Mexican pifion and
evergreen oak. The nitrophilous lichen, Teloschistes, is abundant on oaks.
IMPROVE AmmS04: 0.43 ug/m3
Forested Mountains: Cascades
4.2
3.5
3.4
3.5
3.5
155
160
164
108
108
IMPROVE AmmSO4: 0
Forested Lowlands and
-2.6
-2.6
41
66
74
49
31
21
0
Toe slope
Mid-slope
Upper
slope
Upper
slope
Ridgetop
.36 ug/m3
Uplands (DENA1);
0
69
Flatland
Toe slope
0.66
0.59
0.62
0.62
Northwestern
0.41
0.44
Letharia vulpina
Letharia vulpina
Letharia vulpina
Letharia vulpina
Forested Mountains:
Flavocetraria
cucullata
Flavocetraria
cucullata/
Masonhalea
richardsonii
Abies
magnifica
Abies
concolor
Abies
magnifica
Pinus
albicaulis
Pinus
albicaulis
Alaska Range
Pice a
mariana
Pice a
mariana
W of the Lodgepole picnic areas and just SE of
Bear Bluff on SW side of Rd. 62, ~1 22 m from the
road.
~120m NWof Rd. 62 and NE of Whitehorse
Pond.
Meadow bench just off Lightning Springs trail,
-0.4 km W of Rim Drive.
On the SW side of Mt. Scott, ~1 .6 km up Mt.
Scott trail, on downhill side of trail.
Top of Mt. Scott on NE side of fire lookout.
(DENA2-6)
Moose Creek, ~ 23 km N of Wonder Lake.
SWend of Wonder Lake on hillslope facing lake
~ 60 m downhill from water tower.
Lodgepole pine stand; regeneration layer is almost entirely fir (esp. red fir) and mountain hemlock. The
ground is mineral soil with pumice stones and a thin layer of pine needle duff. Lupine dominates herb layer.
Mixed conifer stand (white fir, lodgepole pine and mountain hemlock) of multiple age classes. The site is a
bench on rocky ground; ground cover is mostly bearberry manzanita and grouse huckleberry.
Meadow with clumps of old Shasta fir and mountain hemlock. The landform is a W-facing flat bench; the soil
is sandy and dry; cover is mostly grass and buckwheat.
Whitebark pine stand with some mountain hemlock and red fir. Slopes are steep and rocky with very little
vegetation.
Gently sloping rocky summit ridge vegetated by clumps of small whitebark pine and some high-elevation
herbs (pasque flower, paintbrush, Penstemon and bunch grasses).
Black-spruce dominated taiga and peatlands.
Black and white spruce woodland on gentle, NE-facing hillslope. Ground cover is matted blueberry, dwarf
birch, grass, bryophytes, and lichens interspersed with willows and alder. Trees are open-grown with
branches to ground level.
1A-3
-------
Ann. nn. Canopy
Site Elev. Temp. Ppt. Cover
Name Media Lat. Long. (m) (°C) (cm) (%) Landform
Fish, lake
DFNA-V water,
McLeod snow' 63.3696 -151.1003 579 -2.4 70 16 Flatland
sediments,
vegetation
DENA4 Vegetation 63.5520 -150.9670 975 -2.9 68 5 Upper
a slope
DENA5 Vegetation 63.1648 -151.3599 1296 -4.7 122 0 ^^
DENA6 Vegetation 63.1386 -151.3221 1753 -6.9 178 0 Ridgetop
Lichen
N%
dw
0.36
0.43
0.43
0.29
Lichens
collected
Flavocetraria
cucullata/
Masonhalea
richardsonii
Masonhalea
richardsonii
Flavocetraria
cucullata/
Masonhalea
richardsonii
Thamnolia
Conifers
collected
Picea
mariana
Picea
mariana
Location
S side of McLeod Lake, ~ 15 km sw of Wonder
Lake.
On ridge W of Wickersham dome.
Upper Birch Creek, NW footslopesof the Alaska
Range below Peters Dome glacier.
NWside of Alaska Range on plateau at foot of
Westermere Glacier on Peter's Dome; ~ 18 km
NWofMt. McKinley Peak.
Habitat
Black spruce peatland/taiga sloping to shoreline. Trees are open grown, most to 4.5 m tall, others to 9 m.
Transition zone from black spruce forest; tundra on a S-facing slope near ridgetop. Small clumps of willow
shrubs and a few scattered spruce trees; mostly dwarf willows, crowberry, other heaths, and lichens.
Riparian zone above tree line above Birch Creek, a stony, braided stream. 1 .2 m high willows along stream
bank, most browsed but not this year (new growth > 2.5 cm long and untouched). Above stream is tundra
on gentle hillslope, with dwarf willow.
High-elevation, graveled plateau, ~ 50% vegetated (~4 in. tall) and 50% gravels. Mostly sedges and moss,
with trace amounts of other tundra plants. Thamnolia and sparse amounts of other lichens (mainly
Stereocaulon) present.
Park: Gates of the Arctic National Park and Preserve, Alaska (GAAR)
Ag Intensity Index: 0; IMPROVE AmmNOS: 0.05 ug/m3; IMPROVE AmmSO4: 0.42 ug/m3
North American Biome: EPA Ecoregion 3 - Tundra: Brooks Range
Air, fish,
lake water,
M t h k snow' 67.7529 -156.2323 505 -8.6 43 0 Flatland
sediments,
vegetation
0.48
Flavocetraria
cucullata/
Masonhalea
richardsonii
Matcharak Lake, birch-covered side slope, W
side of lake.
Small side slope with dwarf birch and bryophytes.
Park: Glacier National Park, Montana (GLAC)
Ag Intensity Index: 21 .6; IMPROVE AmmNOS: 0.29 ug/m3 IMPROVE AmmSO4: 0.84 ug/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Canadian Rockies
GLAC1 Vegetation 48.6208 -113.9058 961 5.4 74 128 Lake basin
GLAC2 Vegetation 48.6757 -113.8096 1089 2.9 85 164 Toe slope
Air, fish,
„. .„_ lake water,
r" . snow, 48.6261 -113.8031 1609 2.5 178 31 Lake basin
Snyder sediments,
vegetation
Air, fish,
GLAC4- lake water' High
~.~r snow, 48.5104 -113.4552 2024 2.9 85 34 elevation
Oldman sediments, lake basin
vegetation
GLAC5 Vegetation 48.6924 -113.5170 1353 2.9 121 114 Valley
1.26
0.94
0.82
0.97
1.16
Platismatia
glauca
glauca
Platismatia
glauca/Alectoria
sarmentosa
Letharia vulpina
Letharia
vulpina/
Hypogymnia
physodes
Park: Glacier Bay National Park, Alaska (GLBA)
Ag Intensity Index: 0 IMPROVE AmmNOS: 0.04 ug/m3 IMPROVE AmmSO4: 0.46 ug/m3 (2005 Data from PETE1 in Petersburg, SE
North American Biome: EPA Ecoregion 3 - Marine West Coast Forest: Coastal Western Hemlock-Sitka Spruce Forests
Air
GLBA1 ' ... 58.6022 -135.8831 8 4 261 79 Toe slope
vegetation r
GLBA2 Vegetation 58.6061 -135.8801 168 4 261 106 Mid-slope
a bench
GLBA3 Vegetation 58.6093 -135.8724 457 4 261 117 Mid-slope
GLBA4 Vegetation 58.6121 -135.8714 625 4 261 18 Upper
a slope
0.79
0.51
0.57
0.39
Platismatia
glauca
Sphaerophorus
globosus
Sphaerophorus
globosus
Alectoria
sarmentosa
Tsuga
heterophylla
Tsuga
heterophylla
~
Picea
engelmanii
Pseudotsuga
menziesii
AK)
Picea
sitchensis
Picea
sitchensis
Picea
sitchensis
Picea
sitchensis
Wside Continental Divide, past McDonald
Ranger Station to (railhead at end of road; lake is
SE of trail, ~ 0.2 km from (railhead.
Wside Continental Divide, ~ 0.6 km on
Avalanche Lake Trail from (railhead at Avalanche
Campground on NE side of Avalanche Creek.
Wside Continental Divide, Snyder Lake trail to
Snyder Lake; in forest on SWedge of lake across
from food preparation area.
E side Continental Divide, trail from Two
Medicine Campground to Oldman Lake; site is S
of trail where it meets the lake and along the
stream from the lake.
E side Continental Divide, St. Mary Lake; road
across from Rising Sun campground to picnic
area; site is 100 m SW toward stream and along
lake.
In forest near shore of N end of Beartrack Cove
at toe-slope of SW ridge.
On first knob on SW ridge of Beartrack Mountain.
On SW slope of Beartrack Mountain ~200 m E of
first major stream. Just below glacial trim line.
Treeline at the headwaters of first major creek W
of Beartrack Mountain's SW ridge.
Area on other side of trail away from lake was burned 1-2 years ago; trail acted as fire line, preventing
burning along lake edge. Plot was downslope from trail, toward lake, and was not affected by the fire.
On NE edge of creek, with areas of full exposure along creek edge, and partly shaded areas in forest.
In a lake valley, mid-valley slope.
Lake surrounded by many small, little-used foot trails. Stream flowing from lake.
Abundant quaking aspen along stream and lake, with sparse lichens and Douglas-fir.
Sitka spruce stand near the beach. The forest floor is entirely covered in moss with 5-leaf bramble mixed in.
Some devilsclub and Aruncus shrubs in understory, and western hemlock in the regenerating stand.
Sitka spruce stand that appears ~ 100-150 years old. The regenerating stand is almost entirely western
hemlock. The ground is covered in a dense thick carpet of moss. Some huckleberry, Aruncus, and
strawberry-leaf raspberry.
Late-serai mountain hemlock/Sitka spruce stand on a steep SW-facing slope. The forest is dense and the
understory has a high cover of huckleberry; the ground is mossy.
Krumoltz mountain hemlock with a few Sitka spruce with high cover of heather, copperbush, and deer
cabbage. Some ferns and Sphagnum.
1A-4
-------
Site
Name
Media
Lat.
Long.
Ann. nn. Canopy
Elev. Temp. Ppt. Cover
(m) (°C) (cm) (%)
Landform
Lichen
N % Lichens
dw collected
Conifers
collected
Location
Habitat
Park: Great Sand dunes National Park and Preserve, Alaska (GRSA)
Ag Intensity Index: IMPROVE AmmNOS: 0.20 |jg/m3 IMPROVE AmmSO4: 0.82 |jg/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Southern Deserts (GRSA1); Northwestern Forested Mountains: Southern Rockies (GRSA2-5)
GRSA1 Vegetation 37.7258 -105.5323 2469 5.3 32
GRSA2 Vegetation 37.7308 -105.4874 2774 4.3 48
GRSA3 Vegetation 37.7338 -105.4602 2941 3.9 58
GRSA4
Vegetation 37.7223 -105.4699 3109 4.3
48
18
5
46
59
Valley
Mid-slope
Upper
slope
Mid-slope
Pinus edulis
1.46 Xanthoparmelia Pinus edulis
Pinus flexilis
0.77 Xanthoparmelia Pinus flexilis
Near park headquarters, ~ 60 m SWof autoshop
Mosca Pass trail, midway to pass on a steep
slope on the N side of the trail.
Mosca Pass, just down-slope from radar tower.
On the N slope of Carbonate Mtn ~ 0.8 km
upslope; use the drainage ~1.6 km W of Mosca
Pass for access.
Two-needle pinon/Rocky Mountain juniper woodland on valley floor bordering a large grassland. Grass
dominates the ground cover with scattered but abundant prickly-pear cactus. Very windy.
Two-needle pinon/alderleaf mountain mahogany woodland. Steep SW-facing slope with many rocks and
rocky outcroppings.
Meadow dominated by quaking aspen with some limber and ponderosa pines.
NW-facing slope in a quaking aspen/ Engelmann spruce forest with yellow lupine and branch litter. High
cover of nitrophilous lichens, esp. Xanthoria.
GRSA5 vegetation 377149 -105.4704 3338 4.3 48 36 Ridgetop
Pinus flexilis
un N-racmg nage or uaroonate ivnn; grassy
bench just below snag-ringed knob, benchmark
3,435 m.
Nt-racmg siope ooraenng me nagerop in a grassy opening wnn patcny tngeimann spruce/quaKing aspen
forest with some limber pine. Very windy with winds blowing up the W-NW slope. High nitrophilous lichen
(Xanthoria) cover on spruce.
Park: Grand Teton National Park, Wyoming (GRTE)
Ag Intensity Index: 1 0.2 IMPROVE AmmNOS: 0.22 ug/m3 IMPROVE AmmSO4: 0.58 ug/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Middle Rockies
GRTE1 Vegetation 43.7307 -110.7389 2073 2.2 80 36 Valley
GRTE2 Vegetation 43.7256 -110.7601 2362 2.2 80 76 Mid-slope
GRTE3 Vegetation 43.7264 -110.7657 2591 1.1 106 66 Mid-slope
GRTE4 Vegetation 43.7276 -110.7713 2804 1.1 106 47 Mid-slope
Air
GRTE5 ve elation 43-1300 -110.7800 3048 2.2 68 21 Ridgetop
0.99
0.99
0.88
Usnea
Letharia vulpina
Pinus
contorta
Abies
lasiocarpa
Pinus flexilis
Pinus flexilis
Pinus
albicaulis
Park: Katmai National Park and Preserve, Alaska (KATM)
Ag Intensity Index: 0 IMPROVE AmmNOS: 0.10 ug/m3 IMPROVE AmmSO4: 0.50 ug/m3 (Data from monitor at Tuxedni Wilderness, USFWS)
North American Biome: EPA Ecoregion 3 - Tundra: Bristol Bay-Nushagak Lowlands (KATM1-5); Marine West Coast Forest: Alaska Peninsula Mountains
KATM1 Vegetation 58.5459 -155.7836 36 2.2 50 16 Flatland
KATM2 Vegetation 58.5686 -155.7937 213 1.9 54 107 Toe slope
Air
KATM3 ' , ,. 58.5711 -155.8036 370 1.9 54 16 Mid-slope
vegetation r
KATM4 Vegetation 58.5718 -155.8421 563 1.4 68 3 Upper
KATM5 Vegetation 58.5793 -155.8558 724 1.4 68 3 Ridgetop
KATM6 Vegetation 58.4715 -155.4901 1112 0.1 83 0 Upper
a slope
Park: Lassen Volcanic National Park, California (LAVO)
Ag Intensity Index: 7.4; IMPROVE AmmNOS: 0.20 ug/m3; IMPROVE AmmSO4: 0.61 ug/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Sierra Nevada
0.52
0.71
0.97
0.46
0.44
0.52
Hypogymnia
physodes
Hypogymnia
physodes
Hypogymnia
physodes
Flavocetraria
cucullata
Flavocetraria
cucullata
Flavocetraria
cucullata
Picea glauca
Picea glauca
Picea glauca
Picea glauca
Picea glauca
Wedge of Lupine Meadows, just off trail; - 0.53
km from Lupine Meadows (railhead.
Slope above Bradley Lake on trail to
Amphitheatre Lake; off second switchback.
On the SE-facing slope below Amphitheater
Lake; mid-slope half-way between Amphitheater
Lake and Valley floor just up trail from the Garnet
Canyon Junction.
-0.5 km E of Surprise Lake, just off trail where
the slope steepens and switchbacks shorten.
S rim above Amphitheater Lake.
(KATM6)
- 2 km from Brooks camp on road to Three Forks
Overlook.
Dumpling Mountain trail on small knoll facing
Naknek Lake.
Dumpling Mountain at 370 m.
On S face of Dumpling Mountain 1 80 m below
peak, downhill from trail overlooking Naknek
Lake, Brooks River, and Brooks Lake.
Top of Dumpling Mountain; SE-facing slope
W slopes of Mt. Katolinat, accessed from Iliuk
Arm of Naknek Lake, - 61 0 m below peak.
On valley floor at edge of a subalpine meadow. Lodgepole pine is dominant tree with subalpine fir, quaking
aspen, and Engelmann spruce. Many snags and dying trees. Some nitrophilous lichens (esp. Xanthoria).
Subalpine forest dominated by subalpine fir and Douglas-fir. S-facing slope with dense herb groundcover.
SE-facing slope in subalpine forest dominated by subalpine fir. Ground cover mostly grasses with some
heaths and a few large boulders (lots of marmot scat on boulders).
On a steep E-facing slope in a mixed conifer stand. Ground has a high cover of thinleaf huckleberry and
grouse whortleberry and large granite outcrops.
On an E-facing ridge above a glacial cirque. Trees are patchy mixed pine, fir, and spruce. On a glacier cut
and mostly all granite with some alpine herbs. No lichens, a few crusts.
Forest uneven age with fallen conifers interspaced hardwoods. Height of dominant trees is 18-21 m.
Scattered white spruce 6-12 m tall on steep sloped knob with Sitka alder and deep moss mats. Oldest trees
probably 100-200 years old. Collected over larger area, including a meadow and slope on far side of the
meadow. Meadow had black cottonwood and Calamagrostis.
Vegetation dominated by 3 m Sitka alder, heavily infested and defoliated by inch worms.
At tree line, scattered short spruce (up to 1 .2 m tall) among tundra vegetation, dominated by crowberry and
other heaths.
Tundra on gently sloping top of Dumpling Mountain. Spruce needles were collected from very short trees
(up to 0.4 m) widely scattered on mountain top.
Alpine tundra, gently stone and gravel slope with thin soils near uppermost NW slopes of Mt. Katolinat.
LAV01
LAV02
Vegetation 40.5568 -121.5315 1829 7.4
Vegetation 40.5314 -121.5342 2012
6.6
LAVO3 Vegetation 40.4550 -121.5399 2271 4.1
103
109
303
Mid-slope
62
76
0.59
0.51
0.85
Lethana vulpina f^
Letharia vulpina f^
Letharia
columbiana
Abies
magnifica
At Sunflower Flat-120 m SWof main park road.
-2.4 km on Chaos Crags trail from (railhead.
Just below the Ridge Lakes Basin - 0.8 km up
Ridge Lakes Trail.
A nearly flat ponderosa pine, western white fir woodland with some shrubby manzanita and chinquapin.
Some boulders from adjacent rocky slope; ground cover mostly grasses. Several downed trees in various
decay states.
Late-serai, mixed pine and white fir woodland. W-facing rocky slope with a dense covering of bearberry
manzanita, and older fir trees have dense cover of the wolf-lichen, Letharia.
In a California red fir/mountain hemlock stand bordered by a creek and wet meadow on the N side and dry
meadow (lily and waterleaf) on the S side. Very little ground vegetation in the forested section, mostly duff
and branch litter with some lupine.
1A-5
-------
Ann. nn.
Site Elev. Temp. Ppt.
Name Media Lat. Long. (m) (°C) (cm)
LAVO4 Vegetation 40.4392 -121.5576 2499 4.8 254
Air
LAVO5 ' , ,. 40.4476 -121.5662 2713 4.8 254
vegetation
Canopy Lichen
Cover N %
(%) Landform dw
18 U,pper
slope
21 Ridgetop
0.87
0.94
Lichens
collected
Letharia vulpina
Letharia vulpina
Conifers
collected
Abies
magnified
Abies
magnifica
Location
On S slope of Brokeoff Top Mtn, ~3.2 km up trail.
Near summit of Brokeoff Top Mtn, on NW ridge.
Habitat
In a mountain hemlock stand on a mild sloping rocky bench. The herb layer is almost all lupine and wolf-
lichens (Letharia spp.) are abundant.
On a mountain top with nearly krumholtz mountain hemlock and some California red fir. The slope is W-
facing and rocky with some bearberry and manzanita. The lichen community consisted of mostly of the wolf
lichens, Letharia vulpina and L. columbiana.
Park: Mount Rainier National Park, Washington (MORA)
Ag Intensity Index: 6; IMPROVE AmmNOS: 0.20 |jg/m3; IMPROVE AmmSO4: 1 .1 1 |jg/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Cascades
MORA1 Vegetation 46.7433 -121.8915 654 6.4 197
MORA2 Vegetation 46.7697 -121.7893 985 3.5 244
Air, fish,
MORA-V lake water'
IPIQ snow> 46.8239 -121.8953 1372 4 222
sediments,
vegetation
Air, fish,
..„_... lake water,
MUKA4. sn 46.8878 -121.8987 1369 4.6 220
Golden sediments,
vegetation
MORAS Vegetation 46.8006 -121.7831 1809 2.4 260
77 Valley
91 Mid-slope
85 Lake basin
36 Lake basin
36 U,pper
slope
Park: Noatak National Preserve, Alaska (NOAT)
Ag Intensity Index: 0 ; IMPROVE AmmNOS: NA ug/m3; IMPROVE AmmSO4: NA ug/m3
North American Biome: EPA Ecoregion 3 - Tundra: Arctic Foothills (NOAT1 , NOAT3); Tundra: Brooks
NOAT1 Vegetation 68.2847 -161.4657 227 -7.4 41
Air, fish,
. |nAT_ lake water,
P snow, 68.4063 -159.2223 388 -8.8 39
Dunai ,. .
sediments,
vegetation
NOAT5 Vegetation 68.4625 -161.4612 675 -8.8 50
0 Toe slope
0 Flatland
0 uPPer
slope
0.63
0.47
0.42
0.54
Alectoria
sarmentosa
Alectoria
sarmentosa
Alectoria
sarmentosa
Alectoria
sarmentosa
Tsuga
heterophylla
Tsuga
heterophylla
Abies
amabilis
Abies
amabilis
Abies
procera
Along Tahoma Creek, off Rd. 706.
Ricksecker Point Picnic Area; trail from rear of
parking lot, past restrooms, towards river behind
outhouse.
Unnamed lake LP1 9 vicinity; 80-90 degrees up
steep slope above St. Andrews Creek drainage,
NE of Puyallup Lakes.
N side Golden Lake.
Mildred Point; in middle of flat area.
Silver fir/western hemlock/Douglas-fir stand along creek. Average dbh <53 cm, age 200+ years, but some
young trees (1 .5 m) present also.
Woodland/riparian area, with old Douglas-fir (200+ years) and an understory of mostly Pacific silver fir,
western hemlock, mountain hemlock, and a few pines. Many paths through site to river edge.
Stand 300+ years old, with mature Douglas-fir, 10-100-year-old Pacific silver fir. Fir most abundant, but 100-
200-year-old western and mountain hemlock also present in low abundance. Red huckleberry and oval-leaf
blueberry cover 50-75% of forest floor.
Site is on a bench on high mountain. Woodland, age 150+ years, with tall Douglas-fir and Pacific silver fir in
understory. Much Vaccinium in understory, and lots of moss near lake. Alectoria dominant lichen at site.
Flat bench at base of Mt. Rainier glacier overlooking steep and deep glacial drainage. Stand has noble fir
and Alaskan yellow cedar with very few lichens. Pacific silver fir and mountain hemlock also present in low
abundance.
Range (NOAT5)
0.38
0.53
0.35
Masonhalea
richardsonii
Masonhalea
richardsonii/
Flavocetraria
cucullata
Masonhalea
richardsonii
Knoll above Middle Kugururok River.
Burial Lake; gravel drainage to lake.
SW of Copter Peak.
Dwarf birch on tundra-covered knoll.
Dwarf birch in well-drained area.
Alpine Dryas tundra with mountain heather.
Park: North Cascades National Park, Washington (NOCA)
Ag Intensity Index: 3.7; IMPROVE AmmNOS: 0.13 ug/m3; IMPROVE AmmSO4: 0.78 ug/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: North Cascades
NOCA1 Vegetation 48.6493 -121.3070 198 8.6 207
NOCA2 Vegetation 48.6420 -121.3370 614 8.6 206
NOCA3 Vegetation 48.6641 -121.3266 945 8.3 243
NOCA4 Vegetation 48.6716 -121.3187 1228 8.7 205
Air
NOCA5 ' , ,. 48.6824 -121.3217 1600 8.2 205
vegetation
74 Toe slope
84 Mid-slope
92 Mid-slope
Upper
a' slope
47 Ridgetop
0.34
0.52
0.50
0.37
0.48
Alectoria
sarmentosa
Platismatia
glauca
Alectoria
sarmentosa
Alectoria
sarmentosa
Alectoria
sarmentosa
Pseudotsuga
menziesii
Tsuga
heterophylla
Abies
amabilis
Abies
amabilis
Abies
amabilis
On Thornton Creek, just below single-lane bridge
(off dirt road to Thornton Lake (railhead), 60 m
from road.
Lower S slope of Mt. Triumph, 4 km up Thornton
Lake Rd., 90 m N of road.
SE slope of Mt. Triumph ~ 4 km up the Thornton
Lakes trail.
Upper SE slope of Mt. Triumph, off Thornton
Lakes trail just below the park boundary sign.
S ridge of Trappers Peak, neartreeline, above
lower Thornton Lake.
Steep, granite outcrop on the E side of a major perrenial creek. Forest is dominated by Douglas-fir and
regeneration is mostly red cedar. Ground vegetation is almost all salal.
E slope in a western hemlock forest which was logged <100 years ago; oldest stand found at this elevation
in area. Ground is mossy with some scattered herbs and shrubs.
SE slope in a western hemlock forest with Pacific silver fir as the regeneration stand. Ground is mossy;
thinleaf huckleberry and beard lichens abundant.
SE slope in an old-growth, mixed-conifer stand (western hemlock, Alaska yellow cedar and Pacific silver fir).
Ground is mossy; thinleaf huckleberry and beard lichens abundant.
Site is on a S-facing ridgeline (rim of glacial cirque) and at tree-line. Forest of Alaska yellow cedar,
mountain hemlock and Pacific silver fir. Slope is steep and rocky with open soil, alpine herbs and thinleaf
huckleberry.
1A-6
-------
Ann. nn. Canopy
Site Elev. Temp. Ppt. Cover
Name Media Lat. Long. (m) (°C) (cm) (%) Landform
Park: Olympic National Park, Washington (OLYM)
Ag Intensity Index: 2.2; IMPROVE AmmNOS: 0.39 |jg/m3; IMPROVE AmmSO4: 0.99 |jg/m3
North American Biome: EPA Ecoregion 3 - Marine West Coast Forest: Puget Lowlands (OLYM1); Marine
OLYM1 Vegetation 48.0926 -123.4338 137 9.4 92 85 Valley
OLYM2 Vegetation 47.9535 -123.8381 518 6.7 360 94 Valley
Air, fish,
™ vr/i-3- lake water, . .
°L™3' snow, 47.8973 -123.7831 1448 6.9 458 31 U,pper
Hoh sediments, slope
vegetation
Air, fish,
OLYM4- lake water'
pj snow, 47.9463 -123.4136 1392 4.9 216 101 Lake basin
sediments,
vegetation
OLYM5 Vegetation 47.9307 -123.4105 1850 4.9 216 38 Ridgetop
Lichen
N % Lichens
dw collected
Conifers
collected
West Coast Forest: Coast Range (OLYM2),
L ob aria
oregana
Alectoria
sarmentosa
Alectoria
0 _R sarmentosa/
Bryoria
fuscescens
Platismatia
0.44 glauca/ Bryoria
fuscescens
Alectoria
0.63 sarmentosa/
Bryoria
Tsuga
heterophylla
Tsuga
heterophylla
Abies
lasiocarpa
Abies
amabilis
Abies
lasiocarpa
Location
Habitat
Northwestern Forested Mountains: North Cascades (OLYM3-5)
Outskirts of Port Angeles; Peabody Creek Loop
Trail, along w side of creek, ~ 2/3 way around the
loop.
~ 3.2 km SE of Sol Due Hot Spgs Resort on N
side of Sol Due River, ~0.4 km from junction with
Canyon Creek.
NW slope above Hoh Lake.
SW side of PJ Lake; ~0.8 km e of Hurricane
Ridge Rd. to Obstruction Point, and 0.8 km N of
Eagle Point.
Ridgetop approximately 100 m E of Hurricane
Ridge Rd. to Observation Point, and ~ 1 km S of
Eagle Point.
Mature mixed conifer/deciduous forest bordering urban area. Douglas-fir dominant, with western hemlock in
understory.
Old-growth western hemlock forest along Sol Due River. Floodplain of river is approximately 61 m wide with
mainly rounded stones and small boulders with islands of alder and willow. Forest understory has low moss
cover, and some blueberry, ferns.
NW-facing slope above a subalpine lake. Stand is mixed mountain hemlock and subalpine fir with a high
cover of blueberry.
Mixed fir/red cedar old-growth forest adjacent to meadow with willow. Willow also present along lake shore.
Little understory, site has deep snow cover in winter. Lake has significant algal growth.
Ridgetop with mature old-growth stand of subalpine fir in subalpine habitat. Site is wind-blasted from the E
and W, high cover of lichens on large trees in most sheltered areas.
Park: Rocky Mountain National Park, Colorado (ROMO)
Ag Intensity Index: 14.4; IMPROVE AmmNOS: 0.35 |jg/m3; IMPROVE AmmSO4: 0.83 |jg/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Southern Rockies
Air
ROMO1 ' , ,. 40.2381 -105.7995 2560 1.7 65 92 Valley
vegetation '
Air
ROMO2 ' , ,. 40.2305 -105.7611 2720 1.2 79 36 Mid-slope
vegetation r
Air, fish,
pnum- lake water' High
KUIVIUO. sn 40.2310 -105.7310 3018 0.1 104 21 elevation
Lone Pine sediments |ake basin
vegetation
Air
ROMO4 ' , ,. 40.2300 -105.7130 3232 -0.4 116 18 Mid-slope
vegetation
ROMO5 Vegetation 40.3916 -105.6867 3451 1.4 92 16 Ridgetop
Air, fish,
RDMnfi- lake water> H'9n
M-M snow, 40.2916 -105.6438 3042 1.3 113 62 elevation
sediments, lake basin
vegetation
Park: Sequoia and Kings Canyon National Parks, California (SEKI)
Ag Intensity Index: 16.6; IMPROVE AmmNOS: 2.19 ug/m3; IMPROVE AmmSO4: 1 .98 ug/m3
North American Biome: EPA Ecoregion 3 - Mediterranean California: Southern and Central Chaparral and
SEKI2 vegetation 36-5762 -118.7862 1573 16.9 36 88 Valley
SEKI3 Vegetation 36.5536 -118.7492 2071 10.3 100 78 tench'0^
SEKI4 Alr' , ,. 36.5985 -118.7212 2332 5.3 108 31 Mid-slope
vegetation bench
Air, fish,
lake water, High
P .. snow, 36.6005 118.6789 2816 3.4 89 47 elevation
tmeraia sediments, lake basin
vegetation
1 .2 Xanthoparmelia
Picea
engelmanii
Abies
lasiocarpa
Abies
lasiocarpa
Abies
lasiocarpa
Abies
lasiocarpa
Picea
engelmanii
Wside of Continental Divide, S of East Inlet
(railhead, near creek.
Wside of Continental Divide, halfway between
Cats Lair Camp and Lower East Inlet Camp; East
Inlet Trail, on slope above falls.
Wside of Continental Divide, on S shore of Lone
Pine Lake.
Wside of Continental Divide, on S-facing rock
outcrop plateau above Lake Verna.
Wside of Continental Divide, near Ute Crossing
on Sundance Mountain.
E side of Continental Divide, on NW corner of
Mills Lake, on large flat rock at outlet.
Flat floodplain forested with lodgepole pine and Engelmann spruce. Much willow and some aspen in
understory. Grasses abundant; most herbs have withered. Evidence of moose in area: lots of browsed
willow and pellets.
Steep, dry, rocky slope with a lodgepole pine dominated stand. Quaking aspen is dominant hardwood, a
few willow shrubs present.
Meadow on the s shore of a subalpine lake with a few large groves of willow; bordered by an Engelmann
spruce forest.
Rock outcrop plateau with sparse forest of subalpine fir and Engelmann spruce. No other vegetation, only
rock.
Rocky ridgetop with patchy forest of subalpine fir; some krumholtz and small patches of willow.
High-elevation Engelmann spruce/subalpine fir with limber pine. Large, smooth, rocky outcrops covered
with crustose lichens. Krumholtz conifers present.
Oak Woodlands (SEKI2); Northwestern Forested Mountains: Sierra Nevada (SEKI3-7)
2.23 Letharia vulpina
1 .59 Letharia vulpina
1 .56 Letharia vulpina
1 .53 Letharia vulpina
Abies
concolor
Abies
concolor
Abies
magnifica
Abies
magnified
Marble Fork of the Kaweah River, just upstream
from Crystal Cove Drive overpass.
Crescent Meadow.
Wolverton Creek Meadow.
Emerald Lake Basin, Topokah valley watershed.
Mature forest of pine, incense cedar, and California red fir along boulder-filled, granite riverbed ~ 25 m wide
bordered by willow.
Meadow and nearby stand along Crescent Creek. Vegetation is diverse and in good condition. Tree cover is
clumped.
Meadow of sedges surrounded by tall California red fir and open-grown lodgepole pine encroaching on
meadow. Vegetation appears healthy. Site is on a bench on a mid-slope.
Mixed red fir/pine stand with Labrador tea understory; many granite boulders.
1A-7
-------
Site
Name
SEKI6:
Pear
SEKI7
Media
Air, fish,
lake water,
snow,
sediments,
vegetation
Air,
vegetation
Elev.
Lat. Long. (m)
36.6040 -118.6690 2911
36.5165 -118.8017 630
Ann.
Temp.
(°C)
2.4
14.5
Park: Stikine-LeConte Wilderness, Tongass National Forest, Alaska
Ag Intensity Index: 0.1; IMPROVE AmmNOS: 0.04 ug/m3; IMPROVE
North American Biome: EPA Ecoregion 3 - Marine West Coast Forest
STLE1
STLE2
STLE3
STLE4
STLE5
Air,
vegetation
Air,
vegetation
Air,
vegetation
Air,
vegetation
Vegetation
56.7910 -132.5110 0
56.8047 -132.5317 254
56.8095 -132.5407 567
56.8250 -132.5715 815
56.8180 -132.6090 1064
4.5
3.6
3.6
2.7
3.6
nn. Canopy Lichen
Ppt. Cover N %
(cm) (%) Landform dw
75 34
76 49
High
elevation
lake basin
Valley
(STLE)
AmmS04: 0.46 ug/m3 (2005 IMPROVE data
: Pacific Coastal Mountains
318 31
378 119
378 34
488 3
431 5
Park: Wrangell-St. Elias National Park and Preserve, Alaska (WRST)
Ag Intensity Index: 0; IMPROVE AmmNOS: 0.04 ug/m3; IMPROVE AmmSO4: 0.46
North American Biome: EPA Ecoregion 3 - Marine West Coast Forest: Pacific Coastal
WRST1
Vegetation
60.0476 -141.3066 7
3.1
312 101
Marine
beach U'4y
Mid-slope 0.49
Mid-slope 0.39
Ridgetop 0.37
Ridgetop 0.45
Lichens
collected
Conifers
collected
Pin us
contorts
Location
Outflow area of Pear Lake.
Potwisha Campground n of highway on Deep
Canyon Creek.
Habitat
Drainage area for alpine lake, but currently dry. Pine stand with willow on granite slabs. Algal and aquatic
moss lines indicate that the lake floods widely over the granite slabs in some places.
Creek with one main channel and granite boulders on either side. Tree canopy overhangs river and
provides intermittent shade.
from PETE1, Petersburg SE AK).
Platismatia
glauca/
Alectoria
sarmentosa
Platismatia
glauca/Lobaria
oregana
Alectoria
sarmentosa
Platismatia
glauca/
Alectoria
sarmentosa
Platismatia
Picea
sitchensis
Picea
Picea
sitchensis
Picea
sitchensis
Picea
sitchensis
ug/m3 (2005 data from PETE1 , Petersburg SE AK)
Mountains (WRST1); Northwestern Forested Mountains: Copper
Marine Q 51
Hypogymnia
apinnata
Picea
sitchensis
Bussy Creek outlet NE side of creek in upper
beach meadow 60 -75 m from inter-tidal flats.
Bussy Creek drainage on lower slopes at 254 m
following ridge line between the N and S main
tributaries.
SE of small lake above Bussy Creek. In long,
narrow Muskeg bench parallel to and overlooking
drainage on long edge.
Ridgetop knob on Wilderness boundary ~ 0.4 km
NW of Bussy Lake and 0.4 km due W of small
lake beside Wilderness. To NWa prominent cliff
face overlooks headwaters of the Muddy River.
Top of Thunder Mountain.
Plateau (WRST2-3); Northwestern Forested Mountains
Kageets Point on E shore of Icy Bay, ~ 0.4 km S
of landing strip, in Sitka spruce/alder forest at
shnrplinp
Upper beach meadows with scattered Sitka spruce. Vegetation samples were collected from open-grown
spruce and along forest edge.
Old growth mixed coniferous forest of Alaska yellow cedar and western hemlock. Canopy cover -80%.
Understory of mixed forms and shrubs with high moss ground cover. Area slopes steeply facing Bussy
Creek drainage.
Muskeg bench with scattered shorepine (Pinus contorts) on SE-facing slope of Thunder Mountain
overlooking Bussy Creek drainage.
Small knob with several scattered ponds. Subalpine heath with clumps of old mountain hemlock.
Alpine mountain peak dominated by sedges, avues, and dwarf heath.
: Interior Highlands (WRST4-5)
Oblong collection area along beach fringe. Recently deglaciated (50-100 years ago) forelands, ~ 30 m into
the Sitka spruce forest from the beach. Age class of trees varying depending on distance from the shore
anr! isnstatir rphnunr!
WRST2 Vegetation 61.5219 -144.4002 219 -1.9 31
Air
WRST3 ye etation 61 -3856 -143.6014 648 -1.7 62
WRST3B Vegetation 61.3844 -143.6063 607 -1.7 62
WRST4 Vegetation 61.4964 -142.8684 1020 -2.2 85
WRST5 Vegetation 61.5014 -142.8381 1421 -2.7 127
16
85
Valley
Toe slope
Picea glauca
Picea glauca
S side of McCarthy Rd. along silt bluffs over g of forest a, river b|uff mu|tip|e age dass Of white spruce. Many old dead trees of possibly black
r8lr^otUroTd%0;dCchan:;gronundCOPPer ^ SP— C°""'ed ^ *>«>< ^ * ^
From highest point along Crystalline Hills trail on
N side McCarthy Rd., just E of marble grotto, take of quaking aspen and white spruce.
uphill side trail -23 m to highest point, scale small v, i v r^ar r
cliff, and go uphill another 60 m.
31 Toe slope
43 Mid-slope
0 60 Hypogymnia
physodes
Picea glauca
Crystalline Hills loop trail at 607 m.
Trail to Bonanza Mine from Kennicott, Wside of
trail at 1,020 m; exposed to the Kennicott Valley,
glaciers.
White spruce/quaking aspen woodland regenerating from fire.
Conifers from 2 to 12 m tall, declining in health. Area gets 2.5 -3m snow in winter. Exposed to Kennicott
glacier and Kennicott, Nizina, and Chitna River valleys with views to Chugach Mountains ~80 km distant.
Flavocetraria
0.49
arbuscula-mitis
On trail to Bonanza Mine below pass between Dwarf ericaceous vegetation with Dryas, moss and willow. Slope is exposed to Kennicot glacier, river
of°KennbS headwaters, and basin.
Park: Yosemite National Park, California (YOSE)
Ag Intensity Index: 13.8; IMPROVE AmmNOS: 0.46 ug/m3; IMPROVE AmmSO4: 0.98 ug/m3
North American Biome: EPA Ecoregion 3 - Northwestern Forested Mountains: Sierra Nevada
YOSE1 Vegetation 37.6783 -119.7541 661 12.1
82
YOSE2 Vegetation 37.7150 -119.6801 1433 10.7 86
YOSE3 Vegetation 37.7237 -119.5336 1829 10.3 98
36 Valley
39 Mid-slope
77 Mid-slope
1.35 Xanthoparmelia Pln"s.
sabmiana
Yosemite National Park on Hwy. 140 at turnout on
left side of road just inside SW park boundary on Large boulders with canyon live oak and poison oak; a few foothill and ponderosa pines present.
N bank of river.
0.98 Letharia vulpina
1.12 Letharia vulpina
P/""f
ponderosa
On N-facing slope of Turtleback Dome.
On top of Nevada Falls on the Merced River ~30
m S of falls off trail.
Dry, rocky slope; mixed conifer forest (pine, cedar, and Douglas-fir), with abundant manzanita in the
understory. All conifer species in the regeneration layer. Wolf-lichen (Letharia vulpina) abundant; some
nitrophilous lichens (Xanthoria) on oaks.
Incense cedar-dominated stand with multiple conifer species present. Gentle NW-facing slope with branch
litter, a thin layer of duff, and two herbs (western rattlesnake plantain and Pacific Rhododendron).
1A-8
-------
Site
Name
YOSE4
YOSE5
Media
Vegetation
Air,
vegetation
Lat.
37.7506
37.7744
Long.
-119.3631
-119.3371
Elev.
(m)
2713
3048
Ann.
Temp.
(°C)
4.2
3.1
nn.
Ppt.
(cm)
110
108
Canopy
Cover
(%)
21
16
Landform
Mid-slope
Upper
slope
Lichen
N % Lichens
dw collected
Conifers
collected
Pin us
contorts
Pin us
contorts
Location
On Lewis Creek near confluence with trail around
Cony Crags into the Lyell Forte Basin.
Valley at head of Lewis Creek watershed at the
confluence of Gallison Lake Outlet Creek.
Habitat
Lodgepole pine woodland on slab granite; a few young western hemlock and California
shrubby chinquapin and western brackenfern. Some pines with charred trunk bases.
Alpine meadow with pine woodland; willow-lined creek runs through site center.
red fir and scattered
Key to Table:
Park = name, state and acronym of the park or wilderness.
Ag Intensity = agricultural intensity in a 150 km radius around the park, see methods section for more details.
IMPROVE AmmNOS and AmmSO4 = mean annual nitrate and ammonium sulfate concentrations in micrograms per cubic meter in ambient fine particulates under 2.5 urn in diameter sampled for 24 hours, air is sampled every 3rd day at IMPROVE monitors located in WACAP parks and
wilderness, if no IMPROVE site exists for the park then data is from the nearest IMPROVE site and the name of that site is given, value is the 1998-2004 mean for annual data that meets IMPROVE quality assurance criteria, see methods section for more details.
Biome = Level three US EPA ecological region in which the site is located, see http://www.epa.gov/wed/pages/ecoregions/ecoregions.htm for general information.
Long. = latitude and longitude of site center in decimal degrees, mapping datum WGS84, sites were approximately 1 ha.
Elev. (m) = elevation in meters, derived from site location on USGS topographic 15' quadrangles.
Ann. Temp, and Ann. Ppt. = mean annual temperature in degrees Celsius and mean annual precipitation in centimeters estimated from the PRISM climate model—see Chapter 3 for details.
Canopy Cover (%) = field ocular estimate of the canopy cover of dominant and co-dominant trees on the site as a percentage of total site area.
Landform = physiographical feature on which the site was located, determined from USGS 15' topographic quadrangles.
Lichen N % dw = mean nitrogen concentration in lichen thalli collected on the site as percent of dry weight, if more than one species was collected, species N concentrations were averaged after averaging laboratory and field replicates.
Lichens collected = lichens collected for nitrogen, sulfur, metals and SOC analysis.
Conifers collected = conifer species collected for SOC analysis. If vegetation was collected, but both "conifers collected" and "lichens collected" fields are blank, then leaves or bark of deciduous shrubs were collected and results are not reported.
Location = site location description.
Habitat = ecological characteristics of the collection site.
1A-9
-------
APPENDIX 3A
Summary of Sampling and Analysis Plan by Environmental Medium
Medium
Purpose
Frequency
Samples
Sample
Processing
Analytes
Laboratory
SNOW
Measure of direct atmospheric contaminant loading, and in many cases, 90% of the annual precipitation, interannual variability
Annually; 14 sites in 8 core parks, and additional snow-only sites for elevational transect
Organic
intPnratPri wrStnlLnark nmfilp Integrated vertical snowpack profile 6
integrated vertical snowpacK pronie -r~n~., D^,^.^ on r*^^ ~r ™^ ^~~i-
•WJ&'Mr^s^ ™~SSF-
Filtration thru
0.45um
Filtered,
acidified: Ca,
Mg, Na, K (1C)
Filtered: NO3,
S04, Cl, NH4
(1C) DOC (IR)
Unfiltered:
specific
conductance,
pH,ANC
USGS-CWSC
Unfiltered,
acidified
Metals: Cd,
Cu, Pb, Ni,
V, Zn, plus
additional
metals listed
in Table
2.2.1 (ICP-
MS)
USGS-NRP
Boulder
Unfiltered
Hg
(oxidation,
purge and
trap;
CVAFS)
USGS-
WWSC
Filtered thru
GF/C (1 .2um)
Spheroidal
carbonaceous
particle
analysis
ECRC
Filtration thru 0 . . .„
GF/F(0.7um) SorbantASE
Total
particulate C SQC
and N a (rr/M
-------
Medium
Purpose
Frequency
Samples
Sample
Processing
Analytes
Laboratory
FISH
Direct measure of food web impacts, bioaccumulation and link to the terrestrial component; evaluation of health and condition effects
Once per site: 4 to 6 sites (2 to 3 core parks) per year
~30 fish/lake (3 fish from each of 5 age classes, from both sexes, from a single species); samples frozen on dry ice in field, shipped to
WRS, then distributed to appropriate lab.
Condition factors
Weight, fork
length,
Macroscopic
health index; ages
from scales and
otoliths
In field, and
OSU-Fish
Hematology/
Physiology
Blood obtained by
caudal vein puncture,
plasma collected and
frozen in the field
Hematocrits, plasma,
cortisol, glucose, sex
hormones, and
vitellogenin
In field, and
OSU-Fish
Histopathology (gills,
kidney, liver, spleen,
gonads)
Organs preserved in
10% neutral buffered
formalin
Evaluation of
pathological changes,
macrophage
aggregate analysis;
and reproductive state
OSU-Fish
Whole fish tissue
Liquid N2 homogenization;
subsample solvent extracted
(ASE) for SOC analyses
Hg
(Direct Hg
Analyzer)
WRS
Target SOC
analyses
(GC/MS)
SEC
Livers and fillets
(from up to 10 additional
fish collected for metals
analysis)
Homogenization, freeze
drying, microwave
digestion
Metals: Cd, Cu, Pb, Ni, V,
Zn (ICP/MS)
USGS-NRP
Boulder
3A-2
-------
Medium
LICHENS
CONIFER NEEDLES and LICHENS
SUBSISTENCE
NATIVE FOOD (MOOSE)
Purpose
Direct measure of food web impacts and
bioaccumulation; used primarily to evaluate N,
S, and heavy metal impacts
Measure of ecosystem exposure, large
"n" for statistical comparisons within
and among sites, parks, regions, and
elevations
Direct measure of food sources (moose)
used by native people
Frequency
Once per site: from 12 sites in 8 core parks in
2004
Once per site: Elevational transects
(~5 sites/park) from 8 core (2004) and
12 secondary parks (2005). Pilot study
(4 sites) in SEKI in 2003
Once: Alaska only, 3 moose collected
Samples
6 lichen samples collected per site
(3 samples each of 2 species);
~20 g dry weight of material for each sample;
Shipped with ice to WRS
One lichen species and second-year
needles from one conifer species at 5
sites at different elevations per park; 3
samples collected at each core park
site, 1 sample collected at each
secondary park site;
Shipped with ice to WRS
Samples provided to Parks by native
hunters; Shipped with dry ice to WRS
Sample
Processing
Ground thru 20 mesh, then
oven dried at 65°C to constant weight
SOCs: Extraction using ASE
N: Ground thru 20 mesh, then
oven dried at 65°C to constant weight
Hg & SOCs: Liquid N2 homogenization;
subsample solvent extracted (ASE) for
SOC analyses
Metals: Homogenization, freeze drying,
microwave digestion
Analytes
Metals: Cd, Cu,
Pb, Ni, V, Zn
(ICP-MS)
Hg
(Direct Hg
Analyzer)
N
Target SOC
analytes
(GC/MS)
Hg
(Direct Hg
Analyzer)
Target
SOC
analytes
(GC/MS)
Metals:
Cd, Cu, Pb,
Ni, V, Zn
(ICP-MS)
Laboratory
UMNRAL
USGS-NRP
Boulder
WRS
UMNRAL
SEC
WRS
SEC
USGS-
NRP
Boulder
3A-3
-------
Medium
Purpose
Frequency
Samples
Analytes
Laboratory
WATER
System characterization; Hydrophilic current-
standard water quality use chemicals and
information SOCs
Once per site: 4 to 6 sites Once per site: 4 to 6
(2 to 3 core parks) per sites (2 to 3 core
year parks) per year
Organic
2 L wat r s'amp™ 2 60-ml ~f° L water sample
, ' . . fi tered in situ; fNters
syringe samp es; shipped .. . .... . ,
-*u • * in/no shipped with dry ice to
with ice to WRS KK \nn=>c
VVrxO
In situ: specific
conductance, DO,
temperature, turbidity
Filtered: Ca, Mg, Na, K,
Zn, Se (AAS), NO3, SO4, Target SOC analytes,
Cl, (1C) SiO2, NH4 (AA), particulate and
DOC (IR), color dissolved phases
(GC/MS)
Unfiltered: TN, TP (FIA),
ANC TSS
Syringe "closed system"
samples: pH, DIG
WRS SEC
LAKE SEDIMENT
Historic trends (~150 years) of contaminant loading to catchments
Once per site: 4 to 6 sites (2 to 3 core parks) per year
Sediment cores, sectioned in 0.5 cm intervals to 10 cm, then 1.0 cm intervals
to 30 cm.; shipped with ice packs to WRS
Dating
profiles
(210Pb,
137Cs,
241 Ami
ERRC
Spheroidal
carbonaceous
particle
analysis
ECRC
%moisture
Ash-free dry
weight (loss-
on-ignition) or
total organic
carbon
Hn
ny
(Direct Hg
Analyzer)
WRS
Target SOC
analytes
(GC/MS)
SEC
Metals: Cd,
Cu, Pb, Ni,
V, Zn
(ICP-MS)
USGS-NRP
Boulder
3A-4
-------
Abbreviations:
AAS Atomic absorption spectrophotometry
ASE Accelerated solvent extraction
CVAFS Cold vapor atomic fluorescence spectrometry
FIA Flow injection analysis
GC/MS Gas chromatography with mass spectrometry
1C Ion chromatography
ICP-AES Inductively coupled plasma with atomic emission spectrometry
ICP-MS Inductively coupled plasma with mass spectrometry
IR Infrared detection
Laboratories:
Laboratory
Abbreviation
CBL
ECRC
ERRC
OSU-Fish
SEC
UMNRAL
USGS-NRP Boulder
USGS-CWSC
USGS-WWSC
WRS
Laboratory
Chesapeake Biological Laboratory, Univ. of Maryland, Solomons, MD
Environmental Change Research Centre, University College London, London, UK
University Environmental Radioactivity Research Centre, University of Liverpool, Liverpool, UK
OSU Kent Laboratory, Corvallis, OR
Simonich Environmental Chemistry Laboratory, OSU, Corvallis, OR
University of Minnesota Research Analytical Laboratory, St. Paul, MN
National Research Program Laboratory, Boulder, CO
USGS Colorado Water Science Center, Alpine Hydrologic Research Team, Lakewood, CO
USGS Wisconsin Water Science Center, Mercury Research Laboratory, Middleton, Wl
Willamette Research Station Analytical Laboratory, USEPS, Corvallis, OR
3A-5
-------
APPENDIX 3B
Sampling Information, Methods, and Data Quality
The WACAP Quality Assurance Project Plan (QAPP), May 2004, outlines the quality assurance
and quality control objectives for WACAP.
Snow QA/QC
Field
Quality assurance and quality control procedures for handling US Geological Survey (USGS)
snow chemistry samples were well established, with annual regional surveys dating back to
1993. A detailed description of sampling protocols was contained in each field kit, and
experienced personnel led each site visit. Information about snowpack physical characteristics
was recorded on prepared data sheets. All original data sheets were carried as personal baggage
during transit and were photocopied and kept in separate locations, as soon as facilities
permitted.
Approximately 10% of the total number of samples were field processing blanks and field
replicates. Field blanks were collected to detect possible contamination from collection methods,
laboratory processing, DI rinse water, filtering apparatus, and Teflon collection bags. Field
replicates were also useful for this purpose, but in addition to contamination, they also reflect the
natural variability in snow chemistry and the precision of analytical techniques.
Laboratory
Detailed laboratory QA/QC procedures are specified in the WACAP QAPP. Quality control at
the USGS Colorado District laboratory involves systematically analyzing blanks, an internal
reference sample, USGS standard reference water samples, and certified nutrient standards from
High Purity Standards, Inc. International blind audit samples from Environment Canada were
analyzed twice per year. Approximately 40% of sample batch run time for the analytical
instrumentation was dedicated to analyzing blanks, duplicates, reference samples, and standards.
Calibration verifications were made with standards at the beginning and end of each batch of
sample analyses on the ion chromatograph.
Quality control at the USGS National Research Program laboratory involves systematic analysis
of blanks, standard reference materials, and spike addition samples. Details, results, and figures
are described in the Quality Assurance/Quality Control section of the database.
Ionic charge balance was calculated as the sum of cations (hydrogen ion, calcium, magnesium,
sodium, potassium, and ammonium) minus the sum of anions (alkalinity, chloride, nitrate, and
sulfate) divided by the total cations and anions in solution. Alkalinities were predominantly
negative for snow samples; only positive values for alkalinity were included with the sum of
anions in charge-balance calculations. Analytical results and charge balance values were
examined and outliers for the snow sample database and rerun were performed as necessary.
3B-1
-------
SOCs
Detailed laboratory QA/QC procedures for SOCs are specified in the WACAP QAPP. The
analyte recovery over the entire analytical method and the estimated method detection limits for
snow is given in Table 3B-1.
Metals
Detailed laboratory QA/QC procedures are specified in the WACAP QAPP. Quality control at
the USGS Colorado District laboratory involves systematically analyzing blanks, an internal
reference sample, USGS standard reference water samples, and certified nutrient standards from
High Purity Standards, Inc. International blind audit samples from Environment Canada were
analyzed twice per year. Approximately 40% of sample batch run time for the analytical
instrumentation was dedicated to analyzing blanks, duplicates, reference samples, and standards.
Calibration verifications were made with standards at the beginning and end of each batch of
sample analyses on the ion chromatograph.
Quality control at the USGS National Research Program laboratory involves systematic analysis
of blanks, standard reference materials, and spike addition samples. Details, results and figures
are described in the Quality Assurance/Quality Control Section of the database (Table 3B-2).
Ionic charge balance was calculated as the sum of cations (hydrogen ion, calcium, magnesium,
sodium, potassium, and ammonium) minus the sum of anions (alkalinity, chloride, nitrate, and
sulfate) divided by the total cations and anions in solution. Alkalinities were predominantly
negative for snow samples; only positive values for alkalinity were included with the sum of
anions in charge-balance calculations. Analytical results and charge balance values were
examined and outliers for the snow sample database and rerun were performed as necessary.
Passive Air Sampler QA/QC
Passive Air Sampler Deployment Summary
Table 3B-3 lists the latitude, longitude, and elevation of each site where a PASD was deployed.
SOCs
Detailed laboratory QA/QC procedures for SOCs are specified in the WACAP QAPP. The
analyte recovery over the entire analytical method and the estimated method detection limits for
PASDs is given in Table 3B-4.
3B-2
-------
Table 3B-1. SOC Recovery and EDLs in Snow Over the Entire Analytical Method (Usenko et al.,
2005).
Chemical Class
Compounds
Amide Pesticides
Propachlor
Alachlor
Acetochlor
Metolachlor
logK™
2.4
2.6
3.031
3.1
50 L Melted Snow2
Avg. % RSD
% Rec
139.5
79.7
65.6
89.0
19.5
1.0
6.9
1.4
EDL3 Chemical Class
pg/L
Compounds
3.7
43.4
25.2
13.8
Organochlorines Pesticides and Metabolites
HCH, gamma
HCH, alpha
HCH, beta
HCH, delta
Methoxychlor
Heptachlor epoxide
Endrin aldehyde
Endrin
Heptachlor
o,p'-DDE
Chlordane, oxy
Dieldrin
Chlordane, cis
p,p'-DDD
Nonachlor, trans
o,p'-DDD
Chlordane, trans
Nonachlor, cis
Aldrin
o,p'-DDT
p,p'-DDE
Mi rex
p,p'-DDT
3.8
3.8
4.0
4.1
4.5
4.6
4.8
5.2
5.2
5.5
5.5
5.5
5.9
5.9
6.1
6.1
6.1
6.1
6.4
6.5
6.8
6.9
6.9
87.9
71.7
100.7
111.8
59.1
31.8
40.6
90.2
49.9
55.3
28.1
109.1
32.7
66.5
56.4
41.5
60.9
30.2
43.7
36.4
50.1
51.5
61.9
6.3
7.4
7.2
5.2
20.9
32.0
13.8
26.8
19.6
12.9
31.5
23.5
29.1
14.6
16.7
25.7
15.3
27.3
25.9
5.6
19.6
10.6
24.3
12.3
18.2
32.1
20.7
16.4
14.7
23.2
47.6
121.7
24.7
9.4
105.6
16.3
44.0
0.9
24.7
0.4
0.6
107.6
23.4
10.3
27.1
26.2
Organochlorine Sulfides and Metabolites
Endosulfan sulfate
Endosulfan I
Endosulfan II
3.7
4.7
4.8
65.4
51.3
53.3
17.3
17.7
18.1
1.0
4.9
2.0
Phosphorothioate Pesticides
Methyl parathion
Malathion
Diazinon
Parathion
Ethion
Chlorpyrifos
2.7
2.9
3.7
3.8
5.1
5.1
74.6
54.8
75.0
56.9
46.7
59.7
1.0
13.6
11.7
9.6
30.0
22.5
52.0
8.4
9.1
3.2
6.2
6.9
Thiocarbamate Pesticides
EPIC
Pebulate
Triallate
3.2
3.8
4.6
64.8
99.9
73.6
25.2
33.2
18.5
45.0
63.8
10.1
logK™
50 L Melted Snow2
Avg. % RSD
% Rec
EDL3
P9/L
Triazine Herbicides and Metabolites
Atrazine desisopropyl
Atrazine desethyl
Simazine
Cyanazine
Atrazine
Prometon
Miscellaneous Pesticides
Metribuzin
Etridiazole
Dacthal
Trifluralin
Hexachloro benzene
1.361
1.781
2.2
2.2
2.3
2.7
1.701
2.6
4.3
5.3
5.5
nd5
nd5
nd5
107.8
105.8
62.8
77.4
206.2
109.9
47.6
55.3
nd5
nd5
nd5
2.3
4.2
15.6
2.1
26.1
10.5
30.2
14.6
na6
na6
na6
26.2
11.5
34.6
24.5
22.5
1.7
0.7
0.2
Polycyclic Aromatic Hydrocarbons
Acenaphthylene
Acenaphthene
Fluorene
Anthracene
Phenanthrene
Pyrene
Fluoranthene
Chrysene + Triphenylene
Benzo(a)anthracene
Retene
Benzo(k)fluoranthene
Benzo(a)pyrene
Benzo(b)fluoranthene
lndeno(1,2,3-cd)pyrene
Dibenz(a, hjanthracene
Benzo(e)pyrene
Benzo(ghi)perylene
3.9
4.0
4.2
4.5
4.5
5.1
5.2
5.7
5.9
6.4
6.5
6.5
6.6
6.7
6.8
6.9
7.0
52.7
101.3
93.2
73.0
82.7
74.4
77.9
71.2
70.5
61.0
66.7
59.3
68.4
61.5
62.9
59.3
59.2
1.8
2.1
4.7
7.3
5.1
10.5
10.7
11.2
11.1
4.0
10.3
10.9
11.4
9.1
8.5
10.9
9.4
19.8
11.3
8.3
19.9
8.8
4.9
4.0
13.3
14.6
33.4
5.0
7.9
6.9
31.5
28.9
8.9
16.5
Polychlorinated Biphenyls (PCBs)
PCS 74
PCB 1 01
PCS 138
PCB 1 53
PCB 118
PCB 187
PCB 1 83
6.3
6.4
6.7
6.9
7.0
7.2
8.3
45.5
48.5
53.3
51.3
52.8
56.0
55.1
23.2
21.4
18.1
17.7
21.8
18.1
17.8
124.8
31.0
2.8
1.3
1.3
0.9
1.2
Average Recoveries and Standard Deviations4
Average
Max
Min
68.3
206.2
28.1
14.8
33.2
1.0
21.9
124.8
0.2
Estimated log «„. Recoveries validated at 6 ng/L and were corrected for background concentrations of SOCs in snow. Sample-Specific
Estimated Method Detection Limits. 4Average recoveries and percent relative standard deviations do not include compounds that were not
detected or not applicable. 5Not Detected (nd) due to lost during silica cleanup. 6Not Applicable (na) due to lost during silica cleanup.
3B-3
-------
Table 3B-2. Trace Metals and Detection Limits for Snow Sample Analyses at the USGS National
Research Program Laboratory, Boulder, Colorado. Concentrations are in |jg/L.
Analyte
Al
As
B
Ba
Be
Bi
Cd
Ce
Co
Cr
Cs
Cu
Dy
Er
Eu
Gd
Ho
La
Li
Lu
Mn
Mo
Detection Limit
<0.2
<0.02
<3
O.005
< 0.005
< 0.0009
< 0.002
< 0.0002
< 0.002
<0.2
< 0.009
<0.04
< 0.0004
< 0.0002
< 0.0002
< 0.0002
< 0.0001
< 0.0002
< 0.008
< 0.0001
<0.01
<0.03
Analyte
Nd
Ni
Pb
Pr
Rb
Re
Sb
Se
Sm
Sr
Tb
Te
Th
Tl
Tm
U
V
W
Y
Yb
Zn
Zr
Detection Limit
< 0.0006
<0.02
< 0.004
< 0.0003
< 0.0006
< 0.0002
< 0.001
<0.05
< 0.0002
<0.03
< 0.0001
< 0.005
< 0.0004
< 0.001
< 0.0001
< 0.0004
<0.07
< 0.001
< 0.0002
< 0.0002
<0.04
< 0.0008
3B-4
-------
Table 3B-3. Extended Details of Passive Sampling Device (PASD) Locations. Mapping datum is
WGS84.
Park
Code
BAND
BIBE
Elevational
Gradient
CRLA
DENA
GAAR
GLAC
GLBA
GRSA
GRTE
KATM
LAVO
MORA
NOAT
NOCA
OLYM
ROMO
Elevational
Gradient
SEKI
Elevational
Gradient
STLE
Elevational
Gradient
WRST
YOSE
#of
PSDs
1
4
1
2
1
2
1
1
1
1
1
2
1
1
2
5
4
4
1
1
Target
Watershed
Wonder N
Wonder S
Matcharak
Snyder
Oldman
Golden
LP19
Burial
PJ
Hoh
Lone Pine
Mills
Emerald
Latitude
35.8642
29.1870
29.3079
29.2534
29.2465
42.9233
63.5421
63.4549
67.7500
48.6264
48.5126
58.6022
37.7149
43.1300
58.5711
40.4476
46.8866
46.8226
68.4100
48.6824
47.9500
47.9000
40.2368
40.2203
40.2303
40.2922
40.2290
36.5176
36.5762
36.5985
36.6005
56.7910
56.8047
56.8095
56.8250
61 .3856
37.7744
Longitude
-106.4178
-102.9718
-103.1828
-103.2979
-103.3049
-122.0162
-150.9781
-150.8761
-156.2300
-113.8050
-113.4564
-135.8831
-105.4704
-110.7800
-155.8036
-121.5662
-121.9002
-121.8963
-159.2200
-121.3217
-123.4200
-123.7900
-105.7992
-105.7582
-105.7335
-105.6420
-105.7117
-118.8003
-118.7862
-118.7212
-118.6789
-132.5110
-132.5317
-132.5407
-132.5715
-143.6014
-119.3371
Elev
(m)
2926
560
1067
1920
2316
2713
564
686
505
1609
2036
8
3338
3048
370
2713
1369
1372
388
1600
1392
1433
2560
2720
3018
3042
3536
658
1573
2332
2816
1
254
567
815
648
3048
Veg Site
BANDS
BIBE1
BIBE2
BIBE4
BIBE5
CRLA5
DENA2
DENA2
GAAR1
GLAC3
GLAC4
GLBA1
GRSA5
GRTE5
KATM3
LAV05
MORA4
MORAS
NOAT3
NOCA5
OLYM4
OLYM3
ROM01
ROMO2
ROM03
ROMO6
ROM04
None (Potwisha)
SEKI2
SEKI4
SEKI05
STLE1
STLE2
STLE3
STLE4
WRST3
YOSE5
3B-5
-------
Table 3B-4. SOC Recovery and EDLs in Passive Air Sampling Devices Over the Entire Analytical
Method.
Propachlor
Alachlor
HCH, gamma0
HCH, alpha0
HCH, beta0
HCH, delta0
Methoxychlor
Heptachlor epoxide
Endrin aldehyde
Endrin
Heptachlor
o,p'-DDEd
Chlordane, oxy
Dieldrin
Endosulfan sulfate
Endosulfan I
Methyl parathion
Malathion
Diazinon
Parathion
Simazine
Cyanazine
Atrazine
Metribuzin
Etridiazole
Triallate
Dacthal
Acenaphthylene
Acenaphthene
Fluorene
Anthracene
Phenanthrene
Pyrene (Pyr)
Fluoranthene (Fla)
Chrysene/Triphenylene
Benzo[a]anthracene
PCB74
PCB 101
PCB 138
PCB 153
Averages and % RSD
average 93.7 5.6 0.03 max 210.0 154.8 0.2
min 20.9 0.0 0.00
" Recoveries were corrected for background concentrations of SOCs in needles. "Sample-specific estimated method detection limits calculated from
a sample taken from Hoh Lake in Olymic National Park. cHexachlorocyclohexane. "Dichlorodiphenyldichloroethylene.
"Dichlorodiphenyldichloroethane. 'Dichlorodiphenyltrichloroethane. 9Data obtained from one sample.
XADa EDLb
Avg. % Rec % RSD ng/g dw
XADa
Avg. %Rec
%RSD
EDLb
ng/g dw
Amide Pesticides
100.7
97.0
3.8 0.05
2.1 0.1
Acetochlor
Metolachlor
87.9
102.6
3.1
1.9
0.1
0.02
Organochlorine Pesticides and Metabolites
92.2
89.9
94.5
102.9
110.0
122.4
92.9
107.3
111.6
104.2
118.2
95.2
0.4 0.01
1.0 0.01
1.1 0.00
0.8 0.02
1.4 0.01
1.3 0.03
1 .4 0.003
2.2 0.03
2.6 0.01
7.7 0.02
1.4 0.03
1.8 0.02
Chlordane, cis
p,p'-DDDe
Nonachlor, trans
o,p'-DDDe
Chlordane, trans
Nonachlor, cis
Aldrin
o,p'-DDTf
p,p'-DDEd
Mirex
p,p'-DDTf
82.6
106.3
99.3
94.9
104.1
93.9
99.2
67.5
91.0
86.5
94.4
3.7
3.2
1.6
1.7
1.1
2.5
1.3
8.8
1.8
2.5
1.5
0.02
0.05
0.00
0.02
0.001
0.001
0.01
0.04
0.01
0.004
0.01
Organochlorine Sulfide Pesticides and Metabolites
94.7
102.0
3.6 0.0002
1.1 0.003
Endosulfan II
97.8
2.3
0.003
Phosphorothioate Pesticides
80.7
74.0
81.2
77.1
1.4 0.1
5.8 0.1
2.2 0.04
3.4 0.1
Ethion
Chlorpyrifos
Chlorpyrifos oxon
100.4
81.8
150.6
8.5
2.6
9.9
0.1
0.003
0.2
Triazine Herbicides and Metabolites
102.7
210.0
90.2
1.3 0.1
2.0 0.1
1.0 0.04
Atrazine desethyl
Atrazine desisoproply
107.7
102.7
3.4
1.4
0.1
0.02
Miscellaneous Pesticides
90.8
116.5
91.9
95.4
7.0 0.02
0.7 0.1
2.2 0.01
3.7 0.002
Trifluralin
Hexachlorobenzene
EPIC
Pebulate
82.6
93.3
83.8
88.8
4.5
1.0
1.4
1.3
0.001
0.0002
0.2
0.1
Polycyclic Aromatic Hydrocarbons
48.4
81.2
92.1
20.9
99.4
89.4
92.2
87.5
63.8
25.6 0.03
4.4 0.04
2.2 0.04
154.8 0.1
2.2 0.1
2.7 0.01
3.0 0.01
1 .9 0.005
44.8 0.01
Benzo[k]fluoranthene (BkF)
Benzo[a]pyrene (BaP)g
Benzo[b]fluoranthene (BbF)
lndeno[1 ,2,3-cd]pyrene (Ind)
Dibenz[a,h]anthracene
Benzo[e]pyrene (BeP)
Benzo[ghi]perylene (BghiP)
Retene
79.6
88.2
99.2
93.7
89.9
101.8
88.9
114.2
2.4
0.0
0.7
1.4
2.4
3.6
2.5
3.0
0.01
0.02
0.007
0.01
0.02
0.009
0.01
0.02
Polychlorinated Biphenyls
93.5
88.7
111.8
103.9
0.6 0.1
3.1 0.003
1.7 0.001
1.6 0.001
PCB118
PCB187
PCB183
70.9
91.0
91.9
4.6
1.5
1.6
0.001
0.001
0.0002
3B-6
-------
Vegetation QA/QC
Vegetation Sample Summary
Table 3B-5. Vegetation Sample Summary by Park.
Park Type
Core
Secondary
Total:
Park
DENA
GAAR
GLAC
MORA
NOAT
OLYM
ROMO
SEKI*
BAND
BIBE
CRLA
GLBA
GRSA
GRTE
KATM
LAVO
NOCA
STLE
WRST
YOSE
20
Sampling
Month
8/2004
7/2004
8/2004
8/2004
6/2004
9/2004
9/2004
10/2004
6/2005
6/2005
8/2005
7/2005
6/2005
7/2005
6/2005
8/2005
7/2005
7/2005
7/2005
8/2005
8
#
Sites
6
1
5
5
3
5
5
8
5
5
5
4
5
5
6
5
5
5
5
5
98
#
Conifer
samples
12
0
15
18
0
15
18
32
6
5
6
4
5
6
5
5
5
6
7
6
176
#
Lichen
samples
29
7
25
19
15
21
6
26
6
2
5
4
2
3
6
5
5
10
7
4
207
3B-7
-------
Table 3B-6. Vegetation Sample Summary. Species collected at each site are recorded in Appendix
1A-3.
Sample type
Conifer needles
Lichens
Needles
Count:
Lichens
Count:
Total Count:
Genus
Abies
Picea
Pin us
Psuedotsu
93
Tsuga
Alectoria
Bryoria
Cladina
Flavocetra
ria
Hypogymn
ia
Letharia
Lobaria
Masonhal
ea
Platismati
a
Sphaerop
horus
Thamnolia
Usnea
Xanthopar
melia
5
13
19
Scientific name
Abies amabilis
Abies concolor
Abies lasiocarpa
Abies magnifica
Abies procera
Picea engelmanii
Picea glauca
Picea mariana
Picea sitchenis
Pinus albicaulis
Pin us
cembroides
Pinus contorts
Pinus edulis
Pinus fiexiilis
Pinus
lambertiana
Pinus ponderosa
Pinus sabiniana
Pseudotsuga
menziesii
Tsuga
heterophylla
A. sarmentosa
Bryoria spp.
C. arbuscula
F. cucullata
H. apinnata
H. physodes
L. Columbian a
L. vulpina
Lobaria oregana
M. richardsonii
P. glauca
S. globosus
Thamnolia sp.
Usnea spp.
Xanthoparmelia
spp.
Common name
Pacific silver fir
white fir
subalpine fir
Red fir
Noble fir
Engelmann
spruce
white spruce
black spruce
Sitka spruce
white pine
Mexican pinyon
lodgepole pine
twoneedle pine
limber pine
sugar pine
ponderosa pine
California
foothill pine
Douglas-fir
western
hemlock
old man's beard
lichen
horsehair lichen
reindeer lichen
reindeer lichen
tube lichen
tube lichen
wolf lichen
wolf lichen
Oregon lung
lichen
Mason Male's
lichen
ragged lichen
globe ball lichen
whiteworm
lichen
beard lichen
xanthoparmelia
lichen
19
16
36
Parks where samples were
collected
MORA, NOCA, OLYM,
ROMO, SEKI
CRLA, LAVO
GLAC, GRTE, OLYM
CRLA, LAVO, SEKI
MORA
GLAC, ROMO
KATM, WRST
DENA
GLBA, STLE, WRST
CRLA, GRTE
BIBE
GRTE, SEKI, YOSE
BAND, GRSA
GRSA, GRTE
YOSE
BAND, YOSE
YOSE
GLAC, NOCA
GLAC, MORA, NOCA,
OLYM
GLAC, GLBA, MORA,
NOCA, OLYM, STLE
OLYM
STLE, WRST
DENA, GAAR, KATM,
NOAT, WRST
WRST
GLAC, KATM, WRST
LAVO
CRLA, GLAC, GRTE, LAVO,
SEKI, YOSE
OLYM, STLE
DENA, GAAR, NOAT
GLAC, GLBA, NOCA,
OLYM, STLE, WRST
GLBA
DENA
BAND, BIBE, GRTE
BAND, GRSA, ROMO,
YOSE
soc
samples
13
9
22
12
4
9
10
12
12
3
3
7
5
5
1
5
1
4
20
26
6
2
8
3
7
1
29
4
23
15
2
1
8
8
157
143
300
N
samp
les
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
6
2
0
3
7
1
23
4
11
10
2
1
8
5
0
94
94
N.S&
metals
sample
s
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
15
0
0
11
0
0
0
6
0
12
5
0
0
0
3
0
52
52
3B-8
-------
Figure 3B-1. Vegetation Sampling. First row: lichen sampling from tundra, rocks and trees; second row:
conifer needle sampling from mid and alpine elevations; third row: lichen and conifer samples in Kapak
bags; fourth row: weighing and sealing vegetation samples.
3B-9
-------
SOCs
Detailed laboratory QA/QC procedures for SOCs are specified in the WACAP QAPP. The
analyte recovery over the entire analytical method and the estimated method detection limits for
conifer needles are given in Table 3B-7 and for lichen in Table 3B-8.
Table 3B-7. SOC Recovery and EDLs in Conifer Needles Over the Entire Analytical Method.
Local Conifer Needles3 EDLb Local Conifer Needles3 EDLb
Avg. % Rec % RSD ng/g dw
Avg. %Rec %RSD ng/g dw
HCH, gamma0
HCH, alpha0
HCH, beta0
HCH, delta0
Methoxychlor
Heptachlor epoxide
Endrin aldehyde
Endrin
Heptachlor
o,p'-DDEd
Chlordane, oxy
Dieldrin
Endosulfan sulfate
Endosulfan I
Chlorpyrifos
Dacthal
Hexachlorobenzene
Acenaphthylene
Acenaphthene
Fluorene
Anthracene
Phenanthrene
Pyrene (Pyr)
Fluoranthene (Fla)
Chrysene/Triphenylene
PCB74
PCB 101
PCB 138
PCB 153
79.1
80.2
74.8
91.5
84.9
75.4
24.6
79.5
85.6
67.0
78.8
75.1
80.6
62.4
68.8
83.2
71.0
Organochlorine Pesticides and Metabolites
1.7
2.1
1.3
2.1
2.4
6.7
3.7
5.4
3.2
1.0
7.1
9.5
Organochlorine Sulfide Pesticides and Metabolites
4.6 0.6 Endosulfan 11
2.6 0.2
1.9
1.5
1.7
3.1
5.3
1.2
0.9
14.6
3.3
3.6
1.6
5.8
Chlordane, cis
p,p'-DDDe
Nonachlor, trans
o,p'-DDDe
Chlordane, trans
Nonachlor, cis
Aldrin
o,p'-DDTf
p,p'-DDEd
Mirex
p,p'-DDTf
0.6
Phosphorothioate Pesticides
0.4 Methyl parathion
Miscellaneous Pesticides
3.9 0.1 Triallate
1.5 0.0 Trifluralin
Polycyclic Aromatic Hydrocarbons
53.2
80.4
66.3
79.1
51.2
79.7
85.6
86.6
2.5
9.5
10.7
1.2
5.8
3.1
5.3
2.3
2.3
7.1
3.2
10.4
4.8
0.6
3.7
4.3
Benzo[a]anthracene
Benzo[k]fluoranthene (BkF)
Benzo[a]pyrene (BaP)
Benzo[b]fluoranthene (BbF)
lndeno[1,2,3-cd]pyrene (Ind)
Dibenz[a,h]anthracene
Benzo[e]pyrene (BeP)
Benzo[ghi]perylene (BghiP)
Polychlorinated Biphenyls
97.3 2.3 16.7 PCB 118
81.3 2.4 2.2 PCB 187
78.8 1.9 0.2 PCB 183
81.2 1.7 0.05
57.6
71.7
58.9
71.8
82.8
30.5
72.6
57.7
81.1
87.9
66.8
63.8
51.1
92.8
77.2
78.2
71.9
92.6
76.3
84.3
62.5
81.7
87.7
89.2
85.7
79.8
0.9
4.8
3.1
0.5
4.6
1.8
3.8
1.8
1.2
0.9
0.9
1.0
44.4
11.2
0.3
2.7
2.7
0.8
2.9
1.0
3.0
2.7
1.8
0.8
0.9
1.1
0.6
6.0
0.2
5.4
0.05
0.1
2.2
1.7
1.8
0.4
2.5
0.7
72.3
1.7
0.1
13.0
6.5
8.4
7.9
16.4
58.5
9.4
3.0
0.2
0.04
0.04
Averages, % RSD, andPD°
average 73.2 3.7 5.7 max 97.3 44.4 72.3
min 24.6 0.3 0.01
"Samples collected at Walnut Park located in Corvallis, OR, USA. Recoveries were corrected for background concentrations of SOCs in needles.
"Sample-specific estimated method detection limits calculated from a sample taken from Mount Rainier National Park. cHexachlorocyclohexane.
"Dichlorodiphenyldichloroethylene. "Dichlorodiphenyldichloroethane. 'Dichlorodiphenyltrichloroethane.
3B-10
-------
Table 3B-8. SOC Recovery and EDLs in Lichen Over the Entire Analytical Method.
Wolverton Creek"
EDL°
Wolverton Creek"
EDL°
Avg. % Rec % RSD ng/g lipid
Avg. %Rec %RSD ng/g lipid
Organochlorine Pesticides and Metabolites
HCH, gamma0
HCH, alpha0
HCH, beta0
HCH, delta0
Methoxychlor
Heptachlor epoxide
Endrin aldehyde
Endrin
Heptachlor
o,p'-DDEd
Chlordane, oxy
Dieldrin
Endosulfan sulfate
Endosulfan I
Chlorpyrifos
Dacthal
Hexachloro benzene
Acenaphthylene
Acenaphthene
Fluorene
Anthracene
Phenanthrene
Pyrene (Pyr)
Fluoranthene (Fla)
Chrysene/Triphenylene
PCB74
PCB 101
PCB 138
PCB 153
73.8
81.6
80.9
88.9
71.3
58.7
52.0
93.8
81.8
68.0
57.5
120.4
5.6
4.8
0.6
6.5
28.4
13.2
9.7
13.6
3.3
1.0
12.0
10.0
1.0
0.9
1.9
1.0
7.3
4.6
0.5
6.9
1.5
7.1
1.7
8.0
Chlordane, cis
p,p'-DDDe
Nonachlor, trans
o,p'-DDDe
Chlordane, trans
Nonachlor, cis
Aldrin
o,p'-DDTf
p,p'-DDEd
Mirex
p,p'-DDTf
Organochlorine Sulfide Pesticides and Metabolites
38.4 22.6 0.4 Endosulfan II
62.0 10.9 1.1
Phosphorothioate Pesticides
92.7 1.6 0.2 Methyl parathion
Miscellaneous Pesticides
68.6 20.0 0.2 Triallate
72.8 1.7 0.01 Trifluralin
Polycyclic Aromatic Hydrocarbons
Benzo[a]anthracene
Benzo[k]fluoranthene (BkF)
Benzo[a]pyrene (BaP)
Benzo[b]fluoranthene (BbF)
lndeno[1,2,3-cd]pyrene (Ind)
Dibenz[a,h]anthracene
Benzo[e]pyrene (BeP)
Benzo[ghi]perylene (BghiP)
Polychlorinated Biphenyls
89.0 2.3 9.1 PCB 118
77.3 2.0 3.7 PCB 187
76.2 5.6 0.2 PCB 183
75.5 3.3 0.12
50.3
68.5
74.6
82.9
67.1
75.5
77.7
87.6
14.5
19.2
6.2
0.4
17.8
2.0
2.8
1.3
13.7
6.3
2.7
4.9
2.3
1.7
1.7
1.8
62.4
76.8
56.9
79.5
59.0
31.3
76.0
52.4
77.3
139.5
90.8
64.8
80.0
99.8
94.1
87.3
54.8
71.4
55.7
82.4
80.5
70.0
90.2
89.9
75.7
75.9
13.2
7.2
16.8
0.7
15.9
6.8
4.0
25.9
1.6
7.6
40.0
5.9
3.7
37.2
2.0
2.4
87.3
30.0
90.5
2.9
6.7
16.1
3.1
0.8
4.5
10.7
2.7
5.3
0.4
7.3
0.21
0.2
2.6
13.5
6.4
1.0
6.3
0.2
54.3
0.9
0.1
2.4
9.2
5.3
9.1
5.9
23.0
7.0
2.4
0.3
0.11
0.09
Averages and % RSD
average 73.9 12.3 4.6 max 139.5 90.5 54.3
min 31.3 0.4 0.01
"Samples collected at Wolverton Creek in Sequoia and Kings Canyon National Park, CA in 2003. Recoveries were corrected for background
concentrations of SOCs in lichen. "Sample-specific estimated method detection limits calculated from a sample taken from Mount Rainier National
Park. °Hexachlorocyclohexane. dDichlorodiphenyldichloroethylene. eDichlorodiphenyldichloroethane. fDichlorodiphenyltrichloroethane.
Metals (Lichen)
Detailed laboratory QA/QC procedures are specified in the WACAP QAPP. Quality control at
the USGS National Research Program laboratory in Boulder, Colorado (see Table 3B-9),
involves systematic analysis of blanks, replicates, standard reference materials, and spike
addition samples. Standard Reference Materials used for the quality control of lichen analysis
included Commission of European Communities CRM 482 Trace Elements in Lichen and
International Atomic Energy Agency IAEA-336 Trace and Minor Elements in Lichen. Details,
results and figures are described in the Quality Assurance/Quality Control Section of the
database.
3B-11
-------
Table 3B-9. Metals and Detection Limits for Lichen Sample Analyses Performed at the USGS
National Research Program Laboratory, Boulder, Colorado. Concentrations in dry weight, assuming a
0.2 g sample size (1:10 dilution).
Analyte
Al
As
B
Ba
Be
Bi
Ca
Cd
Ce
Co
Cr
Cs
Cu
Dy
Er
Eu
Fe
Gd
Ho
K
La
Li
Lu
Mg
Units
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
Wt%
Detection
Limit
<1
<0.05
<14
<0.03
<0.03
<0.01
<0.001
<0.01
< 0.001
<0.01
<0.5
<2
<0.1
< 0.003
< 0.004
< 0.001
<20
< 0.003
< 0.001
<0.006
< 0.001
<0.04
< 0.0007
< 0.0003
Analyte
Mn
Mo
Na
Nd
Ni
Pb
Pr
Rb
Re
Sb
Se
Sm
Sr
Tb
Te
Tl
Tm
U
V
W
Y
Yb
Zn
Zr
Units
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Detection Limit
<0.2
<0.3
< 0.0008
< 0.004
<0.1
<0.04
< 0.001
<0.02
< 0.002
<0.01
<0.2
< 0.005
<0.08
< 0.0007
<0.04
<0.02
< 0.0007
< 0.004
<0.1
<0.01
< 0.001
< 0.003
<0.9
<0.09
Nitrogen and Sulfur (Lichen)
As macronutrients, nitrogen and sulfur are used in relatively large quantities by lichens in
cellular metabolism and in the production of biomolecules. Even in geographic areas with low
nitrogen and sulfur deposition, these elements occur in relatively high concentrations in lichen
thalli (~ 1 and 0.1 % dry weight, respectively), and therefore, compared to other contaminants
analyzed by WACAP, their quantification is relatively easy. Four types of quality control checks
were employed:
3B-12
-------
1. Randomization of samples. Samples were analyzed in random order before analysis to
prevent unintentional bias within and between batches.
2. Field triplicates. Triplicate samples of each lichen species were collected at each collection
site in the core parks as an indicator of error due to field methodology. Triplicate samples
that are truly representative of the lichen population at a site will have low variability.
3. Laboratory replicates. Duplicate measurements were made of every 10th sample to assess
precision of laboratory measurements.
4. Standard Reference Materials. NIST 1515 Apple Leaves and NIST 1547 Peach Leaves to
assess accuracy of laboratory measurements.
5. Lichen reference materials. A 1998 US Forest Service bulk collection ofAlectoria
sarmentosa from Willamette Pass, Oregon, dried, ground and stored in air tight container at
UMRAL. This lichen has a relatively low N and S content compared to most lichen species
and all NIST SRMS and therefore is more challenging to analyze. An aliquot of the bulk
collection was analyzed every 10 samples to assess worst-case precision of laboratory
measurements and to compare to laboratory performance to prior years.
Table 3B-10 shows that variability in N and S concentrations between sites (WACAP lichens),
measured either as the standard deviation or as the size of the standard deviation relative to the
mean (100* sd/mean), was greater than that of the field triplicates, which was in turn, greater
than variability among laboratory and AlesarWIL replicates. Laboratory precision of nitrogen
analyses was excellent, with most values for individual replicates falling within 1% of means;
precision of sulfur analyses was good, most individual values were <5% of means. UMRAL
measurements of NIST SRMs fell within certified ranges for N. UMRAL values were close to
non-certified values for S (NIST does not certify means or ranges for S).
Table 3B-10. Statistical Summary of Quality Control Measures for Total Nitrogen and Sulfur (% dw)
in Lichen Samples from the WACAP Core Parks.
Element Material
N WACAP lichens
Field triplicates
Lab replicate pairs
AlesarWIL
NIST 1515
NIST 1547
S WACAP lichens
Field triplicates
Lab replicate pairs
AlesarWIL
NIST 1515
NIST 1547
Count
58
17
5
7
3
4
56
17
8
6
5
5
UMRAL
Mean
0.567
0.585
0.456
0.423
2.313
3.010
0.044
0.045
0.028
0.034
0.193
0.163
UMRAL
sd
0.303
0.096
0.003
0.012
0.012
0.022
0.030
0.005
0.001
0.004
0.011
0.019
100*
(sd/mean)
53.44
16.33
0.76
2.84
0.50
0.72
68.28
11.49
4.57
10.87
5.60
11.54
NIST
Mean
NA
NA
NA
NA
2.25
2.94
NA
NA
NA
NA
0.180
0.200
NIST
Certified
Range
NA
NA
NA
NA
2.06-2.44
2.82-3.06
NA
NA
NA
NA
NA
NA
3B-13
-------
Lake Water QA/QC
SOCs
Detailed laboratory QA/QC procedures for SOCs are specified in the WACAP QAPP. The
analyte recovery over the entire analytical method and the estimated method detection limits for
water are given in Table 3B-11.
Table 3B-11. SOC Recovery and EDLs in Water Over the Entire Analytical Method (Usenko et al.,
2005).
Chemical Class
Compounds
Amide Pesticides
Propachlor2
Alachlor2
Acetochlor
Metolachlor
log Kow
2.4
2.6
3.031
3.1
1 L RO Water
Avg. % RSD
% Rec
110.2
101.5
96.9
109.9
7.0
4.0
2.9
4.8
50 L RO Water3 Chemical Class
Avg. % RSD
% Rec Compounds
111.3
104.6
102.4
114.7
1.9
0.8
2.7
1.0
Organochlorines Pesticides and Metabolites
HCH, gamma2'4
HCH, alpha2
HCH, beta2
HCH, delta2
Methoxychlor
Heptachlorepoxide2
Endrin aldehyde2
Endrin
Heptachlor
o,p'-DDE2'5
Chlordane, oxy
Dieldrin
Chlordane, cis2
p,p'-DDD2'6
Nonachlor, trans
o,p'-DDD2
Chlordane, trans
Nonachlor, cis
Aldrin
o,p'-DDT7
p,p'-DDE2
Mirex
p,p'-DDT2
3.8
3.8
4.0
4.1
4.5
4.6
4.8
5.2
5.2
5.5
5.5
5.5
5.9
5.9
6.1
6.1
6.1
6.4
6.5
6.8
6.9
6.9
6.9
99.2
105.1
103.0
108.4
127.7
57.5
78.3
147.2
132.3
86.4
55.1
100.8
44.5
105.1
49.4
91.5
42.4
62.5
57.7
73.4
83.2
110.3
82.3
3.5
7.8
5.7
4.9
21.3
19.2
15.1
30.1
28.0
14.9
21.7
12.6
18.4
20.8
14.1
18.3
12.4
18.8
21.0
3.1
11.5
10.2
11.6
103.5
115.9
113.8
118.9
158.8
72.1
74.4
138.6
157.4
101.9
71.8
74.5
60.6
122.0
66.3
110.1
55.5
75.7
70.6
91.5
97.9
118.3
97.5
1.0
1.4
2.2
2.0
2.4
6.3
32.0
3.8
4.0
0.8
4.2
3.5
3.3
3.7
2.6
2.0
3.0
1.4
1.7
3.5
0.8
5.5
0.4
Organochlorine Suffide Pesticides and Metabolites
Endosulfan sulfate
Endosulfan I2
Endosulfan II2
3.7
4.7
4.8
88.4
55.8
88.1
19.8
17.8
19.2
93.3
69.0
98.7
5.2
6.8
2.8
Phosphorothioate Pesticides
Methyl parathion2
Malathion
Diazinon2
Parathion2
Ethion
Chlorpyrifos
2.7
2.9
3.7
3.8
5.1
5.1
107.9
97.5
100.9
97.3
99.6
81.9
5.2
4.9
9.5
7.3
24.4
20.4
114.4
111.2
114.5
106.1
115.4
96.2
2.4
4.7
3.0
4.1
3.8
4.3
Thiocarbamate Pesticides
EPTC2
Pebulate2
Triallate
3.2
3.8
4.6
100.6
125.3
61.1
1.3
4.6
20.0
104.2
128.9
79.7
1.9
3.2
7.4
log Kow
1 L RO Water3
Avg. % RSD
% Rec
50 L RO Water3
Avg. % RSD
% Rec
Triazine Herbicides and Metabolites
Atrazine desisopropyl
Atrazine desethyl
Simazine
Cyanazine
Atrazine2
Prometon2
Miscellaneous Pesticides
Metribuzin2
Etridiazole
Dacthal2
Trifluralin2
Hexachloro benzene2
1.361
1.781
2.2
2.2
2.3
2.7
1.701
2.6
4.3
5.3
5.5
106.0
62.8
115.2
60.5
102.1
68.7
86.3
124.6
98.7
71.2
81.0
6.3
7.3
3.6
10.9
2.1
42.7
14.7
4.1
8.6
5.0
13.8
89.3
82.8
117.8
62.7
104.6
90.5
96.1
127.7
104.1
62.9
89.1
2.4
2.7
0.6
4.6
0.7
8.9
4.3
2.0
2.9
7.8
3.3
Polycyclic Aromatic Hydrocarbons
Acenaphthylene2
Acenaphthene
Fluorene
Anthracene2
Phenanthrene
Pyrene
Fluoranthene2
Chrysene + Triphenylene2
Benzo(a)anthracene
Retene
Benzo(k)fluoranthene2
Benzo(a)pyrene2
Benzo(b)fluoranthene
lndeno(1 ,2,3-cd)pyrene2
Dibenz(a,h)anthracene2
Benzo(e)pyrene
Benzo(ghi)perylene
Polychlorinated Biphenyls
PCB 742
PCB 1012
PCB 1382
PCB 1532
PCB 1182
PCB 1872
PCB 1832
3.9
4.0
4.2
4.5
4.5
5.1
5.2
5.7
5.9
6.4
6.5
6.5
6.6
6.7
6.8
6.9
7.0
6.3
6.4
6.7
6.9
7.0
7.2
8.3
60.3
86.1
96.9
41.1
118.3
84.5
101.2
92.1
76.7
121.9
84.6
98.6
99.9
87.9
102.4
112.4
87.1
74.5
66.2
94.9
99.3
57.1
80.5
85.3
9.0
5.6
0.4
73.6
3.5
8.6
9.3
11.5
22.3
9.6
11.8
9.1
14.2
15.7
9.4
19.9
9.1
21.8
22.3
3.6
4.3
24.8
5.3
4.5
63.8
91.9
102.2
24.7
104.8
89.3
105.1
106.0
76.8
142.0
100.8
117.4
117.2
103.4
113.2
126.4
96.9
106.6
95.1
105.1
110.0
82.6
88.5
94.1
10.9
1.6
2.5
69.5
1.3
1.3
2.7
0.9
20.1
3.4
5.3
5.7
7.4
0.5
2.7
9.2
3.0
0.9
1.2
3.4
3.5
0.9
3.5
3.9
Average Recoveries and %RSD
Max
Min
89.4
147.2
41.1
13.1
73.6
0.4
99.0
158.8
24.7
4.8
69.5
0.4
'Estimated log Kow. Recoveries not statistically different: two sided t-test (p<0.01). Recoveries determined at 300 ng total of each compound (300 ng/L
for 1 L experiment and 6 ng/L for 50 L experiment). 4Hexachlorocyclohexane. 5Dichlorodiphenyldichloroethylene. 6Dichlorodiphenyldichloroethane.
Dichlorodiphenyltrichloroethane
3B-14
-------
Inorganic Compounds
Table 3B-12. Inorganic Lake Water Analytes, Methods, and Detection Limits
Analyte
Specific Conductance
Temperature
Dissolved Oxygen (DO)
Turbidity
pH (syringe, closed system)
Acid Neutralizing Capacity (ANC)
Chlorophyll a
Total Suspended Solids (Residue)
True Color
Dissolved Organic Carbon (DOC)
Dissolved Inorganic Carbon (DIG),
syringe, closed system
Ammonium (NH4)
Nitrate + Nitrite Nitrogen
Silica (SiO2)
Total Nitrogen (TN)
Total Phosphorus (TP)
Chloride (Cl)
Nitrate (NO3)
Sulfate (SO4)
Calcium (Ca)
Sodium (Na)
Potassium (K)
Magnesium (Mg)
Method1
EPA 1 20.6; USEPA (1987)
USEPA (1 987)
USEPA (1987), YSI Model 6920 Datasonde
YSI Model 6920 Datasonde
USEPA (1 987)
EPA 31 0.1 (modified),
USEPA (1987)
APHA(1989)
EPA 1 60.2; APHA (1989)
APHA (1989), EPA 100.2 (modified),
USEPA (1987)
EPA 41 5.2, USEPA (1987)
USEPA (1987)
Lachat10-107-06-3-D
EPA 353.2
EPA 370.1 (modified), U.S. EPA (1987)
EPA 353.2 (modified),
USEPA (1 987)
EPA 365.1 (modified),
USEPA (1 987)
EPA 300.6; USEPA (1987)
EPA 300.6; USEPA (1987)
EPA 300.6; USEPA (1987)
EPA 21 5.1; USEPA (1987)
EPA 273.1; USEPA (1987)
EPA 258.1; USEPA (1987)
EPA 242.1; USEPA (1987)
Detection
Limit2
NA
NA
NA
0.1 NTU
NA
NA
1M9/L
0.1 mg/L
NA
0.1 mg/L
0.1 mg/L
2ug/L
1 M9/L
5ug/L
10ug/L
2ug/L
0.03 mg/L
0.03 mg/L
0.05 mg/L
0.02 mg/L
0.02 mg/L
0.04 mg/L
0.01 mg/L
1 American Public Health Association. 1989. Standard Methods for the Examination of Water and Wastewater.
Seventeenth Edition. American Public Health Association, Washington, D.C.
U.S. EPA. 1983. Methods for Chemical Analysis of Water and Wastes. Environmental Monitoring and Support
Laboratory. EPA/600/4-79/020, U.S. Environmental Protection Agency, Office of Research and Development,
Cincinnati.
U.S. EPA. 1987. Handbook of Methods for Acid Deposition Studies: Laboratory Analyses for Surface Water
Chemistry. EPA 600/4-87/026. U.S. Environmental Protection Agency, Office of Research and Development,
Washington, D.C.
2 The method detection limit is determined as a one-sided 99% confidence interval from repeated measurements
of a low-level standard across several calibration curves.
3B-15
-------
Sediment QA/QC
SOCs
Detailed laboratory QA/QC procedures for SOCs are specified in the WACAP QAPP. The
analyte recovery over the entire analytical method and the estimated method detection limits for
sediment are given in Table 3B-13.
Table 3B-13. SOC Recovery and EDLs in Sediment Over the Entire Analytical Method.
Propachlor
Alachlor
HCH, gamma
HCH, alpha0
HCH, beta"
HCH, delta"
Methoxychlor
Heptachlor epoxide
Endrin aldehyde
Endrin
Heptachlor
o,p'-DDEe
Chlordane, oxy
Dieldrin
Endosulfan sulfate
Endosulfan I
Methyl parathion
Malathion
Diazinon
Simazine
Cyanazine
Metribuzin
Etridiazole
Triallate
Acenaphthylene
Acenaphthene
Fluorene
Anthracene
Phenanthrene
Pyrene (Pyr)
Fluoranthene (Fla)
Chrysene/Triphenylene
PCB101
PCB138
PCB153
Waldo Lake8
Avg. % Rec
49.8
53.1
% RSD
3.3
12.2
EDL"
ng/g dw
7.8
13.3
SRM1941b
Waldo Lake8
ng/g dw PDC % RSD Avg. %Rec
Amide Pesticides
Acetochlor 46.1
Metolachlor 58.6
%RSD
9.3
12.2
EDL"
ng/g dw
9.3
14.2
SRM1941b
ng/g dw
PDC
% RSD
Organochlorine Pesticides and Metabolites
29.6
50.8
36.2
51.8
67.4
46.8
51.8
70.4
32.5
57.7
43.7
74.0
9.4
9.0
9.1
9.4
14.8
13.8
7.9
11.5
12.4
11.2
14.8
13.1
117.5
133.3
175.7
59.5
18.6
89.4
19.6
204.7
111.9
11.3
12.2
114.8
1.0
0.3
Chlordane, cis
P,P'-DDD'
Nonachlor, trans
o.p'-DDD'
Chlordane, trans
Nonachlor, cis
Aldrin
o,p'-DDTB
p,p'-DDEe
Mirex
p,p'-DDTa
45.7
60.3
46.8
55.6
46.8
53.6
29.0
44.4
55.9
56.3
54.7
14.7
10.2
17.2
10.8
15.2
13.0
12.5
12.0
12.7
6.3
13.5
18.4
16.5
3.7
4.3
2.0
1.5
83.2
23.6
3.4
41.4
37.9
0.7
5.1
0.1
1.1
0.4
0.1
3.1
10.0
0.0
53.2
5.8
55.4
0.0
29.5
40.0
32.8
28.2
24.2
15.8
Organochlorine Sulfide Pesticides and Metabolites
61.4
50.2
9.6
13.2
4.4
8.1
Endosulfan II
58.5
10.3
9.0
Phosphorothioate Pesticides
49.9
48.3
47.9
5.1
7.9
5.4
33.0
65.8
5.1
Parathion
Ethion
Chlorpyrifos
54.0
60.0
45.3
6.5
10.4
9.7
15.7
10.8
1.2
Triazine Herbicides and Metabolites
63.2
136.0
3.4
19.3
58.3
171.2
Atrazine
57.6
6.3
9.5
Miscellaneous Pesticides
43.6
21.6
41.1
20.6
13.9
8.6
30.0
29.1
24.2
Dacthal
Trifluralin
Hexachlorobenzene
55.5
32.9
33.5
11.5
10.8
8.0
6.4
1.7
1.0
7.6
24.1
22.8
Polycyclic Aromatic Hydrocarbons
20.9
33.5
25.5
34.8
26.0
50.6
50.5
59.9
14.7
13.5
12.7
8.0
20.0
5.7
5.1
9.2
13.3
11.2
7.2
24.6
13.0
1.0
1.1
0.8
138.8
51.6
59.2
163.3
382.6
402.8
442.6
171.2
12.7
1.5
0.0
24.0
24.3
48.1
Benzo[a]anthracene
Benzo[k]fluoranthene (BkF)
22.3 Benzo[a]pyrene (BaP)
13.6 Benzo[b]fluoranthene (BbF)
18.6 lndeno[1,2,3-cd]pyrene(lnd)
22.1 Dibenz[a,h]anthracene
20.8 Benzo[e]pyrene (BeP)
22.3 Benzo[ghi]perylene (BghiP)
64.5
68.5
46.7
64.8
60.1
58.2
64.6
55.0
10.2
10.0
9.3
9.5
9.5
9.8
9.1
11.1
11.4
3.3
2.1
4.0
29.0
23.7
6.5
5.1
250.4
205.6
220.7
468.2
239.9
76.4
285.5
227.2
17.8
0.6
33.6
0.0
12.9
25.3
4.5
11.3
17.2
21.6
23.7
18.7
17.3
22.9
22.4
22.0
Polychlorinated Biphenyls
70.7
74.9
73.2
14.2
11.7
11.8
129.1
9.7
3.5
4.1
4.3
4.0
13.2
12.3
21.6
29.9 PCB118
30.4 PCB187
20.1 PCB183
74.2
76.1
76.5
11.6
13.1
13.1
10.2
3.9
3.7
3.3
2.0
0.7
17.2
0.0
23.0
34.5
22.1
20.4
BDE7
BDE8
BDE10
BDE17
BDE25
BDE28
BDE30
BDE32
BDE35
BDE37
BDE49
BDE47
BDE66
BDE71
BDE75
BDE77
Polybrominated Diphenyl Ethers
58.6 3.0 0.2 BDE85/155
77.8 2.2 0.1 BDE99
42.7 6.9 0.2 BDE100
78.1 3.6 0.4 BDE116
83.3 3.3 0.8 BDE118
70.5 4.7 4.1 BDE119
70.6 3.9 0.6 BDE126
77.2 1.7 0.7 BDE138
82.6 3.2 0.7 BDE153
80.3 4.0 1.3 BDE154
69.4 5.4 1.3 BDE155
71.9 4.5 15.6 1.3 BDE166
75.2 5.1 0.6 BDE181
67.7 4.8 1.3 BDE183
70.0 5.4 4.9 BDE190
70.5 6.1 0.8
73.0
75.5
74.1
72.8
76.5
75.0
69.2
76.0
76.7
84.8
101.6
72.4
99.9
73.3
104.4
2.0
2.4
2.3
3.2
4.8
2.9
1.7
1.2
1.3
0.8
0.9
2.0
2.3
2.3
2.1
15.1
3.3
2.1
3.3
26.0
0.9
15.6
2.8
5.8
31.3
5.7
0.6
0.9
Averages, % RSD, and PD
average 60.3 8.5 23.8 109.3 16.8 23.6 max 136.0 20.6 204.7 468.2 55.4 40.0
min 20.9 0.8 0.1 0.1 0.0 13.6
Recoveries validated at 26 ng/g wet wt and were corrected for background concentrations of SOCs in sediment. °Sampie-specific estimated method detection limits. cPercent Difference from
SRM 1941b certified values n=5. dHexachlorocyclohexane. eDichlorodiphenyldichloroethylene. fDichlorodiphenyldichloroethane. BDichlorodiphenyltrichloroethane.
3B-16
-------
Metals
Detailed laboratory QA/QC procedures are specified in the WACAP QAPP. Quality control at
the USGS National Research Program laboratory in Boulder, Colorado, involves systematic
analysis of blanks, replicates, standard reference materials, and spike addition samples (see Table
3B-14). Standard reference materials used for the quality control of sediment analysis included
National Institute of Standards and Technology SRM 2704 and 8704 Buffalo River Sediment;
and SRM 2702 Inorganics in Marine Sediment. Details, results and figures are described in the
Quality Assurance/Quality Control Section of the database.
Table 3B-14. Metals and Detection Limits for Sediment Sample Analyses Performed at the USGS
National Research Program Laboratory, Boulder, Colorado. Concentrations in dry weight, assuming a
0.1-g sample size (1:10 dilution).
Analyte
Al
As
B
Ba
Be
Bi
Ca
Cd
Ce
Co
Cr
Cs
Cu
Dy
Er
Eu
Fe
Ga
Gd
Ho
K
La
Li
Lu
Mg
Mn
Units
Wt%
i-jg/g
i-jg/g
|jg/g
|jg/g
|jg/g
wt%
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
wt%
|jg/g
|jg/g
|jg/g
wt%
|jg/g
|jg/g
|jg/g
wt%
|jg/g
Detection
Limit
< 0.0008
<0.07
<4
<0.08
<0.07
<0.01
<0.002
<0.01
<0.01
<0.01
<0.5
<0.02
<0.1
< 0.005
< 0.007
< 0.003
<0.009
<0.01
< 0.005
< 0.002
<0.009
< 0.007
<0.1
< 0.001
< 0.0005
<0.1
Analyte
Mo
Na
Nd
Ni
Pb
Pr
Rb
Re
Sb
Se
Sm
Sr
Tb
Te
Th
Ti
Tl
Tm
U
V
W
Y
Yb
Zn
Zr
Units
|jg/g
wt%
i-jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
wt%
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
|jg/g
Detection
Limit
<0.2
< 0.003
<0.01
<0.06
<0.03
< 0.002
<0.09
< 0.003
<0.01
<0.6
< 0.009
<0.1
< 0.001
<0.05
<0.01
<0.0001
<0.06
< 0.001
< 0.007
<0.4
<0.01
< 0.006
< 0.004
<0.7
<0.01
3B-17
-------
Fish QA/QC
SOCs
Detailed laboratory QA/QC procedures for SOCs are specified in the WACAP QAPP. The
analyte recovery over the entire analytical method and the estimated method detection limits for
fish are given in Table 3B-15.
Table 3B-15. SOC Recovery and EDLs in Fish Over the Entire Analytical Method.
Compounds
HCH4, gamma
HCH4, alpha
HCH4, beta
HCH4, delta
Methoxychlor
Heptachlor epoxide
Endrin
Heptachlor
Hexachlorobenzene
o,p'-DDE5
Chlordane, oxy
Dieldrin
Chlordane, cis
p,p'-DDD6
Nonachlor, trans
o,p'-DDD6
Chlordane, trans
Nonachlor, cis
Aldrin
o,p'-DDT7
p,p'-DDE5
Mi rex
p,p'-DDT7
Endosulfan sulfate
Endosulfan I
Endosulfan II
Parathion
Ethion
Chlorpyrifos
Etridiazole
Log
KOW
3.8
3.8
4.0
4.1
4.5
4.6
5.2
5.2
5.5
5.5
5.5
5.5
5.9
5.9
6.1
6.1
6.1
6.4
6.5
6.8
6.9
6.9
6.9
3.7
4.7
4.8
3.8
5.1
5.1
2.6
Method
Recovery1 (%)
Avg.
38.2
37.6
44.3
42.2
62.1
33.6
89.1
48.5
37.8
53.8
35.1
95.3
32.6
67.8
32.0
55.2
31.4
40.3
39.4
61.1
63.7
54.0
68.1
46.4
36.0
49.0
44.4
48.8
45.5
34.8
SD
1.6
1.6
1.7
1.7
1.8
2.0
2.2
1.3
1.9
2.1
1.9
3.6
1.0
1.0
1.0
2.1
1.0
1.5
1.6
4.8
4.7
3.3
2.1
4.0
3.2
3.5
9.6
10.5
8.9
1.8
Estimated Method
Detection Limit2
(pg/g ww)
Avg.
17
0.2
7.8
0.6
99
14
170
1.6
5.0
58
5.5
8.4
16
99
2.9
68
1.6
5.0
21
97
98
6.8
94
3.7
4.9
8.9
9.1
1.9
5.5
15
%RSD
7.5
8.1
1.7
3.0
73
2.2
26
1.42
1.9
23
1.9
21
6.8
39
1.3
16
0.96
1.0
3.5
63
12
1.5
50
0.83
2.46
5.8
1.0
2.59
0.88
2.2
Determined Values
for NISTSRM 1946
(ng/g ww)
Avg.
1.0
5.4
0.46
5.3
4.7
0.38
6.6
0.91
16
34
31
12
90
1.8
9.7
49
16
350
6.1
34
0.44
0.10
%RSD
46
6.5
34
1.1
0.22
37
2.7
15
7.9
4.8
8.9
9.0
7.1
25
66
5.9
20
9.3
3.2
6.1
12
10
Deviation
from
Certified
Values3
% Diff
0
0
0
0
0
15
0
0
30
9.5
17
16
16
28
0
0
0
3B-18
-------
Table 3B-15. SOC Recovery and EDLs in Fish Over the Entire Analytical Method.
Compounds
Dacthal
Triallate
Trifluralin
PCB 74
PCB 101
PCB 138
PCB 153
PCB 118
PCB 183
PCB 187
Average
Min
Max
Log
KOW
4.3
4.6
5.3
6.3
6.4
6.7
6.9
7.0
8.3
7.2
6.1
2.6
9.4
Method
Recovery1
Avg.
62.2
88.0
42.9
78.9
66.5
77.3
65.0
74.5
75.9
77.3
61.4
31.4
98.3
Estimated Method
.„. . Detection Limit2
( °' (99/9 ww)
SD
2.2
2.3
3.4
1.2
4.5
5.7
4.6
6.1
5.3
5.0
4.1
0.3
12
Avg.
2.6
11
7.2
48
1.1
2.6
2.2
2.2
0.84
1.4
79
0.2
920
%RSD
1.6
1.80
0.89
15
2.6
2.9
0.87
0.96
3.7
2.2
11
0.83
86
Determined Values De.viation
for NIST SRM 1946 certified
Avg. %RSD % Diff
4.6 11
4.1 20 15
28 29 20
134 33 21
110 30 0
51 6.2 0
23 8.6 0
54 13 0
30 15 7
0.10 0.22 0
350 66 30
Polycyclic Aromatic Hydrocarbons
Acenaphthylene
Acenaphthene
Fluorene
Anthracene
Phenanthrene
Pyrene
Fluoranthene
Chrysene
/Triphenylene
Benzo(a)anthracene
Retene
Benzo(k)fluoranthene
Benzo(a)pyrene
Benzo(b)fluoranthene
lndeno(1,,3-cd)pyrene
Dibenz(a,h)anthracene
Benzo(e)pyrene
Benzo(ghi)perylene
PolyBrominated Diphenyl
BDE 10
BDE 7
BDE 8
BDE 12
3.9
4.0
4.2
4.5
4.5
5.1
5.2
5.7
5.9
6.4
6.5
6.5
6.6
6.7
6.8
6.9
7.0
Ethers8
5.0
5.0
5.0
5.8
36.0
54.4
41.7
51.8
56.3
63.7
58.4
59.3
59.4
55.3
64.6
43.4
64.4
60.5
58.0
57.8
60.1
64.2
49.7
52.0
45.2
2.5
5.5
1.6
5.4
3.8
5.4
4.0
0.9
2.3
5.8
0.3
5.2
0.9
0.3
1.6
0.7
0.7
6.4
2.4
5.3
2.3
38
50
16
59
56
6.7
7.6
20
26
44
23
17
20
18
19
100
6.3
920
120
710
880
4.1
2.5
1.7
6.8
10
3.5
1.8
12
0.96
14
0.9
1.7
1.6
3.33
8.9
34
1.3
26
43
23
18
3B-19
-------
Table 3B-15. SOC Recovery and EDLs in Fish Over the Entire Analytical Method.
Compounds
BDE 13
BDE 15
BDE 30
BDE 32
BDE 17
BDE 25
BDE 28
BDE 35
BDE 37
BDE 75
BDE 49
BDE 71
BDE 47
BDE 66
BDE 77
BDE 100
BDE 119
BDE 99
BDE 116
BDE 85/1 55
BDE 126
BDE 118
BDE 155
BDE 154
BDE 153
BDE 138
BDE 166
BDE 183
BDE 181
BDE 190
Log
KOW
5.8
5.8
5.9
5.9
5.8
5.9
5.9
6.7
6.7
6.8
6.8
6.8
6.8
6.8
7.6
7.7
7.7
7.7
7.7
111
8.6
8.5
7.7
8.6
8.6
8.6
8.6
8.6
9.4
9.4
9.4
Method
Recovery1 (%)
Avg.
50.4
82.2
47.2
46.9
55.7
55.9
51.1
52.6
52.3
86.9
94.1
84.8
91.1
83.6
93.6
79.0
78.9
85.7
75.6
91.8
88.6
75.0
80.8
79.7
78.6
81.6
98.3
81.5
76.8
72.4
SD
2.7
6.3
6.6
2.2
2.4
2.3
2.1
2.0
2.1
6.7
7.1
5.2
7.5
8.5
8.0
8.4
7.2
6.3
7.9
8.3
9.2
11.9
7.0
7.4
6.7
7.1
7.8
5.8
4.1
5.0
Estimated Method
Detection Limit2
(pg/g ww)
Avg. %RSD
910 21
860 15
240 37
38 7.6
32 8.4
43 7.1
23 2.8
57 3.8
40 8.1
24 5.3
30 3.6
22 1.9
14 1.1
120 26
83 24
6.7 1.1
19 14
23 1.95
91 48
37 10
36 9.2
200 86
2.3 1.0
8.3 2.7
6.5 3.1
1.1 1.1
1.9 1.7
1.6 0.95
3.5 3.14
5.0 2.5
Determined Values De.viation
for NIST SRM 1946 „ [f.m .
'•*""«> 52?
Avg. %RSD % Diff
0.94 1.9 26
29 10 0
n/a9
8.4 2.7 0
18 5.4 0
0.68 11
6.2 18 0
2.9 9.3 0
0.23 14
1 Triplicate recoveries across entire method of ~8 ng/g ww tissue spikes. Blank and sample background corrected.
2 3:1 S:N of IS normalized response factors in three separate fish from Denali, Sequoia, and Rocky Mountain
National Parks according to EPA Method 8280A
3 Percentage difference between this method and NIST certified values for SRM # 1946 LakeTrout, 0% difference
when method average is within certified confidence interval, n=5
3B-20
-------
Table 3B-15. SOC Recovery and EDLs in Fish Over the Entire Analytical Method.
Compounds
Log Method
Kow Recovery1 (%)
Estimated Method
Detection Limit2
(pg/g ww)
Determined Values
for NIST SRM 1946
(ng/g ww)
Certified
Values3
Avg.
SD
Avg.
%RSD
Avg.
%RSD
% Diff
HexachloroCycloHexane
5 DichloroDiphenyldichloroEthylene
6 DichloroDiphenylDichloroethane
7 DichloroDiphenylTrichloroethane
8 Log Kow Estimated by EPI Suite
9 Interferant prohibited quantitation
Blank Cells indicate no certified, or refernce value for the SRM, and/or not detected here.
Metals
Detailed laboratory QA/QC procedures are specified in the WACAP QAPP. Quality control at
the USGS National Research Program Laboratory in Boulder, Colorado, involves systematic
analysis of blanks, replicates, standard reference materials, and spike addition samples (see
Tables 3B-16 and 3B-17). Standard Reference Materials used for the quality control offish
tissue analysis included National Research Council of Canada SRM DOLT-1 Dogfish Liver,
DORM-1 Dogfish Muscle, TORT-1 Lobster hepatopancreas, and National Institute of Standards
and Technology Standard Reference Materials SRM 2976 Bivalve Tissue. Details, results and
figures are described in the Quality Assurance/Quality Control Section of the database.
3B-21
-------
Table 3B-16. Metals and Detection Limits for Fish Fillet Tissue Analyses Performed at the USGS
National Research Program Laboratory, Boulder, Colorado. Concentrations in dry weight, assuming a
0.2-g sample size (1:2 dilution).
Analyte
Al
As
B
Ba
Be
Bi
Ca
Cd
Ce
Co
Cr
Cs
Cu
Dy
Er
Eu
Fe
Gd
Ho
K
La
Li
Lu
Mg
Units
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
Wt%
Detection
Limit
<0.9
<0.03
<1
<0.008
<0.02
< 0.002
<0.0008
< 0.005
< 0.001
< 0.009
<0.3
<0.02
<0.03
< 0.002
< 0.002
< 0.0005
<11
< 0.001
< 0.0003
<0.002
< 0.0006
<0.03
< 0.0004
< 0.0008
Analyte
Mn
Mo
Na
Nd
Ni
Pb
Pr
Rb
Re
Sb
Se
Sm
Sr
Tb
Te
Tl
Tm
U
V
W
Y
Yb
Zn
Zr
ii .. Detection Limit
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
<0.2
<0..02
< 0.002
< 0.001
<0.05
<0.01
< 0.0003
< 0.007
< 0.0009
< 0.003
<0.2
< 0.001
<0.03
< 0.0002
<0.01
< 0.006
< 0.0004
< 0.0009
<0.05
< 0.001
< 0.0003
< 0.009
<0.4
< 0.002
3B-22
-------
Table 3B-17. Metals and Detection Limits for Fish Liver Tissue Analyses Performed at the USGS
National Research Program Laboratory, Boulder, Colorado. Concentrations in dry weight, assuming a
0.1-g sample size (1:2 dilution).
Analyte
Al
As
B
Ba
Be
Bi
Ca
Cd
Ce
Co
Cr
Cs
Cu
Dy
Er
Eu
Fe
Gd
Ho
K
La
Li
Lu
Mg
Units
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
Wt%
Detection
Limit
<0.6
<0.1
<5
<0.03
<0.02
< 0.005
<0.003
<0.01
< 0.002
<0.01
<0.4
<0.1
<0.1
< 0.002
< 0.003
< 0.0009
<16
< 0.002
< 0.0005
<0.004
< 0.0008
<0.04
< 0.0005
< 0.001
Analyte
Mn
Mo
Na
Nd
Ni
Pb
Pr
Rb
Re
Sb
Se
Sm
Sr
Tb
Te
Tl
Tm
U
V
W
Y
Yb
Zn
Zr
Units
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Detection Limit
<0.5
<0..04
< 0.004
< 0.002
<0.06
< 0.009
< 0.0005
<0.01
< 0.001
< 0.005
<0.3
< 0.003
<0.04
< 0.0005
<0.02
<0.03
< 0.0004
< 0.001
<0.05
< 0.004
< 0.0006
< 0.002
<0.5
<0.01
3B-23
-------
Moose QA/QC
SOCs
Detailed laboratory QA/QC procedures for SOCs are specified in the WACAP QAPP. Because
so few moose samples were analyzed for SOCs, detailed recovery and estimated method
detection limits experiments for moose were not conducted. However, the SOC recoveries and
estimated method detection limits were similar to those for fish (see Table 3B-15).
Metals
Detailed laboratory QA/QC procedures are specified in the WACAP QAPP. Quality control at
the USGS National Research Program laboratory in Boulder, Colorado, involves systematic
analysis of blanks, replicates, standard reference materials, and spike addition samples (see Table
3B-18). Standard reference materials used for the quality control of moose tissue analysis
included National Institute of Standards and Technology SRM 8414 Bovine Muscle Powder and
SRM 1577b Bovine Liver. Details, results, and figures are described in the Quality
Assurance/Quality Control Section of the database.
Table 3B-18. Metals and Detection Limits for Moose Tissue Analyses Performed at the USGS
National Research Program Laboratory, Boulder, Colorado. Concentrations in dry weight, assuming a
0.2-g sample size (1:2 dilution).
Analyte
Al
As
B
Ba
Be
Bi
Ca
Cd
Ce
Co
Cr
Cs
Cu
Dy
Er
Eu
Fe
Gd
Ho
K
Units
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
Wt%
Detection
Limit
<7
< 0.007
<9
<0.03
< 0.006
< 0.004
<0.001
< 0.004
< 0.002
< 0.004
<0.04
<03
<0.02
< 0.0003
< 0.0004
< 0.0002
<1
< 0.0005
< 0.0001
<0.0005
Analyte
Mn
Mo
Na
Nd
Ni
Pb
Pr
Rb
Re
Sb
Se
Sm
Sr
Tb
Te
Tl
Tm
U
V
W
Detection Limit
Units
M9/9
M9/9
Wt%
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
M9/9
<0.03
< 0..006
< 0.0002
< 0.0006
<0.03
< 0.006
< 0.0002
< 0.003
< 0.0004
<0.01
<0.04
< 0.0003
<0.01
< 0.0001
< 0.006
< 0.003
< 0.0001
< 0.0001
< 0.005
< 0.003
3B-24
-------
Analyte Detection Analyte Detection Limit
Units Limit units
La |jg/g < 0.0008 Y |jg/g < 0.0003
Li |jg/g < 0.009 Yb |jg/g < 0.0003
Lu |jg/g < 0.0001 Zn |jg/g < 0.9
Mg Wt% < 0.002 Zr \iglg < 0.002
3B-25
-------
APPENDIX 4A
Detailed Information on Contaminants in Vegetation, Including Elevation Trends
Appendix 4A.1. Comparison of Mean SOC Concentrations in Different Lichen Species Sampled from the Same Sites.
Notes
Standard error uses a pooled estimate of variance.
One way analysis of variance by site and SOC was conducted for all sites where
more than one species
of lichen was sampled.
If no values for a site were > EDLs, then no data was used for that SOC.
If any value for a site was > EDLs, then 1/2 EDLS were used
for samples below
EDLS.
DENA5
Flavocetraria cucullata had 7-50X higher concentrations than
One-way Analysis of Dacthal by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
One-way Analysis of Endosulfans by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii of all SOCs but dacthal
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
Sum of Squares
0.01020833
0.27226667
0.282475
Mean
0.77
0.886667
Sum of Squares
76.608533
0.170467
76.779
Mean
11.87
Mean Square F Ratio Prob > F Increase (fold)
0.010208 0.075 0.8099 NA
0.136133
Std Error Lower 95% Upper 95%
0.36896 -0.8175 2.3575
0.21302 -0.0299 1.8032
Mean Square F Ratio Prob > F
76.6085 898.8095 0.0011 6.7
0.0852
Std Error Lower 95% Upper 95%
0.29195 10.614 13.126
4A-1
-------
Masonhalea richardsonii
1.7633
0.16856
1.038
2.489
One-way Analysis of HCB by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
One-way Analysis of a-HCH by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
One-way Analysis of g-HCH by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
DF
1
1
2
Number
1
2
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
Sum of Squares
219.85707
0.0098
219.86687
Mean
19
0.84
Sum of Squares
111.2643
0.0518
111.3161
Mean
13
0.82
Sum of Squares
18.0075
0.00005
18.00755
Mean
5
0.1
Mean Square
219.857
0.01
Std Error
0.09899
0.07
Mean Square
111.264
0.026
Std Error
0.16093
0.09292
Mean Square
18.0075
0
Std Error
0.005
0.00289
F Ratio
22434.39
Lower 95%
17.74
-0.05
F Ratio
4295.919
Lower 95%
12.308
0.42
F Ratio
720300
Lower 95%
4.9785
0.0876
Prob > F
0.0043
Upper 95%
20.258
1.729
Prob > F
0.0002
Upper 95%
13.692
1.22
Prob > F
<.0001
Upper 95%
5.0215
0.1124
22.6
15.9
50.0
4A-2
-------
One-way Analysis of PAHs by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
One-way Analysis of Total Current Use by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
One-way Analysis of Total Historic Use by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
Sum of Squares
81870.642
154.106
82024.748
Mean
337.4
7.005
Sum of Squares
74.850075
0.5154
75.365475
Mean
12.64
2.65
Sum of Squares
946.2528
0.80735
947.06015
Mean
37
1.48
Mean Square
81870.6
77.1
Std Error
8.778
5.068
Mean Square
74.8501
0.2577
Std Error
0.50764
0.29309
Mean Square
946.253
0.404
Std Error
0.63535
0.36682
F Ratio
1062.527
Lower 95%
299.6
-14.8
F Ratio
290.4543
Lower 95%
10.456
1.389
F Ratio
2344.096
Lower 95%
34.27
-0.1
Prob > F
0.0009
Upper 95%
375.17
28.81
Prob > F
0.0034
Upper 95%
14.824
3.911
Prob > F
0.0004
Upper 95%
39.734
3.058
48.2
4.8
25.0
4A-3
-------
One-way Analysis of Total Pesticides by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
One-way Analysis of % Current Use by Species
Source
Species
Error
C. Total
Level
Flavocetraria cucullata
Masonhalea richardsonii
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
Sum of Squares
1553.3701
2.6115
1555.9816
Mean
49.64
4.13
Sum of Squares
0.11955714
0.00901114
0.12856828
Mean
0.254633
0.653895
Mean Square
1553.37
1.31
Std Error
1.1427
0.6597
Mean Square
0.119557
0.004506
Std Error
0.06712
0.03875
F Ratio Prob > F
1189.615 0.0008 12.0
Lower 95% Upper 95%
44.723 54.557
1.291 6.969
F Ratio Prob > F
26.5354 0.0357 26.5
Lower 95% Upper 95%
-0.0342 0.54344
0.4872 0.82064
WRST1
Platismatia glauca has 2.5x higher HCBs and PAHs compared to
One-way Analysis of Dacthal ng/g Lipid by Species
Source
Species
Error
C. Total
Hypogymnia
DF
1
1
2
apinnata.
Sum of Squares
1.7066667
0.08
1.7866667
Mean Square
1.70667
0.08
F Ratio Prob > F Increase (fold)
21.3333 0.1357 NA
4A-4
-------
Level
Hypogymnia apinnata
Platismatia glauca
One-way Analysis of Endosulfans ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia apinnata
Platismatia glauca
One-way Analysis of HCB ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia apinnata
Platismatia glauca
One-way Analysis of a-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia apinnata
Number
2
1
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
Mean
1.8
3.4
Sum of Squares
1.706667
44.18
45.886667
Mean
50.8
52.4
Sum of Squares
5280.6667
18
5298.6667
Mean
61
150
Sum of Squares
682.66667
50
732.66667
Mean
22
Std Error
0.2
0.28284
Mean Square
1.7067
44.18
Std Error
4.7
6.6468
Mean Square
5280.67
18
Std Error
3
4.2426
Mean Square
682.667
50
Std Error
5
Lower 95%
-0.7412
-0.1939
F Ratio
0.0386
Lower 95%
-8.92
-32.06
F Ratio
293.3704
Lower 95%
22.881
96.092
F Ratio
13.6533
Lower 95%
-41.53
Upper 95%
4.3412
6.9939
Prob > F
0.8765
Upper 95%
110.52
136.86
Prob > F
0.0371
Upper 95%
99.12
203.91
Prob > F
0.1683
Upper 95%
85.53
NA
2.5
NA
4A-5
-------
Platismatia glauca
54
7.0711
-35.85
143.85
One-way Analysis of g-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia apinnata
Platismatia glauca
One-way Analysis of Chlordanes ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia apinnata
Platismatia glauca
One-way Analysis of PCBs ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia apinnata
Platismatia glauca
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
Sum of Squares
26.46
2.42
28.88
Mean
7.7
14
Sum of Squares
1.0250667
2.3328
3.3578667
Mean
2.81
4.05
Sum of Squares
0.7280167
1.56645
2.2944667
Mean
3.455
4.5
Mean Square
26.46
2.42
Std Error
1.1
1.5556
Mean Square
1.02507
2.3328
Std Error
1.08
1.5274
Mean Square
0.72802
1.56645
Std Error
0.885
1.2516
F Ratio
10.9339
Lower 95%
-6.277
-5.766
F Ratio
0.4394
Lower 95%
-10.91
-15.36
F Ratio
0.4648
Lower 95%
-7.79
-11.4
Prob > F
0.187
Upper 95%
21.677
33.766
Prob > F
0.6273
Upper 95%
16.533
23.457
Prob > F
0.6191
Upper 95%
14.7
20.403
NA
NA
NA
4A-6
-------
One-way Analysis of PAHs ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia apinnata
Platismatia glauca
WRST5
Platismatia glauca has 5-1 7X higher dacthal, endosulfans, HCBs,
One-way Analysis of Dacthal ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of Endosulfans ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
DF
1
1
2
Number
2
1
a-HCH, g-HCH,
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
Sum of Squares
4676368.2
7080.5
4683448.7
Mean
1697.5
4346
chlordanes, pcbs, &
Sum of Squares
7.3704167
0.01445
7.3848667
Mean
0.375
3.7
Sum of Squares
3952.41
0.1891
3952.5991
Mean
4.7825
81.78
Mean Square
4676368
7081
Std Error
59.5
84.146
PAHs compared
Mean Square
7.37042
0.01445
Std Error
0.085
0.12021
Mean Square
3952.41
0.19
Std Error
0.3075
0.43487
F Ratio
660.4573
Lower 95%
941.5
3276.8
Prob > F
0.0248 2.6
Upper 95%
2453.5
5415.2
to Alectoria sarmentosa
F Ratio
510.0634
Lower 95%
-0.705
2.173
F Ratio
20899.78
Lower 95%
0.875
76.254
Prob > F Increase (fold)
0.0282 9.9
Upper 95%
1.455
5.2274
Prob > F
0.0044 17.1
Upper 95%
8.69
87.306
4A-7
-------
One-way Analysis of HCB ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of a-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of g-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of Chlordanes ng/g Lipid by Species
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
Sum of Squares
1713.66
1.28
1714.94
Mean
5.3
56
Sum of Squares
468.16667
0.98
469.14667
Mean
3.5
30
Sum of Squares
18.235267
0.2178
18.453067
Mean
1.07
6.3
Mean Square
1713.66
1.28
Std Error
0.8
1.1314
Mean Square
468.167
0.98
Std Error
0.7
0.98995
Mean Square
18.2353
0.2178
Std Error
0.33
0.46669
F Ratio
1338.797
Lower 95%
-4.86
41.62
F Ratio
477.7211
Lower 95%
-5.39
17.42
F Ratio
83.7248
Lower 95%
-3.123
0.37
Prob > F
0.0174
Upper 95%
15.465
70.375
Prob > F
0.0291
Upper 95%
12.394
42.579
Prob > F
0.0693
Upper 95%
5.263
12.23
10.6
8.6
5.9
4A-8
-------
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of PCBs ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of PAHs ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
DF
1
1
2
Number
2
1
Sum of Squares
2.57415
0.01805
2.5922
Mean
0.485
2.45
Sum of Squares
4.2909127
0.00605
4.2969627
Mean
0.553
3.09
Sum of Squares
2623046.5
41.9
2623088.4
Mean
267.43
2251
Mean Square
2.57415
0.01805
Std Error
0.095
0.13435
Mean Square
4.29091
0.00605
Std Error
0.055
0.07778
Mean Square
2623047
42
Std Error
4.575
6.47
F Ratio
142.6122
Lower 95%
-0.7221
0.7429
F Ratio
709.2418
Lower 95%
-0.146
2.102
F Ratio
62660.49
Lower 95%
209.3
2168.8
Prob > F
0.0532 5.1
Upper 95%
1.6921
4.1571
Prob > F
0.0239 5.6
Upper 95%
1.2518
4.0783
Prob > F
0.0025 8.4
Upper 95%
325.6
2333.2
STLE1
Platismatia glauca has 3.4 to 17.1x higher concentrations of dacthal, endosulfans.HCB, a-HCH, chlordanes, PCBs and PAHs than Alectoria sarmentosa
4A-9
-------
One-way Analysis of Dacthal by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of Endosulfans by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of HCB by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
DF
1.00
1.00
2.00
Number
2.00
1.00
DF
1.00
1.00
2.00
Number
2.00
1.00
DF
1.00
1.00
2.00
Number
2.00
1.00
Sum of Squares
7.37
0.01
7.38
Mean
0.38
3.70
Sum of Squares
3952.41
0.19
3952.60
Mean
4.78
81.78
Sum of Squares
1713.66
1.28
1714.94
Mean
5.30
56.00
Mean Square
7.37
0.01
Std Error
0.09
0.12
Mean Square
3952.41
0.19
Std Error
0.31
0.43
Mean Square
1713.66
1.28
Std Error
0.80
1.13
F Ratio
510.06
Lower 95%
-0.71
2.17
F Ratio
20899.78
Lower 95%
0.88
76.25
F Ratio
1338.80
Lower 95%
-4.86
41.62
Prob > F
0.03
Upper 95%
1.46
5.23
Prob > F
0.00
Upper 95%
8.69
87.31
Prob > F
0.02
Upper 95%
15.47
70.38
9.9
17.1
10.6
One-way Analysis of a-HCH by Species
Source
DF Sum of Squares Mean Square
F Ratio
Prob > F
4A-10
-------
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of g-HCH by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of Chlordanes by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of PCBs by Species
Source
Species
1.00
1.00
2.00
Number
2.00
1.00
DF
1.00
1.00
2.00
Number
2.00
1.00
DF
1.00
1.00
2.00
Number
2.00
1.00
DF
1.00
468.17
0.98
469.15
Mean
3.50
30.00
Sum of Squares
18.24
0.22
18.45
Mean
1.07
6.30
Sum of Squares
2.57
0.02
2.59
Mean
0.49
2.45
Sum of Squares
1.19
468.17
0.98
Std Error
0.70
0.99
Mean Square
18.24
0.22
Std Error
0.33
0.47
Mean Square
2.57
0.02
Std Error
0.10
0.13
Mean Square
1.19
477.72
Lower 95%
-5.39
17.42
F Ratio
83.72
Lower 95%
-3.12
0.37
F Ratio
142.61
Lower 95%
-0.72
0.74
F Ratio
196.98
0.03
Upper 95%
12.39
42.58
Prob > F
0.07
Upper 95%
5.26
12.23
Prob > F
0.05
Upper 95%
1.69
4.16
Prob > F
0.05
8.6
5.9
5.1
3.4
4A-11
-------
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
One-way Analysis of PAHs by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Platismatia glauca
OLYM5
Bryoria has 2.5-5x higher concentrations of dacthal, endosulfans,
One-way Analysis of Trifluralin ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Bryoria
1.00
2.00
Number
2.00
1.00
DF
1.00
1.00
2.00
Number
2.00
1.00
0.01
1.20
Mean
0.55
1.89
Sum of Squares
2623046.50
41.90
2623088.40
Mean
267.43
2251.00
0.01
Std Error
0.06
0.08
Mean Square
2623047.00
42.00
Std Error
4.58
6.47
Lower 95% Upper 95%
-0.15 1.25
0.90 2.88
F Ratio Prob > F
62660.49 0.00
Lower 95% Upper 95%
209.30 325.60
2168.80 2333.20
and pcbs compared to Alectoria sarmentosa
DF
0
2
2
Number
3
Sum of Squares
0
5.2866667
5.2866667
Mean
2.23333
Mean Square
2.64333
Std Error
0.93868
F Ratio Prob > F
Lower 95% Upper 95%
-1.805 6.2721
8.4
NA
One-way Analysis of Chlorpyrifos ng/g Lipid by Species
4A-12
-------
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of Dacthal ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of Endosulfans ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of HCB ng/g Lipid by Species
Source
Species
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
DF
1
Sum of Squares
10.120033
7.886667
18.0067
Mean
0.16
3.83333
Sum of Squares
280.33333
60.66667
341
Mean
13
32.3333
Sum of Squares
60847.521
5048.667
65896.188
Mean
64.5
349.333
Sum of Squares
4.083333
Mean Square
10.12
3.9433
Std Error
1.9858
1.1465
Mean Square
280.333
30.333
Std Error
5.5076
3.1798
Mean Square
60847.5
2524.3
Std Error
50.243
29.008
Mean Square
4.0833
F Ratio
2.5664
Lower 95%
-8.384
-1.1
F Ratio
9.2418
Lower 95%
-10.7
18.65
F Ratio
24.1044
Lower 95%
-151.7
224.5
F Ratio
0.1012
Prob > F
0.2503
Upper 95%
8.7041
8.7663
Prob > F
0.0933
Upper 95%
36.697
46.015
Prob > F
0.0391
Upper 95%
280.68
474.14
Prob > F
0.7805
NA
2.5
5.4
NA
4A-13
-------
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of a-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of g-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of Chlordanes ng/g Lipid by Species
Source
Species
Error
C. Total
2
3
Number
1
3
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
DF
1
2
3
80.666667
84.75
Mean
35
32.6667
Sum of Squares
2581.3333
980.6667
3562
Mean
36
94.6667
Sum of Squares
918.75
294
1212.75
Mean
11
46
Sum of Squares
7.061002
9.671667
16.732669
40.3333
Std Error
6.3509
3.6667
Mean Square
2581.33
490.33
Std Error
22.143
12.785
Mean Square
918.75
147
Std Error
12.124
7
Mean Square
7.061
4.83583
Lower 95%
7.674
16.89
F Ratio
5.2644
Lower 95%
-59.28
39.66
F Ratio
6.25
Lower 95%
-41.17
15.88
F Ratio
1.4601
Upper 95%
62.326
48.443
Prob > F
0.1487
Upper 95%
131.28
149.67
Prob > F
0.1296
Upper 95%
63.167
76.119
Prob > F
0.3504
NA
NA
NA
4A-14
-------
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of PCBs ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
One-way Analysis of PAHs ng/g Lipid by Species
Analysis of Variance
Source
Species
Error
C. Total
Level
Alectoria sarmentosa
Bryoria
Number
1
3
DF
1
2
3
Number
1
3
DF
1
2
3
Number
1
3
Mean
1.765
4.83333
Sum of Squares
195.8592
20.8712
216.7304
Mean
3.94
20.1
Sum of Squares
15624.083
49162.167
64786.25
Mean
3841.5
3697.17
Std Error
2.1991
1.2696
Mean Square
195.859
10.436
Std Error
3.2304
1.8651
Mean Square
15624.1
24581.1
Std Error
156.78
90.52
Lower 95%
-7.697
-0.629
F Ratio
18.7684
Lower 95%
-9.96
12.08
F Ratio
0.6356
Lower 95%
3166.9
3307.7
Upper 95%
11.227
10.296
Prob > F
0.0494 5.1
Upper 95%
17.839
28.125
Prob > F
0.5089 NA
Upper 95%
4516.1
4086.6
GLAC5
Letharia vulpina has higher concentrations of 50 x more chlorpyrifos and 2x more g-HCH than Hypogymnia physodes.
H. physodes has higher concentrations of 2-1 OX higher HCB, a-HCH (p > F =0.06), chlordanes, ddts, PCBs and PAHs than L vulpina.
One-way Analysis of Triallate ng/g Lipid by Species
4A-15
-------
Source
Species
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of Chlorpyrifos ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of Dacthal ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of Endosulfans ng/g Lipid by Species
Source
Species
DF
1
2
3
Number
3
1
DF
1
2
3
Number
3
1
DF
1
2
3
Number
3
1
DF
1
Sum of Squares
0.440833
18.526667
18.9675
Mean
12.7667
12
Sum of Squares
161.48003
0.02082
161.50085
Mean
0.3267
15
Sum of Squares
2700
1400
4100
Mean
220
160
Sum of Squares
3104.0833
Mean Square
0.44083
9.26333
Std Error
1.7572
3.0436
Mean Square
161.48
0.01
Std Error
0.0589
0.10202
Mean Square
2700
700
Std Error
15.275
26.458
Mean Square
3104.08
F Ratio
0.0476
Lower 95%
5.206
-1.095
F Ratio
15514.49
Lower 95%
0.073
14.561
F Ratio
3.8571
Lower 95%
154.28
46.16
F Ratio
1.5081
Prob > F
0.8475
Upper 95%
20.327
25.095
Prob > F
<.0001
Upper 95%
0.58
15.439
Prob > F
0.1885
Upper 95%
285.72
273.84
Prob > F
0.3443
45.9
NA
NA
4A-16
-------
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of HCB ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of a-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of g-HCH ng/g Lipid by Species
Source
Species
Error
C. Total
2
3
Number
3
1
DF
1
2
3
Number
3
1
DF
1
2
3
Number
3
1
DF
1
2
3
4116.6667
7220.75
Mean
814.333
750
Sum of Squares
752.08333
44.66667
796.75
Mean
53.6667
22
Sum of Squares
690.08333
104.66667
794.75
Mean
48.3333
18
Sum of Squares
1656.75
98
1754.75
2058.33
Std Error
26.194
45.369
Mean Square
752.083
22.333
Std Error
2.7285
4.7258
Mean Square
690.083
52.333
Std Error
4.1767
7.2342
Mean Square
1656.75
49
Lower 95%
701.63
554.79
F Ratio
33.6754
Lower 95%
41.927
1.666
F Ratio
13.1863
Lower 95%
30.36
-13.13
F Ratio
33.8112
Upper 95%
927.04
945.21
Prob > F
0.0284
Upper 95%
65.406
42.334
Prob > F
0.0682
Upper 95%
66.304
49.126
Prob > F
0.0283
2.4
2.7
1.7
4A-17
-------
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of Chlordanes ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of DDTs ng/g Lipid by Species
Source
Species
Error
C. Total
Level
Hypogymnia physodes
Letharia vulpina
One-way Analysis of PCBs ng/g Lipid by Species
Analysis of Variance
Source
Species
Error
C. Total
Number
3
1
DF
1
2
3
Number
3
1
DF
1
2
3
Number
3
1
DF
1
2
3
Mean
63
110
Sum of Squares
48.400833
0.926667
49.3275
Mean
15.2333
7.2
Sum of Squares
13722.803
50.667
13773.47
Mean
150.667
15.4
Sum of Squares
25.172033
1.020867
26.1929
Std Error
4.0415
7
Mean Square
48.4008
0.4633
Std Error
0.39299
0.68069
Mean Square
13722.8
25.3
Std Error
2.9059
5.0332
Mean Square
25.172
0.5104
Lower 95%
45.611
79.881
F Ratio
104.4622
Lower 95%
13.542
4.271
F Ratio
541.6896
Lower 95%
138.2
-6.3
F Ratio
49.315
Upper 95%
80.39
140.12
Prob > F
0.0094
Upper 95%
16.924
10.129
Prob > F
0.0018
Upper 95%
163.17
37.06
Prob > F
0.0197
2.1
9.8
1.8
4A-18
-------
Level
Number
Mean
Std Error Lower 95% Upper 95%
Hypogymnia physodes
Letharia vulpina
One-way Analysis of PAHs ng/g Lipid by Species
Source
Species
Error
C. Total
DF
1
2
3
12.7533
6.96
Sum of Squares
387129488
8466232
395595720
0.41249
0.71445
Mean Square
387129488
4233116.1
10.979
3.886
F Ratio
91.4526
14.528
10.034
Prob > F
0.0108
3.1
Level
Hypogymnia physodes
Letharia vulpina
Number
3
1
Mean
33464.3
10744.9
Std Error
1187.9
2057.5
Lower 95%
28353
1892
Upper 95%
38575
19597
4A-19
-------
Appendix 4A.2. Mean Total Pesticide Burdens (ng/g lipid) in WACAP Parks by Lichen
Genus. Parks not connected by the same letter are significantly different. Levels not connected
by same letter are significantly different.
Park Within Genus
Alectoria
Park
MORA
OLYM
NOCA
GLBA
STLE
Flavocetraria
Park
KATM
WRST
DENA
Hypogymnia
Park
GLAC
WRST
KATM
Letharia
Park
GLAC
SEKI
YOSE
CRLA
GRTE
LAVO
Lobaria
Park
OLYM
STLE
Masonhalea
Park
NO AT
DENA
GAAR
Platismatia
Park
GLAC
NOCA
STLE
WRST
GLBA
Usnea
Park
BAND
BIBE
GRTE
Xanthoparmelia
Park
GRSA
BAND
YOSE
ROMO
A
A B
A B
A B
B
A
A
A
A
B
B
A
A
A B
B
A B
B
A
A
A
A B
B
A
A B
B
A B
A B
A
A
A
A
A B
A B
B
Total Pesticides (ng/g lipid)
Mean
168.23
139.68
102.88
74.98
23.97
Mean
37.30
25.80
18.94
Mean
1378.33
108.44
82.97
Mean
947.20
899.59
615.57
306.51
264.44
218.42
Mean
58.33
46.33
Mean
3.88
3.08
1.96
Mean
1504.40
485.10
327.78
277.85
258.80
Mean
257.95
222.89
212.61
Mean
899.95
179.29
173.04
28.45
Notes
MORA > STLE
OLYM = NOCA = GLBA =
KATM = WRST= DENA
GLAC > WRST , KATM
WRST = KATM
= STLE
GLAC, SEKI > CRLA, LAVO
GLAC = SEKI = YOSE =
CRLA = LAVO = GRTE =
OLYM = STLE
GAAR > NOAT
DENA = NOAT
DENA = GAAR
GLAC > STLE
GLAC =NOCA = WRST=
BAND = BIBE = GRTE
GRSA > ROMO
BAND = YOSE = ROMO
GRTE
YOSE
GLBA
4A-20
-------
Appendix 4A.3. Comparisons of SOC Concentrations in the Epiphytic Lichen, Hypogymnia
physodes, and the Tundra Lichen, Flavocetraria cucullata, from Three Sites Each in Katmai
National Park. Comparisons provide evidence that concentrations of endosulfans, HCB, HCHs,
PCBs and PAHs were 1.5- to 4.6-fold higher in H. physodes than in F. cucullata (t-tests, assuming
equal variances, p < 0.05).
SOC
Endosulfan
HCB
a-HCH
g-HCH
PCBs
PAHs
Chlordanes
Dacthal
H. physodes
Park Mean (ng/g lipid)
32.30
30.30
12.30
3.50
2.91
406.00
3.75
0.72
F. cucullata
Park Mean (ng/g lipid)
7.10
20.00
6.70
1.72
0.67
118.00
1.08
0.44
H. ph./F. cu.
Ratio
4.55
1.52
1.84
2.03
4.34
3.44
3.47
1.64
Significant?
(p < 0.05)
Y
Y
Y
Y
Y
Y
N
N
Appendix 4A.4. Comparisons of SOC Concentrations in the Epiphytic Lichens, Platismatia
glauca and Alectoria sarmentosa, from Three Sites Each in the Stikine LeConte Wilderness.
Comparisons provide evidence that concentrations of dacthal, endosulfans, HCB, HCHs, PCBs
and PAHs were 3.8 to 22.2 fold higher in P. glauca than A. sarmentosa (t-tests, assuming equal
variances, p < 0.05).
SOC
Endosulfan
HCB
a-HCH
g-HCH
PCBs
PAHs
Chlordanes
Dacthal
P. glauca
Park Mean (ng/g lipid)
12.67
156.16
81.50
58.75
14.07
4.62
4.45
1648.00
A. sarmentosa
Park Mean (ng/g lipid)
0.70
7.03
6.32
7.20
2.01
0.71
1.15
156.00
P. gl./A. sa.
Ratio
18.10
22.21
12.90
8.16
7.00
6.51
3.87
10.56
Significant?
(p < 0.05)
Y
Y
Y
Y
Y
N
Y
Y
4A-21
-------
Appendix 4A.5. Mean Total Pesticide Burdens (ng/g lipid) in WACAP Parks by Conifer
Genus. Parks not connected by the same letter are significantly different. Levels not connected
by same letter are significantly different
Park within Genus
True fir (Abies)
Park
SEKI
LAVO
CRLA
MORA
GLAC
NOCA
GRTE
ROMO
OLYM
Spruce (Picea)
Park
GLAC
GLBA
ROMO
STLE
WRST
KATM
DENA
Pine (Pinus)
Park
YOSE
SEKI
GRTE
BIBE
CRLA
GRSA
BAND
Douglas-fir (Pseudotsuga)
Park
GLAC
NOCA
Western hemlock (Tsuga)
Park
GLAC
MORA
NOCA
OLYM
A
A B
B
B
B
B
A B
B
B
A
B
B C
BCD
BCD
C D
D
A
A B
A B
A B
A B
B
B
A
B
A
B
A B
B
Total Pesticides (ng/g lipid) Notes
Mean
SEKI > CRLA, MORA, GLAC, NOCA,
ROMO, OLYM
270.61
180.19
173.29
136.23
118.82
86.12
73.70
67.93
Mean
.-.„ GLAO GLBA, ROMO, STLE, WRST,
103.67 KATM, DENA
48.88 GLBA > KATM, DENA
42.52
35.97
19.46
15.17
11.18
Mean
118.64 YOSE > GRSA, BAND
94.27
25.19
23.88
21.94
15.01
12.60
Mean
155.59 GLAO NOCA
70.52
Mean
550.92 GLAC > MORA, OLYM
1 93.50 MORA = NOCA = OLYM
178.94
119.19
4A-22
-------
Appendix 4A.6. Comparison of Inter-generic Differences among Conifer SOC Concentrations (ng SOC/g conifer needle lipid) within
WACAP Parks Where More than One Genus of Conifer Was Sampled. Within park and contaminant, genera sharing a common letter do not
have different mean concentrations (Tukey-Kramer multiple means comparison test, a = 0.05). Results show that: (1) concentrations of many
SOCs in pine (Pinus) were significantly lower than concentrations in true fir (Abies) from the same parks, (2) western hemlock (Tsuga) SOC
concentrations were similar to or higher than true firs, Douglas fir (Pseudotsuga), and spruce (Picea), and (3) spruce (Picea) and true firs were
similar*.
Genus
GRTE Abies
Pinus
SEKI Abies
Pinus
CRLA Abies
Pinus
OLYM Tsuga
Abies
MORA Tsuga
Abies
NOCA Tsuga
Abies
Pseud.
GLACE Abies
Pseud.
ROMO Abies
Picea
GLACW Tsuga
Picea
Trifluralin
Mean
5.80 A
4.13 A
0.34 A
0.18 B
Trial late
Mean
5.1 B
17.3 A
22.7 A
0.7 A
Chlorpyrifos
Mean
1.10 A
1.12 A
2.63 A
2.00 A
5.33 A
0.39 B
0.77 B
3.50 A
0.31 A
1.05 A
3.70 A
0.29 A
Endosulfans
Mean
30.3 A
3.4 B
309.6 A
29.9 B
77.7 A
4.3 A
21.6 A
18.9 A
96.2 A
91.8 A
62.0 A
42.1 B
24.4 C
84.8 A
83.9 A
29.8 A
5.6 B
231.8 A
28.3 B
Dacthal
Mean
20.0 A
7.9 B
75.8 A
38.3 A
33.7 A
6.1 A
2.9 A
3.7 A
12.7 A
6.7 B
13.0 A
9.5 B
5.2 C
18.7 B
25.3 A
20.5 A
12.7 A
112.3 A
22.3 A
Current Use
Mean
57.2 A
16.5 B
388.0 A
70.3 B
111.8 A
10.5 A
29.3 A
23.0 B
110.6 A
98.6 A
75.0 A
51.6 B
29.6 C
109.3 A
130.0 A
50.4 A
19.4 A
370.6 A
51.4 B
g-HCH
Mean
8.36 A
2.90 A
5.80 A
0.63 B
8.3 A
3.4 B
8.0 A
5.8 A
11.0 A
5.2 B
2.0 B
6.0 A
5.9 A
3.4 A
16.2 A
41.2 A
43.0 A
a-HCH
Mean
14.0 A
3.50 B
16.6 A
5.3 A
26.2 A
4.20 B
47.5 A
25.3 B
39.7 A
31.7 A
49.0 A
31.0 B
17.0 B
11.7 A
8.7 A
9.2 A
2.8 A
67.7 A
3.2 A
HCB
Mean
14.0 A
4.9 B
15.7 A
5.8 A
33.8 A
6.40 A
29.0 A
15.3 B
24.2 A
23.3 A
35.0 A
25.3 A
19.0 A
7.1 A
8.2 A
8.2 A
2.1 A
50.5 A
4.3 A
Historic Use
Mean
28.9 A
8.7 B
84.5 A
24.0 A
68.4 A
11.4 B
89.9 A
45.0 B
83.0 A
74.7 A
103.9 A
67.2 A
40.9 A
26.9 A
25.6 A
23.4 A
23.2 A
180.4 A
52.3 A
PCBs
Mean
0.56 A
0.60 A
4.66 A
0.38 B
1.39 A
0.06 B
2.76 A
1.12 A
2.54 A
3.44 A
2.27 A
1.30 A
0.50 A
0.87 A
0.79 A
1.09 A
0.49 A
3.23 B
6.90 A
PAHs
Mean
870 A
16 B
3960 A
1140 B
1825 A
48 A
5787 A
245 B
4129 A
506 B
4265 A
2542 A
241 A
2345 A
2209 A
382 A
88 A
47312 A
1043 B
Notes: The analysis ignores site effects within parks but sites on different sides of the continental divide (GLACeast, GLACwest) were not compared and sites influenced by local
sources (OLYM1) were excluded.
* Contaminant concentrations in Abieswas generally substantially higher than Pinus: especially in endosulfans ~ 10 x higher, dacthal 2-5X higher, historic use g- and a-HCH, and
HCB ~ 3-5x higher: Tsuga was usually somewhat higher than Abies by 1/3 to 3x. Pseudotsuga was similar to Abies but could be 2-3x lower in endosulfans and dacthal. Picea was
similar to Abies and Tsuga but 5 (Abies) to 10 (Tsuga) x lower in endosulfans.
4A-23
-------
Appendix 4A.7. Comparison of Lichen and Conifer SOC Concentrations. Results are given of paired t-tests from sites within the 20 WACAP
parks at which both vegetation types were collected. Lichen concentrations were 2- to 9-fold higher, or not different than, conifer needle
concentrations. N, number of sites, varies with detection frequency among sites. Bold-faced SOCs indicate lichen and conifer concentrations were
significantly different (Prob > t < 0.05).
SOC Statistic
Trifluralin Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Triallate Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Chlorpyrifos Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Dacthal Lichen mean
Conifer mean
Mean Difference
ng/g lipid
-0.25
6.24
11.46
-5.22
54
4.36
5.45
-15.90
8.47
2.80
5.67
302
1.54
8.96
2.39
63.73
22.38
41.35
T-test Parameter
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > Itl
T-test result Notes
No difference, but only one pair
0
-2.0000
2.0000
-1.0000
1
-1.1972 No difference; but only 7 pairs
6
0.2764
0.8618
0.1382
7
0.33
3.6789 Lichens 3x higher
15
0.0022
0.0011
0.9989
16
0.67
4.0682 Lichens 3x higher
59
0.0001
4A-24
-------
Endosulfans
HCB
a-HCH
g-HCH
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
Mean Difference
285
10.16
61.69
21.01
188.36
51.38
136.98
367
26.22
189.34
84.62
24.66
14.40
10.26
171
3.53
17.33
3.20
18.60
15.45
3.15
120
3.00
9.15
-2.86
13.21
11.86
1.35
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > Itl
0.0001
0.9999
60
0.59
5.2249
65
0.0000
0.0000
1.0000
66
0.45
2.9045
62
0.0051
0.0025
0.9975
63
0.01
1.0468
63
0.2992
0.1496
0.8504
64
0.10
0.4303
46
0.6690
Lichens 4x higher
Lichens 2x higher; no correlation
between veg types
Many pairs, no difference; poor
correlation between veg types
Many pairs, no difference; poor
correlation between veg types
4A-25
-------
Chlordanes
Dieldrin
DDTs
PCBs
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
Lichen mean
Conifer mean
111
3.13
7.64
-4.95
6.59
2.69
3.90
245
0.78
5.46
2.34
7.27
4.51
2.76
161
1.28
8.26
-2.75
77.73
8.60
69.13
904
18.70
108.99
29.26
4.92
1.75
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
t-Ratio
DF
0.3345
0.6655
47
0.27
5.0010 Lichens 2x higher
57
0.0000
0.0000
1.0000
58
0.46
2.1543 No difference, but only 3 pairs
2
0.1640
0.0820
0.9180
3
0.85
3.6962 Lichens 9X higher
15
0.0022
0.0011
0.9989
16
0.33
6.8672 Lichens 3X higher
46
4A-26
-------
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
3.17
281
0.46
4.10
2.24
Prob > |t|
Prob > t
Prob < t
N
Correlation
0.0000
0.0000
1.0000
47
0.44
PAHs
Lichen mean
Conifer mean
Mean Difference
% Difference
Std Error
Upper95%
Lower95%
7294.10
2711.62
4582.48
269
2651.98
9882.05
-717.09
t-Ratio
DF
Prob > |t|
Prob > t
Prob < t
N
Correlation
1.7279 Lichens 3X higher, strong
63 correlation between veg types
0.0889
0.0444
0.9556
64
0.87
4A-27
-------
Appendix 4A.8. SOC Concentrations in Lichens
In general, lichens growing on trees accumulated higher concentrations of SOCs than lichens
growing on the ground, but there was much variability among species. For example, pesticide
concentrations in the best accumulators among the tundra lichens, F. cucullata and C. arbuscula
(grays), were comparable to or higher than those of the poorest epiphytic accumulator, A.
sarmentosa (black). Smoke from a forest fire in DENA during sampling could have boosted
PAHs at this park. Bars indicate one standard error.
10-
2L
o-
SO-
ii
ra 25-
~&)
t 20-
-------
Appendix 4A.9. SOC Concentrations in Lichens Xanthoparmelia and L/sneafrom BAND
Despite major differences in growth habit and form, the lichens Xanthoparmelia and Usnea from
BAND accumulated similar concentrations of many SOCs. Xanthoparelia is a flat, leafy lichen
that adheres closely to its rock substrates; Usnea is a hair-like or filamentous lichen that hangs
from trees; they were sampled at the two lowest and three highest elevations, respectively. Snow
burial was not expected to limit exposure period at any of the sites. Bars indicate one standard
error.
Oxanthoparmelia QUsnea
'
BAND
BAND
400-
BAND
4A-29
-------
Appendix 4A.10. Relationship between Elevation and SOC Concentrations in Lichens Showing
Significant Elevational Trends.
Pesticides and PCBs increase with elevation as predicted by the cold condensation hypothesis:
PAHs decrease with elevation. See Chapter 3 for data selection criteria for elevational trends
analyses. See Appendix 4A. 11 for park name acronyms, lichen species, and evidence of
significant trends. Sites within parks are listed in order of increasing elevation. Bars represent
one standard error.
Dacthal
Endosulfans
1000-
5 100-5
g8ggbgSgSS228S83B556MS^«^
Chlordanes
4A-30
-------
Appendix 4A.10. (continued).
HCB
100=
CO 1-
o
0.1-
LJJLLJLLJLlJ|
53?g2>i**•**«<<
1 O O O O O O O O °7 °? 9: °T
-------
Appendix 4A.10. (continued).
DDTs
1-
ooooo:o:a:o:
00000000***
O
o o o
PCBs
15-
t
s Ill....l.llllllllll
-i—i—r ii ii M<-r <-f «-r n n n n v- co "^ LQ T— CN
J
PAHs
4A-32
-------
Appendix 4A.11. Analysis of Elevation Trends. June 06, 2007.
Notes:
Parks and lichen species used in analyses:
BAND = Bandolier National Monument, Usnea (all other parks were compared to BAND)
CRLA = Crater Lake National Park, Letharia vulpina
GLAC = Glacier National Park, Platismatia glauca
KATMF = Katmai National Park, Flavocetraria cucullata
KATMH = Katmai National Park, Hypogymnia physodes
LAVO = Lassen Volcanic National Park, Letharia vulpina
MORA = Mt. Rainier National Park, Alectoria sarmentosa
NOCA = North Cascades National Park, Alectoria sarmentosa
SEKI = Sequoia-Kings Canyon National Park, Letharia vulpina
STLEA = Stikine LeConte Wilderness, Alectoria sarmentosa samples
STLEP = Stikine LeConte Wilderness, Platismatia glauca
See Methods chapter for detailed description of multiple linear regression modeling used in this analysis.
Best fit models are reported here. All SOC concentrations in ng/g lipid.
Chlorpyrifos
formula = Chlorpyrifos ~ Park + Elevm
Coefficients:
Value Std. Error t value Pr(>|t|)
(Intercept) -3.0289 2.9688 -1.0202 0.3187
ParkGLAC 0.6053 1.8372 0.3295 0.7449
ParkMORA 5.1879 1.9115 2.7140 0.0127
ParkNOCA 5.2466 2.1913 2.3943 0.0256
Elevm 0.0035 0.0013 2.7512 0.0117
Residual standard error: 2.182 on 22 degrees of freedom
Multiple R-Squared: 0.5338
F-statistic: 6.297 on 4 and 22 degrees of freedom, the p-value is 0.001554
Residuals plot range between -2 and +4. Outliers cases 3, 8, 7.
Dacthal
formula = log(Dacthal)
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
1
0
-2
2
-3
-1
0
0
-0
1
-2
0
0
Value
.3079
.4283
.6363
.9540
.0502
.8507
.9066
.1803
.0177
.6882
.1385
.5618
.0010
~ Park + Elevm
Std. Error
0.4548
0.3229
0.3684
0.3446
0.4448
0.5066
0.3368
0.3457
0.4050
0.2750
0.4717
0.4509
0.0002
t
2
1
-7
8
-6
-3
2
0
-0
6
-4
1
5
value
.8756
.3266
.1561
.5730
.8582
.6535
.6919
.5216
.0437
.1383
.5336
.2460
.9044
Pr(>|t|)
0.0055
0.1895
0.0000
0.0000
0.0000
0.0005
0.0091
0.6038
0.9653
0.0000
0.0000
0.2174
0.0000
Residual standard error: 0.4726 on 62 degrees of freedom
Multiple R-Squared: 0.9654
F-statistic: 144.2 on 12 and 62 degrees of freedom, the p-value is 0
Residuals plot range between -1 and +1. Case 23 still very low outlier? Cases 2 and 9 outliers.
4A-33
-------
Endosulfan I
formula = log(Endosulfanl) ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Value
1.0719
0.3379
-2.3074
1.0709
-0.8374
-0.1960
0.0536
0.7714
0.3777
1.2972
-0.7928
0.4945
0.0009
Std. Error
0.4124
0.2927
0.3340
0.3124
0.4032
0.4593
0.3053
0.3134
0.3672
0.2494
0.4277
0.4088
0.0001
t value
2.5994
1.1545
-6.9082
3.4278
-2.0767
-0.4267
0.1754
2.4610
1 .0286
5.2023
-1.8538
1.2095
6.4791
Pr(>|t|)
0.0117
0.2527
0.0000
0.0011
0.0420
0.6711
0.8613
0.0167
0.3077
0.0000
0.0685
0.2311
0.0000
Residual standard error: 0.4285 on 62 degrees of freedom
Multiple R-Squared: 0.9454
F-statistic: 89.46 on 12 and 62 degrees of freedom, the p-value is 0
Residuals plot range between -1 and +1.5. Outliers 23 (low) and 9 and 19.
Endosulfan
II
formula = log(Endosulfanll) ~ Park
Coefficients:
(Intercept)
ParkCRLA
ParkGLAC
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Value
0.8008
0.7646
2.6674
0.2030
1.0557
0.7703
2.2890
-3.5596
-1.2996
0.0008
Std. Error
0.6029
0.4062
0.4423
0.4230
0.4466
0.5208
0.3455
0.6148
0.5856
0.0002
+ Elevm
t value
1.3283
1 .8824
6.0307
0.4799
2.3641
1.4790
6.6257
-5.7897
-2.2192
3.4741
Pr(>|t|)
0.1905
0.0660
0.0000
0.6335
0.0223
0.1458
0.0000
0.0000
0.0313
0.0011
Residual standard error: 0.5928 on 47 degrees of freedom
Multiple R-Squared: 0.9297
F-statistic: 69.02 on 9 and 47 degrees of freedom, the p-value is 0
Residual plot range between -1 and +1. Case 11 very low, case 2 low, case 9 high outliers.
Endosulfan Sulfate
formula = log(Endosulfansulfate) ~
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
2
0
-3
2
-1
0
0
0
Value
.3087
.5475
.2553
.9316
.6192
.7742
.2440
.7754
Std. Error
0
0
0
0
0
0
0
0
.4924
.3496
.3988
.3730
.4815
.5484
.3646
.3743
Park + Elevm
t
4
1
-8
7
-3
1
0
2
value
.6883
.5664
.1618
.8584
.3626
.4117
.6693
.0717
Pr(>|t|)
0
0
0
0
0
0
0
0
.0000
.1224
.0000
.0000
.0013
.1630
.5058
.0425
4A-34
-------
ParkNOCA
ParkSEKI
ParkSTLEA
0.4146
1.3638
-1.0649
0.4384
0.2978
0.5107
0.9457
4.5803
-2.0853
0.3480
0.0000
0.0412
Residual standard error: 0.5116 on 62 degrees of freedom
Multiple R-Squared: 0.9541
F-statistic: 107.3 on 12 and 62 degrees of freedom, the p-value is 0
Residual plot range between -1 and +1. Case 23 very low, case 2 low and 19 high outliers.
Sum Endosulfans
formula = log(SumEndosulfans) ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Value
2.7285
0.5354
-3.0838
2.7030
-1.5187
0.4639
0.1960
0.8161
0.4569
1.5097
-1.1371
1.7579
0.0009
Std. Error
0.4776
0.3390
0.3868
0.3618
0.4670
0.5319
0.3536
0.3630
0.4252
0.2888
0.4953
0.4735
0.0002
t value
5.7130
1.5792
-7.9718
7.4708
-3.2520
0.8721
0.5541
2.2482
1.0745
5.2277
-2.2959
3.7129
5.1807
Pr(>|t|)
0.0000
0.1194
0.0000
0.0000
0.0019
0.3865
0.5815
0.0281
0.2867
0.0000
0.0251
0.0004
0.0000
Residual standard error: 0.4962 on 62 degrees of freedom
Multiple R-Squared: 0.9541
F-statistic: 107.3 on 12 and 62 degrees of freedom, the p-value is 0
Residual plot range between -1 and +1. Case 23 very low, 73 and 19 high outliers.
HCB
formula = sqrt(HCB) ~
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Value
0.1100
1 .2685
-0.0181
7.1881
3.3725
5.1370
0.5347
1.5388
1.5773
0.8270
1.9689
8.2182
0.0012
Park + Elevm, data = HCB
Std. Error
0.6700
0.4715
0.5564
0.5048
0.6519
0.7439
0.4916
0.5070
0.5935
0.4015
0.6928
0.6619
0.0002
t value
0.1641
2.6906
-0.0325
14.2398
5.1730
6.9051
1.0876
3.0351
2.6579
2.0598
2.8419
12.4167
5.1381
Pr(>|t|)
0.8702
0.0092
0.9741
0.0000
0.0000
0.0000
0.2811
0.0036
0.0101
0.0438
0.0061
0.0000
0.0000
Residual standard error: 0.6897 on 60 degrees of freedom
Multiple R-Squared: 0.9368
F-statistic: 74.15 on 12 and 60 degrees of freedom, the p-value is 0
Residual plot range between -1 and +2. Cases 2 (low) and 9 and 18 high.
4A-35
-------
~ Park + Elevm
Value
-0.8944
0.7264
-0.6901
3.2538
1.7755
3.1398
0.3980
2.2500
1 .9666
0.0906
2.2205
4.1586
0.0013
Std. Error
0.5394
0.3829
0.4369
0.4087
0.5275
0.6008
0.3994
0.4100
0.4803
0.3262
0.5594
0.5348
0.0002
t value
-1.6580
1.8971
-1.5795
7.9623
3.3660
5.2263
0.9965
5.4876
4.0947
0.2778
3.9691
7.7764
6.7546
Pr(>|t|)
0.1024
0.0625
0.1193
0.0000
0.0013
0.0000
0.3229
0.0000
0.0001
0.7821
0.0002
0.0000
0.0000
g-HCH
formula = log(aHCH)
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Residual standard error: 0.5605 on 62 degrees of freedom
Multiple R-Squared: 0.8793
F-statistic: 37.65 on 12 and 62 degrees of freedom, the p-value is 0
Residual plot range between -1 and +1. Cases 23, 73, and 19 high.
Y-HCH
formula = sqrt(gHCH) ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Residual standard error: 0.7102 on 62 degrees of freedom
Multiple R-Squared: 0.897
F-statistic: 45.01 on 12 and 62 degrees of freedom, the p-value is 0
Residual plot range between -1 and +1. Cases 23 (very low) and 19 and 73 high outliers.
trans-Chlordane
formula = transChlordane ~ Park + Elevm
Coefficients:
Value
-1.0997
-0.0675
0.2013
6.3376
1.1518
2.6427
-0.3969
2.3683
1 .7265
0.9654
1 .9234
3.9268
0.0016
Std. Error
0.6836
0.4853
0.5537
0.5179
0.6684
0.7613
0.5062
0.5196
0.6087
0.4134
0.7089
0.6777
0.0002
t value
-1.6087
-0.1392
0.3635
12.2379
1.7231
3.4712
-0.7841
4.5580
2.8365
2.3354
2.7131
5.7945
6.4561
Pr(>|t|)
0.1128
0.8898
0.7175
0.0000
0.0899
0.0009
0.4360
0.0000
0.0062
0.0228
0.0086
0.0000
0.0000
Value
(Intercept)
ParkCRLA
ParkGLAC
ParkKATMF
ParkKATMH
-2
1.
3.
1.
2
.3755
.7337
.9573
.1313
.7274
Std. Error
1
0
0
1
1
.3069
.8895
.9647
.2488
.4353
t
-1.
1.
4.
0.
1.
value
.8176
.9490
.1020
.9060
.9003
Pr(>|t|)
0
0
0
0
0
.0750
.0568
.0001
.3692
.0631
4A-36
-------
ParkLAVO 1.8793 0.9267 2.0280 0.0478
ParkMORA 2.0931 0.9728 2.1516 0.0362
ParkNOCA 1.3339 1.1355 1.1747 0.2456
ParkSEKI 4.8500 0.7568 6.4087 0.0000
ParkSTLEA 1.9919 1.3370 1.4898 0.1424
ParkSTLEP 2.9709 1.2744 2.3312 0.0237
Elevm 0.0018 0.0005 3.7280 0.0005
Residual standard error: 1.299 on 51 degrees of freedom
Multiple R-Squared: 0.7684
F-statistic: 15.38 on 11 and 51 degrees of freedom, the p-value
Residual plot range between -2 and +2. Cases 51 (low) and 61 and
is 1.306e-01 2
9 high outliers.
cis-Nonachlor
formula = cisNonachlor ~ Park + Elevm
Coefficients:
Value Std. Error t value Pr(>|t|)
(Intercept) -1.3992 0.5406 -2.5884 0.0125
ParkCRLA 0.7191 0.3679 1.9546 0.0561
ParkGLAC 1.7262 0.3990 4.3263 0.0001
ParkKATMF 0.8340 0.5165 1.6148 0.1125
ParkKATMH 1.7377 0.5936 2.9271 0.0051
ParkLAVO 0.5762 0.3833 1.5034 0.1389
ParkMORA 0.8960 0.4024 2.2268 0.0304
ParkNOCA 0.7204 0.4697 1.5339 0.1312
ParkSEKI 1.8485 0.3130 5.9055 0.0000
ParkSTLEA 1.2299 0.5530 2.2241 0.0306
ParkSTLEP 2.0254 0.5271 3.8424 0.0003
Elevm 0.0009 0.0002 4.8589 0.0000
Residual standard error: 0.5373 on 51 degrees of freedom
Multiple R-Squared: 0.7595
F-statistic: 14.64 on 11 and 51 degrees of freedom, the p-value is 3.254e-012
Residuals plot range between -1 and +1. Cases 11 (low) and 61 and 9 high outliers.
trans-Nonachlor
formula = transNonachlor ~ Park + Elevm
Coefficients:
Value Std. Error t value Pr(>|t|)
(Intercept) -2.5202 1.3352 -1.8875 0.0648
ParkCRLA 1.8376 0.9088 2.0221 0.0484
ParkGLAC 3.7946 0.9856 3.8502 0.0003
ParkKATMF 1.3459 1.2757 1.0550 0.2964
ParkKATMH 3.3647 1.4663 2.2947 0.0259
ParkLAVO 1.7117 0.9467 1.8081 0.0765
ParkMORA 1.8813 0.9938 1.8930 0.0640
ParkNOCA 1.3925 1.1601 1.2004 0.2355
ParkSEKI 4.1004 0.7731 5.3036 0.0000
ParkSTLEA 2.2491 1.3659 1.6466 0.1058
ParkSTLEP 3.5636 1.3020 2.7371 0.0085
Elevm 0.0017 0.0005 3.5885 0.0007
Residual standard error: 1.327 on 51 degrees of freedom
Multiple R-Squared: 0.6971
F-statistic: 10.67 on 11 and 51 degrees of freedom, the p-value is 8.093e-010
Residuals plot range between -2 and +2. Cases 17, 61 and 9 high outliers.
4A-37
-------
Sum of Chlordanes
formula = sqrt(SumChlordane) ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Value
0.1250
0.8772
1.5560
0.1564
1.5729
1.1679
1.0416
0.1812
2.2368
0.3788
1 .4204
0.0009
Std. Error
0.6599
0.4492
0.4871
0.6305
0.7247
0.4679
0.4912
0.5734
0.3821
0.6751
0.6435
0.0002
t value
0.1894
1.9530
3.1943
0.2480
2.1704
2.4959
2.1206
0.3161
5.8537
0.5612
2.2074
3.9338
Pr(>|t|)
0.8505
0.0563
0.0024
0.8051
0.0347
0.0158
0.0388
0.7532
0.0000
0.5771
0.0318
0.0003
Residual standard error: 0.6559 on 51 degrees of freedom
Multiple R-Squared: 0.7878
F-statistic: 17.22 on 11 and 51 degrees of freedom, the p-value is 1.558e-013
Residuals plot range between -1 and +1. Cases 61 and 9 high outliers.
Sum of DDTs
formula = log(DDTSum) ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkGLAC
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
Elevm
Value
2.0223
-0.4842
2.5460
0.4262
-1.4277
-1.5304
2.1791
0.0004
Std. Error
0.5692
0.3569
0.4011
0.3707
0.4077
0.4727
0.3028
0.0002
t value
3.5531
-1.3567
6.3469
1.1497
-3.5015
-3.2379
7.1970
1.7519
Pr(>|t|)
0.0010
0.1825
0.0000
0.2571
0.0012
0.0024
0.0000
0.0875
Residual standard error: 0.5186 on 40 degrees of freedom
Multiple R-Squared: 0.9321
F-statistic: 78.46 on 7 and 40 degrees of freedom, the p-value is 0
Residual plot range between -1 an +1. Cases 2 (low) and 18 and 33 high outliers.
PCB118
formula = log(PCB118)
Coefficients:
(Intercept)
ParkCRLA
ParkGLAC
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Value
-1.1580
0.3751
1.2140
0.6093
0.3561
0.8471
0.7244
0.1868
0.1196
1.3307
0.0006
~ Park + Elevm
Std. Error
0.6202
0.4939
0.4376
0.6653
0.6363
0.4454
0.5166
0.3318
0.6301
0.6724
0.0002
t value
-1.8671
0.7594
2.7739
0.9159
0.5596
1.9018
1 .4024
0.5628
0.1899
1.9790
2.5688
Pr(>|t|)
0.0694
0.4522
0.0085
0.3654
0.5789
0.0646
0.1687
0.5768
0.8504
0.0549
0.0141
4A-38
-------
Value
-0.1434
0.2482
1.5311
-0.0854
0.7329
-0.1258
1.0255
0.1088
0.1757
0.2515
1 .0623
0.0006
Std. Error
0.6019
0.4096
0.4443
0.5751
0.6610
0.4267
0.4480
0.5229
0.3485
0.6157
0.5869
0.0002
t value
-0.2383
0.6060
3.4464
-0.1485
1.1088
-0.2948
2.2891
0.2081
0.5040
0.4084
1.8101
2.9983
Pr(>|t|)
0.8126
0.5472
0.0011
0.8825
0.2727
0.7693
0.0262
0.8360
0.6164
0.6847
0.0762
0.0042
Residual standard error: 0.5684 on 39 degrees of freedom
Multiple R-Squared: 0.3786
F-statistic: 2.376 on 10 and 39 degrees of freedom, the p-value is 0.02623
Residuals range between -1 and +1. Case 8 very low outlier.
PCB 153
formula = PCB153 ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Residual standard error: 0.5982 on 51 degrees of freedom
Multiple R-Squared: 0.5345
F-statistic: 5.324 on 11 and 51 degrees of freedom, the p-value is 0.00001521
Residual plot range between -1 and +1. Cases 11 (low) and 9 and 61 high outliers.
PCB 183
formula = PCB183 ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Residual standard error: 0.1483 on 50 degrees of freedom
Multiple R-Squared: 0.4552
F-statistic: 3.798 on 11 and 50 degrees of freedom, the p-value is 0.0005441
Residual plot range between -.2 and +.2. Outliers 60, 47 and 48 high.
Sum of PCBs
formula = PCBSum ~ Park + Elevm
Coefficients:
Value Std. Error t value Pr(>|t|)
(Intercept) -1.3328 2.3896 -0.5577 0.5795
ParkCRLA 0.7753 1.6264 0.4767 0.6356
ParkGLAC 7.8398 1.7639 4.4446 0.0000
Value
-0.0418
0.0133
0.0769
-0.0353
0.2192
-0.0787
0.1935
0.0463
0.1099
0.0446
0.1799
0.0002
Std. Error
0.1495
0.1064
0.1103
0.1427
0.1641
0.1058
0.1112
0.1298
0.0864
0.1529
0.1457
0.0001
t value
-0.2798
0.1247
0.6974
-0.2476
1.3356
-0.7441
1 .7402
0.3566
1.2719
0.2918
1 .2349
2.9410
Pr(>|t|)
0.7807
0.9012
0.4888
0.8055
0.1877
0.4603
0.0880
0.7229
0.2093
0.7717
0.2226
0.0049
4A-39
-------
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
-0.4112
3.6197
-1.1251
4.2087
1.9161
1.1741
1 .4407
4.1668
0.0030
2.2832
2.6243
1 .6943
1.7787
2.0762
1.3837
2.4446
2.3301
0.0009
-0.1801
1.3793
-0.6641
2.3662
0.9229
0.8485
0.5894
1.7882
3.5145
0.8578
0.1738
0.5096
0.0218
0.3604
0.4001
0.5582
0.0797
0.0009
Value
2.3852
2.1241
-1.3529
4.8701
-0.6778
0.9093
1.1731
1.4319
0.8034
2.3795
-0.4440
2.1383
-0.0007
Std. Error
0.6061
0.5901
0.5818
0.4977
0.6617
0.6266
0.4635
0.5047
0.5392
0.4319
0.6011
0.5821
0.0002
t value
3.9355
3.5996
-2.3254
9.7859
-1.0242
1.4512
2.5307
2.8370
1.4899
5.5098
-0.7387
3.6736
-4.1979
Pr(>|t|)
0.0003
0.0008
0.0248
0.0000
0.3115
0.1540
0.0151
0.0069
0.1435
0.0000
0.4641
0.0007
0.0001
Residual standard error: 2.375 on 51 degrees of freedom
Multiple R-Squared: 0.6174
F-statistic: 7.481 on 11 and 51 degrees of freedom, the p-value is 1.879e-007
Residual plot range between -4 and +4. Cases 38, 9 and 10 high outliers.
FLO
formula = log(FLO) ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Residual standard error: 0.4004 on 43 degrees of freedom
Multiple R-Squared: 0.9479
F-statistic: 65.24 on 12 and 43 degrees of freedom, the p-value is 0
Residual plot range between -.5 and +1. Cases 16 (low) and 18 and 33 high outliers.
PHE
formula = log(PHE) ~ Park + Elevm
Coefficients:
(Intercept)
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
Residual standard error: 0.3659 on 49 degrees of freedom
Multiple R-Squared: 0.9754
Value
6.5184
-3.5566
4.6367
-3.0210
-0.9352
-1.0698
-0.6393
-1.2356
0.2895
-3.0623
0.6759
-0.0006
Std. Error
0.3276
0.3037
0.2378
0.3624
0.3694
0.2674
0.2347
0.2852
0.1961
0.3402
0.3226
0.0001
t value
19.8970
-11.7123
19.4966
-8.3357
-2.5319
-4.0000
-2.7238
-4.3325
1 .4764
-9.0024
2.0951
-4.2772
Pr(>|t|)
0.0000
0.0000
0.0000
0.0000
0.0146
0.0002
0.0089
0.0001
0.1462
0.0000
0.0413
0.0001
4A-40
-------
F-statistic: 176.4 on 11 and 49 degrees of freedom, the p-value is 0
Residual plot range between -.5 and +.5. Cases 25 and 3 low and 8 high outliers.
Retene
formula = log(Retene + 1e-006) ~ Park
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
4
4
-3
6
0
0
3
2
1
4
-0
1
-0
Value
.3334
.7735
.5747
.8797
.9801
.1757
.9156
.4660
.5408
.9443
.7905
.4773
.0015
Std. Error
2.9265
2.0253
2.5152
2.1943
2.8216
3.2315
2.1109
2.1962
2.5672
1.7239
3.0098
2.8721
0.0010
t
1
2
-1
3
0
0
1
1
0
2
-0
0
-1
+ Elevm
value
.4807
.3570
.4212
.1352
.3474
.0544
.8549
.1229
.6002
.8681
.2626
.5144
.4802
Pr(>|t|)
0.1442
0.0219
0.1607
0.0027
0.7296
0.9568
0.0688
0.2662
0.5508
0.0058
0.7938
0.6090
0.1443
Residual standard error: 2.96 on 57 degrees of freedom
Multiple R-Squared: 0.4881
F-statistic: 4.529 on 12 and 57 degrees of freedom, the p-value is 0.00004422
Residual plot better, but messy, range -5 and +5. Many very low outliers, especially low cases 4 and 14.
CHR/TRI
formula = log(CHR.TRI) ~ Park + Elevm,
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
3
1
-2
5
-1
-1
-0
0
0
0
-1
-0
-0
Value
.7637
.3432
.7154
.5466
.2986
.7043
.6584
.7754
.1692
.8921
.6393
.3516
.0005
Std. Error
0.6259
0.4425
0.5061
0.4760
0.6107
0.6961
0.4616
0.4748
0.5560
0.3769
0.6483
0.6195
0.0002
t
6
3
-5
11
-2
-2
-1
1
0
2
-2
-0
-2
value
.0133
.0351
.3647
.6536
.1264
.4483
.4265
.6331
.3043
.3667
.5289
.5676
.3518
Pr(>|t|)
0.0000
0.0035
0.0000
0.0000
0.0375
0.0172
0.1588
0.1076
0.7619
0.0211
0.0140
0.5724
0.0219
Residual standard error: 0.6476 on 61 degrees of freedom
Multiple R-Squared: 0.9271
F-statistic: 64.65 on 12 and 61 degrees of freedom, the p-value is 0
Residual plot range between -1 and +1. Cases 7 (low) and 8 and 19 high outliers.
B(a)A
formula = log(BaA) ~ Park + Elevm
Coefficients:
Value Std. Error t value Pr(>|t|)
(Intercept) 4.3467 0.8383 5.1850 0.0000
ParkCRLA -0.1428 0.5875 -0.2431 0.8088
ParkDENA -4.5253 0.6755 -6.6989 0.0000
4A-41
-------
ParkGLAC
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
4.7703
-1.1255
-1.2420
-3.2363
0.6786
-3.0583
-2.1140
-0.0014
0.6338
0.6126
0.6331
0.7408
0.5002
0.8657
0.8268
0.0003
7.5271
-1.8373
-1.9618
-4.3688
1.3565
-3.5327
-2.5568
-4.6703
0.0000
0.0714
0.0547
0.0001
0.1803
0.0008
0.0133
0.0000
Residual standard error: 0.8593 on 57 degrees of freedom
Multiple R-Squared: 0.8998
F-statistic: 51.21 on 10 and 57 degrees of freedom, the p-value is 0
Residual plot better, range between -1 and +1. Cases 57 (low) and 58 and 19 high outliers.
SUM OF PAHs
formula = log(PAHSum) ~ Park + Elevm
Coefficients:
(Intercept)
ParkCRLA
ParkDENA
ParkGLAC
ParkKATMF
ParkKATMH
ParkLAVO
ParkMORA
ParkNOCA
ParkSEKI
ParkSTLEA
ParkSTLEP
Elevm
7
1
-4
5
-1
-0
0
0
-0
1
-2
0
-0
Value
.0345
.5044
.1538
.8868
.7076
.8984
.3340
.5403
.2387
.5335
.0292
.8172
.0009
Std. Error
0.6864
0.4853
0.5551
0.5220
0.6697
0.7634
0.5062
0.5207
0.6098
0.4134
0.7109
0.6794
0.0002
t
10
3
-7
11
-2
-1
0
1
-0
3
-2
1
-3
value
.2479
.0996
.4830
.2776
.5496
.1768
.6597
.0377
.3915
.7094
.8542
.2029
.5744
Pr(>|t|)
0.0000
0.0029
0.0000
0.0000
0.0133
0.2439
0.5119
0.3035
0.6968
0.0005
0.0059
0.2337
0.0007
Residual standard error: 0.7102 on 61 degrees of freedom
Multiple R-Squared: 0.9374
F-statistic: 76.11 on 12 and 61 degrees of freedom, the p-value is 0
Residual plot range between -1 and +1. Case 19 is high outlier. Several other outliers. Cases 17 and
others low.
4A-42
-------
Appendix 4A.12. Summary Statistics of Element Concentrations (ppm) in Lichen Samples from the
Core WACAP Parks.
Al
As
Ba
Bi
Ca
Cd
Ce
Co
Cu
Dy
Er
Eu
Fe
Ga
N
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
GAAR
Flcu
3
675.5
131.5
75.9
0.66
0.06
0.03
9.49
1.98
1.14
0.023
0.003
0.002
21956
1506
870
0.220
0.050
0.029
2.38
0.25
0.15
0.47
0.08
0.05
2.51
0.27
0.15
0.118
0.034
0.019
0.049
0.013
0.008
0.045
0.007
0.004
1656.7
119.3
68.9
0.211
0.032
0.019
Mari
2
90.5
5.0
3.5
0.19
0.02
0.01
3.31
0.05
0.03
0.023
0.008
0.006
18090
857
606
0.202
0.003
0.002
0.44
0.05
0.03
0.12
0.02
0.01
1.04
0.01
0.01
0.041
0.000
0.000
0.019
0.001
0.001
0.010
0.001
0.001
245.8
3.2
2.3
0.033
0.002
0.001
NO AT
Flcu
2
133.0
1.9
1.3
0.20
0.05
0.04
70.00
3.34
2.36
0.056
0.003
0.002
4139
158
112
0.194
0.021
0.015
0.18
0.01
0.01
0.16
0.01
0.01
1.70
0.06
0.04
0.021
0.002
0.001
0.011
0.001
0.001
0.005
0.003
0.002
272.0
2.7
1.9
0.054
0.002
0.002
Mari
4
48.2
13.5
6.7
0.07
0.03
0.01
41.02
26.72
13.36
0.026
0.025
0.013
2517
1410
705
0.136
0.053
0.026
0.12
0.05
0.02
0.11
0.03
0.02
0.75
0.09
0.05
0.013
0.005
0.003
0.005
0.002
0.001
0.004
0.001
0.001
98.9
19.1
9.5
0.015
0.003
0.001
DENA
Flcu
6
237.9
29.5
12.0
0.39
0.09
0.04
9.59
1.57
0.64
0.017
0.010
0.004
1341
42
17
0.101
0.027
0.011
0.39
0.04
0.02
0.28
0.06
0.02
1.55
0.12
0.05
0.027
0.004
0.002
0.015
0.002
0.001
0.009
0.001
0.000
405.4
92.1
37.6
0.080
0.012
0.005
Mari
6
47.0
9.0
3.7
0.16
0.04
0.02
3.38
1.08
0.44
0.013
0.006
0.002
1252
378
154
0.064
0.013
0.005
0.13
0.05
0.02
0.12
0.02
0.01
0.80
0.08
0.03
0.017
0.007
0.003
0.008
0.004
0.002
0.004
0.001
0.000
101.2
24.6
10.0
0.018
0.004
0.002
OLYM
Alsa
6
34.0
8.9
3.6
0.06
0.02
0.01
9.94
2.43
0.99
0.007
0.001
0.000
3000
1358
554
0.077
0.034
0.014
0.08
0.02
0.01
0.11
0.06
0.02
0.72
0.14
0.06
0.008
0.002
0.001
0.003
0.002
0.001
0.002
0.001
0.000
50.7
15.8
6.5
0.014
0.003
0.001
Pigi
2
422.5
116.7
82.5
0.31
0.03
0.02
56.17
16.34
11.56
0.028
0.006
0.004
2514
414
293
0.084
0.036
0.026
0.36
0.00
0.00
0.25
0.08
0.06
2.50
0.08
0.06
0.023
0.004
0.003
0.011
0.000
0.000
0.007
0.001
0.000
525.0
91.9
65.0
0.128
0.028
0.020
MORA
Alsa
6
60.3
16.3
6.6
0.10
0.04
0.02
1.66
0.59
0.24
0.016
0.009
0.004
4234
1128
460
0.068
0.020
0.008
0.20
0.03
0.01
0.07
0.03
0.01
1.06
0.30
0.12
0.015
0.003
0.001
0.009
0.003
0.001
0.004
0.001
0.000
64.7
19.0
7.8
0.022
0.007
0.003
SEKI
Levu
3
283.3
55.1
31.8
0.30
0.05
0.03
5.72
0.84
0.49
0.017
0.002
0.001
1564
235
136
0.072
0.006
0.003
0.55
0.12
0.07
0.15
0.03
0.02
2.93
0.31
0.18
0.031
0.007
0.004
0.016
0.004
0.002
0.008
0.003
0.002
363.3
51.3
29.6
0.087
0.013
0.008
GLAC
Alsa
3
99.7
12.4
7.2
0.18
0.04
0.02
17.49
3.51
2.02
0.021
0.007
0.004
1754
251
145
0.100
0.012
0.007
0.24
0.04
0.02
0.10
0.00
0.00
1.22
0.03
0.01
0.019
0.005
0.003
0.012
0.004
0.002
0.005
0.001
0.001
132.3
9.2
5.3
0.043
0.001
0.001
Levu
3
383.3
79.3
45.8
0.58
0.09
0.05
11.22
1.94
1.12
0.051
0.007
0.004
2729
737
425
0.337
0.077
0.044
1.44
0.22
0.13
0.27
0.03
0.02
1.97
0.35
0.20
0.115
0.019
0.011
0.064
0.011
0.006
0.028
0.004
0.002
562.7
106.3
61.4
0.137
0.024
0.014
Pigi
3
897.6
184.8
106.7
0.60
0.03
0.02
71.38
4.64
2.68
0.071
0.018
0.010
2913
92
53
0.379
0.043
0.025
1.78
0.11
0.06
0.36
0.03
0.02
3.40
0.26
0.15
0.106
0.008
0.005
0.056
0.003
0.002
0.029
0.002
0.001
1325.6
172.1
99.4
0.370
0.057
0.033
ROMO
Xant
3
3066.7
957.1
552.6
31.24
12.64
7.30
17062
4870
2812
0.663
0.137
0.079
0.24
0.00
0.00
10.43
4.51
2.60
2905.8
891.1
514.5
4A-43
-------
Gd
Hg
Ho
K
La
Li
Lu
Mg
Mn
Mo
Na
Nd
Ni
P
Pb
Pr
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
0.192
0.038
0.022
0.017
0.002
0.001
0.020
0.005
0.003
1840
159
92
1.133
0.139
0.080
0.837
0.135
0.078
0.007
0.002
0.001
793.2
10.9
6.3
58.0
4.2
2.4
626.94
58.67
33.87
1.2
0.1
0.1
1.274
0.229
0.132
544.378
45.214
26.104
1
0
0
0.30
0.03
0.02
3.924
0.325
0.188
0.051
0.003
0.002
0.023
0.003
0.002
0.007
0.001
0.000
1418
33
23
0.212
0.013
0.009
0.096
0.069
0.049
0.003
0.001
0.001
192.6
7.1
5.0
21.3
0.2
0.2
0.61
0.22
0.16
94.3
4.9
3.5
0.230
0.029
0.020
0.225
0.033
0.023
330
23
16
0.86
0.02
0.01
0.055
0.004
0.003
0.027
0.001
0.001
0.022
0.003
0.002
0.004
0.000
0.000
2611
264
187
0.084
0.000
0.000
0.135
0.002
0.001
0.001
0.000
0.000
686.2
21.0
14.8
385.3
30.9
21.8
0.51
718.3
23.8
16.9
0.107
0.001
0.001
0.802
0.300
0.212
763
16
11
0.25
0.02
0.01
0.024
0.001
0.001
0.019
0.007
0.004
0.026
0.003
0.002
0.003
0.001
0.001
1476
88
44
0.054
0.022
0.011
0.022
0.000
0.000
0.001
0.000
0.000
186.9
27.0
13.5
97.2
28.9
14.4
0.34
0.08
0.06
226.6
192.5
96.2
0.073
0.027
0.013
0.413
0.063
0.032
491
116
58
0.42
0.13
0.07
0.016
0.006
0.003
0.039
0.010
0.004
0.012
0.002
0.001
0.005
0.001
0.001
2603
270
110
0.182
0.024
0.010
0.224
0.051
0.021
0.002
0.000
0.000
383.5
57.7
23.6
208.8
52.8
21.5
144.7
38.8
15.8
0.197
0.015
0.006
0.717
0.198
0.081
538
143
59
0.53
0.04
0.01
0.049
0.006
0.002
0.020
0.008
0.003
0.021
0.006
0.003
0.003
0.002
0.001
1462
234
95
0.057
0.022
0.009
0.038
0.026
0.010
0.001
0.000
0.000
116.7
25.5
10.4
31.1
13.7
5.6
0.29
150.3
162.0
66.1
0.083
0.033
0.013
0.325
0.228
0.093
327
113
46
0.48
0.17
0.07
0.018
0.007
0.003
0.008
0.004
0.002
0.232
0.036
0.015
0.002
0.000
0.000
1583
183
75
0.036
0.009
0.004
0.028
0.012
0.005
0.000
0.000
0.000
346.6
42.8
17.5
100.1
48.0
19.6
0.30
0.11
0.08
137.0
35.8
14.6
0.042
0.013
0.005
0.446
0.114
0.047
433
98
40
1.09
0.23
0.09
0.010
0.002
0.001
0.028
0.005
0.003
0.268
0.025
0.018
0.005
0.000
0.000
1494
114
80
0.178
0.000
0.000
0.204
0.051
0.036
0.001
0.000
0.000
386.0
29.5
20.9
164.0
118.2
83.6
0.86
104.6
56.2
39.7
0.187
0.004
0.003
2.124
0.810
0.573
740
142
101
4.22
0.44
0.31
0.046
0.000
0.000
0.022
0.005
0.002
0.154
0.023
0.009
0.004
0.001
0.000
1821
130
53
0.089
0.014
0.006
0.027
0.018
0.007
0.001
0.001
0.000
261.4
16.2
6.6
153.7
67.9
27.7
166.33
190.53
77.78
0.1
0.0
0.0
0.323
0.262
0.107
674.208
117.317
47.894
1
0
0
0.03
0.01
0.00
3.623
0.875
0.357
0.041
0.009
0.005
0.301
0.017
0.010
0.006
0.001
0.001
2319
64
37
0.285
0.053
0.031
0.238
0.072
0.042
0.002
0.001
0.000
453.9
33.8
19.5
97.3
2.4
1.4
67.2
54.2
31.3
0.230
0.047
0.027
0.916
0.229
0.132
738
73
42
1.39
0.18
0.10
0.062
0.015
0.009
0.026
0.002
0.001
0.136
0.017
0.010
0.003
0.001
0.001
1917
166
96
0.113
0.016
0.009
0.125
0.050
0.029
0.001
0.001
0.000
325.3
9.6
5.6
160.6
23.7
13.7
0.58
0.39
0.28
137.1
173.2
100.0
0.125
0.024
0.014
0.810
0.393
0.227
648
96
55
0.96
0.23
0.13
0.030
0.005
0.003
0.143
0.029
0.017
0.388
0.067
0.039
0.022
0.005
0.003
2246
190
110
0.684
0.113
0.065
0.311
0.031
0.018
0.008
0.002
0.001
600.2
51.7
29.8
262.2
113.4
65.5
0.35
95.5
33.7
19.5
0.746
0.124
0.071
0.594
0.102
0.059
583
110
63
3.90
0.182
0.029
0.017
0.143
0.009
0.005
0.266
0.020
0.012
0.021
0.003
0.002
2047
27
16
0.864
0.070
0.041
0.557
0.155
0.089
0.007
0.001
0.001
639.4
37.8
21.8
126.3
5.1
2.9
0.42
105.6
35.1
20.2
0.873
0.060
0.035
2.128
0.077
0.054
705
56
32
6.59
0.223
0.016
0.009
2963
195
113
2.067
0.505
0.292
932.0
171.0
98.7
77.0
42.2
24.3
1.82
0.31
0.18
92.1
11.8
6.8
4.330
0.410
0.237
1112
165
95
21.16
1.65
0.95
4A-44
-------
Rb
S
Sb
Sm
Sr
Tb
Th
Tm
U
V
W
Y
Yb
Zn
Zr
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
Mean
s.d.
s.e.
330.93
39.88
23.03
0
0
0
0.237
0.027
0.016
15.941
1.211
0.699
0.03
0.00
0.00
0.414
0.004
0.002
0.007
0.000
0.000
0.064
0.010
0.006
1.341
0.191
0.110
0.01
0.52
0.10
0.06
0.042
0.010
0.005
27.4
2.4
1.4
0.86
0.06
0.03
2.46
0.05
0.03
227
47
33
0.032
0.005
0.004
0.052
0.010
0.007
8.68
0.77
0.55
0.007
0.000
0.000
0.030
0.008
0.006
0.003
0.000
0.000
0.041
0.008
0.006
0.34
0.16
0.11
0.23
0.00
0.00
0.021
0.000
0.000
20.7
0.8
0.6
0.28
0.06
0.04
3.38
0.24
0.17
296
6
4
0.101
0.008
0.006
0.023
0.007
0.005
6.46
0.06
0.04
0.004
0.001
0.000
0.031
0.013
0.009
0.001
0.001
0.001
0.007
0.002
0.002
0.48
0.02
0.01
0.13
0.00
0.00
0.008
0.001
0.000
0.5
0.3
0.2
2.42
0.26
0.13
244
29
15
0.016
0.008
0.004
0.016
0.005
0.003
2.60
0.85
0.42
0.002
0.001
0.000
0.023
0.026
0.013
0.001
0.000
0.000
0.006
0.004
0.002
0.12
0.03
0.01
0.09
0.03
0.02
0.006
0.002
0.001
25.0
2.2
1.1
0.35
0.25
0.13
8.05
1.44
0.59
244
32
13
0.070
0.016
0.007
0.040
0.002
0.001
3.17
0.23
0.10
0.005
0.000
0.000
0.041
0.018
0.007
0.002
0.000
0.000
0.024
0.009
0.004
0.50
0.07
0.03
0.16
0.02
0.01
0.012
0.002
0.001
23.3
1.4
0.6
0.30
0.06
0.02
4.72
1.52
0.62
240
60
25
0.021
0.010
0.004
0.018
0.007
0.003
1.59
0.22
0.09
0.003
0.001
0.000
0.028
0.029
0.012
0.001
0.001
0.000
0.011
0.004
0.002
0.14
0.02
0.01
0.017
0.006
0.004
0.09
0.04
0.02
0.009
0.003
0.001
13.8
1.3
0.5
0.29
0.20
0.08
1.70
0.79
0.32
353
75
31
0.012
0.008
0.003
0.010
0.004
0.002
12.18
2.53
1.03
0.001
0.000
0.000
0.007
0.000
0.000
0.000
0.000
0.000
0.003
0.005
0.002
0.10
0.05
0.02
0.05
0.01
0.00
0.004
0.001
0.001
17.3
0.8
0.3
0.30
0.16
0.06
2.50
0.45
0.32
585
83
59
0.017
0.005
0.003
0.037
0.002
0.001
15.81
6.73
4.76
0.005
0.001
0.000
0.061
0.016
0.012
0.002
0.000
0.000
0.021
0.006
0.004
0.97
0.14
0.10
0.016
0.005
0.003
0.12
0.01
0.01
0.011
0.001
0.000
23.7
0.8
0.6
0.65
0.42
0.30
409.43
88.62
36.18
0
0
0
0.023
0.007
0.003
5.753
1.728
0.705
0.00
0.00
0.00
0.021
0.022
0.009
0.001
0.000
0.000
0.001
0.001
0.000
0.108
0.103
0.042
0.01
0.00
0.00
0.10
0.02
0.01
0.007
0.002
0.001
22.7
4.3
1.8
0.35
0.09
0.04
8.10
0.20
0.11
1017
64
37
0.016
0.006
0.004
0.044
0.009
0.005
6.14
0.92
0.53
0.005
0.001
0.001
0.115
0.022
0.013
0.002
0.000
0.000
0.059
0.004
0.002
0.50
0.11
0.07
0.022
0.004
0.002
0.16
0.03
0.02
0.015
0.003
0.002
27.0
4.4
2.5
0.46
0.21
0.12
1.51
0.28
0.16
568
22
13
0.052
0.005
0.003
0.025
0.002
0.001
5.92
0.96
0.55
0.003
0.000
0.000
0.024
0.012
0.007
0.002
0.000
0.000
0.011
0.009
0.005
0.22
0.06
0.03
0.018
0.004
0.003
0.11
0.02
0.01
0.010
0.002
0.001
20.0
0.6
0.3
0.26
0.03
0.02
1.80
0.03
0.02
1073
107
62
0.124
0.008
0.005
0.144
0.023
0.013
3.88
0.58
0.34
0.019
0.002
0.001
0.205
0.115
0.066
0.009
0.002
0.001
0.022
0.003
0.002
0.89
0.20
0.12
0.020
0.002
0.001
0.65
0.13
0.07
0.054
0.011
0.006
38.7
2.3
1.3
0.82
0.24
0.14
3.11
0.29
0.16
985
46
27
0.140
0.014
0.008
0.165
0.005
0.003
19.55
0.08
0.05
0.021
0.001
0.001
0.384
0.149
0.086
0.008
0.001
0.001
0.059
0.005
0.003
1.94
0.04
0.03
0.026
0.009
0.005
0.55
0.02
0.01
0.052
0.004
0.002
24.9
1.5
0.9
1.77
0.34
0.20
1227
154
89
21.75
4.11
2.37
5.30
1.76
1.02
54.9
2.8
1.6
Notes: Flcu= Flavocetraria cucullata, Man = Masonhalea richardsonii, Alsa = Alectoria sarmentosa, Plgl = Platismatia glauca,
= Letharia vulpina, Xant = Xanthoparmelia sp.
s.d.= standard deviation; s.e. = mean standard error; N = number of samples. Laboratory replicates were averaged before
calculating means, field replicates were treated as independent measurements.
Levu
4A-45
-------
Appendix 4A.13. Summary Statistics for Nitrogen Concentrations (ppm) in Lichens from the 20 WACAP Parks.
Alsa Ave
s.d.
s.e.
N
Bryo Ave
s.d.
s.e.
N
Clar Ave
s.d.
s.e.
N
Flcu Ave
s.d.
s.e.
N
Hyph Ave
s.d.
s.e.
N
Levu Me.,
s.d.
s.e.
N
Loor Ave
s.d.
s.e.
N
Mari Ave
Alaska
GAAR NOAT DENA KATM WRST GLBA STLE
3890 3183
640
320
1 4
5360 3380
1 1
4800 5318 4074 4335 4370
144 364 370 303 .
83 258 117 175 .
3 2 10 3 1
7347 7133
2288 1895
1321 1094
3 3
1886
0
1
4150 3618 3985
Pacific Northwest
NOCA OLYM MORA CRLA
4223 4094 5141
763 505 909
382 191 262
4 7 12
7778
299
172
3
5759
1111
497
5
22050
501
289
3
5200 5115
California
LAVO YOSE SEKI
9265
1
7508 10800 175^
o
1877 580 3531
840 410 1117
5 2 10
N Rockies
GLAC GRTE ROMO
8197
451
260
3
10300
3162
1826
3
10498 8768
1706
853
4 -1
9883
S Rockies
GRSA BAND BIBE
4A-46
-------
s.d.
s.e.
N
Plgl Ave
s.d.
s.e.
N
Spgl Ave
s.d.
s.e.
N
Tham Ave
s.d.
s.e.
N
Usne Ave
s.d.
s.e.
N
Xant Ave
s.d.
s.e.
N
141 536 853
100 169 246
2 10 12
2920 7860 5498
793
397
1 1 2
5425
403
285
2
2920
1
559
395
1 2
13460
1
698
493
0
10557
1557
519
9
15667
2050
1184
3
1609 1475
3 5
735 1138
367 805
4 2
11120 152Q
4851 3748
3430 2650
2 2
Notes: Alsa= Alectoria sarmentosa, Bryo = Bryoria spp., Clar = Cladina arbuscula, Flcu= Flavocetraria cucullata, Hypphy = Hypogymnia physodes, Levu =
Letharia vulpina, Loor = Lobaria oregana, Mari = Masonhalea richardsonii, Plgl = Platismatia glauca, Spgl = Sphaerophorus globosus, Tham = Thamnolia sp.,
Usne = Usnea spp., Xant = Xanthoparmelia spp.; s.d.= standard deviation; s.e. = mean standard error; N = number of samples. Laboratory replicates were
averaged before calculating means, field replicates were treated as independent measurements.
4A-47
-------
Appendix 4A.14. Distribution of Cadmium, Mercury, Nitrogen, Lead, and Sulfur Concentrations in Lichen Genera in National Parks and
Forests of the United States, 1977-2005. Ninety percent quantiles (yellow highlight) were used as thresholds for background ranges to assess
enhancement in WACAP lichen samples. Data were obtained from the NPS national database NPEIement (Bennett, 2007), the NPS Arctic Parks
database courtesy of P. Neitlich (all Masonhalea and some Flavocetraria data), and the USFS National Lichens and Air Quality database (US
Forest Service 2007). Fora list of public lands from which distributions were calculated, see Appendix 3.15.
Cd ppm
Quantiles
100.00%
99.50%
97.50%
90.00%
75.00%
50.00%
25.00%
10.00%
2.50%
0.50%
0.00%
Mean
Std Dev
Std Err
Upper 95%
Lower 95%
N
Alectoria
9.95
1.07
0.50
0.24
0.18
0.12
0.10
0.02
0.01
0.00
0.00
0.15
0.33
0.01
0.17
0.14
1491
Bryoria
3.45
3.35
0.50
0.30
0.20
0.15
0.12
0.10
0.10
0.01
0.00
0.20
0.29
0.02
0.24
0.17
262
Cladina
17.80
17.80
1.54
0.47
0.30
0.15
0.12
0.10
0.06
0.06
0.06
0.41
1.69
0.16
0.73
0.09
111
Flavocetraria
0.48
0.48
0.46
0.24
0.21
0.14
0.10
0.07
0.06
0.06
0.06
0.16
0.08
0.01
0.18
0.14
48
Hypogymnia
3.76
1.55
0.90
0.40
0.30
0.22
0.18
0.10
0.04
0.00
0.00
0.27
0.25
0.01
0.29
0.26
1079
Letharia
5.13
4.06
2.01
0.34
0.22
0.16
0.12
0.10
0.00
0.00
0.00
0.28
0.54
0.02
0.32
0.24
819
Lobaria
9.81
8.75
0.87
0.20
0.12
0.10
0.10
0.10
0.07
0.01
0.01
0.21
0.72
0.05
0.30
0.12
251
Masonhalea
0.52
0.52
0.52
0.28
0.24
0.17
0.06
0.05
0.05
0.05
0.05
0.17
0.11
0.02
0.22
0.12
22
Platismatia
17.30
1.65
0.64
0.40
0.27
0.19
0.14
0.12
0.10
0.00
0.00
0.25
0.54
0.02
0.28
0.22
1192
Sphaerophorus
0.32
0.32
0.22
0.18
0.12
0.12
0.10
0.10
0.10
0.08
0.08
0.12
0.04
0.00
0.13
0.12
110
Usnea
3.43
3.43
3.24
3.04
0.32
0.18
0.10
0.06
0.03
0.01
0.01
0.72
1.15
0.08
0.87
0.57
230
Xanthoparmelia
3.40
3.40
3.14
0.79
0.60
0.50
0.40
0.31
0.20
0.10
0.10
0.64
0.60
0.05
0.75
0.53
122
Hg ppm
Quantiles Alectoria Bryoria Cladina Flavocetraria Hypogymnia Letharia Lobaria Masonhalea Platismatia Sphaerophorus Usnea Xanthoparmelia
100.00% 0.518 0.290 0.070 0.090 0.330 7.195 0.120 0.033 0.287 0.960 0.470
99.50% 0.518 0.290 0.070 0.090 0.330 7.195 0.120 0.033 0.287 0.960 0.470
97.50% 0.518 0.290 0.070 0.090 0.330 4.675 0.120 0.033 0.287 0.810 0.470
4A-48
-------
90.00%
75.00%
50.00%
25.00%
10.00%
2.50%
0.50%
0.00%
Mean
Std Dev
Std Err
Upper 95%
Lower 95%
N
0.399
0.270
0.185
0.137
0.010
0.010
0.010
0.010
0.204
0.118
0.023
0.250
0.157
27
0.208
0.170
0.160
0.120
0.078
0.060
0.060
0.060
0.149
0.050
0.010
0.170
0.127
23
0.070
0.063
0.050
0.040
0.040
0.040
0.040
0.040
0.052
0.012
0.005
0.064
0.039
6
0.048
0.033
0.025
0.018
0.013
0.010
0.010
0.010
0.029
0.017
0.003
0.034
0.023
40
0.264
0.200
0.090
0.060
0.040
0.030
0.030
0.030
0.128
0.086
0.014
0.156
0.099
37
0.717
0.418
0.170
0.070
0.026
0.010
0.010
0.010
0.379
0.964
0.130
0.640
0.119
55
0.120
0.105
0.045
0.013
0.010
0.010
0.010
0.010
0.056
0.045
0.016
0.094
0.019
8
0.032
0.026
0.022
0.018
0.015
0.014
0.014
0.014
0.023
0.005
0.002
0.026
0.019
12
0.287
0.287
0.263
0.249
0.247
0.247
0.247
0.247
0.267
0.019
0.009
0.290
0.243
5
0.630
0.403
0.210
0.050
0.030
0.020
0.010
0.010
0.254
0.237
0.021
0.296
0.212
126
0.405
0.285
0.255
0.173
0.030
0.030
0.030
0.030
0.230
0.121
0.032
0.300
0.160
14
N ppm
Quantiles
100.00%
99.50%
97.50%
90.00%
75.00%
50.00%
25.00%
10.00%
2.50%
0.50%
0.00%
Mean
Std Dev
Alectoria
10300
8371
7042
5300
4353
3600
3100
2603
2100
1500
1400
3825
1182
Bryoria
20000
19953
18300
14700
12200
9650
7095
5667
3829
1605
1480
9875
3543
Cladina
5360
5360
5360
5360
5360
4370
3380
3380
3380
3380
3380
4370
1400
Flavocetraria
5630
5630
5609
5355
4943
4560
4263
3710
1244
1180
1180
4489
820
Hypogymnia
25500
20214
15690
10300
7500
5800
4900
4180
3368
2112
1700
6684
2971
Letharia
26100
23777
16220
10940
8800
6830
5430
4640
3818
3115
2020
7539
3228
Lobaria
27800
27800
25970
24100
22500
21100
19050
16660
14930
13500
13500
20756
2799
Masonhalea
5430
5430
5430
4964
4200
3640
3120
2632
2440
2440
2440
3678
765
Platismatia
17300
15200
12355
7768
5690
4500
3700
3176
2657
2000
910
5116
2271
Sphaerophorus
8800
8800
6974
5027
4400
3800
3225
2672
2265
1870
1870
3913
1067
Usnea
16300
16300
15202
13240
10200
5960
4000
3418
2567
1200
1200
7308
3817
Xanthoparmelia
24400
24400
18980
17520
15500
13460
11500
10280
8592
0
0
13625
3126
4A-49
-------
Std Err
Upper 95%
Lower 95%
N
39
3901
3749
942
226
10320
9430
246
990
16949
-8209
2
112
4713
4265
54
101
6883
6485
861
122
7779
7298
695
218
21187
20326
165
129
3941
3415
35
66
5245
4987
1195
93
4097
3729
132
285
7869
6747
180
242
14103
13147
167
Ni ppm
Quantiles
100.00%
99.50%
97.50%
90.00%
75.00%
50.00%
25.00%
10.00%
2.50%
0.50%
0.00%
Mean
Std Dev
Std Err
Upper 95%
Lower 95%
N
Alectoria
93.96
17.61
13.42
9.00
5.50
3.00
2.00
1.68
1.00
0.84
0.61
4.34
4.18
0.10
4.54
4.14
1685
Bryoria
39.07
36.07
9.91
4.00
2.99
2.00
1.68
1.68
1.18
0.73
0.68
2.88
3.43
0.20
3.28
2.48
284
Cladina
6.00
6.00
4.00
3.00
1.77
1.61
1.00
1.00
0.84
0.84
0.84
1.65
0.79
0.07
1.79
1.50
116
Flavocetraria
10.50
10.50
9.00
3.37
1.76
0.72
0.64
0.51
0.24
0.24
0.24
1.52
1.70
0.25
2.01
1.03
48
Hypogymnia
186.82
94.92
52.85
19.00
10.00
6.35
4.57
3.36
1.58
0.86
0.42
10.60
15.13
0.46
11.49
9.70
1102
Letharia
5153.00
58.70
26.70
9.20
5.00
2.80
1.80
1.70
1.70
1.20
0.80
10.59
170.91
5.67
21.71
-0.54
909
Lobaria
19.00
15.41
4.00
2.88
2.00
1.71
1.00
1.00
0.23
0.01
0.00
1.85
1.34
0.08
2.02
1.69
255
Masonhalea
5.38
5.38
5.38
4.06
3.36
0.74
0.45
0.30
0.28
0.28
0.28
1.77
1.66
0.35
2.49
1.05
23
Platismatia
94.00
39.23
18.61
8.00
6.00
4.27
3.08
2.25
1.75
1.68
1.68
5.45
6.01
0.17
5.78
5.11
1229
Sphaerophorus
48.24
48.24
38.60
26.40
20.00
20.00
16.80
16.80
11.51
6.23
6.23
20.27
5.38
0.49
21.24
19.29
119
Usnea
15.00
12.55
7.00
5.00
2.79
1.42
0.90
0.59
0.39
0.32
0.31
2.12
1.90
0.11
2.34
1.91
297
Xanthoparmelia
549.00
549.00
148.00
80.45
48.67
21.99
8.29
6.17
5.38
4.46
4.46
36.65
54.08
3.99
44.51
28.78
184
Pb ppm
Quantiles Alectoria Bryoria Cladina Flavocetraria Hypogymnia Letharia Lobaria Masonhalea Platismatia Sphaerophorus Usnea Xanthoparmelia
100.00% 93.96 39.07 6.00 10.50 186.82 5153.00 19.00 5.38 94.00 4.82 41.00 549.00
99.50% 17.62 36.07 6.00 10.50 94.92 59.10 15.41 5.38 39.58 4.82 39.69 549.00
4A-50
-------
97.50%
90.00%
75.00%
50.00%
25.00%
10.00%
2.50%
0.50%
0.00%
Mean
Std Dev
Std Err
Upper 95%
Lower 95%
N
13.47
9.08
5.59
3.00
2.00
1.68
1.00
0.91
0.62
4.39
4.19
0.10
4.59
4.18
1663
9.91
4.00
2.99
2.00
1.68
1.68
1.18
0.73
0.68
2.88
3.43
0.20
3.28
2.48
284
4.00
3.00
1.77
1.61
1.00
1.00
0.84
0.84
0.84
1.65
0.79
0.07
1.79
1.50
116
10.50
3.37
3.23
1.05
0.64
0.64
0.64
0.64
0.64
1.81
1.87
0.31
2.44
1.17
36
52.85
19.00
10.00
6.35
4.57
3.36
1.58
0.86
0.42
10.60
15.13
0.46
11.49
9.70
1102
26.80
9.30
5.00
2.80
1.80
1.70
1.70
1.20
0.80
10.63
171.39
5.70
21.82
-0.56
904
4.00
2.88
2.00
1.71
1.00
1.00
0.23
0.01
0.00
1.85
1.34
0.08
2.02
1.69
255
5.38
5.38
3.98
3.36
3.36
3.35
3.35
3.35
3.35
3.72
0.70
0.23
4.26
3.18
9
18.65
8.01
6.00
4.24
3.08
2.24
1.75
1.68
1.68
5.45
6.02
0.17
5.79
5.11
1223
3.86
2.64
2.00
2.00
1.68
1.68
1.15
0.62
0.62
2.03
0.54
0.05
2.12
1.93
119
19.27
11.75
8.00
3.73
1.87
0.80
0.61
0.44
0.40
5.72
5.83
0.36
6.41
5.02
270
148.60
80.78
49.00
22.00
8.25
6.16
5.36
4.46
4.46
36.90
54.49
4.05
44.90
28.91
181
S ppm
Quantiles
100.00%
99.50%
97.50%
90.00%
75.00%
50.00%
25.00%
10.00%
2.50%
0.50%
0.00%
Mean
Alectoria
2430
820
673
530
430
350
290
240
200
165
140
376
Bryoria
1800
1750
1119
940
800
720
630
520
330
310
310
732
Cladina
Flavocetraria
549
549
527
405
352
310
251
0
0
0
0
283
Hypogymnia
1970
1830
1532
1100
880
690
560
480
380
270
200
748
Letharia
2000
1772
1300
901
700
600
520
460
390
320
87
651
Lobaria
2010
2010
1740
1430
1160
970
810
630
495
440
440
1001
Masonhalea
525
525
525
464
339
255
216
191
191
191
191
Platismatia
3500
1626
1274
910
690
560
470
410
360
300
200
622
Sphaerophorus
770
770
641
501
460
410
340
319
240
200
200
409
Usnea
2800
2800
1948
1410
973
723
558
450
380
310
310
832
Xanthoparmelia
2900
2889
2004
1558
1300
1100
950
776
504
40
0
1156
4A-51
-------
Std Dev
Std Err
Upper 95%
Lower 95%
N
144
5
386
367
949
193
12
756
709
262
127
19
320
245
46
271
9
767
730
832
221
8
666
635
111
295
22
1045
958
179
255
7
637
607
1171
91
8
425
393
128
404
35
901
763
134
366
25
1205
1108
221
Notes: The 50% quantile is the median and the 100% and 0% quantiles are maximum and minimum values in the data sets. Std Dev and Std Error are the
standard deviation and standard error of the mean. Upper 95% and lower 95% are the upper and lower 95% confidence intervals around the mean. N = number of
measurements. Because most samples come from remote sites, field replicates were treated as independent measurements.
4A-52
-------
Appendix 4A.15. List of Public Lands in the Western United States from Which
Nitrogen, Nickel, and Sulfur Concentrations in Table 4A.11 were calculated. N =
Background Distributions of Lichen Cadmium, Mercury,
: number of measurements.
Genus Code
Alectoria CHU
CLE
COL
CRLA
DES
GIP
MBS
WIL
MORA
MTH
NEP
OLYM
BIT
SIU
TON
UMP
WAW
WEN
WIL
WIN
Bryoria CLE
CRLA
DES
FRE
WIN
GIP
GRTE
National Land
Chugach National Forest, AK
Clearwater National Forest, ID
Colville National Forest, WA
Crater Lake National Park, OR
Deschutes National Forest, OR
Gifford Pinchot National Forest, WA
Mt Baker-Snoqualmie National Forest, WA
Willamette National Forest, OR
Mount Rainier National Park, WA
Mt. Hood National Forest, OR
Nez Perce National Forest, ID
Olympic National Park, WA
Selway-Bitterroot Wilderness, ID
Siuslaw National Forest, OR
Tongass National Forest, AK
Umpqua National Forest, OR
Wallowa-Whitman National Forest, OR
Wenatchee National Forest, WA
Willamette National Forest, OR
Winema National Forest, OR
Clearwater National Forest, ID
Crater Lake National Park, OR
Deschutes National Forest, OR
Fremont National Forest, OR
Fremont National Forest, OR
Gifford Pinchot National Forest, WA
Grand Tetons National Park, WY
N
71
3
1
3
121
198
8
1
209
283
2
246
1
3
273
135
12
3
276
15
1
3
120
7
1
12
5
N(Cd)
69
1
1
3
82
169
8
1
173
242
0
142
0
2
246
102
11
3
202
12
0
3
111
6
1
10
5
N(Hg)
0
0
0
3
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
N(N)
0
2
1
0
107
167
8
1
0
239
0
0
0
2
0
110
12
3
251
8
0
0
113
5
1
10
5
N(Ni)
69
2
1
3
98
170
8
1
0
248
2
32
0
2
245
119
11
3
234
14
1
3
114
6
1
10
5
N(Pb)
69
2
1
3
90
176
8
1
209
256
2
246
0
2
245
105
11
3
221
13
1
3
107
6
1
10
5
N(S)
0
2
1
0
102
176
8
1
0
246
2
0
1
3
0
115
10
3
248
9
1
0
110
6
1
10
0
4A-53
-------
KLA Klamath National Forest, CA
MTH Mt. Hood National Forest, OR
NEP Nez Perce National Forest, ID
OLYM Olympic National Park, WA
PAY Payette National Forest, ID
ROMO Rocky Mountain National Park, CO
SAC Salmon-Challis National Forest, ID
SJR San Juan-Rio Grande National Forest, CO
BIT Selway-Bitterroot Wilderness, ID
UMP Umpqua National Forest, OR
WAW Wallowa-Whitman National Forest, OR
WIL Willamette National Forest, OR
WIN Winema National Forest, OR
YELL Yellowstone National Park, WY
Cladina CHU Chugach National Forest, AK
DENA Denali National Park and Preserve, AK
NOAT Noatak National Preserve, AK
TON Tongass National Forest, AK
YELL Yellowstone National Park, WY
Flavocetraria NOAT Noatak National Preserve, AK
ROMO Rocky Mountain National Park, CO
SJR San Juan-Rio Grande National Forest, CO
Hypogymnia ANG Angeles National Forest, CA
CRG Columbia River Gorge National Scenic Area, OR & WA
COL Colville National Forest, WA
DES Deschutes National Forest, OR
FINL Finley National Wildlife Refuge, OR
FRE Fremont National Forest, OR
GIP Gifford Pinchot National Forest, WA
MBS Mt Baker-Snoqualmie National Forest, WA
4
7
3
5
6
2
7
2
1
15
31
26
51
18
63
6
2
52
3
35
2
1
6
137
3
66
2
4
67
4
4
0
4
0
2
0
0
0
9
25
11
49
18
57
3
0
48
3
34
2
0
6
115
2
61
2
4
59
0
0
0
0
0
2
0
0
0
0
0
0
0
18
0
6
0
0
0
27
2
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
13
25
21
43
0
0
0
0
0
0
33
0
0
0
106
2
54
2
4
60
4
4
3
4
6
2
7
2
1
12
25
15
50
18
57
6
2
48
3
33
2
1
6
115
2
61
2
4
59
4
4
3
4
6
2
7
2
1
11
25
16
48
18
57
6
2
48
3
33
2
1
6
115
2
61
2
4
59
0
6
3
0
6
0
7
2
1
14
26
25
44
0
0
0
0
0
0
33
0
1
0
116
3
56
2
4
62
4A-54
-------
Letharia
KLGO Klondike Gold Rush National Historical Park, AK
LOL Lolo National Forest, MT
MBS Mount Baker-Snoqualmie National Forest, WA
MTH Mt. Hood National Forest, OR
OKA Okanogan National Forest, WA
OLYM Olympic National Park, WA
ORCA Oregon Caves National Monument, OR
PORE Point Reyes National Seashore, CA
REDW Redwood National Park, CA
KICA Sequoia and Kings Canyon National Park, CA
SEKI Sequoia and Kings Canyon National Park, CA
SIU Siuslaw National Forest, OR
TON Tongass National Forest, AK
UMP Umpqua National Forest, OR
WAW Wallowa-Whitman National Forest, OR
WEN Wenatchee National Forest, WA
WIL Willamette National Forest, OR
WEN Winema National Forest, OR
WIN Winema National Forest, OR
ANG Angeles National Forest, CA
BEA Beaverhead-Deer Lodge National Forest, MT
BIT Bitterroot National Forest, ID
BRT Bridger-Teton National Forest, WY
CLE Clearwater National Forest, ID
CLV Cleveland National Forest, CA
CRG Columbia River Gorge National Scenic Area, OR & WA
COL Colville National Forest, WA
CODA Coulee Dam National Recreation Area, WA
CRLA Crater Lake National Park, OR
DES Deschutes National Forest, OR
24
1
35
239
7
66
3
14
50
12
12
122
72
44
18
5
181
3
28
18
8
4
2
1
6
20
11
12
12
312
24
1
33
217
7
62
3
14
27
0
12
106
65
38
17
5
163
3
27
18
1
0
0
0
6
19
9
9
12
257
0
0
0
0
0
0
2
14
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
17
24
1
30
193
6
0
0
14
0
0
0
106
0
36
16
4
158
2
26
0
2
0
0
0
0
18
9
10
0
263
24
1
33
217
7
62
3
14
49
12
12
107
65
38
17
5
165
3
27
18
5
3
2
1
6
19
9
9
12
262
15
1
33
216
7
58
3
14
49
12
12
107
65
38
17
5
165
3
27
18
7
2
2
0
6
19
9
9
12
260
0
1
31
197
6
0
0
0
0
0
0
106
0
35
17
4
158
2
25
0
7
4
2
1
0
18
11
11
0
277
4A-55
-------
Lobaria
ELD Eldorado National Forest, CA
FRE Fremont National Forest, OR
WIN Fremont National Forest, OR
GIP Gifford Pinchot National Forest, WA
HEL Helena National Forest, MT
KLA Klamath National Forest, CA
KOO Kootenai National Forest, MT
LABE Lava Beds National Monument, CA
LOL Lolo National Forest, MT
MTH Mt. Hood National Forest, OR
NEP Nez Perce National Forest, ID
OKA Okanogan National Forest, WA
PAY Payette National Forest, ID
SAC Salmon-Challis National Forest, ID
SAW Sawtooth Wilderness, ID
KICA Sequoia and Kings Canyon National Park, CA
SEKI Sequoia and Kings Canyon National Park, CA
STA Stanislaus National Forest, CA
TAR Targhee National Forest, WY
UMP Umpqua National Forest, OR
WAW Wallowa-Whitman National Forest, OR
WEN Wenatchee National Forest, WA
WIL Willamette National Forest, OR
WIN Winema National Forest, OR
YELL Yellowstone National Park, WY
BIT Bitterroot National Forest, ID
CHU Chugach National Forest, AK
CLE Clearwater National Forest, ID
CRG Columbia River Gorge National Scenic Area, OR & WA
FINL Finley National Wildlife Refuge, OR
18
28
2
2
2
12
2
6
2
35
5
8
6
46
4
12
27
12
3
2
108
5
13
236
15
1
26
3
10
4
18
25
2
2
1
12
0
6
2
28
0
6
0
0
0
0
27
12
0
2
94
5
10
214
15
0
24
0
10
2
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14
0
0
0
0
0
0
24
1
2
1
0
0
0
2
24
0
8
0
0
0
0
0
0
0
2
92
5
11
192
0
0
0
0
10
2
18
25
2
2
1
12
0
6
2
28
5
6
6
41
0
12
27
12
3
2
94
5
11
217
15
1
24
3
10
2
18
25
2
2
1
12
2
6
2
28
5
6
6
45
3
12
27
12
3
2
94
5
11
216
15
0
24
1
10
2
0
23
1
2
2
0
2
0
2
31
5
6
6
46
4
0
0
0
3
2
100
5
11
188
0
1
0
3
10
4
4A-56
-------
GIP Gifford Pinchot National Forest, WA
MBS Mt Baker-Snoqualmie National Forest, WA
KOO Kootenai National Forest, MT
MTH Mt. Hood National Forest, OR
NEP Nez Perce National Forest, ID
OLYM Olympic National Park National Park, WA
REDW Redwood National Park, CA
SIU Siuslaw National Forest, OR
TON Tongass National Forest, AK
UMP Umpqua National Forest, OR
WIL Willamette National Forest, OR
Masonhalea NOAT Noatak National Preserve, AK
Platismatia CLE Clearwater National Forest, ID
CRG Columbia River Gorge National Scenic Area, OR & WA
COL Colville National Forest, WA
CODA Coulee Dam National Recreation Area, WA
DES Deschutes National Forest, OR
FINL Finley National Wildlife Refuge, OR
GIP Gifford Pinchot National Forest, WA
MBS Gifford Pinchot National Forest, WA
KLGO Klondike Gold Rush National Historical Park, AK
MBS Mount Baker-Snoqualmie National Forest, WA
MTH Mt. Hood National Forest, OR
OLYM Olympic National Park, WA
SIU Siuslaw National Forest, OR
UMP Umpqua National Forest, OR
WAW Wallowa-Whitman National Forest, OR
WEN Wenatchee National Forest, WA
WIL Willamette National Forest, OR
WIN Winema National Forest, OR
29
1
1
38
2
6
8
17
86
3
79
9
2
179
5
8
30
3
221
19
13
49
291
20
30
141
47
3
304
3
21
1
0
31
0
5
6
13
83
3
52
8
1
171
4
6
21
2
187
16
13
44
262
13
23
120
45
3
250
2
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
25
1
0
35
0
5
0
15
0
3
65
8
1
153
5
6
24
2
187
16
13
43
257
0
28
114
41
2
279
3
25
1
0
33
2
5
8
15
83
3
66
9
1
171
4
6
23
2
192
16
13
44
263
20
23
125
45
3
270
3
22
1
0
31
1
5
8
13
83
3
51
9
1
171
4
6
23
2
192
16
10
44
263
20
23
125
45
3
271
3
26
1
1
36
2
5
0
16
0
3
71
8
2
150
4
8
27
2
190
17
0
41
258
0
30
116
43
2
269
3
4A-57
-------
Sphaerophorus CRG Columbia River Gorge National Scenic Area, OR & WA
GIP Gifford Pinchot National Forest, WA
MTH Mt. Hood National Forest, OR
OLYM Olympic National Park, WA
SIU Siuslaw National Forest, OR
UMP Umpqua National Forest, OR
WIL Willamette National Forest, OR
Usnea BIBE Big Bend National Park, TX
CHIR Chiricahua National Monument, AZ
CLE Clearwater National Forest, ID
CRG Columbia River Gorge National Scenic Area, OR & WA
DENA Denali National Park and Preserve, AK
ELMO El Morro National Monument, NM
FINL Finley National Wildlife Refuge, OR
GIP Gifford Pinchot National Forest, WA
GILA Gila National Forest, NM
GRTE Grand Tetons National Park, WY
OLYM Olympic National Park, WA
ORCA Oregon Caves National Monument, OR
PORE Point Reyes National Seashore, CA
REDW Redwood National Park, CA
ROMO Rocky Mountain National Park, CO
SAC Salmon-Challis National Forest, ID
SJR San Juan-Rio Grande National Forest, CO
SIU Siuslaw National Forest, OR
TAR Targhee National Forest, WY
UMP Umpqua National Forest, OR
WIL Willamette National Forest, OR
Xanthoparmelia ANG Angeles National Forest, CA
BIBE Big Bend National Park, TX
3
31
9
13
72
3
28
44
7
1
18
1
5
1
5
10
6
1
3
38
65
6
3
18
50
2
1
6
3
4
3
26
6
2
53
3
17
44
0
0
17
1
0
1
5
0
6
1
3
37
46
6
0
0
37
0
1
6
3
4
0
0
0
0
0
0
0
44
0
0
0
1
0
0
0
0
0
0
2
37
36
6
0
0
0
0
0
0
0
4
3
28
8
0
63
3
25
44
0
0
15
0
0
1
3
0
6
0
0
38
0
0
0
0
46
0
1
6
0
4
3
28
6
13
66
3
25
44
7
1
17
1
5
1
5
10
6
1
3
37
62
4
3
18
43
2
1
6
3
4
3
26
6
9
54
3
18
44
7
0
17
1
5
1
5
9
6
1
3
37
62
6
3
17
38
2
0
6
3
4
3
25
8
0
63
3
26
0
7
1
14
0
5
1
3
10
0
0
0
0
0
0
3
17
45
2
1
6
0
0
4A-58
-------
BRT
CHCU
CHIR
CLE
CRG
DES
DINO
GILA
HUT
MAL
MEB
NEP
PAY
ROMO
ROO
ROU
SAC
SJR
MAL
WAW
WAC
WHR
YELL
Bridger-Teton National Forest, WY
Chaco Culture National Historical Park, NM
Chiricahua National Monument, AZ
Clearwater National Forest, ID
Columbia River Gorge National Scenic Area, OR & WA
Deschutes National Forest, OR
Dinosaur National Monument, CO
Gila National Forest, NM
Humboldt-Toiyabe National Forest, NV
Manti-La Sal National Forest, UT
MedicineBow National Forest, WY
Nez Perce National Forest, ID
Payette National Forest, ID
Rocky Mountain National Park, CO
Roosevelt National Forest, CO
Routt National Forest, CO
Salmon-Challis National Forest, ID
San Juan-Rio Grande National Forest, CO
Uinta National Forest, UT
Wallowa-Whitman National Forest, OR
Wasatch-Cache National Forest, UT
White River National Forest, CO
Yellowstone National Park, WY
4
5
11
1
86
3
2
7
1
2
3
27
2
9
2
25
5
26
2
27
1
8
3
0
0
0
0
62
2
0
0
0
0
0
22
0
2
0
0
0
0
0
21
0
0
3
0
5
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
3
0
0
0
0
63
2
0
0
0
0
3
22
0
7
2
25
0
0
0
23
0
8
0
2
0
11
1
62
2
2
7
1
2
0
22
2
0
0
0
5
26
2
21
1
0
3
4
5
10
1
62
2
2
7
1
2
0
22
2
0
0
0
5
22
2
21
1
0
3
2
0
11
1
65
3
2
7
1
2
3
22
2
7
2
25
5
26
2
21
1
8
0
4A-59
-------
APPENDIX 5A
Fish Biological Data
Table 5A-1. Fish Biological and Mercury Data from SEKI and Year Sampled. Data are mean (min -
max) except vitellogenin (min - max). Sex (listed only for fish with accompanying gonad samples),
Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11keto-testosterone (11kT ng/ml), Testosterone (ng/ml), MAs
(average % area), Hg (ng/g ww), Age (years).
SEKI
Emerald Lake
Pear Lake
2003
Species
Total Nos. of Fish
Condition Factor
Vitellogeninmaie1
17B-estradiolfemaie
17B-estradiolmaie
11 kTma|e
Testosteronefemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology3
Numbers in () are
affected fish out of 15
Hgtotal whole-body
Age1
Age Frequency
Histograms1
Salvelinus fontinalis
28
0.9(0.6-1.2, N = 28)
6/4
DH (none)
DL (0.44 - 0.62)
ND (<0.20)
1.04(<0.25-2.67, N = 4)
O.252 (N = 6)
2.14(0.79-4.08, N = 6)
1.06(0.38-1.73, N = 4)
0.80(0.40-1.45)
18.98(10.59-35.48, N = 10)
0.26(0.04-1.25, N = 10)
7.24(1.86-16.05, N = 10)
Kidney: cl(2), iN(1) fF(1), Gr(1), CaD(2)
Liver: none
Spleen: CaD(1)
Gonad: Spn(1), CaD(1)
Gill: none
99.52(52.03-151.67, N = 10)
5(2-10, N = 27)
Salvelinus fontinalis
27
0.9(0.7-1.2, N = 27)
8/2
DH (none)
DL (0.40 - 0.76)
ND (O.20)
2.31 (1.94-2.68, N = 2)
0.212(<0.25-0.50, N = 8)
2.64(0.63-8.11, N = 8)
1.55(1.31 -1.78, N = 2)
1.78(0.46-4.66, N = 8)
11.27 (4.05-19.31, N = 10)
0.15(0.00-0.88, N = 10)
7.82(0.35-21.14, N = 10)
Kidney: CaD(1), cl(1)
Liver: fL(1), pC(2)
Spleen: Spn(2), Gr(1), CaD(1),
mgC(1)
Gonad:none
Gill: none
114.26 (40.56-212.99, N = 10)
5(3-10, N = 25)
1 U
8
6
4
2
r
—
— i
V^
n 1 1 n
1
0 2 4 6 8 10 1
Age (years)
1 U
£ 8
(A
j_ o
'o
«> 4
o
n
—
—
—
n
h
0 2 4 6 8 10 12
Age (years)
1Data are from all fish regardless if analyzed for SOC, SOC & Biology (Biol), or trace elements (Elem). 2Data are from
fish for which there are corresponding SOC and Hg data (N = 10). 3DH = detectable high (>1ug/ml), DL = detectable
low, ND = non-detectable. 4>50% non-detects. 5cl = chronic inflammation, IN = interstitial nephritis, fF= focus of
fibroplasia, Gr = granulomas, CaD = calcium deposit(s), fl_ = foci of lymphocytes, pC = perivascular cuffing, Spn =
embedded spine characteristic of setae from Lepidopteran larvae & associated fibroplasia, mgC = multi-nucleated
giant cell.
5A-1
-------
Table 5A-2. Fish Biological and Mercury Data for ROMO and Year Sampled. Data are mean (min -
max) except vitellogenin (min - max). Sex (listed only for fish with accompanying gonad samples),
Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11keto-testosterone (11kT ng/ml), Testosterone (ng/ml), MAs
(average % area), Hg (ng/g ww), Age (years).
ROMO
Mills Lake
Lone Pine Lake
2003
Species
Total Nos. of Fish
Condition Factor
Vitellogeninmaie1
17B-6StradiOl female
17B-estradiolmaie
11 kTma|e
Testosteronefemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology2
Numbers in () are
affected fish out of 15
Hgtotal whole-body
Age
Age Frequency
Histograms
Oncorhynchus mykiss
Oncorhynchus clarki
28
1.2(0.7-1.5, N = 28)
4/6
DH (11.89-25.85, N = 2)
DL (0.40-0.58, N = 2)
ND (none)
1.78(<0.25-2.86, N = 6)
0.26(0.25-0.36, N = 4)
2.85(0.62-4.60, N = 4)
3.88(0.25-9.52, N = 6)
3.61 (0.25-7.22, N = 4)
7.34(0.40-22.44, N = 10)
0.28(0.00-1.35, N = 10)
0.58(0.05-3.82, N = 10)
Kidney: ciN(1)
Liver: cdl(1),bDH(1),fl(1)
Spleen: none
Gonad: none
Gill: none
55.65(24.40-85.77, N = 10)
4 (1 - 8, N = 27)
8
6
4
9
n
— i
-^
Salvelinus fontinalis
25
1.0(0.6-1.1, N = 25)
6/4
DH (1.17-2.72, N = 2)
DL (0.29 - 0.49, N = 4)
ND (none)
18.90(4.69-26.11, N = 4)
0.27(0.25-0.57, N = 6)
11.58(0.25-32.28, N = 6
15.89(12.10-20.03, N = 4)
5.57(0.25-16.18, N = 6)
8.93(0.83-20.12, N = 10)
0.02(0.00-0.10, N = 10)
3.55(0.03-7.78, N = 10)
Kidney: none
Liver: none
Spleen: none
Gonad: IS(1)
Gill: mfH(1)
75.94(35.58-137.14, N = 10)
4 (1 - 8, N = 25)
1 VJ
fi 8
± 6
0
4
o
z 2
n
r
—
—
i — i
—
01 23456789 10
Age (years)
01 23456789 10
Age (years)
1DH = detectable high (>1|jg/ml), DL = detectable low, ND = non-detectable. 2ciN = chronic interstitial nephritis, cdl
chronic, diffuse inflammation, bDH = bile duct hyperplasia, fl = focal inflammation, IS = intersex male, mfH = mild
focal hyperplasia.
5A-2
-------
Table 5A-3. Fish Biological and Mercury Data for OLYM and Year Sampled. Data are mean (min -
max) except Vitellogenin (min - max). Sex (listed only for fish with accompanying gonad samples),
Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11keto-testosterone (11kT ng/ml), Testosterone (ng/ml), MAs
(average % area), Hg (ng/g ww), Age (years).
OLYM
PJ Lake
2003
Species
Total Nos. of Fish
Condition Factor
Sex(M / F)
Vitellogeninmaie
17B-estradiolfemaie
17B-estradiolmaie
11 kTma,e
Testosteronefemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology
Out of 15 fish
Hgtotal whole-body
Age
Age Frequency
Histogram
Salvelinus fontinalis
29
1.0(0.9-1.2, N = 29)
4/6
DH (none)
DL (0.44, N = 1)
ND (<0.20, N = 3)
3.50(1.54-5.44, N = 6)
0.192(<0.25-0.38, N = 4)
1.74(1.41 -2.00, N = 4)
1.49(0.73-3.40, N = 6)
1.36(1.05-1.49, N = 4)
10.32(1.07-19.58, N = 10)
0.07(0.00-0.24, N = 10)
2.40(0.05-7.39, N = 10)
Kidney: none
Liver: none
Spleen: none
Gonad: none
Gill: none
102.37(52.29-202.29, N = 10)
5 (1 - 8, N = 25)
10
8
6
4
2
0
2468
Age (years)
10
1DH = detectable high (>1ug/ml), DL = detectable low, ND = non-detectable. 2>50% non-detects.
5A-3
-------
Table 5A-4. Fish Biological and Mercury Data from the NOAT/GAAR and Year Sampled. Data are
mean (min - max, N) except Vitellogenin (min - max, N). Sex (listed only for fish with accompanying
gonad samples), Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11 keto-testosterone (11kT ng/ml),
Testosterone (ng/ml), MAs (average % area), Hg (ng/g ww), Age (years).
GAAR, Matcharak Lake
NOAT, Burial Lake 2004
Species
Total Nos. of Fish
Condition Factor
Sex(M / F)
Vitellogeninmaie
17B-eStradJOl female
17B-estradiolmaie
11 kTmale
Testosteronefemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology3
Numbers in () are
affected fish out of 15
Hgtotal whole-body
Age
Age Frequency
Histograms
Salvelinus namaycush
20
1.0(0.7-1.4, N = 20)
5/5
DH (none)
DL (0.47-0.81, N = 2)
ND (<0.20, N = 3)
4.38(<0.25-14.36, N = 5)
0.162(<0.25-0.27, N = 5)
4.39(1.07-10.14, N = 5)
11.28 (<0.25-29.63, N = 5)
7.88(<0.25-21.56, N = 5)
5.97(1.69-11.21, N = 10)
0.05(0.00-0.18, N = 10)
1.82(0.04-5.49, N = 10)
Kidney: none
Liver: WGr(3), Gr(3), Li(1), nNW(4)
Spleen: none
Gonad: tMA(1)
Gill: mifH(1), eH(1), mCp(1), tpL(1),
cpL(1), HtL(1)
Gut: WGr(1), nNW(1)
129.71 (31.59-204.50, N = 10)
19.5(7-29, N = 20)
10
Salvelinus namaycush
20
1.0(0.7-1.2, N = 20)
5/5
DH (none)
DL (0.25 - 0.34, N = 2)
ND (<0.20, N = 3)
3.27(0.26-8.87, N = 5)
0.172(<0.25-0.31, N = 5)
3.29 (< 0.63-13.16, N = 5)
15.40(<0.25-39.73, N = 5)
4.592(<0.25-22.18, N = 5)
6.43(0.57-14.65, N = 10)
0.18(0.00-1.54, N = 10)
0.39(0.00-1.28, N = 10))
Kidney: FcD(2), F(2), W(1)
Liver: Ll(1), dcLI(1), mLI(1), miLI(1), fL(1)
Spleen: none
Gonad: none
Gill: rC(1), HMW(1)
217.54 (68.27-411.01,N = 10)
17.9 (5-41, N = 19)
1 U
^ 8
w
it 6
o
8 4
Z 2
£.
n
— 1
— 1 [— 1
0 10 20 30 40 50
Age (years)
0 10 20 30 40 50
Age (years)
1DH = detectable high (>1ug/ml), DL = detectable low, ND = non-detectable. 2>50% non-detects. 3WGr =
worms in granulomas, Gr = granulomas, LI = lymphocyte infiltration, nNW= numerous Nematodes or
worms, tMA = testis with MA pigments, mifH = mild focal hyperplasia, eH = epithelial hyperplasia, HMW =
Monogene worm with hyperplasia, mCp = mucus cell proliferation, tpL = thickened cartilage element of
primary lamellae, cpL = cortical proliferation of primary lamellae, HtL = hyperplasia on tips of lamellae,
FcD = flukes in collecting duct, F = flukes, W = worms, dcLI = diffuse chronic lymphocyte infiltration, mLI =
moderate lymphocyte infiltration, miLI = mild lymphocyte infiltration, fL = foci of lymphocytes, rC = rare
ciliates no pathology.
5A-4
-------
Table 5A-5. Fish Biological and Mercury Data for DENA and Year Sampled. Data are mean (min -
max) except vitellogenin (min - max). Sex (listed only for fish with accompanying gonad samples),
Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11keto-testosterone (11kT ng/ml), Testosterone (ng/ml), MAs
(average % area), Hg (ng/g ww), Age (years).
DENA
McLeod Lake
Wonder Lake 2004-05
Species
Total Nos. of Fish
Condition Factor1
Sex(M / F)
Vitellogeninmaie
17B-6StradiOl female
17B-estradiolmaie
1 1 \tT
1 i "^ i male
Testosteronefemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology4
Numbers in () are
affected fish out of 15
Gonad: none
Hgtotal whole-body
Age
Age Frequency
Histograms
Lota lota
Prosopium cylindraceum
6
0.7(0.5-0.8, N = 6)
1 10
NA2
NA
0.52 (N = 1)
0.70(N = 1)
NA
<0.25 (N = 1)
NA
NA
NA
Kidney: none
Liver: none
Spleen: none
Gonad: oMA(2)
Gill: none
58.34(26.64-75.73, N = 4)
4 (2 - 7, N = 6)
10
8
6
Salvelinus namaycush
24
1.1 (0.8-1.4, N = 24)
6/4
DH (none)
DL (0.56 -0.66, N = 2)
ND (<0.20, N = 4)
4.49(0.30-9.84, N = 4)
0.173(<0.25-0.26, N=6)
12.13(4.14-18.22, N = 6)
23.95(0.29-63.11, N = 4)
10.22(4.06-17.23, N = 6)
10.34(4.84-18.67, N = 10)
0.24(0.00-0.55, N = 10)
7.27(2.54-13.29, N = 10)
Kidney:WU(1)
Liver: none
Spleen: none
112.59(87.61 -140.30, N = 10)
17(2-29, N = 24)
8
4
2
n
01 2345678
Age (years)
5 10 15 20 25 30 35
Age (years)
1DH = detectable high (>1ug/ml), DL = detectable low, ND = non-detectable. 2NA = not available. 3>50%
non-detects. 4WU = worms in ureters, oMA = ovary with MA pigments, eM = encysted metacercariae.
5A-5
-------
Table 5A-6. Fish Biological and Mercury Data for MORA and Year Sampled. Data are mean (min -
max) except Vitellogenin (min - max). Sex (listed only for fish with accompanying gonad samples),
Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11keto-testosterone (11kT ng/ml), Testosterone (ng/ml), MAs
(average % area), Hg (ng/g ww), Age (years).
MORA
Lakel_P19
Golden Lake
2005
Species
Total Nos. of Fish
Condition Factor
Sex(M / F)
Vitellogeninmaie
17B-estradiolfemaie
17B-estradiolmaie
11 kTma,e
Testosteronefemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology3
Numbers in () are
affected fish out 15
Hgtotal whole-body
Age
Age Frequency
Histograms
Salvelinus fontinalis
25
1 (0.7-1.2, N = 25)
5/5
DH (none)
DL (none)
ND (<0.20, N = 5)
7.95(<0.25-19.29, N = 5)
0.24 (<0.25-0.28, N = 5)
5.06(2.13-7.94, N = 5)
3.94(2.66-6.54, N = 5)
3.41 (2.06-6.00, N = 5)
13.98(1.11 -31.22, N = 10)
0.24(0.00-2.17, N = 10)
4.79(0.07-14.47, N = 10)
Kidney: none
Liver: BKDGr(1), mGr(1)
Spleen: none
Gonad: none
Gill: none
145.68(56.63-267.50, N = 15)
5 (2 - 9, N = 25)
10
8
6
o
co 4
o
468
Age (years)
10 12
Salvelinus fontinalis
25
1.0(0.8-1.2, N = 25)
7/3
DH(6.92, N = 1)
DL (none)
ND (<0.20, N = 6)
9.89(7.35-11.35, N = 3)
0.212(<0.25-0.53, N=7)
6.74(4.87-8.71, N =7)
3.70(3.04-4.07, N = 3)
5.05(2.50-6.31, N =7)
15.73(3.24-25.46, N = 10)
0.05(0.00-0.13, N = 10)
1.63(0.08-3.36, N = 10)
Kidney: none
Liver: bDH(1)
Spleen: none
Gonad: none
Gill: none
80.60(54.75-102.02, N = 15)
4 (2 - 6, N = 25)
8
6
A
2
n
468
Age (years)
10 12
1DH = detectable high (>1ug/ml), DL = detectable low, ND = non-detectable. 2>50% non-detects. 3BKDGr
= bacterial kidney disease like granuloma, mGr = multiple granulomas, bDH = bile duct hyperplasia
(suspected).
5A-6
-------
Table 5A-7. Fish Biological and Mercury Data for GLAC and Year Sampled. Data are mean (min -
max) except Vitellogenin (min - max). Sex (listed only for fish with accompanying gonad samples),
Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11keto-testosterone (11kT ng/ml), Testosterone (ng/ml), MAs
(average % area), Hg (ng/g ww), Age (years).
GLAC
Oldman Lake
Snyder Lake
2005
Species
Total Nos. of Fish
Condition Factor
Vitellogeninmaie1
17B-6StradiOl female
17B-estradiolmaie
11 kTma,e
Testoste ro n efemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology2
Numbers in () are
affected fish out of 15
gtotal whole-body
Age
Age Frequency
Histograms
Oncorhynchus clarki bouvieri
25
1.1 (0.6-1.3, N = 25)
6/4
DH (4.40, N =1)
DL (none)
ND (<0.20, N = 5)
13.70(11.08-20.46, N = 4)
0.59(0.50-0.76, N = 6)
13.20(8.92-18.67, N = 6)
14.32(9.97-17.80, N = 4)
14.68(7.25-22.90, N = 6)
2.14 (0.32 - 4.11,N = 10)
0.00 (N = 10)
0.18(0.01 -0.53, N = 10)
Kidney: none
Liver: pC(1), fl_i(1)
Spleen: none
Gonad: IS(1)
Gill: none
37.06(24.33-45.62, N = 10)
4 (2 - 5, N = 25)
14
% 12
il 10
o 8
g 6
z 4
2
0 J
C
—
n
) 2 4 6 £
Age (years)
Oncorhynchus clarki lewisi
25
0.9(0.7-1.1, N = 25)
5/5
DH (5.58, N = 1)
DL (0.75, N = 1)
ND (<0.20, N = 3)
2.32 (<0.25-5.73, N = 5)
0.30 (<0.25-0.49, N = 5)
7.32(1.31 -13.57, N = 5)
2.41 (1.18-4.54, N = 5)
6.62(1.24-11.72, N = 5)
10.70(2.93-25.66, N = 10)
0.10(0.00-0.52, N = 10)
1.15(0.23-4.52, N = 10)
Kidney: none
Liver: none
Spleen: none
Gonad: none
Gill: none
36.74(16.90-59.60, N = 15)
5 (2 - 6, N = 25)
14
6
z 4
2
0 J
c
-K
) 2 4 6 £
Age (years)
1DH = detectable high (>1ug/ml), DL = detectable low, ND = non-detectable. 2pC = perivascular cuffing,
fLi = fatty liver, IS = intersex male.
5A-7
-------
Table 5A-8. Fish Biological and Mercury data for OLYM and Year Sampled. Data are mean (min -
max) except Vitellogenin (min - max). Sex (listed only for fish with accompanying gonad samples),
Vitellogenin (|jg/ml), 17B-estradiol (ng/ml), 11keto-testosterone (11kT ng/ml), Testosterone (ng/ml), MAs
(average % area), Hg (ng/g ww), Age (years).
OLYM
Hoh Lake
PJ Lake
2005
Species
Total Nos. of Fish
Condition Factor
Vitellogeninmaie1
17B-6StradiOl female
17B-estradiolmaie
11 kTma,e
Testoste ro n efemaie
Testosteronemaie
Kidney MAs
Liver MAs
Spleen MAs
Histopathology
Out of 15 fish
gtotal whole-body
Age
Age Frequency
Histograms
Salvelinus fontinalis
25
1.0(0.7-1.4, N = 25)
5/5
DH (none)
DL (none)
ND (<0.20, N = 5)
8.07 (<0.25-14.56, N = 5)
0.30 (<0.25-0.49, N = 5)
5.56(2.83-8.66, N = 5)
4.88(0.68-8.26, N = 5)
5.20(3.49-7.77, N = 5)
21.24(3.45-34.91, N= 10)
0.29(0.00-0.94, N = 10)
9.07(0.11 -25.83, N = 10)
Kidney: none
Liver: none
Spleen: none
Gonad: none
Gill: none
141.67 (78.44 - 284.02, N = 15)
7(3-13, N = 25)
1 8
"B 6
2
n
1
1
1
1
0 2 4 6 8 10 12 14 16
Age (years)
Salvelinus fontinalis
25
1.0(0.7-1.1, N = 25)
5/5
DH (none)
DL(0.43, N = 1)
ND (<0.20, N = 4)
8.01 (0.49-15.43)
0.192(<0.25-0.42)
5.48(1.06-11.66)
6.52(0.84-17.39)
4.38(1.14-11.91)
12.66(3.72-35.76, N = 10)
0.29(0.00-1.64, N = 10)
3.98(0.00-12.76, N = 10)
Kidney: none
Liver: none
Spleen: none
Gonad:none
Gill: none
102.36(30.16-227.35, N = 15)
4(2-10, N = 25)
1^.
^ 10
if 8
o 6
8 4
^ 2
n
r
n
n n
0 2 4 6 8 10 12 14 16
Age (years)
1DH = detectable high (>1ug/ml), DL = detectable low, ND = non-detectable. 2>50% non-detects.
5A-8
-------
APPENDIX 5B
Correlations between Hg and Age
Data are first analyzed using all fish, then analyzed by species, then park, and then lake. Data are
provided for the model that had the strongest statistical significance. The categories in bold are significant
at P < 0.05.
Analysis
All fish
Brook trout
Lake trout
Cutthroat trout
Rainbow trout
Sequoia
Pear
Emerald
Rocky Mountain
Mills
Lone Pine
Gates of the Arctic
Matcharak
Noatak
Burial
Denali
Wonder
Mount Rainier
LP19
Golden
Glacier
Oldman
Snyder
Olympic
PJ2003
PJ2005
Hoh
F
58.10
129.38
0.39
7.84
114.89
38.31
42.95
9.11
82.08
114.89
31.14
1.44
5.55
0.98
37.33
38.18
2.68
7.84
4.04
11.07
106.82
5.37
44.74
44.96
d
2,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
.f.
162
98
28
23
8
18
8
8
18
8
8
8
8
8
28
13
13
23
8
13
38
8
13
13
p squared
0.42
0.57
-0.01
0.25
0.93
0.68
0.84
0.63
0.82
0.93
0.80
0.15
-0.41
0.11
0.57
0.75
0.17
0.25
0.33
0.46
0.74
0.40
0.77
0.78
P
<0.0001
<0.0001
0.54
0.01
<0.0001
<0.0001
0.0002
0.02
<0.0001
<0.0001
0.0005
0.26
0.05
0.35
<0.0001
<0.0001
0.12
0.01
0.08
0.005
<0.0001
0.05
<0.0001
<0.0001
Best Fit Model
2nd order polynomial
(parabolic)
Linear
Linear
Double reciprocal
Double reciprocal
Double reciprocal
S-curve
Double reciprocal
Double reciprocal
Double reciprocal
Squared X
Reciprocal Y; Squared X
Reciprocal Y; Squared X
Double squared
Square root Y; Squared X
Log Y; Square-root X
Double reciprocal
Double reciprocal
Double squared
Double reciprocal
Double squared
Double reciprocal
Double squared
Squared X
5B-1
-------
APPENDIX 5C
Correlations between Macrophage Aggregates and Hg
Data analyzed using all fish and then separated into species age classes when possible. Data are
provided for the model that had the strongest statistical significance. The categories in bold are significant
at P < 0.05.
Analysis
All Fish
Spleen MAs
Kidney MAs
Sum of MAs
Brook trout
Spleen MAs
1-3 y
4-6 y
7-1 3 y
Kidney MAs
Log10(X)
1-3 y
4-6 y
7-1 3 y
Sum of MAs
1-3 y
4-6 y
7-1 3 y
Lake trout
Spleen MAs
<20y
>20y
Kidney MAs
<20y
>20y
Sum of MAs
<20y
>20y
Cutthroat trout
Spleen MAs
Kidney MAs
Sum of MAs
Rainbow trout
Spleen MAs
Kidney MAs
Sum of MAs
SEKI - Brook Trout
Spleen MAs
Kidney MAs
Sum of MAs
Pear
Spleen MAs
Kidney MAs
Sum of MAs
F
52.25
35.89
48.69
82.82
6.03
26.27
2.59
2.51
6.65
1.8
66.92
3.16
13.06
3.21
6.99
4.77
22.64
0.32
0.00
0.00
1.62
1.48
0.67
15.46
16.10
17.15
4.13
28.83
33.83
35.65
6.12
15.05
31.24
9.85
19.18
d.f.
1,163
1,163
1,163
1,98
1,28
1,46
1,20
1,28
1,46
1,20
1,98
1,28
1,46
1,20
1,28
1,15
1,11
1,28
1, 15
1, 11
1,28
1, 15
1,11
1,23
1,23
1,23
1,8
1,8
1,8
1,18
1,18
1,18
1,8
1,8
1,8
p squared
0.24
0.18
0.23
0.45
0.18
0.36
0.11
42.64
0.08
0.13
0.08
0.41
0.10
0.22
0.14
-0.2
-0.24
-0.67
-0.01
0.0001
0.0002
-0.05
-0.09
-0.06
0.40
0.41
0.43
0.34
0.79
0.81
0.66
0.25
0.45
0.80
0.55
0.71
P
<0.0001
<0.0001
<0.0001
<0.0001
0.02
<0.0001
0.12
1,98
0.12
0.01
0.19
<0.0001
0.09
0.0007
0.09
0.01
0.04
0.0006
0.57
0.97
0.96
0.21
0.24
0.43
0.0007
0.0005
0.0004
0.08
0.0007
0.0004
<0.0001
0.02
0.001
0.0005
0.01
0.002
Best Fit Model
Log10(X)
Log10(X)
Log10(X)
Log10(X)
Double Squared
Log10(X)
Squared Y; Reciprocal X
0.30 <0.0001
Double Squared
Double Reciprocal
Double Squared
Log10(X)
Double Squared
Double Reciprocal
Double Squared
Linear
Squared Y
Reciprocal Y; Squared X
Squared Y; Log X
Linear
Linear
Squared Y; Square root X
Double squared
Reciprocal Y; Square root X
Double squared
Double squared
Double squared
Linear
Double reciprocal
Double reciprocal
Square-root Y; Log X
Multiplicative
Square-root Y; Log X
Linear
Linear
Linear
5C-1
-------
Analysis
Emerald
Spleen MAs
Kidney MAs
Sum of MAs
ROMO
Spleen MAs
Kidney MAs
Sum of MAs
Mills - rainbow trout
Spleen MAs
Kidney MAs
Sum of MAs
Lone Pine - brook trout
Spleen MAs
Kidney MAs
Sum of MAs
GAAR
Matcharak - lake trout
Spleen MAs
<20y
>20y
Kidney MAs
<20y
>20y
Sum of MAs
<20y
>20y
NOAT
Burial - lake trout
Spleen MAs
<15y
> 15y
Kidney MAs
< 15y
> 15y
Sum of MAs
< 15y
> 15y
DENA - Lake Trout
Wonder
Spleen MAs
Kidney MAs
Sum of MAs
MORA - Brook Trout
Spleen MAs
Kidney MAs
Sum of MAs
F
4.46
8.35
7.98
37.35
16.73
35.87
4.13
28.83
33.83
21.21
16.38
34.55
4.75
4.53
5.35
0.26
6.35
0.18
1.17
7.12
0.88
12.56
67.70
1.11
1.47
8.07
0.01
2.42
9.56
0.01
4.21
5.49
7.61
46.03
7.26
11.20
d.f.
1,8
1,8
1,8
1,18
1,18
1,18
1,8
1,8
1,8
1,8
1,8
1,8
1,8
1,3
1,3
1,8
1,3
1,3
1,8
1,3
1,3
1,8
1,2
1,4
1,8
1,2
1,4
1,8
1,2
1,4
1,8
1,8
1,8
1,28
1,28
1,28
p squared
0.36
0.51
0.50
0.67
0.48
0.67
0.34
0.79
0.81
0.73
0.67
0.81
-0.37
0.60
-0.64
0.03
0.68
-0.06
-0.13
0.70
-.23
-0.64
0.97
-0.21
-0.15
0.80
0.21
-0.23
0.83
0.13
0.34
0.41
0.49
0.62
0.21
0.29
P
0.07
0.02
0.02
<0.0001
0.0007
<0.0001
0.08
0.0007
0.0004
0.002
0.004
0.0004
0.06
0.12
0.10
0.63
0.09
0.70
0.31
0.08
0.42
0.009
0.01
0.35
0.26
0.10
0.93
0.16
0.09
0.94
0.07
0.05
0.03
<0.0001
0.01
0.002
Best Fit Model
Reciprocal Y; Log X
Double reciprocal
Double reciprocal
Double squared
Double squared
Squared Y
Linear
Double reciprocal
Double reciprocal
Squared Y; Square-root X
Double squared
Double squared
Squared Y; Reciprocal X
Linear
Squared Y; Reciprocal X
Reciprocal Y; Squared X
Linear
Double reciprocal
Squared Y; Reciprocal X
Linear
Squared Y; Reciprocal X
Squared Y; Reciprocal X
Linear
Linear
Squared Y; Reciprocal X
Linear
Linear
Squared Y; Reciprocal X
Linear
Linear
Reciprocal Y; Squared X
Reciprocal Y
Double squared
Log10(X)
Double reciprocal
Square root Y; Reciprocal X
5C-2
-------
Analysis
LP19
Spleen MAs
Kidney MAs
Sum of MAs
Golden
Spleen MAs
Kidney MAs
Sum of MAs
F
89.51
37.27
48.48
1.87
4.46
4.25
d.f.
1,13
1,13
1,13
1, 13
1,13
1, 13
p squared
0.87
0.74
0.79
0.13
0.26
0.25
P
<0.0001
<0.0001
<0.0001
0.19
0.05
0.06
Best Fit Model
S-curve
Double reciprocal
S-curve
S-curve
Double reciprocal
Double reciprocal
GLAC - West Slope Cutthroat
Spleen MAs
Kidney MAs
Sum of MAs
Oldman
Spleen MAs
Kidney MAs
Sum of MAs
Snyder
Spleen MAs
Kidney MAs
Sum of MAs
OLYM - Brook Trout
Spleen MAs
Kidney MAs
Sum of MAs
PJ2003
Spleen MAs
Kidney MAs
Sum of MAs
PJ2005
Spleen MAs
Kidney MAs
Sum of MAs
Hoh
Spleen MAs
Kidney MAs
Sum of MAs
All Fish
Spleen MAs
Kidney MAs
Sum of MAs
Brook trout
Spleen MAs
Kidney MAs
Sum of MAs
Lake trout
Spleen MAs
Kidney MAs
Sum of MAs
15.46
16.10
17.15
25.26
2.71
3.23
16.53
21.21
23.22
43.10
30.29
40.29
14.45
3.87
5.00
4.98
16.92
13.30
30.86
29.95
34.54
51.46
91.11
75.84
104.67
35.25
61.07
70.02
1,23
1,23
1,23
1,8
1,8
1,8
1,13
1,13
1,13
1,38
1,38
1,38
1,8
1,8
1,8
1,13
1,13
1,13
1,13
1,13
1,13
Failed
Failed
1,163
1,98
1,98
1,98
1,28
1,28
1,28
0.40
0.41
0.43
0.76
0.25
0.29
0.56
0.62
0.64
0.53
0.44
0.52
0.64
0.33
0.38
0.28
0.56
0.51
0.70
0.70
0.73
lack of fit test
lack of fit test
0.24
0.48
0.46
0.52
0.57
0.68
0.71
0.0007
0.0005
0.0004
0.001
0.14
0.11
0.001
0.0005
0.0003
<0.0001
<0.0001
<0.0001
0.005
0.08
0.06
0.04
0.001
0.003
0.0001
0.0001
0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
Double squared
Double squared
Double squared
Double reciprocal
Squared Y; reciprocal X
Squared Y; reciprocal X
Squared X
Double squared
Double squared
Linear
Linear
Linear
Squared Y
Double reciprocal
Double reciprocal
Square-root X
Reciprocal Y; Square-root X
Reciprocal Y; Square-root X
Reciprocal X
Squared Y; Log X
Squared Y; Log X
S-curve
Reciprocal X
Reciprocal X
Reciprocal X
Double reciprocal
Double reciprocal
Double reciprocal
5C-3
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Analysis
Cutthroat trout
Spleen MAs
Kidney MAs
Sum of MAs
Rainbow trout
Spleen MAs
Kidney MAs
Sum of MAs
SEKI
Spleen MAs
Kidney MAs
Sum of MAs
Pear
Spleen MAs
Kidney MAs
Sum of MAs
Emerald
Spleen MAs
Kidney MAs
Sum of MAs
ROMO
Spleen MAs
Kidney MAs
Sum of MAs
Mills
Spleen MAs
Kidney MAs
Sum of MAs
Exponential
Lone Pine
Spleen MAs
Kidney MAs
Sum of MAs
GAAR
Matcharak
Spleen MAs
Kidney MAs
Sum of MAs
NOAT
Burial
Spleen MAs
Kidney MAs
Sum of MAs
DENA
Wonder
Spleen MAs
Kidney MAs
Sum of MAs
F
18.47
11.44
12.88
19.91
52.16
61.84
26.39
8.7
16.23
28.74
7.5
13.83
6.11
7.20
8.49
20.57
23.69
38.64
19.91
52.16
11.53
6.62
11.67
28.45
19.86
38.39
86.15
30.16
34.27
1.83
0.41
1.4
d.f.
1,23
1,23
1,23
1,8
1,8
1,8
1,18
1,18
1,18
1,8
1,8
1,8
1,8
1,8
1,8
1,18
1,18
1,18
1,8
1,8
61.84
1,8
1,8
1,8
1,8
1,8
1,8
1,8
1,8
1,8
1,8
1,8
1,8
p squared
0.44
0.33
0.36
0.71
0.87
0.88
0.59
0.33
0.47
0.78
0.48
0.63
0.43
0.47
0.51
0.53
0.57
0.68
0.71
0.87
1,8
0.59
0.45
0.59
0.78
0.71
0.83
0.93
0.79
0.81
0.19
0.05
0.15
P
0.0003
0.003
0.002
0.002
0.0001
<0.0001
0.0001
0.009
0.0008
0.0007
0.03
0.006
0.04
0.03
0.02
0.0003
0.0001
<0.0001
0.002
0.0001
0.88
0.009
0.03
0.009
0.0007
0.002
0.0003
<0.0001
0.0006
0.0004
0.21
0.54
0.27
Best Fit Model
Square-root Y; Squared X
Squared X
Square-root Y; Squared X
Squared X
Exponential
Exponential
S-curve
Double reciprocal
S-curve
S-curve
Double reciprocal
Double reciprocal
Double reciprocal
Double reciprocal
Double reciprocal
Double squared
Double reciprocal
Double squared
Squared X
Exponential
<0.0001
Double squared
Double squared
Double squared
Square-root Y
Reciprocal Y; Log X
Reciprocal Y; Log X
Double reciprocal
Double reciprocal
Double reciprocal
Double squared
Reciprocal Y; Squared
Double squared
5C-4
-------
Analysis
MORA
Spleen MAs
Kidney MAs
Sum of MAs
LP19
Spleen MAs
Kidney MAs
Sum of MAs
Golden
Spleen MAs
Kidney MAs
Sum of MAs
GLAC
Spleen MAs
Kidney MAs
Sum of MAs
Oldman
Spleen MAs
Kidney MAs
Sum of MAs
Snyder
Spleen MAs
Kidney MAs
Sum of MAs
OLYM
Spleen MAs
Kidney MAs
Sum of MAs
PJ2003
Spleen MAs
Kidney MAs
Sum of MAs
PJ2005
Spleen MAs
Kidney MAs
Sum of MAs
Hoh
Spleen MAs
Kidney MAs
Sum of MAs
F
55.96
61.38
69.46
44.00
61.30
72.66
6.64
5.93
6.17
18.47
11.44
12.88
5.20
2.47
6.01
2.43
2.81
41.97
49.92
58.09
48.23
34.67
37.32
3.2
9.27
7.04
250.45
113.38
104.83
d.f.
1,28
1,28
1,28
1,13
1,13
1,13
1,13
1,13
1,13
1,23
1,23
1,23
1,8
1,8
2.92
1,13
1, 13
1, 13
1,38
1,38
1,38
1,8
1,8
1,8
1, 13
1,13
1,13
1,13
1,13
1, 13
p squared
0.67
0.69
0.71
0.77
0.82
0.84
0.34
0.31
0.32
0.44
0.33
0.36
0.39
0.24
1,8
0.32
0.16
0.18
0.52
0.57
0.60
0.86
0.81
0.82
0.20
0.42
0.35
0.95
0.90
0.89
P
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.02
0.03
0.03
0.0003
0.003
0.002
0.05
0.15
0.27
0.03
0.14
0.11
<0.0001
<0.0001
<0.0001
0.0001
0.0004
0.0003
0.10
0.009
0.02
<0.0001
<0.0001
<0.0001
Best Fit Model
S-curve
Double reciprocal
Double reciprocal
S-curve
Double reciprocal
S-curve
Double squared
Double reciprocal
Double reciprocal
Square-root Y; Squared X
Squared X
Square-root Y; Squared X
Double squared
Squared Y; Square-root X
0.13 Squared Y
Double reciprocal
Log Y; Squared X
Log Y; Squared X
Linear
Linear
Linear
Double reciprocal
Double reciprocal
Double reciprocal
Reciprocal X
Log Y; Squared X
Exponential
S-curve
Squared Y; Log X
Log10(X)
5C-5
-------
-------
Western Airborne Contaminants Assessment Project
Final Report: Volume II Appendices
Site Type
Ail media sampled at
lake sites in core parks
£ Vegetation only sampling sites
(in core parks, in addition to the
lake sites)
A Snow only sampling sites
(in core parks, sites outside
of lake sites)
,- Air sampling sites
EPA Ecoregions-Level 1
ARCTIC CORDILLERA
GREAT PLAINS
|^B MARINE WEST COAST FOREST
MEDITERRANEAN CALIFORNIA
NORTH AMERICAN DESERTS
•B NORTHERN FORESTS
HH NORTHWESTERN FORESTED MOUNTAINS
SOUTHERN SEMI-ARID HIGHLANDS
••TAIGA
HI TEMPERATE SIERRAS
^H TROPICAL DRY FORESTS
•• TUNDRA
3 Kilometers
500 1.000
2.000 3.000
&EPA
United States
Environmental Protection
Agency
PRESORTED STANDARD
POSTAGE & FEES PAID
EPA
PERMIT NO. G-35
Office of Research and Development (81 OR)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
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